The 6th MAFF International

Workshop on Genetic Resources

Genetic Diversity and Conservation
Animal Genetic Resources

NationalofNovember 4-5, 1998
Instituteof Agrobiological
Resources
Tsukuba, Ibaraki, JAPANSponsored by
Research Council Secretariatof MAFFN
ational Instituteof Agrobiological
Resources

in corporation
with
National Instituteof Animal Industry
National Instituteof SericulturalandEntomological
Sciences
National Instituteof Animal HealthJapan International
Research Center
for Agricultural
SciencesContents
page
Opening
SessionLivestock
Effective

Address
1. Improvement
Population

and Conservation

Size and Inbreeding

Genetic Improvement and Inbreeding
Effective Population
Size
Genetic

Improvement

and Inbreeding

N.Katsura
Diversityin1

T.Nomuraand Its Consequence on

5

R.Mrode25

of Genetic

Under Selectionin Dairy

CattleProblem in Swine

Mapping Quantitative
Trait Loci and Marker Assisted
DomesticatedPopulations

Selection

H.Takahashi
in SmallS.Xu

Conservation
Genetic

Sessionof Rare Breed in Spanish

Distance

49

61

Rural Agro-economy
D.L.Vidal

89

Y.Nagamine

99

Between Populations

2. Current

Statusand Perspectivesof Effective

Use ofSperm
1. Basic Aspects of Spermatogenesis and Sperm Maturation
The Role of Sertoli Cells in Differentiationand Exclusion of Spermatogenic
Cells
Y.Nakanishi
Role of Epididymal
Secretory Proteins in Sperm Maturation
N.Okamura
2.Assessment of Sperm Activity and Effective Use of Sperm
Sperm Evaluation:
What Should We Be Testing to Evaluate?
R.Harrison

111
123

135

Using CoordinatedComputer-assisted
Motility
Analysis (CASA) and Western
Phosphoamino Acid Detection to Elucidate
Significant
Signal Transduction
Pathways in Live Sperm
J.TashSpermatogonial
Transplantation
T.Ogawa
Spermatogenic
Cells as Male Gametes
A.OguraSperm Preservation
and Intra-cytoplasmic
Sperm Injection
T.WakayamaClosing

155
171
177
185

Remarks
S.Miyazaki

Photographof Participants

195

Opening

address

NAOKI KATSURA
Director
Kannondai

General,National

Instituteof Agrobiological

2-1-2, Tsukuba,

Ibaraki

305-8602,

ResourcesJapan

First, I would like to extend heartfelt welcome to all participantsof this workshop.
It is great pleasure for me, particularly,
to welcome participants
who have traveled a long
way to join us. Some invited speakers have traveled more than 10 hours, from Europe or
America, and I hope they have overcome the jet lag already. We do appreciate all of you forsparing the time to join in this workshop.
I would like to acknowledge
and thank the Agriculture,
Forestry and Fisheries
Research Council, our sister institutes
and committee members who have helped to makethis workshop possible.
Conservation of genetic resources of animal should be a global concerned and seen
as a way to improve the global food supply. This is needed for the basic security of all
people and a most urgent tasks facing humanity. All countries are interdependentwhen itcomes to conserving, evaluating
and using genetic resources. Many issues related tobio-diversity
are becoming international
issues involving government level meetings but
these issues have implications
for science and scientists. Fostering international
cooperationand interaction
is one of the results which I hope will emerge from the next few days you
all have together in Tsukuba.
Animal genetic resources conservation is the particularfocus of your discussionsof
this workshop. The papers to be presented in this meeting cover a very wide range fromgenetics to economics. Each field has its own particular interest. However, over the next few
days I hope all participants
will discuss and exchange their views and be stimulated by thevarious reports presented. I hope you will find differencesamong research which will be ofinterest in the research you are engaged in. Please, seek commonness which will make thebroad management of conserving animal genetic resources more efficient.
I would like to introduce the National Institute of Agrobiological
Resources (NIAR).
NIAR has 5 main research sections:Genetic

Resources,

Molecular Genetics,
Plant Physiology,
Biotechnology,
Radiation
Breeding.
I would like to explain the connection between these sections by saying that eachaims to provide a foundation for improving agriculture.
Conservationof genetic resources
as well as, basic research on molecular biology is essential
to improve agriculturalscienceand production.
We are trying to use fully the advances in biotechnology
and information
sciences to make scientificprogress.
One of the most important facilities
is the genebank project for plant, animal,
microorganism and DNA genetic resources. For animals, we have more than 700 species
or breeds conserved.
However, an institute does not stand alone. All our research depends on partnershipboth within Japan and abroad. The collaborative
linkages we are hopefully
strengthen
byjoint research are an important component of our institutes activity. We are looking forward
to enhanced collaborationin Japan and, particularly,
with colleagues from overseas in the
future.
I sincerely
hope that those participatingin this workshop from various countries will
have something to offer regarding our study for animal genetic resources and will contributeto their future development.
Thank you very much.

Session

1. Improvement
Genetic Diversity

and Conservationin LivestockofEffective

Population

Size and Inbreeding

TETSURO NOMURA
Department of Biotechnology,

Faculty

Kyoto Sangyo University,

Kyoto 603-8555,

under Selectionof Engineering,Japan

Abstract
Effective

population

size is one of the most importantparametersin the definitionof selection

programmes, because the magnitude of this parameter determines the effect of both random genetic drift
and inbreeding.
In this paper, the basic principle
and several extensions of prediction theory of effective
population
size under selection are presented.
breeding schemes with BLUP selection.

The theory is applied

to the evaluationof long-term

Introduction
The effective population
size is one of the most important parameters in thedefinitionof selection programmes, because the magnitude of this parameter determines
the effects of both random genetic drift and inbreeding(Falconer,
1989; Caballero,1994). In the absence of selection,
all the individuals
of a population
have the same
expected number of offspring,and differencesin family size are due to only random
sampling of individuals
among families.
However, in populations
under selection,families do not have equal probabilities
of contribution
to the next generation because
of inherited or noninherited
causes. This leads to a reduction in effective population
size. Thus, selection inflates the rate of inbreeding
and the amount of random genetic
drift.
In this paper, the basic principle
and several extensions of prediction
theory ofeffective population
size under selection are presented. The theory is applied to theevaluation of long-term breeding schemes with BLUP selection.
Basic formula
Wright (1939) derived a formula for the effectiveof variance of family size (S2k),
4
N
Ne
2

+S2k

population

size (Ne) in terms

[1]

•¢p

where N is the number of parents. This formula can be applied when selection is acting
on a noninherited
trait. When selection
acts on an inherited
trait, the effective size
cannot be predicted
solely from the variance of the family size at a given generation,
because selection affects on the variance of the family size not only in the parental
generation but also in more ascended generations (Wray et al., 1990). Thus, selection
has a cumulative effect on the effective population
size.
The cumulative effect can be understood from the following consideration:
Under
selection on an inherited trait, more offspring are likely to be selected from a geneticallysuperior parent than from a geneticallyinferior parent. Thus, the effective
size isreduced by an increase in variance of family size (S2k in [1]).
However, moregrandoffspring
are also likely to be selected from a geneticallysuperior grandparent for
two reasons. Firstly,
the grandparent
was geneticallysuperior as a parent and has
already contributed more offspring, and therefore has more grandoffspring
available forselection.
Secondly,
the grandoffspring
has inherited
superior
genes from the
grandparent,
and so are more likely to be selected than their contemporaries.
Following Santiago and Caballero (1995), we formulate the cumulative effect of
selection by considering
the change in frequency of a neutral allele. A population inwhich the same number of male and female parents are pair mated to create N/2
families each generation is supposed. We consider an autosomal neutral allele unlinked
to the selected genes with the initial frequency po. Effective population size is computedfrom the variance of change in gene frequency of this allele among infinite replicatesof
the population.
We first consider effective size after one generation of selection. Later,
the asymptotic
prediction
under continued selectionis addressed.
First generation:
Changes in gene frequency arise from three independent process, (a)Random association
between the neutral allele and families with a selective advantage
or disadvantage:
For example, if a family with a high selective
advantage has theneutral allele with a high frequency, the gene frequency in the next generationis
expected to increase, and vice versa, (b) Random sampling of individuals
amongfamilies:
Actual variation in contributionof the family to the next generation is caused
not only by the differencesof selective advantage among families but also random ornoninherited
causes, (c) Mendelian sampling of the neutral allele in the families inwhich one or both parents are heterozygotes.
Changes in gene frequency due to thesethree causes after one generation of selection are denoted by S1,D1 and H1, respectively.
Thus, the total change in gene frequency is=p1 -p0 =S1 + D1 + H1, and thevariance of the change among infinite replicates is expressed asV(•¢P)= E(s12)+ E(D12 )+ E(H12),[2]
where E denotes expectation. According to Santiago and Caballero (1995), the three
expectations are approximated byE(s12)= p0(1-lP0•jC2•@•@•@ 2N
E(D12)_ Po(l-Po)S2‚‹•@•@•@•@2N 4
andrespectively, where C2 is the variance of selective advantage among families.
Substituting the above expressions into [2] and equating the variance of change in genefrequency in an ideal population p0 (1- p0) / 2Ne, we get the effective size in the first
generation
N = 4N
2+S2
k+4C2
[3]
Without selection (C2 =0), [3] reduces to [1], as expected. Under phenotypic selection,
the variance of selective advantage is approximated by
C2 = i2p[4](Robertson, 1961; Burrows, 1984), where i is the selection intensity and p is theintraclass correlation of full-sibs.
Asymptotic prediction: Although the term C2 in [3] reflects the effect of selection in
offspring on change in gene frequency, this change partially persists in descendants. Allof the persisted changes are ascribed to the random association between the neutral
allele and families with a selective advantage or disadvantage, existed in generation O.
Thus, we should incorporate the persisted changes into [3]. This problem was first
addressed by Robertson (1961). He showed that the cumulative effect of selection on
the change in gene frequency increases up to a limiting value of Q times the initialeffect. This means that to obtain the asymptotic prediction, the term arising fromselection ( C2 in [3]) should be multiplied by Q2. Thus, the prediction equation for theasymptotic effective size is
Ne = 4N
2+S2k+4Q2C2[5]
Since the average selective advantage (and, therefore, the changes of gene frequency of

the neutral allele) is expected to be reduced by one-half each generation in descendants
by segregation and recombination, Robertson (1961)
expressed Q by a geometric series
Q = 1 + 1/2 +1/4 + 1/8 + •E•E•E= 2. This expression overpredicts
the cumulative effect of
selection,
because the reduction of genetic variance due to selection (Bulmer effect;
Bulmer, 1980) is not accounted for. Santiago and Caballero (1995)
derived a more
precise expression

where G is the remaining proportion of genetic variance in the selected individuals,
andr is the correlation
between the expected selective
advantages of male and female
parents. Under truncation selection on a normally distributed
trait controlledby aninfinitesimalmodel of gene effects (Bulmer, 1980), G - 1 - kh2, where k = i (i - x),x
is the truncation
point of the standardized
normal distribution,and h2 is theheritability.
The above derivation has been based on the variance of change in gene frequency(variance effective size). Using the rate of inbreeding (inbreeding
effective size), Wrayand Thompson (1990a) developed a method to approximate the effective size of selected
populations,as a function of the mean and variance of the contributionsof ancestors
in the first generation,
to descendants
in the limit. Their method, however, requires
complex recurrence computations. To overcome this problem, Woolliams et al. (1993)
derived equations to predict the mean and variance of the contributionsof ancestors todescendants. Although the prediction
equation derived by Woolliams et al. (1993) isslightly different from [5], simulation studies have shown that both equations give goodpredictions(Woolliams
and Thompson, 1994; Santiago and Caballero,1995).
Several extensions
Nonrandom mating and different
numbers of male and female parents: Suppose
a population in which Nmmales are mated with Nf females (Nm <Nf). The number offemales mated with one male is an integer number of Nf/Nm. The prediction
equationfor the effective size derived by Santiago and Caballero (1995),
but includingsometerms arising from selection
and nonrandom mating (Wang, 1996, 1998; Nomura,1997a),iswhere ƒÊsu ( = NU /Ns ) and S2su are the mean and variance of offspring number of sex
u ( = m or f) from parents of sex s ( = m or f), respectively, Ssm,sf is the covariance
between the numbers of male and female offspring from parents of sex s, ƒ¿0 is thecorrelation between genes of mated parents, ƒ¿I.s is the deviation from Hardy-Weinberg
proportions inparents of sexs, and C2
s is the variance of selective advantage of parentsof sexs.
Overlapping generations: In most domestic animals, the generations are not discrete
but are overlapping. Nomura (1996) formulated the cumulative effect of selection in
populations with overlapping generations by applying the gene-flow technique(transition matrix approach), that was originally proposed by Hill (1974) to describe
the dissemination of genetic gain achieved in a given year. Under random mating, theprediction equation for the effective size per generation iswhere Ns,1is the number of parents of sex s entering the populationeach year and L is
the generationinterval. The variance and covariance terms in [7] is same as [6] but
should be computed based on the lifetime progeny numbers of parents. In comparing[7] and the corresponding
equation for discrete generations(equation[6]), it is foundthat the effective size of a selected population with overlapping
generations is the sameas that for a discrete-generation
population
having the same number (Ns,1 L) of
individuals
entering the populationeach generation, which is in accordance with thecorresponding
result from nonselected populations(Hill, 1979).
Hierarchicalstructure: Animal breeding programmes frequently show a hierarchicalstructure,in which genetic improvement is made in a nucleus herd, and theimprovement is transferredby migration to base or commercial. Extending thetransition
matrix approach, Nomura (1997b)
derived a prediction
equation for theeffective size of populations
with hierarchicalstructure.
Sex-linked locus: With a sex-linked gene, the effect of selection is accumulated throughmore complex process, because the heterogametic
sex has only one copy of the sexlinked gene. Nomura (1997c) described the cumulative effect of selection on the change
in frequency of a sex-linked neutral gene, by applying the transition
matrix method. For
species in which males are heterogametic, the coefficients
accounting for the cumulativechange in gene frequency from parents of sexs to offspring of sex u, Qsu, are expressedaswhere Gs is the remaining proportion of the additive genetic
individualsof sex s. Using these coefficients,
the effective
random mating is predictedbyvariance after selection ofsize of population
underwhere C2suthe varianceof parentsof the relative

selective

advantageof sex s in theircontribution
to offspringof sex u. The equation extended to nonrandom mating
populations
is given by Wang (1998).
Index selection:
In animal breeding practice, selection
on an index including
familyinformation is often carried out. To consider the effect of index selection on the effective
size, we suppose that selection is based on an index of an individual's
phenotype (P) andthe mean of its full sib family (PD , including
the individual):
The optimal(Falconer,••ƒÐ2c/ƒÐ2p phenotypic
ƒÐ2c
ƒÐ2p

index weights

are given by

1989), where r is the genetic correlation
intraclasscorrelation
among full-sibs

among full-sibs(t = h2/2 +commonenvironmental

variance;= the phenotypicof full-sibs
expressed

The intraclasscorrelation

per family.
asvariance)of full-sibs( r = 1/2 ), t is the:theand n is the number

for the index value iswhere ƒÐAw is the genetic variance within family, ƒÐ 2Ab is the genetic variance between
family, ƒÐ2E is the environmental variance, and ƒÐ2
I is the variance of index. With thiscorrelation, the variance of selective advantage C2 is computed from [4]. Thus, theeffective size after one generation of selection is predicted from [3].
Under phenotypic selection (ƒÀ1 =ƒÀ2=h2) in the absence of common environmental
variance(ƒÐ2c = 0),[8]reduces to p = ƒÐ2
Ab/ƒÐ2pfrom= h2/2that parises
only Since the cumulative effect of selection on effective
the ,meaning
breeding valuesof parents.
size is caused by the transmission of breeding values of parents to their descendants,
full portion of the differences of selective advantages among families ( ƒÐ 2
Ab ) have thecumulative effect on the effective size. Thus, as already seen, the asymptotic effect ispredicted by Q2C2 = Q2i2p.
Under index selection, [8] is rewritten aswhere PA is the component of p due to the breeding values of parents (which has the
cumulative effect on Ne), and PE is the component of p due to within-family
varianceand commonenvironmental variance (which has no cumulative effects on Ne). Thus, theasymptotic predictionof the cumulative effect is written as(Wray
prediction

et al.,

1994;

equation

Caballero

et al.,

for the effective

1996).

ReplacingQ2C2

by [9],

we get asize

Evaluationof long-term breeding schemes with BLUP selection
The theory of selection
index has been developed to enable simultaneouspredictionof an individual's
breeding value and fixed effects, such as year and season,
using information from all the available relatives. This is known as best linear unbiased
prediction(BLUP) of breeding values with an animal model (Henderson, 1984). BLUP
selection maximizes the response after one generation. However, through the increasein the probabilityof coselectionof close relatives, BLUP selection reduces the effective
size and, then, inflates the rate of inbreeding.
As long-term selection response is highly
dependent on the effective size (Robertson,1960), BLUP selection applied to long-term
breeding schemes may be less efficientthan simple phenotypic
selection.
Wray and Hill (1989)
have developed a method for predicting
asymptotic rateof response to BLUP selection using an index (pseudo-BLUP)
of an individual's
record,the mean of the records of its full- and half-sibs
and the estimatedbreeding values(EBVs) of its sire, its dam and all the mates of its sire. In this section, this method isused to predict cumulative selection response and inbreedingunder BLUP selection.
Model and assumptions:
The trait under selection is assumed to be determined by an
infinite number of additive loci, each with infinitesimal
effect (the infinitesimalmodel;
Bulmer, 1980). Apopulation
with discrete generations
is supposed, in which Nmmales=are randomly mated with Nf females. The number of females mated with one male isan integer number of d =Nf/Nm. Subscript s (= m or f) is used to specify sex. Eachfemale produces n progeny consisting of nf progeny of each sex ( n= 2 nf). Each malehas nm=dnf progeny of each sex. Among the progeny, the best Nmmales and the best
Nf females are selected on the basis of their EBVs from BLUP with an animal model.
The proportionof selected
for sex s is Ps = 1/ns and the selection
intensity
is
is

zs/Ps

selection, where zs is the normal ordinate

corresponding(xs). The variance reduction

due to the Bulmer effect (Bulmer,

factorthe truncation

point of1980)

is ks =is(is-xs).

EBVs from BLUP are approximatedby the pseudo-BLUP
The selection index assumed in the pseudo-BLUP is1989).

where P is the individual's
individual)
full-sib records,
records,(Wray

and Hill,record, PD is the mean of records of n (includingthePH is the mean of nd (including
the n full-sibs)
half-sib

Am and Af are EBVs of the sire and dam, respectively,

and Af is the meanof EBVs of all dams mated to the sire. The selection index weights are updated andoptimized each generation using the equations given in Appendix 1.
Predictionof effective size and inbreeding:
The asymptotic genetic parameters, which
are required for the predictionof asymptotic
effective size and rate of inbreeding,areobtained in the following way. The total phenotypicvariance at generation t is

ƒÐ2A(t)whereis the additive

ƒÐ2E

environmentof full-sibs,

ƒÐAmvariance

ƒÐAf
ƒÐAw

(

is further

genetic
anddecomposed) and within-full-sib

familyThese components are obtained

variance,oc is the varianceis the environmental
into between-sire(from) components:

familydue to the common

variance. The additive(), between-dam

genetic
familyƒÐI(t)/ƒÐA(t)ƒÐI(t)

where r(t-1) is the accuracy
computed as r(t) =genetic

parametersof selection, whereare computedin generation
t-1. The accuracy of selection isis the standard deviation of the index. The

each generationby using recurrently

equations[12].

Preliminary
analysis showed that all the genetic parameters reach essentiallysteadyvalues after several generations.
Thus, the values after 10 generations
are used as theasymptotic
values (asterisks
will denote asymptotic
values).
The asymptotic effective size of sex s is predictedby the equation combined [6]and[10]where p*s is the asymptotic

intraclassis component of p*s due to the breeding
unweighted
correlations

correlationof families

values of the parents,from parents of sexs, p*Asand i [= (im + if)/2]

is themean of selection intensities
for the two sexes. The expressions of the
ps and p*Asare given in Appendix 2. The effective size of the populationand the asymptotic

rate of inbreeding

are obtained

by

and

Predictionof selection
response:
In the calculationof the selection response, thereduction of the additive genetic variance due to the inbreeding is taken into account byusing the equation suggested by Wray and Thompson (1990) and Wray and Goddard(1994),

The inbreeding

coefficient

at generationt is predictedby(Wright,1931), where Neis the asymptotic
effective size computed from [13] and [14].
The response to selection in generationt is computed from R(t) = ir(t)ƒÐA(t). Then the
cumulative

response

over t generations

is obtainedby

Simulation:
Stochastic
simulation was carried out to check the prediction
equations.
Breeding values of parents in generation 0 were randomly chosen from an N(0, 1)
distribution.
inifinitesimal
individualƒÓiThus, the initial additive geneticvariance,
model of gene effect (Bulmer, 1980),
in the following
generation was generatedoA^0) was 1. On the basis of thethe breedingvalue (Ai) of anaswhere Amand Af are the breeding values of the sire and dam, respectively,
Fmand Ff are
the inbreeding
coefficientsof the sire and dam, respectively,
and is a random number
taken from an N(0, 1) distribution.
Assuming that there are no commonenvironmental
factors (ƒÐc2 = 0 ), the phenotypicvalue (Pi) was obtained as

ƒÕi

where

is a random number taken from an N(0, 1) distribution.

••

Nm 10,Nf= 60 and nf•• 3 was simulated.

The initial

heritabilities

The population

with(h20 ) were assumed

to be 0.1, 0.2, 0.4, 0.6 or 0.8. The number of simulated generations
were 8 and thenumber of replicatedruns were 50. Selection
was based on BLUP with an animalmodel. The rate of inbreeding
between 5 and 8 generations

For comparison,

was obtained

simple phenotypic

selectionfrom the average inbreeding

was also simulated.

coefficients

The predictionsforƒÀl(t)ƒÀ2(t)
ƒÀ3(t)
ƒÀ4(t)

phenotypic

selection

were made by putting

=

=

=

= h2t.

The inbreeding
coefficientsobserved in simulation
are shown in Figure 1. As
seen from this figure, the rate of inbreedingunder BLUP selection is much higher thanunder phenotypic
selection, especiallywhen the selected trait has a low heritability.
Asthe heritability
becomes low, the rate of inbreedingunder BLUP selection tends toincrease. This is because for a trait with low heritability,
BLUP places larger weights
on family information
and, then, the probabilityof coselectionof close relatives
increases.
Table 1 gives the rate of inbreedingand effective
size observed (sim) in
simulation and predicted(pre) from equations [13]-[15].
On the whole, the equations
give a good predictor,
though it slightly
over-predicts
the rate of inbreeding
and underpredicts the effective
size.
Figure 2 shows the inbreeding coefficientsobserved in simulation and predictedfrom [17]. Although the prediction
was made with the asymptotic effective size, it gavea good predictor of initial inbreedingunder phenotypic selection. For BLUP selection,
the presented method over-predicts
the initial inbreeding.
The presented method may
give a conservative predictionof the effective size and rate of inbreedingunder BLUP
selection.
The cumulative selection responses observed in simulation and predicted fromequations [16] and [18] are compared in Figure 3. In all the cases studied, there was agood agreement between the observed and predictedvalues.

Figure 1. Observed inbreeding
different
initial heritabilities.

coefficients

under phenotypicand BLUP selection

withTable 1. Observed (sim) and predicted(pre) effective
population
size and rate of
inbreedingunder phenotypicand BLUP selection with different
initial heritabilities.
R a t e o f in b r e e d i n e
P h e n o t y p ic

E f f e c t iv e p o p u l a t io n s iz e

B U L PP h e n o ty p ic

h 20

S im

p re

s im

p re

s im

p re

0 .1

0 .0 1 6 6

0 .0 1 7 1

0 .0 5 5 8

0 .0 5 7 2

3 0 .0 7

0 .2

0 .0 1 6 7

0 .0 1 9 0

0 .0 4 3 8

0 .0 5 2 3

2 9 .9 0

0 .4

0 .0 1 9 5

0 .0 2 0 7

0 .0 3 4 7

0 .0 4 1 2

0 .6

0 .0 2 0 4

0 .0 2 0 7

0 .0 2 5 7

0 .8

0 .0 1 9 2

0 .0 1 9 5

0 .0 2 1 1

B U L Ps im

p re

2 9 .1 7

8 .9 6

8 .7 42 6 .9 2

1 1 .4 2

9 .5 62 5 .5 9

2 4 .2 0

1 4 .4 2

1 2 .1 4

0 .0 3 1 5

2 4 .4 6

2 4 .1 8

1 9 .4 2

1 5 .8 50 .0 2 3 5

2 6 .0 5

2 5 .6 2

2 3 .6 4

2 1 .3 1

Figure 2. Observed and predicted inbreeding
selectionwith different initial
heritabilities.

coefficients

under phenotypicand BLUP

Figure 3. Observed and predictedcumulative
selection with different initial heritabilities.

responses

under phenotypicand BLUP

Application: Quinton et al. (1992) suggested that it is important to compare selection
programmes for response at the same level of inbreeding. This can be done graphically,
relating the cumulative selection response with the level of inbreeding for a range in thenumber of sire used. As an application of the presented method, breeding programmeswith BLUP and phenotypic selection are compared at the same level of inbreeding. A
population with h2
0 = 0.3 ,Nf= 120 and nf= 3 was supposed. The number of sires waseither Nm =5, 10, 15, 20, 30, 40 or60.
Figure 4 shows the cumulative responses over 10 and 20 generations of selection
relative to the cumulated inbreeding. Except the case of Nm = 5 and 20 generations of
selection, where the effect of selection and inbreeding on the cumulative response ismost severe, BLUP selection always gives a higher response than phenotypic selection,
for a given number of sires selected. However, for lower levels of inbreeding (e.g.
F10 < 10% and F20 < 20%), the cumulative response at the same inbreeding is greaterwith phenotypic selection. This suggests that BLUP selection may not be always
optimal, especially when inbreeding is a limiting factor in the choice of selection
programmes.

Figure 4. Cumulative
response and inbreedingfrom 10 (a) or 20 (b) generationsofphenotypicand BLUP selection.
The number of sires: 5, 10, 15, 20, 30, 40 or 60 (the marked
points on the graphs). The number of dams: 120

FinalremarkThis paper has investigatedthe prediction
theories of effective population
size
and inbreedingunder selection. All the theories are based on the infinitesimalmodel ofgene effects, in which traits are assumed to be determined by an unlinked system ofadditive loci. This model is, however, inappropriate
to make predictionsof the effectof marker-assisted
selection(MAS) on effective size, because quantitative
trait loci
(QTLs) and selected markers are linked and, therefore, they tend to be passed together
over generations.
Due to the hitchhiking
effect, the drift process of genes linked withmarkers is expected to be larger than that predicted by the infinitesimalmodel (Santiago
and Caballero,1998). The prediction
theory for effective size and inbreedingunder
MAS should be developed in future studies.
Appendix 1. Index weights in the pseudo-BLUP
Optimum weights for the pseudo-BLUP (equation

where V(Am) and V(Af) are the variancerespectively.
These variances areof estimatedbreeding[11])

are obtainedby:values of sires and dams,

andAppendix 2. Asymptotic intraclasscorrelationof families for the value of pseudoBLUP
Let p*Dand p*Hbe the asymptotic intraclass correlationsof full- and half-sibs,

respectively.

These are expressed

as

and

where asterisks denote the asymptotic values. With these correlations,correlationof families from parents of sex s is obtained by

The asymptotic
values of parents

andintraclasscorrelations(pA,D and pA,H, respectively)of full- and half-sibs
are expressed

the intraclassdue to the breedingasWith these correlations,obtainedthe componentof p*s to the breedingvalues of parents

isbyReferences
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Burrows, P.M. 1984. Inbreeding
Caballero,A. 1994.

Theoryof Quantitative

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Clarendon

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16: 97-159.
Exposes de Biometrie

et de StatistiqueinGenetic Improvement in Dairy Cattle
Effective Population
Size
MRODE RA, SWANSON GJT and LINDBERG

and its ConsequenceonCMAnimal Data Centre Ltd, Fox Talbot House,
Greenways Business Park, Chippenham SN15 1BN UK

Abstract
Genetic progress that has accompaniedby examining the rate of genetic change in protein
population
size (Ne) were studied by examining

the globalizationof Holstein cattle was investigatedyield in several countries. Current trends in effective
the number of sires of sons used each year and thepercentage in common use across several countries using Interbull
test results. The influence of rates
of inbreedingin several Holstein populations
and the genetic correlation(rg) among countries on Ne was
examined. The results were compared with those for the Jersey breed with a less globalized
breeding
programme. Substantial
rates of genetic progress of about 2% per year in milk traits have been observedin most Holstein Friesian populationsin the last 5 years. This however has been accompanied by adecreasing trend in effective
population
size. The number of bull sires used is decreasing
with time
while the percentage in common usage is increasing.
The average relationshipamong bulls, rates ofinbreeding
and percentage of inbred cows all show an increase with years. International
evaluations by
the Interbull
Centre account for rg among countries and therefore offers opportunities
to select from awide range of top bulls. This, in addition
to national selection
objectives
which include other traits
related to profit in addition to milk traits, should ensure different
countries sample different bulls forprogeny testing and hence increase Ne. The use of selection using indices with restrictions
on family
information which substantiallyreduce levels of inbreeding
while maintaining
present rate of genetic
gain should be exploited.
The current rates of inbreeding
could increase substantially
due to advancesin reproductive
technology. The adoption of special breeding schemes such as factorial mating designs,
equalizing
family sizes and deliberatechoice of less closely related animals would become necessary
to reduce the rate of inbreeding

to conserve available

genetic

diversity.

Introduction
Selection
based on progeny testing remains the major tool for genetic
improvement in dairy cattle. Traditionally
improvement has resulted from selection
applied on the usual four pathways: bulls to breed bulls (BB), bulls to breed cows (BC),cows to breed bulls (CB) and cows to breed cows (CC).
Prior to 1970, this was mainly within the several strains of Holstein andFriesian (black and white) dairy cattle existing in different countries around the world.
Comparisons of the various strains of the black and white (B&W) cattle carried out inthe mid-seventies
indicated
the superiorityof the North American Holstein for milk
production(Jasiorowski
et al., 1988). This stimulated
large scale importationofHolsteins by many countries resulting in the replacement of most strains of European
Friesian
dairy cattle by the North American Holsteins.
In the 1980's,
due toimprovements in reproductive
technology,
this process was accelerated.
This resulted
in bulls with similar parentage being progeny tested simultaneously
in several countries
and began the creation of a single global populationfor B&W dairy cattle. This
globalization
process was accompanied by substantial
rates of genetic progress due tothe genetic lift in importing countries from the between strain selection, opportunities
for higher intensityof selection provided by a large global population
and a certain
amount of heterosis from crossing the North American Holstein with the local Friesian
genotypes.
The major disadvantageof the emergence of the global breeding structure of
B&Wdairy cattle has been a reduction in Ne resulting in the loss of genetic diversity.
In addition,
as a result of several changes in breeding practices in the last decade suchas the availabilityof more accurate genetic evaluations
through improved statisticalprocedures, an even higher intensityof selection
is possible
especiallyin the BB
pathway. This could result in the use of fewer top sires world wide thereby increasing
the rate of inbreeding
and further reducing Ne. Further globalizationof dairy cattle
breeding programmes is intensifying
due to international
progeny testing and acrossborder mergers of major companies involved in progeny testing bulls. It is therefore
important that current trends in rates of inbreeding
and Ne are examined in the B&W
dairy cattle populationand the implicationsin terms of conserving genetic diversity
assessed. Where appropriate,
results are compared with those from the Jersey breed
which is a numericallysmaller population
and with a less globalized
breeding
programme.
Initially,
this paper examines the rates of genetic progress in several countries
that have accompanied the globalizationof the B&W and Jersey breeds. Subsequently,
the consequences of genetic improvement in terms of rates of inbreeding
and Ne and theimplicationsin terms of conservation of genetic diversity
are discussed.
Genetic

Improvement

Scheme in dairy

cattleƒÐG

Currently genetic improvement in dairy cattle in most developed countries isbased on progeny testing (PT), see Figure 1. While initially
it was thought that nucleus
breeding schemes utilizing
Multiple Ovulation and Embryo Transfer (MOET) withselection
based on parent and sib information(Nicholasand Smith, 1983) would
replace progeny testing schemes, this has not been so in practice. The potential
advantages of the nucleus schemes in terms of higher rate of genetic gain were greatly
reduced after the reduction in genetic variance due to the Bulmer effect and levels of
inbreeding
were taken into account (Dekkers,
1992). Generally these schemes haveoperated in conjunction with conventional PT in countries where they exist rather thanreplace it. In a national breeding programme based on PT and selection in the four
traditional
pathways, the rate of response per year (•¢G) based on the expression ofRendel and Robertson, (1950) is:

Where the Ljk is the mean generationindex for the ikth pathway is:interval

for the pathway

jk and Ijk, the selection

With rGGbeing the correlation
between the predicted
and true genetic value (theaccuracy of selection),
i is the standardized
selection differential
andis the genetic
standard deviation.
These are the major factors that determine the rate of genetic
progress.
Schmidt and Van Vleck (1974) indicatedthat the proportionsof genetic gainfrom the four pathways are approximately:Icc 2%, IBC 27%, ICB 32% and IBB 39%.
These values indicate that most of the genetic improvement (about 71%, (BB + CB))would be due to matings to produce young bulls for testing in the next generation. This
proportionis likely to be higher with the availabilityof more accurate estimates ofbreeding values (EBV) from Best Linear Unbiased Prediction(BLUP) as the tendency
in most PT schemes is to use sires with the highest EBV to breed bulls (BB) in the next
generation. In addition selection of cows on the basis of EBV would result in most dams(CB) selected to breed young bulls being daughters and granddaughtersof the elite bulls(BB) of the previous generation. Consequently
as Goddard and Smith (1990) indicated,the bull breeding part of the population
is like a dispersed
nucleus herd with the samebulls used in the BB and CB paths in the nucleus section. The system therefore
effectively
reduces to a two-path system rather than the conventional four path system.
While this could result in substantial
rates of genetic progress, it could also markedly
increase the rate of inbreedingand hence reduce Ne.

Figure 1. Structure of conventional
progeny-testing
shcemes, identifing
the fourpaths of selection:sires of bulls (BB), sires of cows (BC), dams of bulls (CB) anddams of cows (CC).

Current

Rates of Genetic improvement
Theoretically
PT schemes can result in rates of genetic

improvementof 2% to3% per year (Van Vleck, 1981). Presented in Tables 1 and 2 are current rates of genetic
progress for protein yield for Holstein bulls and cows in some countries. These rates
have been expressed where possible as a percentage of the mean protein yield of cows
born in 1990.
Table 1. Genetic trends for Holstein Friesian bulls for protein yield in several countries
L ast 10 yearK g /C o u n tr yp e rio dy ea rC an ada

1 9 8 2 ‑9 2

H o l la n dof

L ast 5 year

K g /

%

e a n*

P e rio dy e ar

m ean

3 .9

1 .4

1 9 8 8 ‑9 2

4 .8

1 .7

1 9 8 0 ‑9 2

3 .6

1 .6

1 9 8 8 ‑9 2

4 .5

2 .0

U K

1 9 8 1 ‑9 0

2 .7

1 .5

1 9 8 6 ‑9 0

3 .4

1 .9

F ra n c e

1 9 8 1‑ 9 1

4 .4

1 .5

1 9 8 6 ‑9 1

4 .8

1 .8

I ta l y

1 9 8 3 ‑9 2

5 .1

1 .9

1 9 8 8 ‑9 2

5 .1

1 .9

*Mean of protein

yieldof cows born in 1990.

Genetic

trends for Holstein

Table 2.

%m

Friesiancows for protein

in several

countries

L ast 10 year

K g /

%

L a st 5 y e ar

K g /

%

C o u n tr yp e rio dyear

m ean *

P e ri o d

year

m ean

C an ada

1 9 8 4 ‑9 4

3 .8

1 .3

1 9 8 9 ‑9 4

4 .3

1 .5

U S A

1 9 8 6 ‑9 5

3 .8

1 .3

1 9 9 1 ‑9 5

3 .7

13

H o l la n d

1 9 8 4 ‑9 4

4 .1

1 .9

1 9 9 0 ‑9 4

4 .1

1 .9

U K

1 9 8 4 ‑9 4

2 .3

1 .3

1 9 9 0 ‑9 4

3 .8

2 .1

F ra n c e

1 9 8 8 ‑9 3

3 .6

1 .3

1 9 8 8 ‑9 3

4 .8

1 .8

I ta l y

1 9 8 6 ‑9 55 .0 2

1 .9

1 9 9 1 ‑9 5

5 .2

1 .9

*Mean of protein

yieldof cows born in 1990.

of

yield

o f

o f

In percentage
terms the rate of genetic progress is similar across countries
ranging from 1.5% to 2.5% for bulls and 1.3% to 2.0% for cows over the last 10 year
period. The similarity
in the rate of change is further confirmed by Figure 2, whichindicates
the genetic trends for bulls for several countries
relativeto the United
Kingdom (UK) base from Interbull results in February, 1998. In the Jersey breed, therate of genetic gain for protein yield varies from 0.7% to 1.5%/year for cows in the last
10 years and about 1.8% per year in Canada and the UK in the last 5 years. In mostcountries studied, the rates of change over the last 5 years are higher than for the last
10 year period in both the B&W and Jersey populations.
This is indicativeof a higher
intensityof selection in recent years and the increasing influence of animals of higher
genetic merit from North America in countries such as the UK with a high rate ofimportation.
These results indicate that the annual change in genetic progress in milk traits
in the global Holstein population
and Jersey populations
are going up at an increasingrate. What then are the implicationsof such change in terms of effective population sizeor rates of inbreeding?
These are examined subsequently
in this paper.

Figure 2. The genetic
on UK basetrend for protein

yield

for some countries

from Interbull

evaluations

•¢F

The consequence of selection
on effective
population
size
One of the consequences of directional
selection
is a reduction in the genetic
variance and increase in the rate of inbreedingas families superior for the selected trait
contribute more offspring to the next generation. Any increase in the rate of inbreeding
implies a reduction in Ne, as both are inversely related;= 1/(2Ne). In the past few
decades, most dairy cattle populations
have been subjected
to intensive
selection foryields of milk, fat and protein. However, in the past decade or so, as a result of several
changes such as the availabilityof more accurate genetic evaluations,
global sirerankings, increasing
international
trade in semen and embryos and the use of new
reproductive
techniques,
even higher intensities
of selection,
especiallyin the BB and
CB pathways, have become possible. These factors are expected to increase the rate of
inbreedingand hence reduce Ne. For instance, the use of BLUP animal model
evaluations to select sires and bull dams increases the probabilityof co-selectionofclose relations (Belonsky
and Kennedy 1988) and hence and increase in inbreeding.
Increased international
trade in stock, semen and embryos may imply the use of fewer
top sires world wide. In a world wide study Leitch (1994)
showed that the 10 mostpopular sires and sons for Holstein Friesians
represented
62% of the young bulls
sampled in each country, and the 10 most popular maternal grandsons represented 63%
of sampled sons. Moreover, the use of reproductive
techniquessuch as artificial
insemination and embryo transfer, imply that superior individuals
can be exploited more
intensively
in the breeding programme
In this section, current trends in effective population size and genetic variancein various dairy cattle populations
are examined and the implications
discussed.
Trends in effective population
size and the influence of various factors
In a theoretical
study, over a wide range of numbers of bulls tested, Goddard(1992)
reported an optimum number of about 6 bulls to be selected to maximize netpresent value (future genetic gains discounted
to an equivalent present value) in the
Holstein global population.
To understand current trends in effective population
size
in the Holstein population,
the number of sires of sons used each year in several
countries was examined based on Interbull results. Figure 3 shows the percentage ofbulls with progeny test results in various countries which are sons of the 5 most usedsires over the period 1981 to 1991. In general, this percentage is increasing
in a numberof countries implyingthat the number of sires of sons used each year is decreasing.

Considering
the same trend in 18 countries, Wickham and Banos (1998) indicatedthat
the 5 most heavily used sires accounted for 50% of all bulls born in 1990 which havebeen progeny tested compared with 14% in 1970.
The percentage of bulls by the 5 sires with most sons each year, summed over
five countries (Canada, Denmark, USA, New Zealand and Australia)
for the Jersey
breed is shown in Figure 4. A similar trend for the Holstein summed over the sevencountries represented
in Figure 3 is included for comparison. While the percentage isincreasing slightly with time, the rate of increase is lower in the Jersey breed than in the
Holstein, over the same time period.

Figure 3. The percentage of bulls with progeny
the 5 most used sires in some countries

test results

which are sons ofFigure4.

Percentage

of bulls

with progeny

test which are sons of the 5 top bullswith most sonsNot only has the percentage of progeny tested sons of the top 5 sires increased
with time, the percentage of these top 5 sires in commonuse across several countries
has also increased. Tables 3 and 4 show the percentages of the top 5 most used sires
commonamong several countries over two five year time periods (1981-1985
and
1988-1992).
The percentages for the top 10 most used sires are also given in these
Tables. The results indicate a higher proportion of the same bulls are being used in thebull breeding section of the progeny testing programmes of several countries between
1988 - 1992 when compared with 1981 - 1985. For instance of the top 5 bulls withmost sons between 1988 -1992, three of the bulls were the same in France and Hollandand 2 were the same in the USA, Germany, the UK and Canada. Considering
the top
10 sires over the 2 time periods indicatesan even higher number of bulls were incommonuse in several countries. A similar analysis for the Jersey population based on
5 countries in Figure 4, also indicatedan increase in the proportion of bulls in common
use. However, these proportions
were not as high as in the Holstein, with at most 2bulls in commonuse between any two of these countries when the top 5 sires with mostsons were considered. Again the proportion of bulls in commonuse were higher when
the top 10 sires with most sons were considered, with Canada, USA and Australia using
the same 5 sires.

Table 3. Percentage of the 5 bulls
among several countries
CAN AD A

with

GERM AN Y

most sons in the period
FR A N C E

IT A L Y

1981-1985

which

H O LL A N D

are common
U SA

CANA D A

100

GERM AN Y

20(1 0 )

100

FR A N C E

0
(0 )

0
(0 )

10 0

IT A L Y

40(3 0 )

20(1 0 )

20(3 0 )100

H O L LA N D

0
(0 )

0
(0 )

20
(2 0 )

0
(10)

100

U SA

20
(3 0 )

20(1 0 )

0(0 )40(2 0 )

0(1 0 )

100

U K

0
(0 )

20
(1 0 )

0
(0 )

0
(0 )

0
(0 )

0
(0 )

Percentages

in brackets

are those when the 10 bullswith the most sons are considered.

U K

100

Table 4. The percentage
among several countriesof the 5 bulls

CA N A D A

G ER M A N Y

with most sons in the period
FRA N CE

1988-1992

which are common

H O L LA N D

IT A L Y

U SA

CA NA D A

1 00

G ER M A N Y0(2 0 )

100

FR A N C E

40(3 0 )

0
(10)

100

IT A L Y0(3 0 )

0(10)

20(5 0 )10 0

H O L LA N D

20(3 0 )

0(0 )60(5 0 )

20(5 0 )

100

U SA

20
(3 0 )

40(2 0 )

20

0
(30 )

20
(3 0 )100

B R IT A IN

40
(5 0 )

20(2 0 )

40(4 0 )

40(4 0 )

40(4 0 )

40(3 0 )

Percentages

in brackets

are those when the 10 bulls

Figure 5.
Trends in genetic
relationshippopulations(Wickham and Banos, 1998)

with the most sons are considered.

among sires

for

18

Holstein

Friesian

UK

100

These results attest to a declining Ne of both the world wide B&W and Jersey
populations.
Wickham and Banos (1998) examined the actual trend in Ne and average
additive genetic relationshipamong bulls in 1 8 countries in the Interbull evaluations andtheir results are presented in Figure 5. The effective population size of bulls each year
was calculated as a function (harmonic mean) of the number of sires of sons and damsof sons. Similar results for UK B&Wdata only is given in Figure 6.

Figure6.

Trends in genetic

relationshipamong sires for UK Holstein

Friesians

Consideringbulls born in 1990 over the 18 countries (Figure 5), Ne was only
22% of the actual population
size compared with 100% for bulls born in 1970. At thesame time the average genetic relationshipof sires of sons born in 1990 was six times
that of sons born in 1970. The results from the UK data followed the same trend withthe average relationshipof bulls in 1990, three time those born in 1974. A similar result
was obtained for the Jersey breed in the UK. These results confirm a decreasing trend
in Ne with time in both the B&W and Jersey populations.

Estimateof total sires to breed bulls
Considering
the top 5 or 10 sires with the most sons in the seven countries
represented
in Figure 2, the total number of different bulls used to breed bulls in the
B&Wbreed across these countries is given in Table 5. Similar results for the Jersey
breed are also represented
in the table. The 10 bulls with most sons in these countries
accounted for 65% to 93% of all bulls with progeny test results in the Interbull
test
analysis, and therefore gives a good approximationof the total of different bulls used
to breed sons in these countries. In general the total number of bulls decreased with timein both breeds with 28 sires used to breed bulls in 1991 in each breed in these countries.
This number is substantiallyhigher than the optimum recommended by Goddard (1992)
and Goddard and Smith (1990)
for the B&W population.
Thus while total Ne isgenerally
decreasing
with time, it seems the current level is higher than therecommended optimum. However, this ignores the average relationshipand therefore
levels of inbreeding
associatedwith the sires used in the several countries.

Table 5. Total number of Holstein Friesian
the top 5 or 10 sires with the most sonssires used to breed bulls

in seven countries*

based onT o ta l n u m b e r o f sire s u s e d to b re e d b u lls
H o lste in F rie sia n B re e d
B toa sep d 5 obn u lls
th e
B oa pse 1d 0 o bnu thllse tJe rs e y B re e d
B as
to ep d 5 ob nu th
llse
Btoa ps e1d0 obn u lls
th e

Y ear
19 81

22

41

27

45

198 3

19

37

22

43

1985

22

42

21

39

198 7

15

36

23

42

198 9

12

31

23

37

199 1

10

28

16

28

1 992

15

29

18

26

*Countries included
were Canada, Germany, France, Italy, Holland,
USA and UK for the Holstein
Friesian breed and Canada, Denmark, USA, New Zealand and Australia for the Jersey breed.

Influenceof genetic correlations
between countries on effective
population
size
The genetic correlation between countries arising from differences
in selection
objectives
and genotype by environment interaction(GxE) for individual
milk traits
determine the extent to which they select the same sires of sons and hence affect global
Ne. In a simulation study Goddard (1992) examined the influence of rg on a number ofbulls selected if2000 bulls were tested in more than two countries under two situations:
In the first situation,
there were differencesin the selection objectivesof the countries
with a correlationof0.9 between each pair of countries and each country was assumed
to have selected the best two bulls in terms of their selection objective
as bull sires. In
this situation,
the total number of bulls selected continued to increase as the number ofcountries increased to at least 10, with a total of about 8 bulls selected. In the second
situation,
selection objectives
were the same but there was GxE in individual
traits andagain with each country selecting the best two local bulls as bull sires. The total numberof bulls selected increased as the number of countries increased but the number of bulls
selected reached a maximum of 5.
In general accounting for rg (rg < 1) due to GxE and/or differences
in selection
objectives
between countries increased the total number of bulls to be selected andhence increased global Ne. In practice, with respect to the first situation,
Leitch (1994)
carried out a comparison of selection
indices for dairy cattle in ten countries. The
largest differences
observed between countries were in terms of the relative emphasis
on production and type and relative emphasis on milk yield (positiveor negative). Also
5 of the 10 countries included non-productive
traits such as mastitis resistance, somatic
cell count, milking speed and temperament in their indices. Leitch also reported rankcorrelationsof0.90 - 0.92 between Canadian LPI and ILQM (Italy),
ISU (France) andPIN II (now ITEM, UK) and 1.00 with US TPL However, considering
only theproduction component of LPI, she observed rank correlationsof 0.88 to 0.92 with the
production index in the UK, Netherlands,
Italy and France, but a correlationof unity
with several countries: USA, New Zealand, Germany and Israel. In spite of these highcorrelations
between the various selection criteria she observed a substantial
variationin the sires and maternal grandsires of sons progeny tested in 1993 in the countries she
studied. Thus while it seems that the present indices ensure variation in bulls sampled
by different
countries, differences
between selection indices in different
countries will
become more obvious as countries include other traits relating
to profitability
in theirindices rather than concentrate on milk traits. Meuwissen (1998) indicatedthat genetic

correlationsof below 0.7 - 0.8 between different selection
objectives
are needed, if
these objectives
are to give rise to breeding schemes that are substantiallydifferent.
Considering the second situation of Goddard (1992), Interbull
Centre estimates
genetic correlations
among countries using sire proofs, which are then utilized by theCentre in multi-trait
across country evaluation(MACE). These correlations
are areflectionof GxE among countries as well as differencesin methods for national
evaluations. Presented in Table 6 are the genetic correlations
among seven countries
from protein yield from Interbull results.

Table

6.

InterbullC A N A D AGenetic

Correlations*

G E R M A N Y

FR A N C E

among countries

for protein

IT A L Y

H O L L A N D

yield
U SA

C A N A D A

1 .0 0G E R M A N Y

0 .9 1

1 .0 0F R A N C E

0 .9 50 .9 2

1 .0 0IT A L Y

0 .9 30 .9 00 .9 5

1 .0 0H O L LA N D

0 .9 30 .9 30 .9 30 .9 1

1 .0 0U SA

0 .9 6

0 .8 90 .9 50 .9 50 .9 3

1 .0 0U K

0 .9 30 .9 00 .9 30 .9 1

0 .9 30 .9 1

run in February

1998*From Interbull

U K

1 .0 0

These correlations
vary from 0.89 between USA and Germany to 0.96 between
USA and Canada. These less than unity correlations
imply bull rankings in eachcountry are different
and should increase the total number of bulls available
for
selection and hence increase Ne. The effect of these correlations
on the total number ofbulls available
for selection and the percentage average relationshipamong these bulls
considering
the top bulls on protein yield in each of the seven countries in the previous
section, is presented in Table 7. Thus if selection for global protein yield is based on the
top 5 bulls on protein yield in all seven countries, a total of 11 bulls will be available
for selection, that is, 120% more than selecting only from the top 5 of the national listof an exporter. If the top 50 bulls are considered,
93 bulls will be available
for
selection,
86% more than selecting from the top 50 of any national list. Considering
18
countries in the Interbull
evaluation,
Wickham and Banos (1998)
reportedthat,
selection for protein yield based on a "top 50" list of all these countries would lead toa total of 128 bulls being available for selection, which is 150% more than selection
based on the top 50 of any national list.

Table 7. Total number of bulls available for selection
5 bulls for protein yield in seven countries*
N o . o ffo rb us elllse ac tv iao iln a b lebased on the top％

A r ev lae rtaiog ne sgh eipn e t ic

N o . o f T o p B u lls*Countries

5

11

3 .3

10

2 4

3 .4

20

4 6

4 .4

30

63

4 .4

40

82

4 .5

5 0

9 3

4 .9

included

were Canada, Germany, France,

Italy,

Holland,

USA and UK

Although the percentage average relationshipamong the available
bulls forselection increases as more top bulls are considered (Table 7), in all cases it was lessthan 6.25%, the additive genetic relationshipbetween a sire and his progeny in the
fourth generation.
It seems that international
evaluationby accounting for genetic
correlations
among countries results in more bulls being available for selection on a
global scale. This should slow down the rate of decrease in global diversity.
Influenceof inbreeding
depression
In a simulation study Goddard (1992) concluded that inbreeding
depression (D)largely determines
the optimal Ne for the global Holstein population.
Inbreeding
depression
results in a reduction of milk production.
Table 8 presents estimates ofinbreeding
depression
for milk traits in some dairy breeds in the UK (Mrode andSwanson, 1998), USA (Wiggans et al., 1995) and Canada (Miglior et al., 1992). The
estimates for the Holstein breed for milk yield vary from -13.1kg in the UK to -29.6kg
in the USA per 1% of inbreeding.
Expressed as a percentage of the mean, these
represent a reduction of 0.25% to 0.31% of the mean milk production of cows born in
1990. The estimates for the Jersey breed are similar to the Holstein when expressed inpercentage terms.

Table 8. Estimates of inbreeding
Friesians and Jerseys

depression

for milk traits

H o lste in F ri es ia n

by per 1% inbreedingin HolsteinJ e rs e y

T ra it

U K

U SA

U K

U S A

C A N A D A

M ilk (k g )‑ 1 3 .1

‑2 9 .6‑6 .9‑2 1 .3‑9 .8

F a t (k g )‑0 .5 4

‑1 .0 8

‑0 .6 4

‑ 1 .0 3‑0 .5 5

P ro te in (k g )‑0 .4 2‑0 .9 7‑0 .2 8‑0 .8However, inbreeding
depression
not only reduces milk production,it alsogreatly affects fertility,
survival and general hardiness. Studies evaluating the influenceof D on Ne have mostly accounted for its effect through reduction of production
andmay therefore have under-estimatedits effects. Over a wide range of bulls tested (50-1000) Goddard and Smith (1990)
observed that the optimum number of bulls tomaximize net response (•¢G - D•¢F) was about 4-7 bulls assuming 0.25% depressionin economic merit per % inbreeding.
With 10 sires used per generation (5 years), they
estimated the level of inbreeding
to give the optimum response to be 0.25% per year.
In general they concluded that 8-12 bulls per generation gave more than 95% of the
maximumresponse except in extreme cases where very low numbers of bulls were
tested.
Howdo current estimates of inbreedingcompare with the levels for the
optimum scheme of Goddard and Smith (1990)?
Table 9 shows current rates ofinbreeding
for several Holstein Friesian populations.

Table

9.

Estimatesof rates of inbreedingin some Holstein

Friesian

populations

R a te (o%f In/y eb arer)e d in g
C o u n tr y

R ef e ren c e

C an ad a

0 .2 1 (b u lls o n ly )M ig lio r a n d B u rn s id e (1 9 9 5 )U SA

0 .2 0W ig g a n s e t a l (1 9 9 5 )F ra n c e

0 .2 0 *M a ig n e l e t a l ( 1 9 9 6 )N e th e rlan d s

0 .1 0T e B r aa k e e t a l (1 9 9 4 )Ita ly0 .1 0 (b u lls o n ly )M ig lio r (p e rso n a l c o m m u n ic a tio n )U K

0 .0 2

M ro d e a n d S w a n s o n (1 9 9 8 )*Assuming a generationintervalof 5.5 years

Most of the estimates are between 0.1% to 0.2% per year and are within thelevels for the maximum response considering
10 bulls per generation. However, thesestudies are retrospective
and future rates of inbreedingare expected to increase. For
instance, considering
all the dairy breeds in the USA, Wiggans et al (1995)
observedthat the rate of change in annual increase in inbreeding
was the greatest in the Holsteinin spite of the large population
size. In the UK, the proportion of bulls and cows withinbreedinglevels greater than zero increased from 45% and 66% respectivelyforanimals born in the period 1971 to 1975 to 81% and 90% for animals born in the period

1986-1990.
Similarly in the Netherlands the percentage of cows which were inbred rosefrom 33% for cows born in 1980 to 98% for cows born in 1996 (Gerben DeJong,
personal communication).
These imply that most subsequent matings are likely toincrease the rate of inbreeding.
This current increasing
trend in the percentageofanimals which are inbred is partly due to selection of sires and bull dams on the basisof estimated breeding value. Thus the same bulls are being used in the BB and CB
pathways as explained earlier.
The rates of inbreeding
observed in the less numerical breeds such as Jerseys
and Ayrshires tend to be slightly
higher than those for the Holstein
Friesian breed(Wiggans et al., 1995, Mrode and Swanson, 1998). In the UK data (Mrode andSwanson, 1998) higher levels of inbreedingin these breeds was due mainly to anincrease in the proportion of animals with higher levels of inbreeding rather than a rapid
increase in total number of animals inbred. The increase in the proportionof animals
with higher levels of inbreedingis due to small population
size and fewer numbers ofbulls tested each year in these breeds.
In addition, further increases in annual rates of inbreedingare expected in thenear future as a result of advances in reproductive
physiology.
Cloning and in vitro
embryo production (IVEP) could result in breeding programmes with intense female
selection and shorter generation intervals. For instance, with IVEP it could be possiblethat large numbers of embryos could be sold into the commercial population,
creatinga large dispersed
nucleus in which both adult and juvenile
MOET schemes can be
exploited.
In this situation,
Goddard (1992) indicated that breeding programmes basedon maximizing net genetic gain would lead to annual rates of inbreeding of 1% per year,with a resultant drastic reduction in Ne.
Trend in genetic variance
One of the major consequences of selection is a reduction in genetic variance.
In spite of the rapid rate of genetic progress in milk traits observed in the Holstein
Friesian population,
most recent estimates of heritabilities
have risen to 0.40 in the last
decade from the traditional
values of 0.25 to 0.30 in several populations(VisscherandThompson, 1992). In European countries such an increase in genetic variance could
partly be attributed
to the substantial
immigration of Holsteins.
However, heritability
estimates in the USA Holsteins have either not changed or show a slight increase (Van
Vleck et al., 1988; Dimov et al., 1995) but definitely
not a decline in genetic variance

(Va). Thus the increases in estimates of Va have not been consistent with expectation
as a result of selection
and reduced population
size. This could possibly
be attributed
to better management, improved recording, better statisticalmethods for data analysis
and the effects of mutation.
NewVa attributableto mutation (Vm) increases at a rate of the order of 10-3
times the environmental
variance (Ve) per generation
in a variety of characters(Falconer and Mackay, 1996). As Hill (1989),
indicated,
if values of Vm =10-3Ve are
realistic,
then mutation can provide
populations,provided a proportion

useful variation in long term directionallyof mutants increase the trait.

selected

Discussion
The results reveal a decreasing trend in Ne in the global Holstein population
and also in the less numerical breeds such as the Jersey. Not only is the number of bullsires used decreasing with time, the percentage in commonuse amongseveral countries
is on the increase. Indications
are that the current downward trend in Ne will continue
given the very high percentage of inbred cows in many Holstein populations.
The
consequence of this is obviously a continual decline in genetic diversity in the Holstein
population,
therefore ways of arresting this decline in Ne and hence conserving theavailable
genetic diversity
must be examined.
Firstly,
as Wickham and Banos (1998)
remarked, the very high usage of
popular sires of sons in several countries, identifies
an opportunity
for co-operation
between bull studs to test sons from a wider range of sires and hence maintain a large
effective
population
size to protect long term genetic gain.
Selectionon the basis of pedigree
information
and BLUP has become thestandard practice in most countries. This will inevitably
push up the rate of inbreedingand hence reduce Ne as it increases the probabilityof co-selectionof close relatives.
Several simulation studies have examined the use of selection indices constructed from
BLUP evaluations
in which the emphasis given to ancestral informationis reduced(Verrier et al., 1993; Grundy et al., 1998). One of the simple indices studied by Grundy
et al (1998)
involved the use of artificiallyinflated heritability
by a factor of 2 or 3 in
BLUP evaluations
to restrict
family information.
They reported
that after 25
generations of selection, cumulative response was about 3% less than standard BLUP
but the final level of inbreedingwas 23% to 35% lower than from BLUP. In general,they observed that the magnitude in reduction in the rate of inbreedingcompared toBLUP selection was inversely
proportional
to the amount of family information
included in the modified selection indices. The possibilityof utilizing
such modified
indices as national selection indices should be examined in practise in order to conserve
the available
genetic diversity.
The substantial
influence of pedigree information on
International
proofs by the Interbull
Centre, especiallyin countries where foreign bulls
have little or no information, has been demonstrated by Mrode and Swanson (1997).
The adoption of indices with restrictions
on family information would therefore also
be beneficial
even with international
evaluations.
One of the factors that influences Ne is the genetic correlation among countries,
resultingfrom differences
in selection objectives
and GxE. This determines to what
extent different countries select the same sires of sons. To ensure different bulls are
sampled by different countries, selection objectives
should be defined to include othertraits that relate to profit such as health and reproduction
rather than concentrate on
milk traits. More comprehensive indices may result in lower genetic correlations
among
the selection objectivesof different countries than the present estimates of 0.88 - 0.92.
Genetic correlationsof lower than 0.7 - 0.8 are needed if these different
selection
objectives
are to result in breeding schemes that are substantiallydifferent (Meuwissen,1998). Also the inclusion of more functional traits in selection objectives
should ensure
genetic variability
in any one trait is not significantly
reduced.
In addition, the results have demonstrated the substantial
increase in the numberof bulls available
for selection following the inclusion of genetic correlations
among
countries in International
evaluations by the Interbull
Centre. For the less numerical
breeds with few bulls tested each year, the effects of international
evaluations are evenmore pronounced, offering greater opportunities
to select from a wider range of bulls
and also increase selection differential.
This should avoid polarizedusage of a few"trendy" bulls and hence conserve global genetic diversity.
The effect of this will be
maximized as all countries adopt Interbull evaluations
and more appropriate
data andmethodology
are utilized
to improve on the current estimate of these genetic
correlations.
Improvements in current advances in reproductive
technology are probable with
the implicationof higher intensityof selection
in females and shorter generationinterval. With IVEP, the applicationof MOET type schemes could be quite pronounced
in the commercial populations.
Under such a situation rates of inbreeding,as Goddard(1992)
indicated,
could be as high as 1% per year and the adoption of appropriatebreeding schemes such as the factorial
mating design (Woolliams,
1989), equalizing
family sizes, constraining
the rate of inbreeding(Meuwissen, 1998) and deliberately
choosing less closely related animals, would become necessary to substantiallyreducethe rate of inbreedingwith little effect on genetic gain.
Conclusion
The results have indicatedsubstantial
rates of progress of about 2% in milk
traits in most B&W populationsin the last 5 years. This however has beenaccompanied by a decreasing trend in Ne. The number of bull sires used is decreasing
with time while the percentage in commonusage is increasing. The average relationshipamong bulls, rates of inbreeding
and percentage of inbred cows all show an increase
with years.
Firstly,
the very high usage of popular sires of sons in several countries
identifies
the opportunity
for co-operation between bull studs to test sons from a wider
range of sires and hence maintain a large Ne. International
evaluations by the Interbull
Centre account for GxE and therefore offers opportunities
to select from a wide rangeof top bulls. This, in addition to comprehensive national selection objectives
which
include other traits which relate to profit in addition
to milk traits, should ensure
different countries sample different bulls when progeny testing and hence increase Ne.
The effectivenessof selection using indices with restrictions
on family information inmaintaining the present rate of genetic gain while reducing the level of inbreeding havebeen demonstrated.
The possibilityof utilizing
such modified indices in national
selection
schemes should be exploited.
Current rates of inbreeding
could increase
substantially
due to advances in reproductive
technology.
Special breeding schemes
such as factorial mating designs and equalizing family sizes may have to be adopted toreduce rates of inbreeding
to conserve available genetic diversity.
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+-Genetic
HIROSHIImprovement

and Inbreeding

Problem in Swine

TAKAHASHI

GlobalPig Farms, Inc. Gunma 377-0.052,

Japan

Abstract
The effects of inbreeding(ABF) were estimatedthicknessregression

coefficientfor D105and ABF of 3,403

of D105 or ABF on the inbreeding

Akachi

Genetics,

effectsof birth-year-monthlinearregression

genetic

effectparents,

the Fs of the 3,403(BYM),(breedingvalue)

sex of the pig, parityof pigs within
pigs

Slightly

litter.

were calculated.
approximately(F) of the pigs. Performance1985 and June 1987,

model used for the analysisof the dam (giltwere litter

withinAfter forming

includedatthe fixed

vs. sow, MAT) and a partial

BYM and MAT and the additive
the relationshipmatrix of the 376
0.033

with theThe number of pigs having

less than

97% of the total pigs. The estimatesin F were + 1.47 d for D105

data

were collected

The average F value was 0.013

more than 71% were not inbred.

which represented

per 10% increasecoefficientborn between Julyon F of the pig. Random effects

F was 3,302,

of inbreeding

Duroc pigs,

Inc., Gunma, Japan. A mixed animal

range of zero and 0.250.
0.100

on age in days at 105 kg (D105) of body weight and average back fat
using a single trait animal model which included
a partial
linearof the effects

and - 0.17 mmfor ABF.

Introduction
Genetic improvement can be accomplishedonly if geneticallysuperior
individuals
are selected and then mated to reproduce the next generation. However, thetrue genetic merit of individuals
are never known but can be estimated from theavailable
data. A mixed animal model (Henderson, 1984) is the model preferred byanimal breeders to obtain estimated breeding values. The estimators and predictors havedesirable properties
and the model is general, so that any known effects peculiar to thedata set can be accounted for.
Most swine breeders today, either consciously or unconsciously, practice some
types of inbreeding.
As Lush (1933) noted, linebreeding,though mild, is a form ofinbreeding.
Inbreeding
can also be used to fix a desirable type. Progeny produced bycrossing two inbred parents of different lines can be expected to be more uniform. This
increased uniformity in phenotype can be considered desirable for the mass production
systems typical of many commercial pork production enterprises.
Swine breeders are now attempting to limit the introductionof new genetic

ƒÐ2a
ƒÐ2a.

components as live pigs, because of the higher risk of introducing
new diseaseorganisms. Hence, almost all the replacement
gilts are reproduced on their own farms
and only when necessary are a minimum number of boars or semen purchased fromoutside sources. Unless replacement herd boars are always purchased from different
outside sources, some level of inbreeding
is likely to build up within a herd.
In theory, two consequences of inbreeding
are well established:(1) changes ofmeanvalue and (2) changes of variance (Falconer,
1981). The most striking observedconsequence of inbreedingis the reductionof the mean phenotypicvalue which is
known as inbreeding
depression.
When the average inbreeding
coefficientof a populationis F, the within-line
additivegenetic

variance is reduced to (1 - F)

while the between line additive

genetic

variance increases to 2FƒÐ2a. The additive genetic variance in the whole population is
the sum, and thus is equal to (1 + F)
Dickerson et al. (1954)
clarified
two methods to estimate the effects ofinbreeding:(1) the comparison of linecrosses
with parental lines of known inbreeding
and relationshipwithin the same season of farrowing, and (2) the linear regression of
performance on F among the unselected
progeny within the same line and season.
Therefore, to estimate the effects of inbreedingon the phenotypicvalue, the effects of
selection and environment must be accounted for if the linear regression coefficientofphenotypicvalue on F is used.
Selectionis the process of discriminationby which individualsof divergent
phenotypes
are made to differ in rate of reproduction(Dickerson
et al., 1954).
Response to selection can be estimated by using mixed model methodology,
even whenrecords from a control group are not available. Results from a Monte Carlo simulation(Sorensen and Kennedy, 1984) and from a selection experiment (Blair and Pollak,
1984) revealed that a mixed model analysis could be used without a control population
to partition
adequately
the phenotypic
trend into its genetic and environmental
components. Both studies strongly suggested
that for the mixed model approach tosucceed in separating
genetic and environmental trends, strong genetic ties between
contemporary groups are required. Swine data collected within a herd will generally
meet this requirement
because of the strong genetic ties among females which aremostly produced within the herd.
A systematic procedure for constructing
a numerator relationshipmatrixamong animals is well established(Emik and Terrill,
1949; Plum, 1954).
Theoff-diagonal

elementsof the relationshipmatrix,when they are multipliedby theadditive genetic variance, represent the additive genetic covariances between animals.
The kth diagonal element of the matrix is (1 + Fk), where Fk is the inbreeding
coefficientof the kth animal. Therefore, when the complete relationshipmatrix of all the animals
is used, not only the known additive genetic covariances between the animals but also
the increased additive
genetic variances due to inbreeding
are accounted for.
The purpose of this study was to estimate the effects of inbreedingon age indays and average backfat thickness
at 105 kg of body weight from a mixed animalmodel. After solving the corresponding
mixed model equations
and for each trait
separately,
estimates were obtained for the linear regression
coefficientsof records on
F of individual
animals.
Materials
Description

and Methodsof data
Performance data for D105 and ABF from 3,403 Duroc pigs were collectedat Akachi Genetics, Inc., Seta-Gun, Japan. The pigs were born between July 1985 andJune 1987 and were from 497 litters. Very little, if any, castration and preselectionprior
to performance testing was practiced, because all boars were candidates for either saleor herd replacement. Hence, unless pigs had severe illness or were injured, all Duroc
pigs born within the period were measured for both traits. Evidence of little preselectionof boars for testing is demonstrated
by the fact that the average number of pigs tested
per litter
females.

was slightly

over 6.8 pigs. Sexes of the pigs were either

intactmales orBirth information was recorded on each.litter.
Individual
pigs were weighed
off-test and ultrasonicallymeasured for backfat thickness
using a Lean-MeaterR atapproximately
105 kg. Measurements of backfat were taken 5 cm off the midline atthree points; above the point of the elbow, the last rib, and the stifle
for ABF. Both D105 and ABF were adjusted to a 105 kg basis

joint; and averaged
using the formulas

recommended by the National Swine Improvement Federation (1987) with the desired
weight of 105 kg.
The number of male and female pigs and the total in each birth-year-month(BYM) subgroup are shown (Table 1).

Table 1. Distributionof male and female pigs and the total number of pigs,
sires and litters in each birth-year-month
(BYM)
B Y M

N o .litte rs

N o .s ire s

N o .p ig s

N o .m a le s

N o .f e m a le s

8 50 7

18

5

103

49

54

8 50 8

21

6

134

57

77

8 5 09

25

7

176

88

88

8 5 10

21

7

137

72

65

8511

24

7

14 6

79

67

8 5 12

21

7

150

72

78

8601

27

7

196

102

94

8 6 02

27

7

2 30

134

96

8 6 03

20

9

137

76

61

8 6 04

20

10

124

70

54

8 6 05

21

10

176

81

95

8606

21

8

136

75

61

86 07

21

9

14 5

74

71

8608

23

11

152

85

67

86 09

20

9

125

67

58

8 6 10

21

13

134

76

58

86 1 1

17

12

127

69

58

8 6 12

23

10

162

88

74

87 0 1

27

11

175

97

78

8 7 02

11

9

77

40

37

53

57

8 7 0314

8110

8 7 04

26

11

19 1

97

94

8 7 05

22

9

13 5

87

48

8706

6

4

25

19

6

T o ta l

4 97

3 ,4 0 3

1 ,8 0 7

1 ,5 9 6

The number of pigs within the BYM were distributed
rather uniformly across the BYM,
except for the last BYM, June 1987. This happened because only a small portion of thepigs born in this BYM had completed the test at the time the data were obtained. The
25 pigs representingthat particular
BYM could have been either the fastest growing
pigs and hence were able to complete the test, pigs born at the beginning of the BYM,
or the mixture of both. The most likely reason would be the latter and hence they were
included in the analysis. The distributionof the number of males and females withineach BYM was quite even, and, hence, also provided
evidence for little or nopreselectionof boars for testing. The numbers of sires and litters were likewise
distributed
uniformly across the BYM, except again for the last BYM.

In addition to the pigs that were tested, 44 of their sires and 226 dams whose
individual
performance had not been recorded were added to the data set to increase
genetic ties among the pigs, making a total of 3,673 animals in the analysis. There were
21 sires and 48 dams with both parents unknown and they comprised the base
population.
The remaining 3,604 pigs had both parents identified
and were traced backto 69 ancestors in the base population.
Calculationof coefficientof inbreeding
Emik and Terrill (1949)
and Plum (1954) developed a systematic procedure
for calculating the coefficientof inbreeding (F) by constructing
a numerator relationshipmatrix. One important aspect of the matrix is that the off-diagonal
elements are
Wright's coefficientof numerator relationshipbetween animals. Hence, a relationshipmatrix comprised of only the parents is required to calculate F of all the animals as:
Fi = asd/2

where Fi is the coefficient[1]of inbreeding.of

the ith animal

off-diagonal
element of the relationshipmatrix.
To calculate F of the 3,673 pigs, the relationshipand asd is the sire by dam
matrix of their 376 parents

was constructed. Then, F of all the pigs was calculated by using the respective
sire bydam off-diagonal
elements as in [1].
ModelsThe data were analyzed separately
for D105 and ABF with the same model:
Yijknq=mi+Sj+pk+fiknb+likn+aijknq+eijknq

where yijknq is an adjusted

recordof either

[2]

D105

or ABF of the qth pig of the jth sex in

the nth litter farrowed in the ith birth-year-month(BYM) from a dam of the kth maturity
group (MAT), mi is the fixed effect of the ith BYM (i = 1,...,24), sj is the fixed effect of
the jth sex (j = 1,2), pk is the fixed effect of the kth MAT of the dam (k = 1,2), fikn is thecovariate term of F , likn is the random effect of the nth litter (n = 1,...,497),
aijknq is therandom effectof the additive

genetic

to the record

value of the qth pig (q = 1,..., 3,403),

residualerror peculiar

and b is the partial

records

on F.
The model in [2] can be expressedin matrix

linearregression

notationas:eijknq is thecoefficient

of

ƒÀ'

ƒÐ2a,
ƒÐ21,
ƒÐ2e

where y, m, s, p, f, 1, a0, a1, and e are vectors of records (3,403 x 1), BYM effects (24x 1), sex effects (2 x 1), MAT effects (2 x 1), F as a covariate (3,403 x 1), random
litter effects (497 x 1), random additive genetic effects of the 270 parents without theirownrecords (270 x 1 ), random additive genetic effects of pigs (3,403 x 1) and residual
errors (3,403 x 1), respectively.
Design matrices for m, s, p, and 1 are X1, X2, X3 andZ, respectively.

Also

geneticof the 270 parents

effects,= [m',s',p'],a' = [a 0', a 1' ] andwithouttheir

X = [X1,X2,X3].

The additive

own records (a0) are augmented

to thebreeding
vales of the 3,403 pigs (a1) to provide extra genetic ties and thus thedimensions of vector a is 3,673 x 1. The design matrix corresponding
to vector a hasdimensions of 3,403 x 3,673; zero matrix of order 3,403 x 270 augmented with a unit
matrix of order 3,403. The partial linear
denoted by b in [3]. With the assumptions

where A is the numerator relationshipvariances of litters,
additivegenetic
equations(MME)

regression
coefficientof records on F isthat the first and the second moments are:matrix of 3,673 pigs andeffects and residual
errors,andarethe mixed model

for model [3] are:

ƒÐ2a
ƒ¿1
ƒÐ21
ƒÐ2e/
ƒÐa
ƒÐ2e/ƒÐ2a.
where
=
and
=
Because A
accounts for all the additive
genetic
ƒÐ21,
variancesand covariancesof the pigs,
accounts for the covariance
betweenƒÐ2aƒÐ21
littermatesafter/ 2 is removed. Or equivalently,
the components of
are:

ƒÐ2d
ƒÐ2c

where cov (LM) represents
variancesof dominancerespectively.

the covariancegenetic
effects

between littermates
and
and
are
and common environmental
effects,ƒ¿1ƒ¿aTakahashi et al (1990) reported the estimates of andfor D105, respectively,
and 6.979 and 2.996 for ABF, respectively.

as 8.716 and 2.295
Those calculated

ƒ¿1's
ƒ¿a's

and
were used in MME to estimate
records on F of the pigs.

coefficient

Results
0.006

the linear

regressionof theand Discussion
The average Fvalues of the 64 sires and the 312 dams were 0.012 +-0.040 and+-0.023, respectively(Table 2). The minimum F values of both the sires anddams were zero, while the maximum F values of sires and dams were 0.250 and 0.125,
respectively.
In Table 3, the average parental F values of sex-status subgroups areshown. Status 1 representsa group of parents without their own performance records
and status 2 a group with records. There were 44 sires of Status 1, all of which had Fof zero. The remaining 20 sires had performance records and their average F was
0.038 +-0.064. There were 226 dams of Status 1. Their F averaged 0.004 +-0.022. The
remaining 86 dams had their own performance records and the averaged 0.011 +-0.025
in F.

Table

2. Average F values of the 64 sires and the 312 dams

S ex

N o .

A v e .F

S .D .

M

S i re s

64

0 .0 1 2

0 .0 4 0

0 .0 0 0

0 .2 5 0

D a m s3 120 .0 0 6

0 .0 2 30 .0 0 0

0 .1 2 5

Table 3. Average parental

i n .FM

a x .FF values of sex by status subgroups

S ex

S ta t u s a

N o.

A v e .F

S .D .

M i n .FM a x .F

S ir e s1

44

0 .0 0 0

0 .0 0 0

0 .0 0 0

0 .0 0 0

S ir e s2

20

0 .0 3 80 .0 6 40 .0 0 0

0 .2 5 0

D am s1

22 6

0 .0 0 4

0 .0 2 20 .0 0 0

0 .1 2 5

D am s2

86

0 .0 1 1

0 .0 2 50 .0 0 0

0 .1 2 5

aStatus 1 represents a group of parents without,performanceand 2 of those with their own records.

records

Table4. Average F values by sex of the 3,403

pigs

S ex

N o .

A v e .F

S .D .

M a les

1 ,8 0 7

0 .0 1 2

0 .0 3 1

0 .0 0 0

0 .2 5 0

F e m a le s

1 ,5 9 60 .0 1 5

0 .0 3 50 .0 0 0

0 .2 5 0

Table 5. Distribution
F

M in .FM a x .Fof pigs by level of FN o .

0 .0 0 0

2 ,4 2 60 .0 0 4

17

0 .0 0 877

0 .0 1 6

2 1 9

0 .0 2 0

20 .0 3 1

3 20

0 .0 3 5

6

0 .0 3 9

6

0 .0 4 7

34

0 .0 6 3

159

0 .0 7 8

90 .0 9 4

27

0 .1 2 553

0 .1 3 3

9

0 .1 4 1

9

0 .1 4 3

4

0 .2 5 026

After constructing
the relationshipmatrix for the 376 parents, Fs of the 3,403
pigs, which included those of 20 sires and 86 dams that had individual
records, were
calculated.
The average Fwas 0.012 +-0.031 for the males and 0.015 +-0.035 for gilts(Table 4). Both sexes had minimum and maximum values of zero and 0.250,
respectively.
In Table 5, the distributionof the pigs for each level of F are shown.
Slightly
more than 71% of the pigs were not inbred. The number of pigs having less

than 0.100

for F was 3,302, which represented

approximately

97% of the total

pigs.

The partial linear regression coefficient(b) of records on F of the pigs in [3]was estimable,
because in the current data set there were two boars born in the first
BYM that were from dams of the second MAT, and had differing
F values (zero and0.037).
The estimates of b were 14.700 and -1.659, respectively,
for D105 and for
ABF. Since the estimates of b for both traits were expressed per unit ofF, the effects
of inbreedingper 10% increase of F were to increase D105 by 1.47 d and to reduce
ABF by 0.17 mm.
By using the method of least squares (LS) and the current data set, the effects
of inbreedingon D105 and ABF were also estimated. The statisticalmodels used for
LS contained the same fixed effects, including
a partial linear regression
coefficient(bLS) of records on F of the pigs, as was used with the mixed model in [2]. But all the
random effects in [2] were not fitted and hence fell into residual error in the LS model
thus permitting
the estimate of bLS to be estimable.
The estimates of the effects ofinbreedingon the phenotypicvalues of D105 and ABF were 1.02 d and -0.14 mm,
respectively,
per 10% increase of F. These results, compared to the results from MME,
indicatedthat the estimates obtained from LS were regressed more toward zero.
The estimates of the effects of inbreedingon D105 and ABF obtained in thisanalysis with MME were similar to those reported by Bereskin and Hetzer (1981,1984). They used data obtained from an unselected control populationof more than
1,000 pigs and collected
over the period of 1955 to 1967. The F values of the pigs
ranged from zero to approximately
0.40. By using the least squares method and a modelthat included the linear regression coefficientsof records on F of the pigs, they obtained
estimates of the effects of inbreedingon age in days to 100 kg (D100) and ABF of 0.89
d and -0.17 mm,respectively,
per 10% increase of F.
Using data obtained from a large crossbreeding
experiment, Schneider et al.
(1982) calculated
estimates of the effects of inbreeding on D100 and ABF. The average
F value of the pigs was approximately
0.05. The estimates for D100 and ABF were
6.00 d and -1.37 mm,respectively,
per 10% increase ofF. Although the magnitude ofthe estimates reported by Schneider et al. (1982)
analysis,
they were in the same direction.

was larger than those obtainedin thisUse of the dominance relationshipmatrix (D) in MME was studied by
Maki-Tanila and Kennedy (1986). Data were simulated for three levels of frequency of
the favorable gene (0.1, 0.5 and 0.9) and several levels of dominance within the range

of -1 and 1 with a limited number of loci involved. The data were generated for three
generations
with selection.
In the third generation,
the average F value was
0.140 +-0.003. Use of D in MME, however, failed to adjust for the effect of inbreeding.
After reviewing the study, Kennedy et al. (1988)
concludedthat with inbreeding,inclusion of D did not account for inbreeding
depression
and the effects of inbreeding
depression might be accounted for by including the inbreeding
coefficient
as a covariate
in the model.
Conclusions
For an accurate genetic evaluation of animals, known sources of effects, both
genetic and environmental, should be incorporated into a model. With an animal model,
the additive animal effect accounts for the change in additive genetic values of animals
that is due to selection. Use of the complete A accounts for the known additive genetic
variance-covariancestructure as well as the increase of the additive genetic variancedue to inbreedingof the whole population.
Inbreeding
depression affects phenotypicvalues and, hence, use of F as a covariate in an animal model can account for thechange in phenotypicvalues resulting from inbreeding.
When the major interest in aswine breeding herd is the evaluationof sires by using progeny performance data,
inbreeding
depression
should not be ignored. Because progeny of sires raised on thefarm will tend to be inbred while progeny of purchased sires from the outside will more
likely be non-inbred, accounting for inbreeding
depression,
especiallyfor D105,
provides an opportunity
for fair comparisons among both sires raised on the farm andthose purchased from the outside.
References
Bereskin,B. and H. O. Hetzer.

1981.

Reciprocal

recurrent

selectionin swine:

postweaning

growth.

1984.

Reciprocal

recurrent

selectionin swine:carcassJ. Anim.

1984.

Estimationof genetic

trend

in a selected

the use of a control

population.

J. Anim. Sci. 58:

Anim. Sci. 53:
Bereskin,

J.

1446.

B. and H. O. Hetzer.

traits.

Sci.58:821.
Blair,

H. T. and E. J. Pollak.
without

Dickerson,

G. E., C. T. Blunn,
Whatley,Emik,Jr. 1954.

A. B. Chapman,
Evaluationof selection

in developing

551.

(Also

No. Central

Reg. Publ.

L. O. and C. E. Terrill.

1949.

Systematic

procedures

with

and

878.

R. M. Kottman, J. L. Krider,Exp. Stn. Res. Bull.

population

inbredlines

E. J. Warwick

and J. A.

of swine.

Mo. Agric.

38).

for calculating

inbreeding

coefficients.

J.

Heredity
Falconer,

40: 51.

D. S. 1981.

Henderson,C.

Introduction

R. 1984.

to Quantitative

Applications

genetics.Longman,of linearmodelsInc., New York, NY.

in animal

breeding.

Univ.

Guelph,Guelph,Canada.
Kennedy,

Lush,

B. W., L. R. Schaeffer

and D. A. Sorensen.

Dairy

Sci. 71 (Supple.

2): 17.

J. L. 1933.

Linebreeding.

Iowa Agric.

Maki-Tanila,

National

A. and B. W. Kennedy.
small

number of additiveProd.

12: 443.

Sorensen,and nonadditive(Rev.

Aid 1157.

U.S.D.A.,of inbreedingJ.

No. 301.
with

loci.

Congr. Genet. Appl.

Livest.

Proc. 3rd Worldfor uniform

Washington,

and relationship1982.

swine improvementDC.

coefficients.

J. Heredity

45: 92.

in swine:

genetic

merit. J. Anim. Sci. 54: 747.
of response

to selectionusingeffects

least-squares

on

and

J. Anim. Sci. 58: 1097.

H., L. L. Christian,T. Sugimoto

environmentalof littermatesof Duroc pigs. Jpn. J. Zootech.

and A. Nibe.

1990.

Estimatesof heritabilities

for age at 105 kg of body weight
Sci. 61: 255.

a

programs

Crossbreeding

pig growtheffectsmodels.

modelsGuidelines

D. A. and B. W. Kennedy. 1984. Estimationof animal

under genetic

and D. L. Kuhlers.

and carcass

properties

model methodology

J. F., L. L. Christianmixed model methodology.
Takahashi,

Mixed

1987.

Plum, M. 1954. Computation
Schneider,

1986.

Federation.

Program

Geneticand Home Econ. Exp. Stn. Bull.

Swine Improvement
Ed.).

1988.

and common

and backfat

thickness

Mapping Quantitative
Trait
Selection in Small Domesticated
SHIZHONG XU
Department of Botany and Plant Sciences,
Universityof California,
Riverside,
CA 92521,

Loci and Marker
Populations

Assisted

USA

Abstract
The general concept and theory of quantitative
genetics of finite loci are described. A
method of partitioning
the genetic variance into variance components contributed by individualloci
are developed. The key to the partitioningof genetic variance components is the variationin theidentical-by-descent(IBD) value shared by relatives. The IBD value of a putative quantitative
trait
locus (QTL) is inferredfrom multiple
markers via a multipoint
method. QTL variances are
estimated
and tested in the general framework of maximum likelihood.
The simplex and EM
algorithms
for solving the maximum likelihood
solutionsare described. Marker assisted selection(MAS) via the best linear unbiased prediction(BLUP) is discussed.
A new concept, called marker
assisted mating (MAM), is proposed. MAM can help breeders to design the best mating strategy to
avoid inbreeding
depression
and maintain genetic variance of small populations.
The new concept
and methodology
of QTL mapping and MAS are fully consistent
with the classicalframework forthe genetic analysis of quantitative
characters.

Introduction
Estimating
genetic variances and covariances is the first step towards
understanding
the genetic mechanisms of quantitative
traits. It is also the basis fordeveloping
efficientselection
procedures because selection
acts by consuming
existing genetic variances. In classical quantitative
genetics, genetic variances are
estimated by the phenotypic
resemblance between relatives. Molecular techniques
allow the new area of molecular quantitative
genetics to further partition
theoverall
genetic
variance into components corresponding
to chromosomal
segments of the genome (Goldgar
1990).
Each chromosomal segment may
contain one or more loci that control the traits of interest. These loci are referred
to as quantitative
trait loci (QTLs). In the terminology
of molecular quantitative
genetics, locating QTLs and estimating QTL effects are called mapping QTLs.
Data collectedfrom well designed
crossing
experiments
are usually
analyzed using the fixed model approach. Usually a single line cross is analyzed.
Linkage disequilibriumis the key to QTL mapping using the fixed model
approach. Under the fixed model, data (phenotypicvalues) are describedby alinear model with a variable that indicates
the QTL genotype as the independent
variableand the effect of allelicsubstitutionof the QTL as the regression
coefficient(Lander & Botstein 1989). The QTL genotype itself, however, is notobservable.
The usual practice is to use the genotypes of linked markers to guessthe missing genotype of the QTL.
The random model approach of QTL mapping is more appropriate
than
the fixed model when data are sampled from outbred populations(Xu & Atchley1995).
Under the random model approach, variance of QTL segregation(thesecond moment) rather than the effect of allelic substitution(the first moment) is
estimated(Haseman & Elston 1972; Schork 1993; Fulker & Cardon 1994).
Sometimes, the random model approach is referred to as the robust method (Amos
1994; Olson 1995) because knowledge of the actual genetic mechanism, such as
the number of alleles,
the allelicfrequencies
or the linkage phases, is not
absolutely
required.
Many populationsof natural organisms
may be inapproximate
linkageequilibrium,
whereby marker genotypes
providenoinformation
about the genotype
of a QTL. This situation
invalidates
theapplicabilityof fixed model approach to outbred populations.
The random model
methodology,
however, is fully consistent
with the classicalframework for the
genetic
analysisof quantitative
characters.
The primary innovationis thepartitioningof the genetic variance into variance components associatedwithparticularmarkers and those associated with the polygenic
background. All of theexisting
models and types of gene action that have been elaborately
studied bybiometricalgeneticists,
includingdominance and epistatic
effects
and fixed
environmental factors are readily incorporated
into the framework.
Theory and Methods
1. Genetic variance in small populations
Assume that a small populationof size Ne was isolated
from a base
populationt generations
ago. In the absence of mutation, the additive
genetic
varianceof a quantitative
trait at the current
generation(t) decreases
monotonicallycompared with that of the base population(generation0). TherelationshipisƒÐ2A(t)ƒÐ2A(0)

where

ƒÐ2A

respectively,
and Ft is the average inbreeding
coefficientof the populationin thecurrent generation (Falconer & Mackay 1996). To estimate the genetic variance ofthe population,
N individuals
are sampled from the current generation (t) and theirphenotypicvalues are analyzed. If the pedigree
relationshipsof the N individuals
are known exactly back to the original
generation(0) and the relationships
arefully taken into account, the estimate will be(0). According to equation (1),

ƒÐ2A
ƒÐ2A.
ƒÐ2A(0).
ƒÐ2A(0)in the absence of mutation, this variance is greater than the variances in all
subsequent
generations.
This perhaps is the reason why one always tries toincorporate
all pedigree
informationin estimating, hoping to obtain amaximumestimate ofHowever, less attention has been paid to the utilities
ofFrom evolutionary
standpoint,
may help us to understand theƒÐ2A(0),ƒÐ2A(t)genetic
history
however,ƒÐ2A(t)determinesandare the additive

geneticof the small population.
may be more useful than

the actual response

to selection

variancesat generation

From breeding
becauseoccurred

point
it is

t and 0,

of

view,
that

in the current generation.

ƒÐ2A
Directly
estimating(t) is impossibleusing individuals
in the current
ƒÐ2A(tgeneration,but- 1) can be estimated. In fact, it is much easier to estimate
ƒÐ2A(tƒÐ2A(0).
-1) than to estimate
When the population
size is not too small,ƒÐ2A(t)•`ƒÐ2A(tƒÐ2A(t-1). To estimate- k) for k < t in general, one simply uses

ƒÐ2A(tƒÐ2A(tthe pedigree
informationback to k generations
and treats all individuals
in thatgeneration as completely
unrelated. For instance, if one uses pedigree information
only back to one generation,
only full-sib
or half-sib
families
are considered
because all parents are treatedas independent.
Then the estimatedgenetic
variance would be-1). Sib analyses(k = 1), are more convenient, andperhapsmore appropriate,

than complex pedigree

one can arbitrarily
increases k to obtain
subsequentdiscussions,only sib analyses
genetic variance is- 1).

analysesa higher estimate
are considered.

(k > l).

Of course,of the variance. In
Thus, the estimatedƒÐ2e)

2. Genetic

covariance between relatives under finite loci
Consider N individuals
with known family origins sampled from a finite
populationin generationt (the current generation).
Assume that we have collectedboth phenotypicand molecular data for all N individuals
and their parents. Our
purpose here is to estimate
the total genetic variance as well as variances
contributedby different
chromosome regionsin the parental
population(generationt - 1).
The phenotypicvalue of a quantitative
trait for the j-th individual,
yj, isexpressed by the following mixed linear model:

where x, is an 1xm

designvector of fixed effects,
and djk are the additiven is the total number of loci contributing
to the trait, ajk
and dominance effects, respectively,
for the k-th locus,

and ej is the environmental

vectorerror with a N(0,are assumed to be absent. All the genetic
distributions,

i.e.,

ajk

~ N[0,(1for a set of fixed

effects+ fjk)ƒÐak2]andeffects,

distribution.

b is an mx1

Epistatic

are random variables
djk~ N(0,ƒÐ2dk

effects

with normal), wherefjk

indicates
the inbreeding
event of individual
j at locus k. Because all parents
assumed to be unrelated,
fjk can be non-zero only if there are selfings.
domesticatedanimals which do not allow selfing,

dominance genetic

variances

The phenotypicvariance

The expectationof yj isE(yj)=xjb.

are defined

fjk = 0. The total additive

are
In
and

by

of the trait is, therefore,(3)

The variance isNote thatprobability;

fjk

is not the usual inbreeding
coefficientdefined in the context of
it indicatesthe status of identical-by-descent(IBD)
between theƒÎkij
ƒÆkij
ƒÎkij
ƒÆkij

paternal and maternal
and yj isof individual

whereis the IBD value shared

the eventthat

variables,

i and j shareandDenoteƒÐ2akfrom the

•¢dk
ƒÐ2dk

denoteaverage

j at locus k. The covarianceby i and j at the k-th locus,

IBD genotype(bothallelesfamilies

andindicates

otherwise,

These

IBD

they are zeroare assumed to be independent.

=-1nƒÐ2A
as the deviation

varianceacrossloci=ƒÐ2dk-1n-ƒÐ2Dso thatcovariance can be expressed

between yi

are IBD).

, can be non-zero if i and j are sibs,

because members of different
•¢ak
ƒÐ2ak

•¢ak
•¢dk

allelesso thatof the locus specificvariance=•¢ak+1nƒÐ2A.Similarly,=•¢dk +1nƒÐ2D, Thevariance

and

by

and

It can be easily shown that the finite locus model approaches
to the classicalpolygenic
model when the assumptionsof polygenic
model hold. With equal
effects across loci,==0 for k= 1,..., n. For infinite

number ofunlinked

the usual inbreeding
coefficient,
Aij is the additive relationshipand Dij is the coefficientof fraternity(Lynch and Walsh 1998).
In matrix notation,

the model can be expressed

as

loci,

between i and j,

ƒÎkj
ƒÆkij

ƒÎkij
ƒÆkij

The expectationand variance-covariancematrix are

andrespectively,

where IIk

Inbreeding
coefficient,additive
relationshipand coefficientof fraternity
can be considered
as average measurements of various kinds of IBD variables
across different
loci. Their values depend on the pedigree
relationshipsofindividuals
concerned. The corresponding
IBD measurements at the locus level,
fjk ,

and

, however, depend not only on the pedigree

relationships

but also

on the actual genotype of each individual
at the locus of interest.
Conditionalonpedigrees,
these locus specific
IBDs are still variables.
The variance of the IBD
measurements across loci provides the key to the random model method of QTL
mapping. In all subsequentdiscussion,
fjk ,
and
are referred to as IBD
variables.
Consider the genotypic
configurationsof progenies from mating type
A1A2 x A3A4. There are four possible
types of progeny, each with an equal
frequency.
The four possible
genotypes
are A1A3, A1A4, A2A3 and A2A4. If two
sibs are sampled from this family, ignoring
the order of sampling,
there are 10
possible
sib-pairs.
Suppose we observe a pair of sibs with genotypic configuration
A1A3-A1A3,weknow immediatelythat they have received exactly the same allelesƒÎkijfrom their parents and thus
=1. The two sibs behave like identicaltwins forthis locus. If A1A3-A2A4 is observed, then we know that they do not share any IBD
alleles,and thus behave like two unrelated individuals.
If we happen to know the
genotypes of two sibs at a particularlocus, the covariance between them at thislocus can be different
from what is expected. For example,
Cov(aik,ajk)

and Cov(aik,ajk)= 1ƒÐ2akfor a pair of sibs with genotypic= OƒÐ2akwith A1A3-A2A4.It is incorrect

configuration

A1A3-A1A3

to sayƒÐ2ak Cov(aik,ajk)=
if one already knows that i and j share no IBD allele at thelocus of interest.
Full-sib families,
half-sib families
and sibs resulting
from a selfing parent
are the three types of families
consideredin this paper. There has been no
scientificterm for sibs derived from a single parent (selfing).
For simplicityofdescription,
I will call them selfed-sibs
in subsequentdiscussion.
In domesticatedanimals, it is common to have a hierarchicalfamily structure of mixed sibships,i.e., a half-sib family consistsof several full-sib
families.
The distributionsofvarious IBD variables are given in Table 1 for full-sibs,
Table 2 for half-sibs
andTable 3 for selfed-sibs.
The expectationsand variances of these IBD variables are
summarized in Table 4. Selfed-sibs
have the largest IBD variances, and thus havethe highest power of separating
QTL variance from the polygenic
variance. Halfsibs have the lowest power due to the smallest IBD variance. In addition,
half-sib
analysis does not provide an estimate of the dominance variance.
3. Mapping QTLs under the random model
Given the expectationand the variance-covariancematrices, under theassumption that y has a multivariate
normal distribution,a likelihood
function canbe constructed.
The maximum likelihood
method requires that the number of
QTLs and the IBD matrices are known a priori, whereas, this information
is notknown. In fact, one of the main purposes of QTL mapping is to find the numberof QTLs. In addition,
it may be difficult
for the maximum likelihood
method tohandle too many unknown parameters. The usual treatment is to decompose thetotal genetic effect into the effect of the q-th QTL and the sum of the effects of theremaining QTLs. The model is written asTable 1. Distributionof the identical-by-descentvariables
shared by full-sibs
resultingfrommatting of A1A2 with A3A4, where A1 and A3 are the paternalalleles of the parents, and A2 and A4
are the maternal alleles of the parents.
The 16 possible
sib-pairs
are classified
into four states, (a)state 11: share both alleles,(b) state 10: share paternal alleles,(c) state 01: share maternal alleles,and (d) state 00: share no allele.
S ib p a irA

P ro b a b ility

A 1A 3‑A 1A 3

1 /1 6

A 1A 3‑A 1A 4
A 1A 3 ‑A 2A 3

π

kij

θkij
S ta te1

1

11

1/ 1 6

1/2

0

10

1/ 1 6

1 /2

0

01

A 1A 3‑A 2A 4

1 /1 60

0

00

A 1A 4‑A 1A 3

1/ 1 6

1 /2

0

10

A 1A 4‑A 1A 4

1/ 1 6

1

1

11

A 1A 4‑A 2A 3

1/16

0

0

00

1A 4‑A 2A 4

1/ 1 6

1 /2

0

01

A 2A 3‑A 1A 3

1/ 1 6

1 /2

0

0 1
00

A 7A 3‑A1 A 4

1/ 1 60

0

A 2A 3‑A 2A 3

1/ 1 6

1

1

11

A 2A 3‑A 2A 4

1/16

1 /2

0

10

A 2A 4‑A 1A 3

1/16

0

0

00

A 2A 4‑A 1A 4

1 /1 6

1 /2

0

01

1 /1 6

1 /2

0

10

1 /1 6

1

1

11

A

2A 4 ‑A 2A 3A

2A4‑A 2A 4

Table 2. Distributionof the identical-by-descentvariables
sharedresultingfrom matings of a father A1A2 with randomly selectedby half-sibs
females.

πk i jS i b p a ir a

P r o b a b i li ty

A 1A x ‑A 1 A y

1 /4

1/2

A 1A x ‑ A 2 A y

1 /4

0

A 2A x‑A1AyA 2A x ‑A 2A ya: Ax or Ay represents

1 /4

0

1 /4

1/2

a random allele

from the population.

Tablesingle3. Distribution
parent A1A2.

of the identical-by-descentvariablesπsharedby selfedsibs

θk i jikj

S ib p a ir

P ro b a b il it y

A 1A 1 ‑ A 1A 1

1 /1 6

2

1

A 2A 2‑A 2A 2

1 /1 6

2

1

A1 A1‑ A1 A2

1 /8

1

0

A 2 A 2 ‑ A 1A 2

1 /8

1

0

A 1A 2 ‑ A 1A 1

1 /8

1

0

A 1A 2 ‑ A 2A 2

1 /8

1

0

A 1A 2 ‑ A 1A 2

1 /4

1

1

2A 2 ‑ A 1 A 1

1 /1 60

0

A 1A 1 ‑ A 2 A 2

1 /1 60

0

A

Table 4. Expectationsand variances of the identical-by-descentfull-sibs,
half-sibs
and selfed-sibs.
π

fjk

of a

variablesof a single

θ

kij

kij

locus inE ( fjk )V a r( fjk)

E (π kij )V a r(π kij )E (θkij)V a r(θ kij)

F u ll‑sib

0

0

1 /2

1 /8

1 /4

3 /1 6H a lf‑s ib

0

0

1 /4

1 /1 60

0

S e lfe d ‑sib

1/2

1 /4

1

1 /4

3 /8

1 5 /6 4ƒÐ2aqƒÆ

where aA =is the sum of the additiveeffects(additive

effect)is the sum of the dominance effects

polygenie

and dD =of the remaining

QTLs

of the remaining
QTLs (dominancepolygenic
effect).
Accordingly,
the total
additivevariance is partitioned
into variance explainedby the q-th QTL and thevariance contributed
collectively
by all other loci. The latter is called thenpolygenic

variance

but withand still denoted

excluded.

Similarly,

byfor notational

the dominance variance

The variance-covariance

where

conveniencecan be partitioned

matrix of y is now rewritten

into
as

With this model, the list of unknown

parameters becomes much shorter:function is

In QTL mapping,

the parameters

The likelihoodof interest

are

the

remaining elements inare called nuisance parameters. These two parameters ofinterest can be tested for significanceby fitting the model with all parameters andobtaining
the natural log of the likelihoodof the data, In(L1), and refitting
without
those two parameters,
obtaining
In(Lo), the log of the likelihood
under the null
hypothesis.
The test statistic,is asymptoticallyof parameters
IBD matrices

distributed
tested.
at thatIf therepositionas a x2 with degrees

of freedom equal to the number

is a QTL at a particularchromosome position,
will match IIq and 0q and the test statistic

the
will

ƒÎqijƒÆqijshow a peak at that position.
The chromosome positionis allowed to vary so thatthe whole chromosome is searched from one end to the other for the evidence of
QTLs.
The IBD matrices of a QTL cannot be observed and must be inferredfrom markers linked to the putative
QTL. There will be some uncertainty
associatedwith the statisticalinferenceof IBD. What we can obtain is theconditional
distributionsof the IBD variables
given marker information.
When
QTL mapping is conducted,
the conditional
expectationsof the IBD variables areused in place of the true IBDs.
For full-sibs,
the conditional
expectationofgiven marker information
is calculatedbywhere Pq11, Pq10, Pq01 and Pq00 represent

the conditional

probabilities

thati and jshare two IBD alleles, one IBD allele (paternal),
one IBD allele (maternal)
and no
IBD allele, respectively.
These conditional
probabilities
are calculatedusing amultipoint
method which will be discussed in the next section. The conditional
expectation

ƒÎqij

of

is

For half-sibs,

the conditional

expectationofiswhere p1q is the conditional probability that i and j share one IBD allele (paternal)
and pq0 is the probability that they share no IBD allele (pq0 + pq
1 = 1).
For selfed-sibs, the conditional expectations of ƒÎqij. and ƒÆqij areandrespectively,where

pq5 is theconditional

probability

identical

homozygotes,

i.e.,

conditional
heterozygote,

probability
that the first sib is homozygotei.e., A1A1-A1A2
or A2A2-A1A2

that

A1A1 -A2A1orA2A2-A2A2,

thetwo sibs

are

Pq4 isthewhile the second one is, pq3 is the conditional

probability that the first sib is heterozygote while the second one is homozygote,i.e., A1A2 -A1A1 or A2A2 -A2A2 , p2q is the conditional probability thatboth sibs are heterozygotes, i.e., A1A2 -A1A2 , and pq
1 is the conditional
probability that the two sibs are different homozygotes, i.e.,
A1A1 - A2A2 or A2A2 - A1A1. These conditional probabilities are calculatedusing a multipoint method. In selfed families, a sib has a chance to be inbred. The
IBD of an individual with itself is ƒÎqjj = 1 + fjq. The conditional expectation ofƒÎqjj iswhere pq is the conditional probability that the two alleles ofj are IBD at the
QTL, i.e.,j is either A1A1 or A2A2.
4. Multipoint inference of IBD states
In outbred populations considered here, markers may be partiallyinformative. In such situations, two flanking markers may not extract the
maximum amount of information about the segregation of a putative QTL, andbecause of this, markers outside the interval can provide additional information.
In these situations, a more desirable procedure than interval mapping is to use all
markers simultaneously, a procedure called multipoint mapping (Fulker et al.
1995; Kruglyak & Lander 1995).
Consider M ordered markers on the chromosome of interest. If a marker
is fully informative, the IBD states of each marker shared by sibs are observed(known). Otherwise, the probabilities of IBD states can be inferred based on theobserved marker genotypes. I will first demonstrate the multipoint method in fullsibs. Denote the probabilities of IBD states of marker k bypk
11, pk10, pkrespectively,
for the four states. Assume that the QTL is located between marker
01 and pk
k00and, k+1 for M-l > k> 1. What we want is to calculate
When the marker IBD states are
not directly observed, Pr(ƒÎqtj|ƒÎ1ij•E•E•EƒÎMij) should be denoted by Pr(ƒÎqjj|IM),
where IM means marker information. Under Haldane map function, the sequenceforms a reversible Markov chain with the following

ƒÎkj
ƒÎlij

transition

probability

matrix between

where

and

:

and rkl is the recombination•@

fraction

between locik and l. With the multipoint
method, we will first calculate
the probabilitiesofobserving the marker genotypes given each of the four IBD states at the QTL.
These four conditional
probabilities
are calculated
as follows:

According to the Bayesian
given marker information iswhere Pr(ƒÎqij

) is the priordistribution

theorem, the probabilityof the IBD state.

of IBD state

Recallthat

The prior probability is the unconditional distribution
which is 1/4 for each of the four possible IBD states. Therefore, the explicit formsof equation(21)areof QTLThe multipoint
IBD states:

method for half-sibs

are similarly
defined. There are two
Therefore, the dimension of theMarkov chain is 2 x 2 , rather
states of loci k and l isthan 4 x 4. The transition

The conditional
probabilities
state of the QTL areof observing

marker genotypes

matrix between the IBD

given the shared IBD

and

where

The prior probabilityof the IBD state of QTL is 1/2 for each state, leading

toIn the multipoint
method of selfed-sibs,
I first define the conditional
probabilities
of marker IBD states, Pt, for t=1,...,5, given each of the five IBD
states of QTL using the Markov chain method. The transition
probability
matrixof the five IBD states between loci k and l is

The prior probabilities
conditional

probabilitieso fth efive

IBD states of QTL areof the IBD states of QTL given marker information

The
are,

The multipoint
calculationof IBD probabilityof an individual
is pq = c-1Pq , where c = Pq + Qq, Pq is the probabilityof observing
genotypes

given the individual

probabilityof observing

heterozygote

being homozygote

the marker genotypes

at the QTL, and Qq is thegiven the individual

at the QTL. Pq and Qq are calculatedwith itself
the marker

being

using the Markov chain

approach:andwhere the transition

matrix is

and the D matrices

are definedasNote that pk is the conditional
probability
that the two alleles of marker k are IBD
given the observed genotype of marker k and qk = 1- pk.
The hidden Markov model method provides a general machinery for themultipoint
calculationof the conditional
probabilities
of IBD states of QTLs. The
additional
work left to the practitioners
is to calculate
the probabilities
of IBD
states of an individual
marker given the observed genotypesof this marker.
Computingmarker IBD probabilities
is much easier than computing
IBD
probabilities
of QTLs. For example, in a selfed family, if the parent is of genotype
A1A1 at a marker locus, all the sibs will be of the same genotype, and there is no
information
about the IBD states in the progenies.
The probabilitiesof the IBD
states will take their prior probabilities.
If the parent is A1A2 and the sib-pair is oftype A1A1 - A1A2 , then the IBD state is type 4, which leads to
pk4 =1and pki = 0for i =1,2,3,5.
For the same parent, if the sib-pair is of type
A1A1 - A?A? , i.e., the second sib has a missing genotype, then the sib-pair canƒÆ

be of type 5 with a probability
1/4 or type 4 with a probability
probability1/4.
Therefore,
pk5 =1/4,pk4=1/2,pk3=0,pk2

1/2 or type 1 with a=0 and pk1 =1/4.

A general method of calculating
marker IBD probabilities
can be derived
following
the Bayesian method suggested by Haseman & Elston (1972).
However,
descriptionof the method is very tedious and one has to resort to a computer
program.
5. Algorithms
of searching for ML solutions
The likelihood
is a complicatedfunctionof the genetic
variancecomponents. Analyticalsolutions
are hard to derive and a numerical approach
must be resorted. There are numerous ways to search for the ML solutions, but
only two algorithms
are commonly used in this context. The first algorithm
is thesimplex method and the second one is the EM (expectation-maximization)
algorithm.
The simplex method: This method was developed by Nelder & Mead
(1965).
It is a general searching algorithm for any complicatedobjectivefunction.
Letbe a kx1 vector of the unknown parameters and f(ƒÆ) is the objectivefunction
which needs to be minimized.
Note that maximizing
a likelihood
function is equivalent
to minimizing
the negative of the likelihood
function. A
simplex is definedby a set of k+1
points in Rk joined by lines. In twoƒÆ(m)dimensions,a simplex is a triangle.
In three dimensions
it is a tetrahedron.
The
basic idea of the simplex method is that iteration
process generates a new simplex
by reflecting
one vertex in the hyperplane
spanned by the other vertices of thesimplex. Suppose the function to be optimized
has a maximum at a given vertex;
then we expect that the function will have a minimum at the reflectedvertex. Let(a k x 1 vector) be vertex such that

ƒÆ(m)The reflectionof the point

is given by

ƒ¿

where

is a reflection

factor.

Contraction

is defined

by

ƒÀ

where

is a contraction

factor. Similarly,

ƒÁ
ƒ¿
1/2
ƒÁ

where
is an expansion factor. The values of the three factors are chosen basedon personal experience. The choice that
=1, (ƒÀ = and = 2 given by Nelder& Mead (1965)
seems to perform well. The iteration
process of the simplex
method consists of the following steps.

the expansion

is definedbyƒÆ(0),ƒÆ(e),ƒÆ(m),ƒÆ(r).
(I)
Determine

ƒÆ(m)
ƒÆ(r)

(II)

ƒÆ(c),
ƒÆ(m)
ƒÆ(e)
ƒÆ(i)
ƒÆ(i)
ƒÆ(e)).

ƒÃ

ƒÆ
ƒÆ
ƒÆ

ƒÌ

reflection,and if f(ƒÆ(r));>f(ƒÆ(e)),replacebyand restartwith the new simplex.
(III)

If f(ƒÆ(r))< f(ƒÆ(e)),
we proceed with expansion.
The expansion succeeds iff(ƒÆ(m))> f(ƒÆ(e)),
and in this casereplaces
In case expansion fails,
we replacealso byand the process starts with the new simplex.

(IV)

If the reflection
is such that f(ƒÆ(m))> f(ƒÆ(r)),
we replaceƒÆ(m)byandmake a contraction
move. Now if f(ƒÆ(m))> f(ƒÆ(c)),
the contractionissuccessful,is replacedbyand a new simplex results.
In case of
failure,the simplex
is shrunk about the lowest function
value
by
,
and
is replacedby 1/2(ƒÆ(i) +ƒÆ(m).
ƒÆ(r)ƒÆ(m)ƒÆ(r),ƒÆ(r)Try

The stopping

criterion

generallyuses a pre-assigned

number> 0 and stop if

The optimalvalue ofmust be searchedwithinthe space of Rk. The

three actions in the simplex method (reflection,
contractionand expansion),
however, do not necessarily
guarantee that all vertices ofare within Rk. For
example, the variance components cannot be negative, but a reflectionofmaycontaina negative
variancecomponent. The non-negativityof variancecomponents can be achieved by reparameterization.
Instead of directly
searching
for the value of a variance component, we search forfor -•‡ <ƒÌ< +<•‡andassign eƒÌ to the variance component. This will automatically
restrict the variancecomponent within (0,+•‡).
The EM algorithm:
This algorithm
was developed by Dempster et al.
(1977)
for ML problem involving missing observations.
The likelihood
functionin this study can be formulated as incomplete data problem. The missing data are
the genetic effects. I now demonstrate the EM algorithm using a simple additiveeffect genetic model:

where aq is an Nx1
additivepolygenie

vectors for the effects

effect.

The expectation

andof QTL and aA =is theand variance of y are E(y)= Xb

The likelihood•@

The actual likelihood

is obtainedƒÆ(o)Instead of maximizingby using an initial estimateƒÆ,with respect

toby integratingout the missing

directly,•@
and maximizing

where the expectationof the complete

data:

the EM algorithm

is taken with respect

data isproceeds

to aq and aA

ƒÆ(1).
ƒÆ(0).
ƒÆ(0)ƒÆ(1)

conditional
The initialon y andThe solution after the maximization
value
is then replaced byand the iteration

ƒÆ(t).

convergence criterion
is satisfied.
The explicit
process of the EM steps is
described below.
In the E-step, we need to calculate
the conditional
expectations
andconditionalvariances of aq, aA and e given y andFirst, we establish
thefollowing

expectationand variance-covariance

Accordingconditional

to the theorem of conditional
expectationsareis denoted byis continued until

matrices:

p 106),

the

These conditional
expectationsand variances are then used to calculateconditional
expectationsof the following quadratic terms:

theThe conditionalvariancesdistribution(Graybill

1976,

are

a

ƒÆ
ƒÆ(t+1)The M-step is quite simple. It only involves replacing the ML estimatesthe complete data case by the conditional
expectations,as given below,

The estimate
E-stepsatisfied,ofin the M-step is denotedin the next cycle.

The iteration

bycontinues

which
until

is used to initiate

a convergencecriterion

in

the
is

e.g.,

Although the EM algorithm appears to involve (IIq )-1 which does notalways exist, directly computing (IIq )-1 is not needed. This can be shown by

I have described
two algorithms,
the simplex method and the EM
algorithm.
Althoughneither
method is the most efficient
one in terms ofcomputational
speed, both are well behaved in the sense that convergence to a
local maximum is usually guaranteed.
6. Marker assisted selection
In animal production,
the ultimate goal of QTL mapping is to use themapped QTLs to facilitate
selection.
Theory and methods of marker assisted
selection
have been well developed
for line crossing
experiments (Lande &Thompson 1990). Because the alleliceffects are treated as fixed, a multipleregression
approach is usually applied.
In outbred populations,however, because
the alleliceffectsof QTLs are treated as random, the best linear unbiased
prediction(BLUP)
technique(Henderson
1975)
must be applied
to marker
assisted
selection.
The BLUP marker assisted
selection
was first introducedbyFernando & Grossman (1989),
and subsequently
extended by Goddard (1992).
Assume that there are n QTLs each mapped on a particular
position of achromosome. The variances of these QTLs and the IBD matrices are assumed tobe known or estimated with sufficientaccuracy. The expectation
step of the EM
algorithm automaticallyprovides the BLUP formula of the QTL effects, i.e.,

Therefore,

the estimatedbreeding

value (EBV)isWhen the number of QTLs is large and each QTL has a small effect,

which is the estimatedbreeding value under the polygenic
model. Therefore, theefficiencyof marker assisted selection relative pedigree selection
depends on thedeviation

of

from
•@

•@•@
The amount of deviation

is expected

to belarge when there are a few QTLs with uneven contributions
to the trait.
In small breeding populations,mating strategy is important in maintaining
genetic variance and diminishinginbreeding
depression.
In addition
to markerassisted selection,
results of QTL mapping can be used to facilitate
mating design,
called marker-assisted
mating. As describedearlier,the reductionin additive
genetic
variance of a population
is proportional
to the average inbreeding
coefficientof the population,i.e.,After individual•@
QTLs areidentified,the above equation becomeswhere Fk is the average

inbreeding

coefficientat locus k (thek-th QTL).

With

molecular
marker information,
we can manipulate
matings of the selectedindividuals
so that their offspring
have minimum chance of inbreedingat moreimportant
QTLs. This will reduce the average inbreeding
coefficientsat these
QTLs, as illustrated
in the following
example. Assume that a major QTL has
been identified
and the population
size is so small that full-sib
mating cannot be
avoided, we can avoid inbreeding
for this locus while the mating is still between
full-sibs.
For example, a highly polymorphic
marker cosegregateswith the major
QTL so that the QTL genotype of each individualis actually observed. Let Q1Q3,
Q1Q4, Q2Q3 and Q2Q4 be the four possible
QTL genotypes of full-sibs
resultedfrom mating parents Q1Q2 x Q3Q4. Assume that the whole populationconsistsof this single full-sib
family. All matings will occur between full-sibs,
but amating between Q1Q3 x Q2Q4 will generate an offspringwith absolutely
nochance of inbreedingat this locus. On the other hand, a mating between
Q1Q3 x Q1Q3 will produce an offspringwith 50% of chance to be inbred. The
distributionof the inbreeding
event in the offspring
resulted from full-sib mating
can be deduced from Table 1. If a pair of full-sibs
share no IBD alleles, theirmating (Type I mating) will generate absolutely
no inbred offspring.
If a pair offull-sibs
share one IBD allele, the mating (Type II mating) will produce offspringwith 1/4 of chance to be inbred. A mating between full-sibs
sharing two IBD
alleles (Type III mating) will generate offspringwith 1/2 of chance to be inbred.
The probabilitiesof the three types of matings are 1/4, 1/2 and 1/4, respectively.
With marker information,
we may be able to choose all Type I full-sib
mating sothat their offspring
are all non-inbred
for the locus in consideration.
If aquantitative
tiait is controlled
by several QTLs, we should first order these QTLs
by their contributions
to the total phenotypicvariance. Avoidance of inbreeding
should be consideredwith high priority
for larger QTLs than for smaller ones.
High priority of avoidance of inbreeding
should also be placed to loci that show ahigh level of inbreeding
depression.

Discussion
Development of the random model QTL mapping is based on the animalmodel approach in which the alleliceffects of all individuals
are includedasmodel effects. Informationof allelic inheritancefrom parents to offspringis notrequired in the method, rather, only the final IBD sharings between sibs are used.
Because of this, the method does not require informationof parental
markerlinkage phases. However, if that information
is available,
a reduced animal model
may be used (Cantet & Smith 1991). In the reduced animal model, the alleliceffects of a progeny are expressed as linear functions of the allelic effects of theparents. Because a full-sib family only share a maximum of four parental alleles,
the number of effects in the model can be reduced significantly
when the family
size is large. The functional relationshipsof allelic effects between parents andprogenies must be derived based on the informationof allelic inheritancefromparents to progenies.
This will need the parental marker linkage phases when
multipoint
method is used to infer the allelic inheritanceof a linked QTL. The
reduced animal model is computationallymore efficientthan the animal model. It
may also provide better estimatesof the genetic variances due to the use of
linkage phase information.
The animal model presented in this study, however,cannot use linkage phase information,even if it is available.
To incorporate
linkage phase information
into the animal model, one needs to modify themultipoint
method. For example, in the full-sib analysis, instead of lumping the 16
sib-pair genotypic
configurationsof a marker (see Table 1) into four IBD states,
one should use all the 16 possible
sib-pair configurationsof all markers to infer
the probabilitiesof all 16 possible
configurationsof a putative QTL. The 16
configurationsof the QTL are then lumped into the four IBD states which are
eventuallyappliedto the animal model. Further investigationis necessarytoquantify
the improvement of using linkage phase information
relativeto thesituation

when linkage phases are not known.
For a given variance explained by a QTL, the IBD-based

linkageanalysisusually shows a lower statisticalpower in QTL detection
than the interval
mapping using line crossing experiments.
This appears to be caused by thedifference
between the statisticalmethods applied,because a fixed modelƒÐ2aqƒÐ2aq

approach has been used in interval mapping of line crosses. In a previous study(Xu 1998),
however, I have shown that the difference
is not due to statisticalmethods, rather, it is due to the differencein genetic samplings.
A simple crossinvolves only two alleles. Traditional
interval
mapping utilizes
only a single
cross or family, and thus deals with a few alleles.
The IBD-based
linkageanalysis,
however, usually deals with a large number of small families and thus alarge number of alleles. When the overall sample sizes and the allelic variances
are the same for the two sampling strategies,
the strategy with smaller number ofalleles is favored and thus has a higher power. When the same sampling strategy
is applied
to both the fixed model and the random model approaches, I found thatthe two approaches have virtually identicalpower.
Statisticalpower of QTL detection
highlydepends on the samplingstrategy. Most power studies tend to ignore the effect of sampling strategy on thestatisticalpower. These studies usuallyassume that the parents sampled areheterozygotes
and the variance of the allelic effects among the parents are given.
The random process only occurs in the sampling of these parental alleles to form
the progenies. In fact, there is another level of random process which occurs in thesampling of the parents. This type of random sampling is called genetic drift. The
variance of the allelic effects of the sampled parents is a representativeof thegenetic variance of the populationin which these parents are sampled. The overall
genetic variance is a parameter, but the variance among the sampled parents is avariable because it can vary depending on the sample size of the parents and theparticular
set of parents selected. The sampled genetic variance has a scaled chisquare distribution
with degrees of freedom determined by the number of parents
sampled. Consider a single locus,
allelic substitutionof the two alleles
vsj and vdj are the paternal
effects

are normallyis the additive

are sampled.

q, and define
as the effect•@carried by parent j, for j = 1, •E•E•E,p , where

and maternalallelic

effects,

distributed,

aj will be distributed

varianceof locus q in the population

The genetic

variance

among the sampledrespectively.

If the allelicas N(0,) , where

in whichparentsthese parents

is actually

of

ƒ¿2j
ƒÐ2aq

Because/has a chi-square

distribution

has a scaled chi-square distribution
expectationand variance of s2aq arewith one degree of freedom,with p degrees

of freedom. Therefore,

the

ƒÐ2a.

This variance is called drift variance which can be diminishedby increasingp.
Genetic drift can cause significant
deviation of the estimated from the true genetic
variance, even though the sample sizes (numbers of progenies within families)
arelarge. From the standpoint of estimating genetic parameter, one should sample as
many parents as possible
to reduce the bias of estimation.
However, when the
total number of progenies is fixed, increasing
the number of parents will decrease
the numbers of progenies within families. The latter will eventually
increase theerror of the estimated genetic variance. There is an optimal sampling strategy inwhich the estimationis the best, i.e., estimation
with the smallest joint effect of
the bias and estimation error. From the standpointof detecting QTL, genetic drift
is not necessarily
a bad thing because, by chance, s2aq may be larger than
If

ƒÐ2a

s2a >

, genetic

ƒÐ2a.

chance,s2a can also be smaller

drift has a positive

impact on the power. Overall,thangenetic

influenceon the power. Unfortunately,
If so, genetic

drift will decrease

due to the asymmetrical

drift

by

has a negative

the power because
nature of the distributionof s2aq.For instance,forfor p = 100. Numerical evaluations
conducted by Muranty (1996)
show that atleast a few parents should be sampled to warrant a reasonable power in QTL
detection.

Again, when the total number of progenies

is fixed,

reducing

drift

error

by increasing
the number of parents may cause a loss in power due to thedecreased numbers of progenies within families.
There is an optimal number ofsampled parents;
any deviationfrom the optimal number will cause a loss inpower (Xu 1998).
The IBD-based variance component method is particularly
designed forlinkage study in outbred populations.
Computer simulations
show that when the
mapping populationconsists of many small families,
the method is inefficientindetectingsmall QTLs. Association
study via the candidate gene approach may be
the choice for mapping QTLs with small effects (Risch & Merikangas 1996).
With the candidate gene approach, one directly observes genotypes of QTLs. The
allelic
effects have a global nature instead of being nested within families.
The
whole population
is now treated as a "single big family". Because genotypes of
the candidate
gene are observed and segregationof the QTL is assured, genetic
drift has been prevented. The number of alleles of a candidate
gene is usuallysmall, resulting
in small estimation
errors of the allelic effects. The consequenceof no drift and small estimation
errors is an increased statisticalpower.
Acknowledgments
This research was supported
the USDA National
Research Initiativeby the National
Institutesof Health Grant GM55321-01
Competitive
Grants Program 97-35205-5075.

andReferences
Amos, C.I. 1994. Robust variance-components
approach for assessing genetic linkage
in pedigrees.
Am.J. Hum. Genet. 54: 535-543.
Cantet, R. J. C. & C. Smith. 1991. Reduced animal model for marker assisted
selectionusing best
linear unbiased
prediction.
Genet. Res. 23: 221-233.
Dempster, A.P., N.M. Laird & D.B. Rubin. 1977. Maximum likelihoodfrom incomplete
data via the
EM algorithm.
J. Roy. Stat. Soc. B 39: 1-38.
Falconer,
D. S. & T. F. C. Mackay. 1996. Introduction
to quantitative
genetics,
ed 4. Longman, NY.
Fernando,
R. L. & M. Grossman. 1989. Marker assisted
selectionusing best linear
unbiasedprediction.
Genet. Sel. Evol. 21: 467-477.
Fulker, D. W. & L. R. Cardon. 1994. A sib-pair approach
to interval
mapping of quantitative
trait
loci. Am. J. Hum. Genet. 54: 1092-1103.
Fulker, D.W., S.S. Cherny & L.R. Cardon. 1995. Multipointinterval
mapping of quantitative
trait
loci using sib pairs. Am. J. Hum. Genet. 56: 1224-1233.
Goddard,M. E. 1992. A mixed model for analyses of data on multiple
genetic
markers. Theor. Appl.
Genet. 83: 878-886.
Goldgar,
D. E. 1990. Multipoint
analysisof human quantitative
genetic
variation.
Am. J. Hum.
Genet. 47: 957-967.

Graybill,

F. A. 1976. Theory and Applicationof the Linear Model. Duxbury Press. North Scituate,
Massachusetts.
Haseman, J. K. & R. C. Elston. 1972. The investigationof linkage between a quantitative
trait and amarker locus. Behave. Genet. 2: 3-19.
Henderson,
C.R. 1975. Best linear unbiased
estimationand prediction
under a selection
model.
Biometrics.
31: 423-447.
Kruglyak,L., & E.S. Lander. 1995. Complete
multipoint
sib-pairanalysisof qualitativeandquantitative
traits. Am. J. Hum. Genet. 57: 439-454.
Lande, R. & R. Thompson. 1990. Efficiencyof marker assisted
selectionin the improvementofquantitative
traits. Genetics
124: 743-756.
Lander, E. S. & D. Botstein.
1989. Mapping Mendelianfactors underlying
quantitative
traits using
RFLP linkage maps. Genetics 121: 185-199.
Lynch, M. & B. Walsh. 1998. Genetics
and Analysisof Quantitative
Traits.
Sinaure Associate,
Sunderland,
Massachusetts.
Muranty, H. 1996. Power of tests for quantitative
trait loci detection
using full-sib
familiesindifferent
schemes. Heredity
76: 156-165.
Nelder, J. A. & R. Mead. 1965. A simplex method for function minimization.
The Computer Journal
7:308-313.
Olson, J.M. 1995. Robust multipointlinkage analysis:an extensionof the Haseman-Elston
method.
Genet. Epidemiol.
12: 177-193.
Risen, N. & K. Merikangas
1996. The future of genetic studies of complex human diseases. Science
273: 1516-1517.
Schork, N. J. 1993. Extended multipoint
identity-by-descentanalysisof human quantitative
traits:efficiency,
power, and modeling considerations.
Am. J. Hum. Genet. 53: 1306-1393.
Xu, S. & W.R. Atchley.
1995. A random model approach to interval
mapping of quantitative
trait
loci. Genetics 141: 1189-1197.
Xu, S. 1998. Mapping quantitative
trait loci using multiple
familiesof line crosses. Genetics
148:
517-524.

Conservation
Agro-economy

of Rare Breeds in Spanish

DEA DE LIMA VIDAL
Agricultural
Economics Unit, University(medea.p@mailcity.com)

of Zaragoza,

Rural

Spain

Introduction
The animal production systems in the Mediterranean
Basin, dominated byherds of small ruminants have diverse characteristics
in time and space, according
to the objectives
and importanceof the physical,
social,economic
andenvironmental
conditions.
All this has lead to different
organizationof the herds,
markets, agrobiologicalresource use, the reproduction
calendar, etc , in accordancewith management adopted. These systems are very flexible
with regard to thecapacityof the territoryuse. Within the Mediterranean
Basin there are many
contrasts
that reflect
differencesin the living
standardof the various peoples(Daza-Andrada,
1997).
The forage system has a well documented effect on the configuration,
management and economic results of the households.
The nutritional,
forage andpastoral
expenditures,
vary according
to the implemented
production
system(Vidal,1997). Among the householdsof specializedzones, the expenditures
which
influence
forage production
can reach 70% to 74% of the total expenditure.
Thus,
forage yield represents the main influence on total income (Henot, 1983).
With regard to autochthonous
agricultural
breeds,in recent decades
governmental
abandomentand extended
neglect
have been contrasted
byexpansion of the territorial
range and increasing
economic and social importance of
these breeds. In addition,some breeds or races have experienced
decline and evenextinction.
During agriculturaldevelopment,
the autochthonous
breeds appear tohave gained equal status with commercial breeds (Sanudo, 1994).
The objectivesof the present work are, to characterize
and differentiate
distinct
livestockhouseholdsof the semi-arid area of Aragon (Spain)with regard tospecific
aspects of structure and economy in relation
to the forage system. In
addition,the ovine racial diversity
in relation
to this householdgroups modelling,with special attention
to the autochthonous
breeds, is discussed.

Methodology
The data was obtainedby direct survey of 101 representative
farmhouseholds
raising
sheep or sheep and cereals, located in the semi-aridzone ofAragdn Community (Fig.1;
Vidal,
1997).
The survey provided
informationon
structural,technical
and economic aspects of the households.
The information
covered the annual production
cycle: October of 1994 - October 1995. A total of
fifteen variables
for the management of the forage systems was classified
in threetypes: seven ratios of structure;
four economic ratios of specific expenditures;
threeof global economic results;
and a productiveorientation
indicator
were considered(Table1).
These
indicators,
were processed
statisticallyby Multiple
Correspondence
Factorial
Analysis.
Subsequently,
the first three factors (32% of
the absorbed
inertia),
were used in Cluster Analysis
to model the households
according to their implemented
system.

Fig.1

Aragonsemi-aridzone delimitationaccording(Vidal,1997)

to two climatologic

classifications(Spain)The types of households
could be differentiatedby their characteristic
economic and structuralelements. The characteristics
and determinantfactors of
the forage systems implemented,
and their relationships
to economic structure,labour and herd productivities
are analyzed and discussed.
In order to analyze the racial diversity
in relation to household
modelling,
the ovine racial classificationof the Official
Inventory of the Spanish MinistryofAgriculture
has been used (MAPA, 1997).

Table

1. Variables

used and theirs

definitions

STRUCTURAL VARIABLES
1 - UAA : hectares Useful Agrarian Area.
2 - % FA/UAA: percentage of Forage Area with respect to the UAA
3 - % Irrig.SFC/FA:
percentage of specific forage cultivation
for sheepwith respect to the FA

and goat in irrigablearea

4 - %Dry SFC/FA: percentage of specific forage cultivation
for sheep and goat in dry area withrespect to the FA
5 - SGLU/ha SFC*FA : stocking rate in units of sheep and goat with respect to the hectares of FA
6 - SGLU/ha
FL: stocking rate in sheep and goat livestock
units with respect to the surfaces in
hectares that are grazed not in the principal
cultivation,
but in by-products: Fallow Lands(fallows,
stubbles andcrops)
7 - Forage Chain: expresses the degree of autonomy in the forage compilation(complete,partial, noforage mechanization,
no forage area). (Variableof qualitativetype).
SPECIFIC
EXPENDITURES
1 - Conc.exp./SGLU:
expendituresin concentrate
feed for the sheep and goat with respect to SGLU
2 - Forage exp./SGLU:
expendituresin voluminous feed for the sheep and goat with respect to
SGLU
3 - Fix.salar./SGLU:
expendituresin fixed salary in relationshipto SGLU
4 - Ev. salar./SGLU:
expendituresin eventual salary in relationshipto SGLU
ECONOMIC RESULTS
1 - FGVA : Farm Gross Value Added
2 - FGVA /ALU: the relationshipbetween the farm gross value added and the units of available
total
annual labour
3 - ov&capFGVA/head(pts/head):the specific ovine and caprine FGVA in relationshipto the eweor goat
PRODUCTIVE ORIENTATION-%OCFP/AFP: the percentage
final production.

of the ovine and caprine

final production

in relationshipto agrarian

Results

and Discussion

The diversityof the forage systems studied has been grouped into five
types (Fig.2).
The average characteristicsof these groups with reference to thevariablesused are shown (Table 2).
The main structuralcharacteristicthat differentiates
the five types offorage systems is its character of dryness or irrigation.
The two irrigabletypes arereferred as G1 and G2, and differ by: (1) the size of the Useful Agrarian Area; (2)by the compositionof the Forage Area (FA);and (3) availability
and type oflabour. Of these the FA and watered area have similar importance.
On the other
hand, the difference
between the three types of dry systems (G3,G4 and G5 ) was
based mainly on: a) the herd size; b) the compositionof the FA; and c) theavailabilityof rented grass surface by Ovine and Caprine Livestock
Units. The
foregoing factors (a-c) are reflected in the stocking rate related to the FA.
The economic differencesin the five defined types were determined
globally
by the productiveorientationand the proportional
incidenceof Ovine andCaprine Final Production
within the Agrarian Final Production.
The value unitary
production
varied with the dimension of the herd in all the defined groups. It can besaid that there are economies of scale as well as influencesby the different
reproductive
management methods in the economy of the groups of dryland
systems.
In the groups of irrigablesystems,
as well as in those of prevailingdryland, the value unitary production
was increased in relation to the importance of
the Specific Forage Cultivationin the compositionof the Forage Area.
There is a relationshipbetween labour productivity
and the stocking ratesof the household(Hamrouni,1993).
In the groups of prevailingdryness,
theincrease in productivityof the labour would depend on the increase in stocking
rateon the Fallow Lands, and has a positive
repercussion
in the specificlivestockproductivity.
The same trend was not observed between the specificstocking
rate
and labour productivity.
In contrast,
in irrigablesystems group, the relationshipshows an opposite
trend: the increase in labour productivity
does not correspond to an increase in thestocking
rate, either by specificor by Fallow Lands. In the case of these, both,stocking
rate and specificlivestockproductivity,
decreased
as the labour
productivity
increased.
This could be caused by the productiveorientationof these

Fig.2Typologicaldiversityof forage systems

Table2.

Means and variation

S tr u c tu r a l V a r ia b le s
H e c ta re s U se fu l A g ra ria n A re a (U A A )%%F A /U A A
Ir rig .S F C /F A

% D ry S F C /F A
S p e c ifi c liv e s to c k lo ad (S G L U / h a S F C * F A )F a llo w L a n d s liv e sto c k lo a d (S G L U /h a F L )1

E x p e n se s in sh e e p & g o at fix e d sa la rie d (p ts/S G L U )fi n a lp ro d u c tio n /ag ra ria n

fi n a lcharacteristics

G R O U P 1

G R O U P 2

G R O U P 3

G R O U P 4v c

M ea n

v c

M ean

v c

M ea n

v c

M ean

v c

4 6 .9 8
4 0 .6 9
2 2 .7 8

0 .7 20 .8 33 5 6 .3 2
3 4 .1 7
1 5 .9 0

1 .6 40 .9 01 8 6 .5
2 5 .4 4

0 .9 6
1 .0 9

1 .9 22 1.1 43 6 .3 5
0 .8 3

1 .5 1
1 .0 6
2 .0 5

1 .2 5
1 .9 1
1 .8 03 5 .0 3

1.4 1
l .9 82 .9 03 5 .0 3
1 5 .0 5
1 9 .8 0

2 7 .3 5
1 3 .8 9

1 .5 1
2 .3 41 4 .5 6
1 .6 1

2 .1 93 .3 2

1 3 .1 0
0 .8 02 .4 3
1 .8 1

1 .3 7
1 .2 2
1 .7 53 .3 7
1 .2 1o rc op martia
p le lte

S p e c ific E x p e n s e s
E x p e n se s in sh e e p & g o at c o n c e n tra te fe e d (p ts/S G L U )E x p e n se s in sh e e p & g o a t v o lu m in o u s fe e d (p ts/S G L U )E x p e n se s in sh e e p & g o at e v en tu a l sa la rie d (p ts/S G L U )(VC) of the forage systems

M ea n2 8 .8 7
3 .2 5F o ra g e c h a in

P r o d u c tiv e O r ie n ta t io n
p% ro d uO cvtio
in ne&
c a p rin e

coefficient

1 7 .4 4
0 .8 3
2 .0 5p artial

1 .2 6

8 .6 0 2.8 1 8

0 .6 02 .2 9

1 0 .9 7 4
1 1 .3 4 8

0 .6 1
0 .9 1170
2 05

2 .3 1
4 .2 4

2 .3 5 6
7 .2 2 77 6 .0 3

0 .3 35 1 .8 9

G R O U P 5

1 0 .4 7
3 .7 06 .8 5
1 .9 82 .4 2
2 .7 4o cr op ma rtia
p le lte
hpaavrtia
e fol rao rg de o chn 'ta inhp avartia
e fol rao rg de o cnh'ta in

8 .6 8 2

0 .4 1

9 .7 1 3

1 .1 91 .0 35 .5 5 2
0
6 .8 1 3

0 .9 20
1 .1 7

9 .3 1 8
0
2 .3 6

0 .6 17 1 .2 8

0 .3 3

9 0 .3 6

0 .4 4
0 .6 81 1 .5 6 2
1 0 .8 8 2

0 .5 3
0 .5 80 .0 03 .2 60
4 .9 7 10
1 .7 1

0 .1 89 3 .6 90 .19

E c o n o m ic R es u lts
F a rm G ro s s V a lu e A d d e d (F G V A ) p ts
F G V A /A L U (p ts/A L U )O v & c a p F G V A /h e a d (p ts/h e a d )7 ,5 4 9 ,0 8 1 0 .5 0
3 ,3 7 2 ,6 6 4 0 .4 3
1 1 .0 6 4 0 .2 5

2 ,7 1 0 5 ,9 9 1 2 .0 4
1 0 ,0 4 6 :3 5 8.8 1
7 .0 3 2 0 .3 8

1 3 ,4 7 6 ,8 9 2 0 .5 9
5 ,6 5 9 ,7 3 9 0 .4 4
1 0 .5 4 7 0 .17

4 ,3 6 8 ,4 3 4 0 .4 3
2 ,4 6 9 ,5 5 4 0 .5 0
9 .0 0 3 0 .2 2

3 ,4 6 8 ,9 8 7 0 .6 0
1 ,7 8 4 ,4 8 5 0 .6 1
6 .5 2 1 0 .3 4

groups, that is a mix between ovine-cereal.
The Forage Area is present in all of the types of forage systems defined.
In the groups of greater
availabilityof FA (G1 and G2), Specific
ForageCultivation
prevailed,by and large in those of smaller FA surface (G4 and G5)comprised the natural grassland and scrub.
Cultivation

Group 1 type is "Forage", since it has an important
SpecificForageirrigablebase, near a high Fallow Land with emphasison alfalfa.

Group 3 type is "Herbaceous"or "Grain Producer",because it represents
privileged
resource
derivatives
from the diversity
in the Specific
ForageCultivation(drynessand irrigable)
and the important percentage
of dry cereals.
On the other hand, according to Guerin et al. (1994),
Groups 4 and 5 both possess
forage area type "Pastoral".
This types represent
at the end of spontaneousvegetation
or of ancientinstallation
involving
direct utilizationby the animals suchas: permanent meadows, mediocre lawns, scrubs, grasslandand fallows. Group 2
consistof a form of FA in transition,
since it possesses
a combinationofmechanized SFC, with an significant
percentage of scrubs and grassland. This type
would be the type "Transition"or "Forage-Pastoral".
The diversityof the Spanish autochthonous
sheep breeds used in thesesystems is shown (Table 3).
The Manchega Spanish
ovine autochthonous
race, whose cache isnormal, appear mainly in the Forage Irrigable
Systems (G1, Forage type and G2,
Transitiontype) with ovine-cereal
mixed productiveorientation
and better global
economic results and labour productivity.
This race is currently
being promoted in
Spain(Abecia,
1998;
Sanudo,1994),and is predominantamong others
autochthonous
races in these irrigables
systems.
In Forage Dry Systems G3, G4 and G5 (defined above) the Ojinegra andRoya Bilbilitana
Spanish ovine autochthonous
races predominate.
The Ojinegra
race is being promoted
like the Manchega race, while the Roya Bilbilitana
race isbeing studed in Spain as a race to officiallyconserved by the European Union,
since its cache is reduced and in danger of extinction(MAPA, 1997; FAO, 1992).
Conclusions
The Rasa Aragonesa race, whose cash is normal, appears in all the
Forage Systems in high proportions.
Nevertheless,the Roya Bilbilitana
race, whichTable3.

The diversityof the Spanish

S pRa an sisa hA Sr ha eg eo pn eR s aa c e(h o u se h o ld s %G ro u p 1

G ro u p 2

G ro u p 3

to ta l g ro u p )4 2 .1

2 6 .75 9 .2 5

auchthonous

sheep breeds

O th e r s A u to c h Io nt co
h o rn po ou rsa Stepda an nis dh S h e e p R a c es *(h o u se h o ld s %to ta l g ro u p )3 1 .5 4

4 6 .6 7

2 5 .9 2

lo c a l n a m e

G ro u p 559

4 4 .4 4

3 1.7 24 4 .4 4

S pN ao nnis‑Ah uS thh eo ec pth Ro an co esu s***(h o u se h o ld s %M an c h e g a

1 5 .6

O j in e g ra

1 0 .6R o y a B ilb ilita n a

5 .3

I. o r W .* * ra c e s

0

M an c h e g a20

O j in e g ra

6 .7

R o y a B ilb ilita n a

6 .7

R ip o lle s a

6 .7

I. o r W .* * ra c e s

6 .7

O j in e g ra

14 .8

R o y a B ilb ilita n a

3 .7

S e g u re n a

3 .7
3 .7

M o n ta n n a P ire n a ic a
G ro u p 4

in the forage systems groupin tra ‑g ro u p )I. o r W .* * ra c e s

3 .7 3

O j in e g ra

1 8 .1 8

R o y a B ilb ilita n a

9 .1

C as te lla n a

4 .5

I. o r W .* * ra c e s

0

M an chega

16 .6

O j in e g ra

5 .6

R o y a B ilb ilita n a

1 6 .6"M a sk "5 .6

I. o r W .* * ra c e s0* Not excluding
cases with the presence of foreign races in the Group 3** I. : incorporated;
W. : without definited
race*** Not excluding
cases with presence of W. in the Group 4(h o u se h o ld s %to ta l g ro u p )2 6 .3 6

2 6 .6 3

14 .8 3

9 .2 8l l .12

is rare, also shows viability
in all the modellingsystems. This demonstrates
thehigh capacity
for adaptationof these races to the great diversityof structural
andeconomics conditionsof the studied area, with emphasis on the dryland systems.
The analysis verifies that an "in situ" conservation
strategy is appropriate
to farm households
in semi-arid
conditions.
Specific
economic to help these
nucleus herds will need to be developed.
References
Abecia, A. 1 998. Personal communication.
Faculty of Veterinaria,
Zaragoza University,
Spain.
Daza-Andrada A. 1997. Reproduction
y sistemas de explotaciondel ganado ovino. Ed. MundiPrensa, Madrid, Spain. 384 pp.
FAO, 1992. Animal Genetic Resources Information.
FAO Animal Production paper, 10: 81 pp.
Guerin, G., Leger, F. and Pflimlin,
A. 1994. "Strategie
d'alimentation:
methodologie
d'analyse et dediagnostic
de l'utilisation
et de la gestion des surfaces fourrageres
et pastorales".
INRA,
Department Alimentation
et Systemes Fourragers et Department Systemes d'Exploitationd'Elevage. 36 pp.
Hamrouni, S. 1993. Tipificacion
estructural,
tecnica y economica y nivel de intensificacionde lasexplotaciones
ovinas del Valle Medio del Ebro en el marco y dinamica de las agriculturas
desarrolladas.
Ph.D. Thesis, Unidad de Economia Agraria de la Universidad
de Zaragoza,
Espana. 400 pp + annexes.
Henot, A.Y. 1983. Incidence
des systemes fourragers sur le revenu d'une explotaition
latiereen
Bretagne. Etude par simulation.
Fourrages 96: 333.
MAPA (Ministeriode Agricultura,
Pesca y Alimentation,
Espana),1997. Evaluationde recursos
agrarios. Direction General de la Production Agraria. Madrid, Spain.
Sanudo, C. 1994. Las razas ganaderas tradicionales(II). Su importancia
y recuperation.
Mundo
ganadero 4: 22-29.
Vidal, D. de L. 1997. The incidenceof the Forage System in the Ovine and Ovine-Cereal
households
in Semi-Arid zones of the Ebro River Middle Valley (Spain).
Ph.D. Thesis, Unidad deEconomia Agraria de la Universidad
de Zaragoza, Espana. 517 pp + annexes.

Genetic Distance and Classification
Using Genetic Markers

of Domestic

YOSHITAKA NAGAMINE, HIDEAKI

TAKAHASHI*

and HISATO TAKEDA*

Tohoku National

StationAgricultural

Experiment

Shimo-kuriyagawa,Morioka,*Nationalof AgrobiologicalKannon-dai,

Institute

Tsukuba,308-8602,

020-0198,

AnimalsJapan
ResourcesJapan

Abstract
Genetic distance is used to classify and understand evolutionary
relationships
betweenpopulations,such as species, which have long divergent
time. The propertiesof genetic distancemeasures in classification
have not been investigated.
We compared the accuracy of 6 genetic distancemeasures to classify populations
which have recently diverged in simulation
studies. Effect of varied
population
sizes from generation to generation
were also considered.
Nei's standard distance andminimum distance generally showed the best results under various conditions.
However, the differencein accuracy among distance measures was not significant.
The number of applied loci had a larger effecton accuracy than the type of distance measures. The four distance measures had a linear relationshipwith generation
at a constant population
size. None of distance measures, however, had a linear
relationshipwhen population
size varied. The effect of varied population
size on the average distance
was large over the first few generations

but became less when number of generations

was increased.

Introduction
Various methods to measure the genetic distances between populations havebeen described by Nei (1987).
Propertiesof these genetic distance measures using
genetic markers, such as blood and protein polymorphismand micro satelliteDNA,
have been discussed mainly from an evolutionary perspective.
Genetic distances help
understand evolutionary
relationships
between populations.
Conservationof traditional
livestock
breeds is receiving
increasing
international
attention (Hall and Bradley 1995). Traditional
livestock breeds may carryuseful genes for agriculture.
In addition,indigenous
breeds are thought worthy toconserve for their cultural merit. Measuring the genetic distance between breeds in a
species provides informationon their genetic diversity
and aids classificationofpopulations.
The main characteristics
of livestock breeds compared to other populations

in evolutionary
perspective
are number of generations from the point of divergence androle of mutation. Since livestock are domesticatedand improved by man, the divergent
time between breeds is short from an evolutionary
perspective.
The role of mutation ofmarker genes might be very small to make the genetic difference
between two breeds.
Consequently
it is necessary to determine if the propertiesof genetic distance measures
are appropriate
for understand the relationships
among livestock breeds. The objectivesof this paper were to investigate
the accuracy of classification
using various genetic
distance measures with small number of generations in simulation study. The effect ofvaried population
sizes from generation to generation are considered to simulate therealistic
divergence between breeds.
Method of computing simulation
Genetic distance measures: The property of eight genetic distance measures werecompared. Mathematical symbols used mainly followed the paper by Takezaki and Nei(1996).
Here, Xijand Yijare the frequenciesof the i th allele at the j th locus in
populationx and y, respectively,
mj is the number alleles at the j th locus and r is thenumber of loci.
1) Sanghvi's(1953)
distance

The Sanghvi's

distance

was multiplied

because it had no effect in this simulation.
2) Roger's (1972)
distanceby 2 originally

but we ignored the multiplier25) Nei's(1983)

6) Cavalli-Sforza

DA

and Edwards'(1967)

distance

Model tree and population:
A simple model tree was used to test the accuracy of the
6 genetic distance measures (Figure 1). Twenty populations
were split from the base
populationand they were named the first split populations.
They were monoecious
diploid populations
and individuals
were mated randomly in each population
through
20 generations. Each population was split into two subpopulations
to generate the 21st
generation.
Then 40 subpopulations,the second split populations,were generated.
Individuals
in each second split population
were mated randomly for 5 to 20
generations. Genetic distances between second split populations
at the 5th, 10th, 15thand 20th generation
after generating
subpopulation,i.e. 25th, 30th, 35th and 40th
generations
from the base population,
were examined. We did 3 types of simulation
with different conditions
regarding population
sizes: (1) All populations,
20 of the firstand 40 of the second split population,
had size 100 constantly,(2) each population
chose a size, 90, 100 or 110 randomly, or (3) 80,100 or 120 randomly from the first to
the final generation.

Fig.1Model tree used in simulation.
The first split populations,randomly through20 generations
and the second split populations,mated randomly through 5, 10, 15 or 20 generations.

P1, P2,.... and P20 are matedP1,1, P1,2,... and P20,2 are

Loci and alleles: Ten and twenty loci were generated and 2 allelic genes were assumed
at each locus. Since one out of two genes were chosen randomly with equal probability
to generate the base population,
expected heterozygosity
at each locus was 0.5.
Accuracy of distance measures: Since there were 40 subpopulations,a particularsubpopulation
could have 39 combinationsof distance with the other subpopulations.
The expected distance between a pair of subpopulations,which are from the same first
split
population,
should be minimum. Subpopulations,i.e. the second split
subpopulations,from the first split population
P1 were designated to be P1,1 andP1,2. P1,1
might have 39 combinations
with all other subpopulations
including the combination
with its partner subpopulations
P1,2. P1,2 might also have 38 combinations
if thecombination
with P1,1 was excluded. The genetic distances
between a pair ofsubpopulations
P1,1 and P1,2 was expected to be minimum from 77 (= 39 + 38)
combinations.
When the distance between a pair of subpopulations
had the minimum
distance from 77 combinations,the genetic distance measure was judged as correct or
accurate.

Results and Discussion
Number of correct pairs:

The average number of correct pairs which showed theminimum distances between paired subpopulations
from 20 pairs after 200 replications
is shown (Table 1 and 2). When a larger number of generationsand varied population
sizes were applied, the number of correct pairs was reduced. The number of correct
pairs for Sanghvi's
distance, for example, were 16.08 at the 5th generation with aconstant population
size, 100, and the number was reduced to 4.36 at the 20th
generation.
Varied population
sizes resulted a reduction in number of correct pairs.
Number of correct pairs was reduced from 16.08 to 11.99 with varied population
sizes,
between 90 and 110, for Sanghvi's distance. When population sizes varied between 80
and 120, it was reduced to 7.40 at the 5th generation.
The largest number of correct
pairs were observed from Nei's standard genetic distance (Table 1). The smallest
number of correct pairs were generally observed from Cavalli-Sforza
and Edwards'
distance. Nei's standard distance showed the largest number and Nei's minimum
distance showed the second largest numbers in all generations at any varied population
size (Table 1 and 2). Generally,
Cavalli-Sforza
and Edwards' distance showed thesmallest number and Roger's distance showed the second smallest number of correct
pairs. However, among the 6 measures of genetic distance, no significant
differenceswere observed under the same conditions for generation and population size. Increasing
the number of loci from 10 to 20, increased the number of correct pairs significantly(< 1%) for the all distance measures. Under the same conditions for population
size andgeneration, the minimum number of correct pairs from all distance measures with 20
loci was always larger than the maximumnumber of correct pairs from all measureswith 10 loci. When 10 loci was used, for example, with constant population
size at the20th generation, the maximumnumber was4.91, which was obtained by Nei's standard
distance, from all distance measures. Though this value was significantly(<1 %) smaller
than the minimum value of 8.65, from Cavalli-Sforza
and Edwards' distance with 20
loci. When a large number of generations was applied, the effect of loci number wasvery clear. Number of correct pairs from Nei's standard distance measure at 20th
generation, for example, increased from 4.91 with 10 loci to 10.66 with 20 loci.
Relationshipbetween distance and generation:
At generation 0 of the subpopulations,which is the 20th generation from the base population,
expected genetic distance is zero.
A distance measures, except Roger's distance and Cavalli-Sforza
and Edwards'
distance, showed the liner relationshipwith generation.
None of distance measures,
however, had a linear relationshipwhen population
size varied. From the 0 to the 5th
generation,
distances increased rapidly in all measures with population
sizes varying

Table1. Number of correct pairs from 20 pairs using
D ista n c e

10 locim e a s u re s

Ga ne dn ePr ao tpio. sn iz e
S ang h vi

R og erN e i M in

N e iS t

N e iD A

C a v a ll i

10 0

1 6 .0 8 ( 2 .0 8 )

1 5 .5 1 ( 2 .1 8 )

1 6 .2 0 ( 2 .0 4 )

90 ‑110

1 1 .9 9 ( 2 .5 6 )1 1 .4 2 ( 2 .5 1 )1 2 .1 8 ( 2 .5 1 )1 6 .3 4 ( 1 .9 9 )1 6 .0 6 ( 2 .0 7 )

1 5 .3 4 ( 2 .1 7 )

1 2 .3 6 ( 2 .5 0 )1 1 .9 8 (2 .5 4 )80 ‑120

7 .4 0 ( 2 .3 1 )6 .9 3 ( 2 .2 9 )

1 1 .2 8 ( 2 .5 2 )7 .5 5 ( 2 .2 7 )7 . 6 3 ( 2 .3 0 )7 .4 3 ( 2 .3 1 )6 .9 4 ( 2 .3 0 )1 00

9 .8 7 ( 2 .5 8 )90 ‑110

6 .9 0 ( 2 .2 7 )9 .5 0 ( 2 .3 4 )1 0 .0 7 ( 2 .5 0 )1 0 .3 6 ( 2 .4 8 )9 .8 5 ( 2 .5 7 )9 . 1 9 ( 2 .4 4 )6 .6 1 ( 2 .3 4 )7 .1 0 ( 2 .2 8 )7 .2 9 ( 2 .2 8 )6 .8 8 ( 2 .3 0 )8 0‑120

6 .4 0 ( 2 .3 7 )4 .3 8 ( 1 .8 4 )

4 . 1 1 ( 1 .8 6 )4 .5 2 ( 1 .9 2 )4 .6 6 ( 1 .9 5 )4 .3 6 ( 1 .8 3 )3 .9 8 ( 1 . 8 7 )G e n .5

G e n .1 0

G e n .1 5
100

6 .2 8 ( 2 .2 1 )6 . 1 2 ( 2 .3 0 )6 .6 7 (2 .4 4 )6 .9 4 ( 2 .4 5 )6 .1 8 ( 2 .2 0 )5 .6 4 ( 2 .3 2 )9 0‑110

4 .5 6 ( 1 .8 9 )4 .4 5 ( 2 .0 1 )3 .0 5 ( 1 .8 5 )2 .9 3 ( 1 .7 4 )4 .8 9 ( 2 .0 2 )3 .3 0 ( 1 .8

4 .4 9 ( 1 .8 9 )3 .0 1 ( 1 .8 3 )4 .1 5 ( 1 .9 6 )8 0‑120

4 .6 5 ( 1 .9 5 )3 .1 7 ( 1 .8 3 )2 .7 3 ( 1 .7 6 )G e n .2 0
100

4 .3 6 ( 1 .9 0 )4 .9 1 ( 2 .0 0 )4 .2 8 ( 1 .8 9 )3 .2 0 ( 1 .6 2 )4 .4 0 ( 1 .9 5 )3 . 1 4 ( 1 .7 0 )4 .6 0 ( 1 .9 6 )9 0‑110

3 .4 0 ( 1 .7 8 )3 .5 8 ( 1 .8 0 )3 .1 2 ( 1 .6 2 )3 .9 5 ( 1 .8 9 )2 .8 9 ( 1 .6 1 )8 0‑120

2 .2 7 ( 1 .4 7 )2 .2 9 ( 1 .4 2 )2 .3 6 ( 1 .5 1 )2 .4 9 ( 1 .5 3 )2 .2 5 ( 1 .4 9 )2 .1 4 ( 1 .3 6 )Nei Min: Nei's minimum distance,
Nei St: Nei's standard
distances.
Cavalli:
Cavalli-Sforza
and Edwards' distance.
Generation:
Gen.5, Gen10, Gen.15 and Gen.20 are 5th,10th,15th
and 20th generation,
respectively,
after
Pop.size:
Population
sizes are (1) constantat 100 (2) varied 90,100
or 110 or (3) varied 80,100
or 120.

the divergence.

Table2. Number of correct pairs from 20 pairs using 20 loci
D is ta n c e

m e a s u re saG nedn eP roa pt .io sn iz e
S ang hv i

R og erN e i M in

N e iS t

N ei D A

C a v a l li

10 0

1 9 .8 2 ( 0 .4 7 )

1 9 .7 7 ( 0 .5 3 )1 9 .8 5 ( 0 .4 5 )1 9 .7 3 ( 0 .5 8 )

1 7 .3 6 ( 1 .6 5 )1 6 .7 7 ( 1 .8 1 )

1 7 .4 1 ( 1 .6 9 )

1 9 .8 7 ( 0 .4 2 )1 7 .5 2 ( 1 .6 0 )1 9 .8 2 ( 0 .4 7 )9 0‑110

1 7 3 9 ( 1 .6 1 )1 6 .7 2 ( 1 .8 2 )8 0‑120

1 1 .3 6 ( 2 .2 9 )

1 0 .6 5 ( 2 .2 4 )

1 1 .3 6 ( 2 .3 6 )1 1 .3 5 ( 2 .3 7 )

1 1 .3 8 ( 2 .3 0 )1 0 .6 2 (2 .2 3 )10 0

1 7 .3 2 ( 1 .7 8 )

1 6 .6 6 ( 2 .0 4 )

1 7 .5 2 ( 1 . 6 6 )1 7 .6 8 ( 1 .6 6 )1 7 .2 7 ( 1 .8 1 )

1 6 .2 5 ( 2 .1 8 )9 0‑110

1 3 . 1 6 ( 2 .3 2 )1 2 .3 2 ( 2 .5 3 )1 3 .4 7 ( 2 .2 6 )1 3 .7 1 ( 2 .3 0 )1 3 .1 0 ( 2 .3 5 )1 2 . 1 4 ( 2 .5 6 )8 0‑120

8 .0 3 ( 2 .2 5 )7 .4 8 (2 .2 6 )8 .1 3 ( 2 .3 2 )8 .2 2 ( 2 .3 1 )8 .0 1 ( 2 .2 3 )7 .3 5 ( 2 .2 6 )10 0

1 3 .0 0 ( 2 .3 7 )1 2 .2 6 ( 2 .4 5 )1 3 .3 5 ( 2 .3 4 )1 3 .6 6 ( 2 .3 6 )1 2 .8 7 ( 2 .3 1 )

1 1 . 6 5 ( 2 .4 1 )9 0‑1109 . 1 4 ( 2 .6 9 )8 .7 3 ( 2 .5 6 )9 .6 0 ( 2 .5 6 )9 .8 1 ( 2 .5 5 )8 .4 1 ( 2 .6 3 )8 0‑120

6 .3 0 ( 2 .3 9 )5 .9 4 ( 2 .3 6 )6 .4 2 ( 2 .3 9 )6 .5 7 ( 2 .3 8 )9 .0 7 ( 2 .6 4 )6 .2 5 ( 2 .3 9 )10 0

9 .9 1 ( 2 .5 0 )9 .3 8 ( 2 .4 6 )1 0 .2 0 ( 2 .4 7 )1 0 .6 6 ( 2 .4 1 )9 .6 9 ( 2 .5 6 )8 .6 5 ( 2 .4 1 )90 ‑110

6 .6 1 ( 2 .1 5 )6 .2 5 ( 2 . 1 5 )6 .9 9 ( 2 . 1 7 )7 .3 1 ( 2 .1 9 )6 .5 0 ( 2 .1 6 )5 .8 5 ( 2 . 1 5 )

8 0 ‑ 12 0

4 .3 7 ( 1 .7 4 )4 .2 2 ( 1 .7 1 )4 .5 8 ( 1 .7 8 )4 .7 8 ( 1 .8 6 )4 .3 7 ( 1 .7 8 )3 .9 8 ( 1 .6 5 )G e n .5

G e n .1 0

G e n .1 5

5 .7 2 ( 2 .3 7 )G e n .2 0

Nei Min: Nei's minimum distance.
Nei St: Nei's standard
distances.
Cavalli:
Cavalli-Sforza
and Edwards' distance.
Generation:
Gen.5, Gen10, Gen.15 and Gen.20 are 5th, 10th, 15th and 20th generation,
respectively,
afterPop. size: Population
sizes are (1) constantat 100, (2) varied 90, 100 or 110 or(3) varied 80,100 or 120.

the divergence.

between 80 and 120. When population
sizes varied between 90 and 110, average
distances also increased but they rose slightly
from the 0 to 5th generation.
The effectof varied population
size was clear over the first few generations.
Though with later
generations,the effect of varied population
size was less.
Nei's standard distance and minimum distance generally showed the largest
number of correct pairs in various conditions.
Numbers of correct pairs among thegenetic distance measures, however, were small and not significantly
different.
Nei(1978)and Gorman and Renzi (1979) recognized
the number of loci was important toestimate unbiased genetic distance. In our simulation,
number of loci had much larger
effect on the accuracy than the type of distance measures. In electrophoretic
surveys,
the number of loci studied is generally
few (Nei 1987). Though the number of locidetected using microsatellite
marker is much larger and easy to increase (Takahashi
etal., 1998). Microsatellite
markers can improve the accuracy in classificationofdomestic animals. The difference in the property of linearity with divergent time among
the distance measures was found. Roger's distance and Cavalli-Sforza
and Edwards'
distance showed the non-linearity
even with a constant population
size. All distancemeasures, however, showed rapid increase of average distance from 0 to 5th generation
with varied population
size. At the 20th generation
the average distance between
populations
with a constant populations
size and varied population size was very small.
This suggests that the effect of varied population
size is smaller with increasing numberof generations.
Acknowledgments
Wethank Dr. D. A. Vaughan for his helpfulcomments on our manuscript.

References
Cavalli-Sforza,

L.L. and Edwards,
procedures.
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S.J.G.

Copeia:and Bradley,
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breed biodiversity.

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10: 267-270.

Nei, M., 1972.

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Nei,

The theoryM., 1973.

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Amer. J. Hum. Genet. 19: 233-257.

Gorman, G.C. and Reinz,
Hall,

A.W.F., 1967.

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betweenpopulations.

Amer. Nat. 106:and estimationof genetic

distance:283-291.

In: Genetic(Morton N, ed). Honolulu:
Universityof Hawaii: 45-54.
Nei, M., Tajima, F. and Tateno, Y., 1983. Accuracy of estimatedphylogenetic

Structureof Populations,trees from moleciiardata.

J. Mol. Evol.

19: 153-170.

Nei, M., 1987. Molecular

Evolutionary

Genetics.

New York; Columbia

University

Press.

of genetic

similarityand genetic

In: Studies

inRoger,

J.S.,1972.

Measures

University
Sanghvi,

L.D.,1953.

of Texas Publication
Comparison

differences.
Takahashi,

polymorphisms.
Takezaki,K., Nagamine,among Japanese

UniversityVII.

145-153.

for a studyof biological

11: 385-404.

Y., Tsudzuki,
nativeof Texas:
methods

Genetics

breedsM. and Yamamoto,of chicken

basedY., 1998.

Genetic

on microsatellite

DNA

J. Hered. 89: 543-546.

N. and Nei, M.,1996.
microsatellite

Austin:and morphologicalAmer. J. Phys. Anthropol.

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and reconstructionof phylogenetic

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from

Session

2. Current Status and Perspectives
Effective Use of SpermofThe Role of Sertoli Cells
Spermatogenic
Cells

in Differentiation

AKIKO SHIRATSUCHI,TOMOJI MAEDA and YOSHINOBU
Faculty of Pharmaceutical
Sciences, Kanazawa University
Kanazawa, Ishikawa 920-0934,
Japan

and Exclusion

of

NAKANISHI

Abstract
In order to clarify the molecular basis of the functional
interaction
between spermatogenic andSertoli cells, we identified
molecules responsible
for the specific adhesion of spermatogenic
cells toSertoli cells and for the phagocytosis
of apoptotic
spermatogenic
cells by Sertoli cells. Adopting an
expression cloning procedure, we isolated a cDNA coding region for a spermatogenic
cell protein whose
expression gave a cultured cell line the ability to bind to Sertoli cells. The protein, 243 amino acids with
the N-terminal signal peptide and a C-terminal Cys-rich region, turned out to be the rat homologue ofa testicular
protein called Tpx-1 whose function was not determined. A polyclonalanti-body raised
against bacteriallyexpressed Tpx-1 significantly
inhibited
the binding of spermatogenic
cells to Sertoli
cells. These results indicatedthat Tpx-1 is a testicularcell adhesion
molecule responsible
for thespecific interaction
between spermatogenic and Sertoli cells. We establisheda quantitative
phagocytosis
assay in which apoptotic spermatogenic
cells were selectively
engulfed by Sertoli cells. Cell surface
exposure of phosphatidylserine,
which is observed in many apoptotic
cells, occurred with apoptotic
spermatogenic
cells, and their phagocytosis
by Sertoli cells was specificallyinhibitedin the presenceof PS-containing
liposomes. These results indicatedthat PS is exposed on apoptotic spermatogenic cells
and serves as a marker for subsequent phagocytosis
by Sertoli cells.

Introduction
The mammalian spermatogenic pathway consists of a complex series of events,
such as proliferationand differentiationof spermatogonial
stem cells, meiotic divisionof differentiating
spermatogenic
cells, and morphogenic maturation of spermatids.
Throughout the spermatogenic pathway, spermatogenic cells remain in close contact
with somatic Sertoli cells in the seminiferous tubules. It is thus presumed that a variety
of materials and information are transferred between the two cell types. On the other
hand, more than half of the differentiating
spermatogenic
cells undergo apoptotic cell
death and are excluded through phagocytosis
possibly by Sertoli cells. We developed
a primary culture of rat testicular cells, in which spermatogenesis
proceeds in terms of
the occurrence of testis-specificgene expression (Tamaru et al, 1990). In our previous•ŽƒÊg/ml)ƒÊg

study (Mizuno et al. , 1996), spermatogenic
differentiation
was abrogated and deadspermatogenic cells accumulated when spermatogenic cells and Sertoli cells were placedon the opposite side of a permeable
membrane. These results indicatedthat bothdifferentiationand exclusion of spermatogenic cells occur in primary culture dependingon their direct association
with Sertoli cells. We here analyzed the molecular basis forthis functional
association
between the two cell types.
Materials
and Methods
Primary culture of rat testicular
cells-Dispersed
testicular
cells were prepared fromtestes dissectedfrom 20-day-old Donryu rats and primary cultured at 32.5
in amixture of F12 and L15 media (1:1)
with 10% fetal calf serum (FCS) andnorepinephrine(1
as described previously(Nagao, 1989).
Cell adhesion assay-Testicular
cells were primary cultured on coverslips (ƒÓ=3 cm)
for 3 days. Cultured spermatogenic
cells consisting
mostly of spermatocytes
weredetached from Sertoli cells by pipetting
and spun down, and the cells (5 x 105) wereadded to a monolayer of the Sertoli cells (1.5 x 106). After a three-hour culture, thecoverslips were taken off with forceps and washed by shaking them exactly five times
in the culture medium. The number of spermatogenic
cells remaining attached to the
Sertoli cells was determined under a phase-contrast microscope. Ten randomly-chosen
microscopic fields were examined, and the cell numbers are presented relative to thatof initiallyloaded spermatogenic
cells taken as 100; i. e., the cell adhesion index. To
determine the function of cloned cDNA, the binding of Jurkat Tag cells expressing theDNAto Sertoli cells was examined as described above.
Cloning of Tpx-1 cDNA-Poly(A)-containing
RNA (5 /ƒÊg) prepared from cultured
spermatogenic
cells was used to synthesize
cDNA using a commercial kit (Great
Lengths cDNA Synthesis Kit; Clontech),and the resulting cDNA was ligated with the
pcDNAI/Amp vector (Invitrogen).
The cDNA library consisted of2 x 106 independent
clones with insert sizes ranging from 0.5 to 3 kbp. The library (200
DNA) wasintroduced into Jurkat Tag cells (5 x 107) by electroporation
using Gene Pulser (BioRad Laboratories)
at 0.25 kV with a capacitance of 960ƒÊF. The cells were cultured in
RPMI1640 medium with 10% FCS for 48 h and then subjected to screening in the celladhesion assay with Sertoli cells. The cells remaining attached to the Sertoli cells weredetached by pipetting
and selected again in the same adhesion assay. Plasmid DNAwas
then recovered from the selected cells and amplified in E. coli TOP10F'. The DNA wasintroduced again into Jurkat Tag cells (1 x 107), and the cells were subjected to anotherround of screening. After four rounds of screening, plasmid DNA was extracted fromthe selected cells and about 50 cDNA clones were sequenced. Weobtained three clones
that contained reasonably long open reading frames. The inserts of these clones wereƒÊg)
ligatedwith the pHook-2 vector (Invitrogen),and the resulting
DNA (40
wasintroduced into Jurkat Tag cells (1 x 107) by electroporation.
After a 48-h culture, thecells expressing the CDNA (1 x 105) were collected using phOx-coated magnetic beads(Capture-Tec
pHook-2 kit; Invitrogen)and subjectedto the final cell adhesion assay
with Sertoli cells. About 80% of the cells selected by the above procedure expressed an
introduced DNA when examined with a control pHook-2-lacZ DNA (data not shown).
Eventually,
one clone coded for a protein possessing
the desired
activity
wasobtained.
Preparation of anti-Tpx-1 antibody-The
region between nucleotide positions 345 and807 (amino acids 89-243) of clone #97 was inserted into a PET-15b (Novagen)-derived
His-tag vector, and the resulting DNA was introduced into E. coli BL21. Expression
ƒÀ-D-thiogalactoside.
of the His-tagged Tpx-1 was induced with 1 mM isopropylThe
His-tagged
Tpx-1 recovered
as inclusion
bodies were separated
on a 15%polyacrylamide
gel containing SDS. Aportion of the gel containing the fusion protein
was excised and the protein was electroeluted
using Maxyield-NP (Atto). Tpx-1 fusedwith glutathione
S-transferase(GST) was similarly
prepared by inserting the region
between nucleotide
positions345 and 807 into a vector (pGX-KG;
AmershamPharmacia Biotech),and E. coli BL21 was transformed with the resulting DNA. The
GST-Tpx-1 fusion protein was purified from E. coli, as was the His-tagged protein.
GST was purified using glutathione
Sepharose (Amersham Pharmacia Biotech) understandard procedures. Rabbits were immunized with about 0.4 mg of the purified
Histagged Tpx-1, and the antibody titer was determined by Western blots of GST-Tpx-1.
The total IgG fraction was obtained from sera by successive
fractionation
withammoniumsulfate precipitation
and DEAE-cellulose
chromatography.
IgG specific to
Tpx-1 was further purified
by affinity
chromatography
with His-tagged
Tpx-1conjugated
Ni-NTA agarose (Qiagen).
Western blots-Proteins
were separated on a 12% polyacrylamide
gel containing SDS
and electrophoreticallytransferred
onto a polyvinylidene
difluoride
membrane(Millipore).
The membrane was blocked with 0.2% skim dry milk and incubated witha primary antibody in a buffer consistingof 10 mM Tris-HCl (pH 8), 0.15 M NaCl,and 0.5% Tween 20. The membrane was washed and reacted with a secondary antibody
conjugated with alkaline phosphatase.
The signals were detected using the Immun-Star•Žsystem (Bio-Rad Laboratories).
Immunohistochemistry-Spermatogenic
cells prepared from 20-day-old rats were fixedwith 4% paraformaldehyde
and 2% sucrose for 15 min at room temperature, then withmethanol for 1 min at room temperature. The fixed cells were treated first with affinitypurified
anti-Tpx-1
IgG, then with a FITC-conjugatedsecondary antibody,andexamined under a confocal laser microscope.
Phagocytosis
assay-Spermatogenic
cells were recovered by gentle pipettingfromtesticular
cells co-cultured in collagen-coatedmultiwell plates for two days. On theother hand, Sertoli cells were obtained by removing the spermatogenic
cells from a coculture of testicular
cells maintained on Chamber Slides (Nunc). Most of the recovered
spermatogenic
cells were spermatocytes,
and the Sertoli cell culture was about 90%
pure. The recovered spermatogenic
cells were maintained with no added cells for twodays, labeled with biotin (NHS-LS-Biotin;
Pierce), and added back to the Sertoli cell
culture maintainedin Chamber Slides. Spermatogenic
cells (about 2.5 x 105) weremixed with Sertoli cells (about 3 x 104) in 0.15 ml of medium, and the phagocytosis
reaction
carried out at 32.5
for 2 h, except in the time course experiment.
Phosphate-bufferedsaline was then added and unreacted spermatogenic cells washedout by pipetting
15 strokes two times. The mixture was further treated with trypsin (0.5mg/ml) for 3 min at room temperature, after which those cells detached from the culture
slides were removed. The remaining cells were fixed with 2% paraformaldehyde/0.1%
glutaraldehyde/0.05%
Triton X-100/phosphate-bufferedsaline. The fixed cells were
supplemented
with Fluorescein
Avidin D (Vector)
and kept for 20 min at room
temperature.
The biotinylated
spermatogenic
cells were detectedunder afluorescence/phase-contrast
microscope. The ratio of the number of positively
stainedSertoli cells to total Sertoli cells (100-150)
was determined in each microscopic field.
Eight to ten fields from different culture wells were examined in each experiment, andthe results statisticallytreated. The mean and standard deviations of a typical example
from at least three independent
experiments were presented as the phagocytic
index.
Under these conditions,
we routinely obtained a phagocytic
index of 13-20.
Liposome preparation-Dried
lipid films containing various phospholipids(2 mmoles)
were swollen in 10 mM Tris-HCl, pH 7.4/0.15 M NaCl and sonicated for 10 min on
ice. The liposomes
were composed of either phosphatidylcholine(PC) only, or a
combination of PC and another phospholipid
at a molar ratio of 7:3. Fluorescencelabeledliposomes
were prepared
as above in the presenceof L-ƒ¿phosphatidylethanolamine-N-(lissamine
rhodamine B sulfonyl) (Avanti Polar-Lipids)

at 1% of total phospholipids.
The engulfment of fluorescent liposomes by Sertoli cells
was analyzed using a confocal laser microscope.
Annexin V-binding assay-Spermatogenic
cells, which had been maintained in singleculture for various periods, were supplemented
with FITC-conjugatedannexin V(Bender MedSystems)
and propidium iodide, and the mixture left on ice for 15 min.
The fluorescence from FITC and propidium iodide was simultaneously
determined withthe cultured cells (104) in a flowcytometer.
Results
Molecular cloning of Tpx-1 responsible
for associationof spermatogenic
andSertoli cells (Maeda et al., 1998)
Expression cloning of a cell adhesion molecule of spermatogenic
cells-When rat
testicular
cells were primary cultured,
spermatogenic
cells were maintainedinassociation
with Sertoli
cells that grew as a monolayer. In order to identify
themolecule(s)responsible
for the specific interaction between the two cell types, we firstestablisheda quantitativecell adhesion assay. Cultured spermatogenic
cells wereisolated and loaded on the Sertoli cell culture, and the mixture was left for 3 h. The
culture was then washed carefully
and cell adhesion
indices were determined as
described in the Materials and Methods section. In this assay, spermatogenic cells wereshown to bind to Sertoli cells much more efficiently
than did a cultured T-cell line,
Jurkat Tag.
Wethen aimed at cloning the molecule(s)responsible
for this interaction,
using
an expression cloning procedure. We chose the human T-cell line Jurkat Tag as parent
cells for the following reasons; the cells grow in suspension, do not bind to Sertoli cells,
and express SV40 large T-antigen that allows a plasmid
containing
the SV40
replication
origin to replicate extrachromosomally.
A cDNAlibrary prepared from themRNAof cultured spermatogenic
cells was introduced into Jurkat Tag cells, and thecells that acquired the ability to bind to Sertoli cells were selected as described in the
Materials and Methods section. After four rounds of screening, plasmids containing the
cDNAwere recovered from the selected cells and sequenced. Weobtained three CDNA
clones that contained the complete coding sequences for reasonably large peptides. To
determine whether these cDNA coded for proteins with the desired activity, the final celladhesion assay was conducted. Two of the clones were inserted into the pHook-2
vector, and the resultant DNA was introduced into Jurkat Tag cells. We were unable
to ligate the insert of the third clone with pHook-2 for an unknown reason. Cells

expressing
the cDNA were selected using magnetic beads, and their ability to bind to
Sertoli cells was examined. The cells expressing one of the clones bound to Sertoli cells
as efficiently
as did spermatogenic
cells, but those expressing the other or the vector
alone did not. These results showed that a cDNA clone that coded for a protein with thedesired activity
was obtained.
Structure of the cell adhesion protein-The
cDNA clone encoded a protein with 243
amino acids including
an N-terminal hydrophobic
sequence and a Cys-rich region at the
C-terminal half. Characteristically,
the Cys residues repeated five times with an intervalof eight amino acids near the C-terminus. The entire amino acid sequence of this protein
showed a significant
similarity
to that of a testicular protein called Tpx-1 or autoantigen
1 (AA1);85, 65, and 68% identitywith mouse, guinea pig, and human proteins,respectively.
We thus concluded that the protein is the rat homologue of Tpx-1. Tpx-1
is a member of the CRISP (standing for Cys-rich secretory proteins)family of proteins,which contain the N-terminal signal peptide
and are rich in Cys residues at the Cterminal half.
Role of Tpx-1 in spermatogenic
and Sertoli cell adhesion-In
order to assess the roleof Tpx-1 in the specific adhesion of spermatogenic cells to Sertoli cells, a polyclonal
antibody was raised against a bacterially-expressed
His-tagged Tpx-1. The specificityof the antibody was first examined by Western blots. E. coli proteins containing a GSTTpx-1 fusion protein were analyzed with anti-Tpx-1 and anti-GST antibodies
in thepresence and absence of corresponding
antigen proteins. The fusion protein of about 44
kDa was detectable with either antibody. When the reaction with primary antibodies
was conducted in the presence of GST-Tpx-1 or GST, the signal with anti-Tpx-1
disappeared
only in the presence of the fusion protein, while that with anti-GST was
abolishedby the additionof either protein. These results showed that the antibody
raised against bacterially-expressed
Tpx-1 specificallyrecognizes the Tpx-1 protein in
Western blots. The same antibody was then examined to determine whether it reacts
with native
Tpx-1. When spermatogenic
cells of 20-day-old
rats were
immunohistochemicallyanalyzed with the affinity-purified
anti-Tpx-1 antibody, mostof the cells showed signals. We then assessed the effect of anti-Tpx-1 on the adhesion
between spermatogenic
and Sertoli cells. The adhesion was inhibitedin a manner
responding
to the amount of added antibody, and the maximum inhibition
was about
50%. The control normal rabbit IgG showed little effect. These results indicatedthatTpx-1 is responsible,
at least in part, for the adhesion between spermatogenic
andSertoli cells.

Phagocytosis
of apoptotic
spermatogenic
cells by Sertoli cells (Shiratsuchi
et al.,
1997)
Establishmentof a quantitative
phagocytosis
assay-In our previous phagocytosis
experiments (Mizuno et al., 1996), spermatogenic
cells that adhered to Sertoli cells
were not rigorously
distinguishedfrom those engulfed. In the present experiment, we
treated the phagocytosis
reaction with trypsin to eliminate spermatogenic cells that wereattached to the surface of Sertoli cells. Even after extensive washing, a significant
number of spermatogenic cells remained associated with Sertoli cells. These cells weredistinguishable
from phagocytosed
cells when examined carefully
under
fluorescence/phase-contrast
microscopy. We speculate that the tight associationofspermatogenic
cells with Sertoli
cells is an important
step toward subsequent
phagocytosis.
Stained particles of various sizes were observed within the Sertoli cells,
and we regarded these as phagocytosed
spermatogenic
cells.
In order to analyze the phagocytosis
reaction in a quantitative
manner, we
defined the phagocytic
index as follows:
the number of Sertoli cells positive forphagocytosis
was determined as a percentage relative to the total number of Sertoli cells
present in each microscopic
field. We first determined
a time course for thephagocytosis
reaction. Spermatogenic
cells that had been single-cultured
for about 40
h were subjected to the phagocytosis
reaction. The reaction seemed to continue during
the first 2 h and reached a plateau at about index 20. Our previous experimentssuggested that Sertoli cells selectively
phagocytose degenerating spermatogenic cells in
culture (Mizuno et al., 1996). To further examine this possibility,
spermatogenic cells
were single-cultured
for one and three days and subjected to a phagocytosis
assay. The
phagocytic index increased as the culture continued, while viabilityof spermatogenic
cells, as assessed by trypan blue exclusion, decreased. These results support the above
hypothesisthat dying or dead spermatogenic
cells are preferable
targetsforphagocytosis
by Sertoli cells.
Inhibitionof phagocytosis
by liposomes containing
acidic phospholipids-For
identifying
the molecule(s)that participatesin the cell-to-cellrecognition
betweendegenerating
spermatogenic cells and Sertoli cells, we examined the effect of several
compounds that are known to be recognized by the putative phagocytosis
receptorspresent on the surface of macrophages. The synthetic RGDS peptide, which contains
an amino acid sequence bound by members of the integrin superfamily,
was first tested,
but the phagocytic
index remained unchanged in the presence of this peptide. We thenexamined whether phospholipids
are involved in cell-to-cellrecognition,by addingliposomesthat consist of various phospholipids
to the phagocytosis
reaction.
Liposomes containing
phosphatidylserine(PS), an acidic phospholipid,
inhibitedphagocytosis
in a dose-dependent
manner, whereas those containing
neutralphospholipids,
PC and phosphatidylethanolamine,
had little effect. The inhibitory
effectwas not specific for PS but seemed to be common to the acidic phospholipid;
theadditionof liposomes containing either phosphatidylinositol
or cardiolipin,
caused asignificant
reductionin the phagocytic
index. Although
the efficacy of inhibition
somewhat varied among those anionic liposomes, the significanceof this difference is
not certain at the present time. These results suggest the involvementof acidic
phospholipids
in the phagocytosisof spermatogenic cells by Sertoli cells. Phosphoester
compounds related to PS were examined next, to determine whether they affected
spermatogenic cell phagocytosis.
The addition of glycerophosphoryl-L-serine
led to asignificant
reduction of phagocytosis.
Phospho-L-serine showed marginal inhibitionathigher concentrations
whereas phagocytosis
was unaffected in the presence of its opticalisomer, phospho-D-serine.
These results suggest that the inhibitory
effect of PSliposomes was executed through not only the serine residue, but also through the more
complicatedstructure of the phospholipid.
To examine the way these liposomes inhibit phagocytosis,
fluorescence-labeledliposomes were used instead of spermatogenic
cells as targets for Sertoli cells.
Liposomes containing acidic phospholipids
were more efficiently
engulfed than were
liposomes consistingof neutral phospholipids.
This coincided well with the above
results, indicatingthat anionic liposomes compete with spermatogenic
cells to bephagocytosed
by Sertoli cells.
Exposure of PS to the surface of dying spermatogenic
cells-Since
the above results
suggested that PS was recognized by Sertoli cells, we examined whether degenerating
spermatogenic
cells expose PS, which is normally restricted in the inner leaflet of theplasma membrane, to the cell surface. Spermatogenic
cells were single-cultured
forvarious periods and subjected to flowcytometricanalysis with FITC-labeledannexin V,
which specificallybinds to PS. Since it was necessary to detect PS on the surface of
spermatogenic
cells, we analyzed only those cells whose plasma membrane remained
intact. For that purpose, the cells were simultaneously
treated with propidium iodide,which binds to DNA and stains the nucleus of cells whose plasma membranes aredamaged and permealized. We observed two distinct populationsof spermatogenic cells
in terms of their propidium iodide positivity;
it was presumed that the cells with lessstaining possessed intact plasma membranes, while the membranes of cells that weremore intensely stained with the reagent were damaged. The ratio of propidium iodidepositive
cells increased
as the culture period was prolonged. This indicatedthatspermatogenic
cells degenerate
during single-culture.
When the binding of annexin V to cells that presumably possessed intact plasma
membranes was analyzed, two peaks were found, which probably represented
annexinV-negative and -positive cell populations.
On the other hand, most of the propidium
iodide-positive
cells appeared to be also positive
with annexin V. It is likely thatannexin V bound to PS present in both the outer and inner leafletsof the plasma
membrane of damaged cells. As the culture continued, the relative number of cells
which were propidium iodide-negativeand FITC-positive,
gradually increased. These
results suggest that spermatogenic cells that are dying, but which still retain membrane
integrity,
are bound by annexin V. From the above results, we presume that during theearly stages of apoptotic death, PS translocatesfrom the inner leaflet to the outer leafletof the spermatogenic
cell membrane and serves as a signal for phagocytosis
by Sertoli
cells.
Discussion
Tpx-1 as a testicularcell adhesion molecule
A number of cell adhesion proteins such as cadherins, Ca2+-independent celladhesion molecules and integrins exist in the testis, seemingly on the surface of bothspermatogenic
and Sertoli
cells. Althoughsuch proteins
are likely candidates
responsible
for the specific contact between these two cell types, no direct evidence of
this has been presented.
We attempted in this study to isolate the cDNA for themolecule(s)that defines the binding of rat spermatogenic
cells to Sertoli
cells,
employingan expression cloning method. One such cDNA clone, obtained after
repeated screening in a cell adhesion assay with Sertoli cells, coded for a protein distinct
from authentic cell adhesion molecules. The protein turned out to be the rat homologue
of a known testicular
protein called Tpx-1 or AA1. The synthesis of the mRNAandprotein of AA1 (guinea pig Tpx-1) becomes detectable at the pachytene spermatocyte
stage, reaching a peak in round spermatids,
and it is thereafter
stably present inspermatogenic
cells. Tpx-1 may not be solely responsible
for the adhesionofspermatogenic cells to Sertoli cells, since an anti-Tpx-1 polyclonal
antibody inhibitedit by only 50%. Other testicular
proteins are likely to also be involved in the specificinteraction
between the two cell types.
Tpx-1 was first identified
as a protein encoded by one of the randomly-clonedƒ¿vƒÀ3

genomic fragments from mouse chromosome 17. It was eventually revealed to be themouse homologue of a guinea pig protein named AA1, a component of the acrosome.
Tpx-1 consists of about 240 amino acids with a presumed N-terminal signal peptide
and a Cys-rich C-terminal half, with no sequences identified
that are considered to betransmembrane regions or N-linked glycosylationsites. Within the Cys-rich region, fiveCys residues appear every nine amino acids near the C-terminus. These characteristics
of Tpx-1 indicate that it is a member of the CRISP family of proteins. Another memberof this family is an epididymal protein called acidic epididymal
glycoprotein(AEG) thatis synthesizedin the epididymal
epithelium and exists attached to the plasma membrane
covering the sperm head. AEG has been suggested to be involved in sperm-egg fusion.
Since more than 55% of the amino acid sequence of Tpx-1 is identicalto that of AEG,
these two proteins could possess similar biological
functions. We here showed that Tpx1 functions as a cell adhesion protein not for the sperm-egg interaction
but for the
association
between spermatogenic and Sertoli cells.
PS-dependent
phagocytosis
of apoptotic
spermatogenic
cells by Sertoli cells
By adopting the phagocytic index, it became feasible to quantitatively
analyzespermatogenic
cell phagocytosis
by Sertoli cells. We showed that Sertoli cells are
responsible,
at least in culture,for heterophagic
eliminationof degenerating
spermatocytes.
As discussed
below, Sertoli
cells appeared
to recognize
andphagocytose apoptotic cells in a manner similar to that of macrophages, a professional
phagocyte. Macrophages attack a variety of cell types by infiltrating
various areas.
Sertoli cells however, are presumed to phagocytose
only testicular
cells since they are
localized
within the seminiferous
tubules in the testis.
Wetried several approaches in order to clarify the mechanism by which Sertoli
cells selectively
recognize and phagocytose
apoptotic
spermatogenic
cells.
integrin, which mediates phagocytosis
of apoptotic neutrophils
by macrophages, wasseemingly not involved in spermatogenic
cell phagocytosis,
since the addition of the
RGDS peptide, which binds to members of the integrin super family and inhibits theirinteraction
with specific ligands, did not influence spermatogenic cell uptake by Sertoli
cells. In contrast,
acidic phospholipids,
PS in particular,
most likely act as aphagocytosis
signal for spermatogenic
cells. Liposomes
containing
acidicphospholipids,
when present in the phagocytosis
reaction, brought about a great
reduction in the phagocytic index, whereas the addition of liposomes containing neutral
phospholipids
had little effect. As degenerationof the cultured spermatogenic
cells
progressed, we observed a significant
increase in the number of cells that exposed PS

to the cell surface. These results suggest that the amount of cell surface PS, andprobablyother acidic phospholipids
as well, increased
during the apoptosisofspermatogenic
cells and served as a signal for phagocytosis
by Sertoli cells.
Membrane phospholipids
of normal cells are localized
asymmetricallywithregard to the two leaflets of the membrane bilayer; that is, PC and sphingomyelinaremostly present in the outer leaflet, while other phospholipids
includinganionic PS,
phosphatidylinositol
and phosphatidic
acid, are confined to the inner leaflet. It has beensuggestedthat such asymmetry is lost upon inductionof apoptosis,
and that all
phospholipids
are then redistributed
evenly in the membrane bilayer. This would resultin the exposure of phospholipids
that normally exist in the cytoplasmicside to the cellsurface, and these phospholipids
could help in the distinguishingof apoptotic cells fromnormal cells. Among those phospholipids,
PS has been proposed to be a phagocytosis
signal. Translocationof PS from the inner to the outer plasma membrane leaflet has
been reported with a variety of apoptotic cells, including thymocytes, vascular smoothmuscle cells, and cultured cell lines, as well as with aged red blood cells. We showed
here that this is also the case with apoptotic spermatogenic
cells.
Cell surface PS was shown to be responsible,
at least in part, for thymocyte
phagocytosis
by macrophages and homophagic eliminationof vascular smooth muscle
cells, since the addition of PS-containing liposomes significantly
inhibitedphagocytosis.
PS thus most likely serves as a commonphagocytosis
signal for several different
phagocytes, such as macrophages, vascular smooth muscle cells, and Sertoli cells, but
the modes of recognitionof target cells by these phagocytes, are presumably somewhat
different.
We speculatethat phagocytosis
receptorspresent on the surface ofmacrophages and vascular smooth muscle cells are related to, but distinct from, thoseof Sertoli cells. It has been proposed that scavenger receptors are responsibleforrecognitionof cell surface PS. Among members of the scavenger receptor family,
CD36, SR-BI, and macrosialin bind to membrane acidic phospholipids
including
PS.
Class B scavenger receptors,
CD36 and SR-BI, were also shown to be involved inrecognitionand phagocytosis
of apoptotic cells. However, only a limited number ofexamples have been reported, and the identity of the phagocyte receptor for PS is stilla matter of conjecture. Whether Sertoli cells express any particular
type of scavenger
receptor, is under investigation.
In conclusion,
our findings should contributeto a deeper understanding
as tothe functional interaction
between spermatogenic
cells and Sertoli cells (see the figure
below).

References
Maeda,T., Sakashita,M., Ohba,

Y. and Nakanishi,

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for the interaction

Y. 1998.

between spermatogenic

Res. Commun.248: 140-146.
Mizuno, K., Shiratsuchi,A., Masamune, Y. and Nakanishi,differentiationand exclusion
Differ. 3: 119-123.

of rat testicular

Molecular
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Y. 1996.
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cloning
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Cell.
Shiratsuchi,that meiotic

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Biophys.
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Cell

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in primary culture
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divisionsin vitro.

of

In Vitro

Dev. Biol. 25: 1088-1098.

A., Umeda, M., Ohba, Y. and Nakanishi,
the surface

of apoptotic

the rat. J. Biol.
activationof testis-specific331-338.

spermatogenic

Y. 1997. Recognition

cells and subsequentof phosphatidylserineonphagocytosis

by Sertoli

cellsM., Masamune, Y. and Nakanishi,

Y. 1990.

SelectiveofChem. 272: 2354-2358.

Tamaru, M., Nagao, Y., Taira,1049:

complete

Biochem.

The role of Sertoli

Nagao, Y. 1989. Viabilityof meiotic prophase spermatocytesof rats is facilitateddispersed
testicular
cells on collagen gel by supplementing
epinephrine
evidenceof the Tpx-1

M., Tatibana,genes in cultured

rat spermatogenic

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Biochim.

Biophys.

Acta

Role of Epididymal

Secretory

Proteins

NAOMICHI OKAMURA
College of Medical Technology& Nursing and Tsukuba Advanced(TARA researcher for Sakabe-Shoun
Project),
Universityof Tsukuba, Tsukuba, Ibaraki 305-8577,
Japan

in Sperm Maturation
Research

Alliance

Abstract
In mammals, testicularsperm must undergo the process of maturation in the male genital tract
and capacitationin the female genital tract before being able to fertilize
in vivo. During transit through theepididymis,
immature sperm are affected by the continuously
changing
microenvironment
which isformed by the secretory and absorptive
activitiesof the epithelial
cells lining the ductus epididymidis.
Many studies have been performed to identify
the epididymis
specific
proteins
which relate to thedevelopment of sperm motility
and fertilizing
ability.
We have purified several region-specificsecretory
proteins from porcine epididymal
fluid, cloned their cDNAs and determined
their roles in the sperm
maturation. Among them, two proteins are reviewed in the present paper. A 16 kDa protein (HE1homologue)
which is specificallysecreted from the middle and distal caput and corpus epididymis
hasbeen found to bind free cholesterol
and regulate the lipid compositionof the sperm plasma membranes
during the maturationin the epididymis.
In addition,
the 23 kDa epididymis-specificglutathione
peroxidase-likeprotein (EGLP) which is specificallysecreted from the proximal caput epididymis.
Thisprotein has been found to be enzymaticallyquiescent in the epididymal
fluid but thought to protect sperm
fromthe premature acrosome reaction and maintain sperm fertilizing
ability in the epididymis.

Introduction
In mammals, structurally
and functionallyimmature sperm leaving the testischange into sperm possessedwith fertilizing
abilityduringthe transit 'throughtheepididymis.
This process is called "post testicularsperm maturation". The epithelial
cells of the ductus epididymidis
provide the microenvironment
for this process by theirsecretory and absorptive
activities.
The interaction
between sperm and the epididymal
secretions
is essential
for the developmentof the fertilizing
capacityin sperm.
Moreover, the exact molecular mechanism involved in sperm maturation remains to beelucidated.
The ductus epididymidis
is differentiatedmorphologicallyand functionallyfrom the initial
segment to cauda epididymis.
Different
genes are expressedin thedifferent
epididymal
regions, resulting
in the changes in the protein contents of theluminal fluids
among various epididymal
regions (Fig.1).
So, the immature spermmove through the duct and have sequential
contact with different
proteins.
It isanticipatedthat region specific secretory proteins in the epididymis
especiallyplay keyroles in sperm maturation. Isolationof the epididymis-specificgene transcripts
fromthe epididymal
cDNA library has been actively attemptedby the differential
screeningmethod (Kirchoff
et al., 1990; 1993; Osterhoff
et al., 1997). In addition,
purificationofepididymis
specific
proteins,
directdeterminationof their biologicalactivities
andcloning their cDNAs have also been tried extensively(Okamura et al., 1995a;
1995b;
1997;
Syntinet al., 1996; Jin et al., 1997).
Among these proteins,
two epididymisspecificproteins
which
were purified
and whose physiological
functionsweredetermined(Kiuchi
et al., 1997; Okamura et al., 1997)
are reviewed in the present
paper with respect to the interaction
between these proteins
and the epididymal
sperm
in their maturation process.

Fig.1
Diagramof the region specificgene expression
in the porcine
epididymis.
Porcineepididymis
was divided
into ten regions.
Regions
1, 2, 3 and 4 are proximal,
middle,anterior
half
of distaland posterior
half
of distal
caput
epididymis,
respectively.
Regions
5, 6 and 7 are proximal,
middleand distal
corpus epididymis,
respectively.
Regions
8, 9 and 10 are proximal,
middleand distal
cauda epididymis,
respectively.
The distributionof mRNA was determinedby Northernblot analysis.
ƒ¿-D-mannosidase.MAN2B2: 135 kDa
ACE: angiotensin
I-converting
enzyme.

16 kDa choresterol-binding
protein
The change in the lipid compositionof the sperm plasma membranes is one of
the important features of epididymal
sperm maturation. It has been shown that the total
cellularcontents of lipids decrease while sperm pass through the epididymis(Davis,1980;
Pouloset al., 1973;
1975;
Adams and Johnson,
1977;
Rana et al., 1991).
Phospholipids
loss during the sperm maturation
is highly
selective;
whereas choline
phospholipids
are rather
stable,theamountsof phosphatidylethanolamine,
phosphatidylserine
and phosphatidylinosotol
declinegreatly(Jones,1998).
The

•Ž
ƒÊM
ƒÊl

cholesterol
content in sperm membranes have also been shown to decrease in boar
(Nikolopoulou,
Soucek and Vary, 1985),
hamster (Awano, Kawaguchi and Mohri,
1993), rat (Hall, Hadley and Doman, 1991), stallion(Lopez and Souza, 1991),
and ram(Parks and Hammerstedt,
1985), whereas they increase in goat (Adams and Johnson,
1977) during maturation. The mechanisms that mediate these changes in lipid content
during the sperm maturation have not been fully elucidated
yet. It is possiblethat somelipid-exchange
proteins
in epididymal
fluid
would
mediatethe transferofphospholipids
and/or cholesterol
from the sperm plasma membrane to other acceptors
in the epididymal
fluid.
We purified
a 16 kDa protein from the porcine cauda epididymal
fluid andidentified
it as a cholesterol-binding
protein. The 16 kDa protein is a major cholesterolbinding protein in epididymal
fluid (Fig.2).
The 16 kDa protein amounted to about 1%of total proteins in the porcine cauda epididymal
fluid. This protein was also found to
be expressed specificallyin the distal caput and corpus epididymis
and to be absent intestis, rete testis, and the proximal caput and cauda epididymis.
It is first secreted into
the luminal fluid as a 19 kDa glycoprotein,whose sugar chain(s)are processed to form
16 kDa protein
during passage throughthe epididymis.
An immunocytochemical
examination, using a polyclonal
antibody
raised against the purified
16 kDa protein,showed that the 16 kDa protein does not bind to the porcine epididymal
sperm.

Fig. 2. Separationof the [3H]-cholesterol-binding
proteins
in the porcine cauda epididymal
fluid bySuperdex 200 FPLC. The cauda epididymal
fluid was incubated with 0.5[3H]-cholesterol
at 0
for
30 min and then was fractionatedby a Superdex 200 FPLC column. Radioactivity
in 50
aliquotsof eachfraction was counted and shown by open circle. Broken line shows absorbance at 280 nm.

Analysis of its cDNA revealed that the 16 kDa protein is a porcine homologueof the major secretory
proteinof the human epididymis,
HE1 (Fig.3;
Kirchoff
et al.,
1990).
mRNA of the HE1 homologue was found in the epididymis
of all species
studied so far (Ellerbrocket al., 1994; Perry et al., 1995; Kirchoff
et al., 1996; Frohlichand Young, 1996; Kiuchi et al,, 1997). This suggests that it plays some fundamentalroles in the epididymis.
Baker et al. (1993)
reported
that some chromatographic
fractions of ram cauda epididymal
fluid and seminal plasma, which have an activity totransfer free cholesterol
from liposome to cauda epididymal
sperm, contain proteins
with the similar N-terminal amino acid sequences to that of HE1.

-19

-1 +1

30

PorcineDog
M.fas.
Human
Chimp.

MHFLAAAFLMRLLVAAFL
MRFLAATFL
MRFLAATFL
MRFLAATFL

LLTLSASALALLALGDLGPG
LLALSTAAQA
LLALSTAAQA
LLALSTAAQAEPVHFRDCGSGAVHFKDCGSEPVQFKDCGSEPVQFKDCGSEPVQFKDCGSGVGVIKEVNVAVGVIKELNVVDGVIKEVNVVDGVIKEVNVVDGVIKEVNVNPCPTQPCQLNPCP-QPCKLSPCF-QPCQLSPCF-QPCQLSPCF-QPCQL

PorcineDog
M.fas.
Human
Chimp.

HKGQSYSVNVHKGQSYSVNVSKGQSYSVNVSKGQSYSVNVSKGQSYSVNVTFTSNTQSKG
TFTSNIPSQSTFTSNIQSKSTFTSNIQSKSTFTSNIQSKSSKAVVHGIVM
SKAVVHGIVLSKAVVHGILMSKAVVHGILMSKAVVHGILMGVPIPFPIPDGVAVPFPIPEGVPVPFPIPEGVPVPFPIPEGVPVPFPIPEPDGCKSGINCADGCKSGINCPDGCKSGINCPDGCKSGINCPDGCKSGINC

PorcineDog
M.fas.
Human
Chimp.

PIQKDQTYSY---KDKTYSY---KDKTYSY---KDKTYSY---KDKTYSYLNKLPVKAEYLNKLPVKNEYLNKLPVKSEYLNKLPVKSEYLNKLPVKSEYPSIKLVVEWKPSIKLVVQWMPSIKLVVEWQPSIKLVVEWQPSIKLVVEWQLQDDNDQCLF
LLGDNNQHLF
LQDDKNQSLF
LQDDKNQSLF
LQDDKNQSLF

CWQIPVQIESCWEIPVQIEGCWEIPVQIVSCWEIPVQIVSCWEIPVQIVS

80

130

--HL
HL
HL

Fig. 3. Alignmentof the amino acid sequences of HE1 homologues. The deduced porcine sequence isalignedwith those of dog (Ellerbrocket al., 1994),
macaque (Perry,
Jones and Hall, 1995),
human(Kirchoff,
Osterhoffand Young, 1996),
and chimpanzee (Frohlichand Young, 1996). -1 and +1 show thesuggestedsignal sequence cleavage site and E numbered with a +1 is N-terminus of the purified 16 kDa
protein.

We have recently
found that the purified
16 kDa protein specificallybinds
free cholesterol
in a time and concentration
dependent
manner and the amounts ofcholesterol-binding
reach saturation.
The dissociation
constantof the cholesterol-16
ƒÊM, kDa protein complex was 2.3
which is similar
to that of the recombinantfatty acidƒÊM binding
protein (L-FABP)
of rat liver: 1.53
(Nemecz and Schroeder,
1991).
The
maximal binding
capacityof the purified
16 kDa protein
for cholesterol
wasdetermined
to be 58.8 pmol/ƒÊg protein.
Binding
stoichiometry
was calculated
to be0.94 mol/mol in this study, which is also similar
to the values reported
for othercholesterol-binding
proteins such as L-FABP: 0.83 (Nemecz and Schroeder,
1991) and
VIP21/caveolin:
about 1.0 (Murata et al., 1995).
This suggeststhat one mole ofcholesterol
binds to one mole of the purified 16 kDa protein.
Analysisof amino acid sequence homology
revealed that the porcine HE1
homologue
showed no significant
homology
with the other cholesterol-binding
proteins
such as heart-FABP (X98558;
Gerbens et al., 1997),
liver-FABP(P49924;
Perozzi et al., 1993), nor the human caveolin (Z18951,
S49856;
Glenney, 1992). It is
concludedthat the porcine HE1 homologue is a novel type of the cholesterol-binding
protein which is specifically
expressed in the restricted
part of the epididymis.
Recently,
it has been reported that bovine milk contains
a protein named
EPV20 which displays
79% sequence similarity
to HE1 (Larsen et al., 1997). EPV20
has six cysteine
residues which are disulfide-linked
in 1-6, 2-3 and 4-5 pattern. Thosesix cysteins are well conserved among HE1 homologues including
the porcine 16 kDa
protein.
So, the fact that the 16 kDa protein
is a cholesterol-binding
protein mightprovide a clue to find out the biologicalactivityof the HE1 homologues
and otherproteins
containing
the same domains with three disulfide
bonds such as Derf2, themajor mite allergen (Ichikawa
et al., 1998) and esr16, a protein found in moth trachea(Meszaros and Morton, 1996).
It was also found that the 16 kDa protein greatly
decreased
the bindingofcholesterolto sperm by competitively
bindingcholesterol.
Furthermore,
the 16 kDa
protein was found to reduce the amount of bound cholesterolin sperm (Fig.4).
This
was not merely due to the fact that 16 kDa protein binds free cholesteroland decreases
its concentrationin the medium. The 16 kDa protein
seems to extract the boundcholesterol
directly
from sperm, because in the absence of the 16 kDa protein,thebound cholesterol
remained on the sperm even after free cholesterol
was washed out.
The change in the cholesterol
contents in the sperm membranes should affectmembrane fluidity,
membrane stability
against the cold shock, protein behavior in themembrane, and the motile and fertile
abilitiesof mature sperm. Although
littleisknown about the molecularmechanism involvedin the alterationof the membraneƒÊM•ŽƒÊg/ml).
ƒÊg/ml•Ž
ƒÊg/ml).

Fig. 4. a: Time course of [3H]-cholesterol-binding
to the porcine cauda epididymal
sperm. 0.05
of [3H]-cholesterol
was incubated withthe porcine cauda epididymal sperm (2x108 cells/ml) at 32
for various intervals of time in the absence (A) or the presence (O) of the 16 kDa
protein (200
At 20 min, 200of the 16 kDa protein (•œ) or phosphate buffered saline (•£) was added to the incubation mixture
which did not contained the 16 kDa protein (A), b: Release of bound [3H]-cholesterol
from the porcine cauda epididymal
sperm. [3H]cholesterol-boundsperm was incubated at 32
for appropriate intervals of time in the absence (A) or the presence (O) of the purified 16 kDa
protein (200Amounts of the [3H]-cholesterol
bound to sperm were then determined.

lipids
during sperm maturation,
the 16 kDa protein, a HE1 homologue, is indicatedtobe one of the important regulatorsof the cholesterol
content of sperm membranes.
The capacitationand subsequent acrosome reaction are preceded by a decreasein the cholesterol
content of the sperm plasma membrane (Hammerstedt
et al., 1979;
Davis, 1980).
Recently,
we have found that the 16 kDa proteinmRNA is also
expressedin female reproductive
organs such as the ovary, oviductand uterus.
Furthermore, the purified
16 kDa protein has been found to stimulate
capacitationandacrosome reaction inducedin vitro in the presence and absence of caffeine,highconcentrationof Ca2+ and fatty acid free BSA. These results suggest that the 16 kDacholesterol-binding
protein,
HE1 homologue,
partlymediates
the post testicularacquisitionof sperm fertility
by binding
and extracting
cholesterol
from sperm
membranes both in the male and female genital tracts. But, in connection
with thefunction of cholesterolin sperm maturation and capacitation,
further study must be
done to know the exact roles of the HE1 homologue in epididymal
sperm maturation.
23 kDa epididymis-specificglutathione
peroxidase-likeprotein
The epididymis
has importantroles in protecting
sperm from the oxidative
damage. Mammalian sperms are rich in unsaturated
fatty acids in membranes, andoxygen free radicals react with them to form organic hydroperoxides.
Although lipid
peroxidationis required
to increase the capacityof sperm to bind to zona pellucida(Aitken,Clarkson and Fishel,1989),
it has been correlatedwith the male infertility
because of the loss of motility
or morphologicalabnormalityof sperm (Aitken
andClarkson, 1987; Alvalez and Storey, 1989; Rao et al., 1989). The peroxidative
processis thoughtto damage the fluidity
and integrityof the sperm plasma membrane,
resultingin a loss of sperm function (Aitken
and Fisher,1994). Lipid peroxidation
enhances the destructionof phospholipids
in sperm plasma membranes, which induces
the premature acrosome reaction in the epididymis(Ungemach et al., 1985; Bennet etal.,1987).

In addition
to superoxide
dismutase(SOD),
it has been suggested
that
glutathione
peroxidase(GPX)
protects
sperm from peroxidative
damage in theepididymis.
It has also been reported that mouse sperm and epididymal
fluid containhigh concentrationsof glutathione
and selenium-dependent
GPX activity(AlvalezandStorey,1984;1989).
Recently,
cDNAs encoding
epididymis-specific
GPX-like
proteins (EGLP) have been cloned from mouse, rat and monkey, and they were foundto be distinct
from either cytosolicor placental
secreted
GPXs (Ghyselinck,
Jimenezand Dufore, 1991; Perry et al., 1992). Cytosolicand placental
secreted GPXs contain aselenocysteine
residue at the catalytic
site, and their mRNAs contain the selenocysteine

•Ž

•Ž

codon (UGA). But in EGLP mRNA, it was found to be replaced by the cysteine
codon(UGY). Although
there is no direct evidence that EGLP is enzymatically
active in theepididymis,
it has been suggested
that it protects
sperm from the deleterious
effects ofoxygen free radicals
by preventing
the accumulationof organic hydroperoxides.
In order to see whether EGLP exerts a physiologicallysignificant
amount ofactivityin the epididymal
fluid, we purified EGLP from the porcine cauda epididymal
fluid and its activities
were quantified
in comparison
with selenocysteine
containing
erythrocyte
GPX. The purified EGLP was found to consist of four identical
23 kDasubunits.
EGLP showed only an extremely low activity
when hydrogen peroxide wasused as substrate(Table 1). The affinityof EGLP for hydrogen peroxide was less than
0.1% of the value for erythrocyte
GPX. The Vmax of EGLP was also much lower(1/150)than that of the erythrocyte
enzyme. The same tendency was observed whenorganic hydroperoxides
were used as substrate;
the activityof EGLP was as much as
1000-foldlower than the erythrocyte
GPX (Table 2).
Table 1. Comparison of the kinetic parameters between the purified
bovine erythrocyte GPX
E nz ym e

EGLP andK m

V m axT u r n o e r r a te(m M )( w m o l/ m in /m g p r o te in )( M in ‑1/ s u b u n it ‑1)

EG L P

0 .3 1 + ‑ 0 .0 1

3 .7 +‑ 0 . 1

85

B o v in e e r y t h r o c y t e G P X( 0 .2 4 +‑ 0 .0 2 ) x 1 0 ‑3

5 8 0 +‑ 2 0

12 00 0

The GPX activities
were determined at 37
concentrationsof hydrogen peroxide.

Table 2. Comparison of the activities
bovine erythrocyte
GPX

in the presencewith differentof 4 mM glutathione

and various

between the purified

EGLP and thesubstrates

G P X a c tiv ity (n m o l/m in /m g p ro te in )B o v in e e ry th ro c yte G P X

E G LPS u b s tra te
H y d ro g e n p e ro x id e

1 .6 9 +‑ 0 .2 6

3 2 0 0 +‑ 9 5

1 3 ‑ H y d r o p e ro x y ‑ (9 ,1 1 )‑ o c ta d e c a d ie n o ic a c id

4 .1 6 +‑ 0 .5 5

4 0 5 0 +‑ 1 3 3

1 5 ‑ H y d r o p e ro x y ‑ (5 ,8 ,1 1 ,1 3 ) ‑ e ic o s a te tra e n o ic a c id

3 .5 4 +‑ 0 .3 3

3 0 0 0 +‑ 1 6 0

The GPX activities
various substrates.

were determinedat 37

in the presenceof 4 mM glutathioneand 20ƒÊM ofFurthermore,
It was found that the concentrationsof glutathione
in the porcine
ƒÊM epididymal
fluidswere 142-24.6
which is much lower than the optimal
concentrationof glutathione
for the GPX activityof EGLP (around 5 mM). All thesedata strongly
suggest that the GPX activityof EGLP is almost negligiblein porcine
epididymai
fluid, though the possibility
can not be dismissedcompletelythat there may
be unknown mechanisms for activationof the protein. It is also possiblethat EGLP has
a substrate specificity
different
from that of the cytosolic
GPX. In porcine epididymal
fluid, the purified EGLP is the only source of GPX. So EGLP is unlikely
to protect
porcine epididymal
sperm from the oxidative
damages at least by its GPX activity.
It has been proposed that some proteins secreted from the epididymal
epithelial
cells bind to sperm and/or affect sperm functions. EGLP is specificallysecreted fromthe caput epididymis
and is a major protein component in the caput epididymal
fluid.
Using polyclonal
antibodies,
the localizationof EGLP on sperm was analyzed.
Consistent
with the results reported by Jimenez et al. (1992),
EGLP is localized
in theacrosomal region of the epididymal
sperm (Fig.5).

Fig. 5. Immunocytochemical
localizationof EGLP on sperm.
Porcine cauda epididymal
sperm
before (a) and after (b) the
inductionof acrosome reaction
were incubated with antisera
raised against the purified
23 kDaEGLP subunit and the
localizationof EGLP was
observed. The stained samples
were observed under the lightmicroscope at x200. The
acrosome reacted sperm areindicatedby arrowheads.

It was also found that EGLP disappeared
from the acrosomal
region during theacrosome reaction induced by 1.8 mM CaCl2, 10 mM caffeine and 0.1% fatty acid free
BSA. EGLP was detected only on the acrosome intact sperm. These results indicatethat EGLP in sperm can be used as a marker to distinguishacrosome-intact
sperm fromthe acrosome-reacted
ones. Furthermore,
it was found that EGLP inhibitedtheacrosome reaction induced by high calcium, caffeine and fatty acid free BSA. Namely,
the pretreatmentof the sperm with the purified
EGLP significantly
reduced thepercentage of the acrosome-reacted
sperm.
It has been reported
that the lipidhydroperoxides
accumulate
on themembranes of maturating
sperm in the epididymis.
Lipidperoxides
are known to
induce acrosome reaction
by enhancing
the activityof sperm phospholipase
A2(Ungemach et al., 1985; Bennet et al., 1987;
Goldman et al., 1992).
Namely, lipidhydroperoxides
stimulates
phospholipase
A2 activity,
which results in the formation oflysophospholipids
in the sperm plasma membrane. This destabilizes
the structure of the
sperm plasma membrane and induces the membrane fusion, leading to the acrosome
reaction (Parrish
et al., 1988).
As the acrosome reaction must not occur in the epididymis,
there should besome mechanisms to restrain the lipid peroxides
from inducing the acrosome reaction.
As the epididymis
expresses relatively
low catalase mRNA (Zini and Schlegel,1996),
it has been indicatedthat GPX and SOD is responsible
for the degradationof the lipid
peroxides
accumulated in sperm. SOD has been reported to be expressed and secreted
in the epididymis(Jow et al., 1993; Perry e tal., 1993). The conventional,
seleniumcontaining
plasma glutathione
peroxidase,
GPX3 was also found to be expressed in themouse epididymis,
but it was not detectedin sperm or in the epididymal
fluid(Schwaab et al., 1995; 1998), suggestingthat GPX3 does not directly
participate
in thedegradationof the lipid peroxides
in sperm. On the other hand, it has recently beenreported that the testis-expressed
phospholipidhydroperoxideglutathione
peroxidase(PHGPX) is present in the rat epididymal
sperm (Godeas et al., 1997). In addition,
Jin
et al. (1997)
have reported that lactoferrin
is secreted
from the epididymis
and binds tosperm. It is suggested
that lactoferrinbinds free iron and prevents it from catalyzing
lipid peroxidation
chain reactions in the epididymal
sperm.
At least in boar, as mentioned above, EGLP is unlikely
to exert a protective
effect on sperm by its enzymatic
activity.
But EGLP has been found to protect
transfectedmammalian culture cells from the oxidative
stress (Vernet et al., 1996).
A
hypothesisis that EGLP binds to the lipid
peroxides
and prevents
them frominteractingwith phospholipase
A2, which resultsin the inhibitionof the acrosome
reaction.
In this connection,
it is interesting
that, in sperm-egg recognition,
enzymessuch as galactosyltransferase
and proacrosin,
produce their effects not by enzymatic
activities
but by specificinteractions
between the catalytic
site of the enzyme and its
substrate. Although
the acceptor in the sperm for EGLP is not identified
yet, EGLP
seems to support sperm maturation in the epididymis
by maintaining
the acrosome
integrity.
Acknowledgments
This work was supported
in part by a grant from the10680682)
to N.O. and partly by Tsukuba Advanced Research Alliance

Ministry(TARAof Education
Sakabe-Shoun(05044121,
project).

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Sperm Evaluation:

What Should

R. A. P. HARRISON
Laboratoryof Gamete Signalling,
Cambridge
CB2 4AT, UK

The Babraham

We Be Testing?

Institute

Abstract
In conserving animal genetic resources, it is likelythat semen evaluation
will need to be
carried out largely to examine the impact of new technology
on sperm function, during developmentof more effective semen preservation
procedures. At the present time, semen evaluation methods are
not yet sufficiently
discerning,
and there is an urgent need for improvement. In seeking these
improvements, it is the author's contention
that (a) the natural heterogeneityof sperm populations
should be more widely recognisedand (b) clear distinction
should be made between "preservation
environments" and the "activating"environment of the female tract: most tests should examine sperm
population
response to a similar
activating
environment.
Recent studies have shown that thefertilizing
spermatozoon exerts several profound post-fertilization
effects on the developmentof theresultantzygote, and these will also need to be taken into account;notably,methodologicalopportunities
now exist for sensitive
assessment of DNA integrity, which have so far been appliedvery largely only to human sperm. There is currently much concern as to the damage that can becaused by reactive
oxygen species. Althoughhuman sperm seem to be particularly
sensitive,knowledge of other sperm species' vulnerability
is limited.
With respect to potential
oxidative
damage,it is often forgottenthat during its natural life in vivo the spermatozoon never encounters oxygen
levels as high as those of the outside atmosphere. It would therefore
seem prudent to consider
preserving and processing semen in an oxygen-depleted
environment.

Amazing advancesin reproductive
technology
are currently
being
achieved (e.g. successful
fertilization
via intracytoplasmic
injectionof oocytes withfreeze-dried
sperm; ability
to transplant
spermatogenic
cells between individuals
and even species - see later articles
in this volume). However, it will almost
certainly
be some time before these advances emerge from the specialised
researchlaboratory,
and they may never become applicableto the maintenance or rescue of
endangered
wild animal species.
At least for the time being, therefore,
theconservationof animal genetic resources must depend very largely upon the use of
AI and conventionalsemen preservation(whetherin liquid or frozen state). It isclear that the success of semen preservation
using current methods is not universal,
varying between species,
breed and even individual.
Obviously,
it is highly
undesirable
simply to abandon semen types that respond poorly to preservation,
because probably those semen samples in themselvesrepresent a specific genetic
resource!
Thus improvement of preservation
methods remains a major goal. This

improvement
will not be achievable
without
evaluating
the impactof themethodologicalmodifications
on sperm fertility.
At present, evaluation
methods
themselves
are inadequate
and give imprecise
results. In this paper I aim to provide
a framework within which choices and developmentof more focused evaluation
methods may be made.
Ideally,
fertility
should be measured as the sperm's ability
to bring about
the eventual live birth of normal offspring.
In the past, field trials have been used toevaluate such overall ability.
However, not only are field trials slow and expensive
but they are likely to be completely
impractical
where rare breeds or species are
involved.
Great efforts therefore
continue to be made to find sperm evaluation
proceduresthat are rapid,
simpleand sensitive.
The problem
is that the
spermatozoon must be capable of many functions in order to play its normal
physiologicalrole (see Table 1). Some of these functions are obvious; for example,
the sperm cell must be viable (i.e. capable of maintaining
its normal intracellular
homeostatic
processes
within an intact plasma membrane). Other functions
aremuch less obvious (e.g. role in supporting
embryo development).
We need to findkey sperm characteristics
that are crucial to fertility.
Many traditional
evaluation
parameters such as viability
are so fundamental to any physiologicalcapabilityas
to be only of very general value (e.g. a dead sperm is obviously
incapableof
fertilizing;on the other hand, a live sperm may also prove to be infertile).

Table

1.

Levels of sperm functional

Cellularintegrity:

maintain

Cellularpotential:

express motility,
undergo capacitationalchanges in response
suitable extracellularenvironment
undergo zona-induced acrosome reaction and express
hyperactivatedmotility

Zona penetration:
Fertilization:basal intracellular

fuse with oocyte and initiate

Embryo development:
Live young: support

environment

capability

early development

form normal implanting
full foetal

development

blastocyst(Na+, K+, Ca2+, pH, ATP etc)to

In order

to achieve

actualfertilization,

it is clear

thatvarioussperm

functionsmust be specifically
activated(e.g. ability
to undergo an acrosome
reaction as a result of contact with the zona pellucida).
This activation,termedglobally"capacitation"(see
Harrison,1996),
apparently
takes
place
via
intracellularsignalling
pathwaysthat are set in train by changes in the sperm's
extracellularmilieu (in vivo, by the environmentsof the female reproductive
tract).
It would therefore
seem logicalto test the sperm's ability
to respond to such"fertilizing"conditions.
In my own laboratory,
we have been focusing on the boar
sperm's response to bicarbonate/CO2
because bicarbonate
has been identified
as anessential
milieu component for successful
IVF in pig as in several other animal
species (see Suzuki et al., 1994 and references therein).
Boar sperm are particularly
good models because they are unusuallyquiescentas they emerge from theepididymis(see Tajima et al., 1987);
they therefore
offer excellent
possibilities
forstudying
the signallingmechanisms that "switch-on"
sperm. We have observed
several cellularchanges which are specificallyinduced by bicarbonate,includingchanges in membrane lipid architecture(Harrison
et al., 1996a)
and enhancementof motility(Tajima et al., 1987; Abaigar et al., 1999). These latter changes are ofparticularinterest since they take place in most cells within a few minutes after thestart of exposure to bicarbonate;
we therefore think they may constitutevery earlystages in the overall capacitation
process (which takes some 2-3 hours to completein the majority of boar sperm: Hunter and Dzuik, 1968; Yoshida,
1987; Yoshida etal.,1993).

Figure 1 shows the change in plasma membrane lipid architecturecausedby bicarbonate,while Figure 2 shows the stimulationof motility
by bicarbonate;
Figure 3 shows differences
between individualboars with respect to their plasma
membrane lipid response to bicarbonate.
A key feature of our strategy has beenthat response to bicarbonate
is measured in relation
to the effect of parallel
incubation
in a control medium which is essentially
identical
in all respects
to thetest medium save for its lack of bicarbonate/CO2.
Figures 1-3 illustratein particular
two of our relevant findings:
namely that individual
cells within the sperm samples
show considerable
differencesin their response, and that proportionsof responding
cells vary greatly between samples from different
boars. It may be emphasized thatexaminationof the extended sperm samples themselves(i.e. under 'preservation'
conditions)do not reveal such differences.
Figure 3 also shows that the membrane•ŽFigure1. Bicarbonate
inductionof changes in plasma membrane lipid architectureinboar spermatozoa, detectedby flow cytometry. "Control":incubationfor 10 min in abicarbonate-freeHepes-buffered
Tyrode's-based
medium; "+Bicarbonate":incubationfor 10 min in the Tyrode's-based
medium supplemented
with 15 mM bicarbonate/5%
CO2. Washed spermatozoa were incubatedat 38
in the presenceof Yo-Pro(fluorescentprobe for viability),
after which aliquants
were mixed with merocyanineand subjected
to flow cytometry
90 sec later. The rectangles
withinthe plots
delineate:
D, dead cells (stainedwith Yo-Pro);L, live cells showing low merocyanine
staining;
H, live cells showing high merocyanine
staining.
Fluorescenceis expressed
as arbitrary units. Further experimental
details are provided in Harrison et al. (1996).

•ŽFigure 2. Bicarbonate
inductionof enhanced motility
in boar spermatozoa.
Washedspermatozoa (preparedseparatelyfrom semen samples from two different
boars) wereincubated
at 38
for 10 min in a bicarbonate-freeHepes-buffered
Tyrode's-based
medium; to half the suspension,
bicarbonate/CO2
was added to a final concentrationof
15 mM/5%, while 15 mM NaCl was added to the other half (as control treatment).
Samples of the suspensions
were observed and recorded by video microscopy,
whencemotion descriptors
for individual
sperm were measured from the recordings
using aHobson Sperm Tracker (W.V.Holt
and R.A.P.Harrison:
unpublisheddata; further
experimental
details
are provided
in Holt et al., 1996;
Abaigar
et al., 1999).
Pathlinearity(LIN = mean straightline velocity/mean
path velocity)
has been plottedagainstmean path velocity(VAP);
LIN is a reflectionof progressive
motility,
VAP areflectionof tail beating.
"At start":
motility
just before additionof bicarbonate;"After bic/CO2
addn": motility
12 min after additionof bicarbonate;"After NaCl addn":
motility
18 min after additionof NaCl.

Figure 3. Variation
among boars with respect to sperm plasma membrane response tobicarbonate.
Sperm-richfractionsfrom the ejaculatesof six different
boars weredilutedand stored at ambient
temperature
in Beltsville
Thawing
Solution(BTS;
Johnson et al., 1988)
for either
1 or 7 days. Spermatozoa
were isolated
from thedilutedsamples by washing through
Percoll
after which they were incubatedinbicarbonate-containing
or bicarbonate-freeHepes-buffered
Tyrode's-based
mediumand analysed for merocyanine staining,as describedin Fig.1. Results from a single setof samples (tested
in parallel)
are presented
as the combined
bicarbonate-induciblechanges in high merocyanine-binding
live cells or dead cells (i.e. those in boxes H
and D respectively
in Fig.1).
Standard
deviationof the assay = +-5.3 cells per 100(estimatedfrom a series of several similar experiments
in which duplicate
assays were
performed).

lipidresponse

to bicarbonate

declines

duringliquidpreservationof boar semen in a

widelyused commercial
extender, though how closely this decline
is correlatedwith the well-establishedloss of fertility
duringliquidstorage (Waberski
et al.,
1994) remains to be demonstrated.
One of the problems in developing
laboratory
tests of sperm function is thedifficulty
in relating
cellularprocesses
to actual fertilizing
ability(see Amann and
Hammerstedt,
1993;
Hammerstedt,
1996).
In most species,
in vivo testingisprecluded
because of the impossibilityof obtaining
statisticallymeaningful
datafrom single sperm samples, while the use of in vitro fertilization(where possible)
is time-consuming
and very demanding on resources and skill.
A simple but
sensitive
laboratory
bioassayis therefore
needed. Of the sperm's several tasks at
fertilization
itself, the ability
to pass through the zona pellucida
appears to be themost demanding (see Liu and Baker, 1994; Yanagimachi,
1994; Johnson et al.,1995).
Recently,
we developed a procedure for testing this in boar sperm, using
cryopreserved
zona-intact pig eggs (Lynham and Harrison, 1998). We were able toshow (Figure4) that the sperm developed
zona penetrating
ability
only under
fertilizing
conditions,and that the time-base of developmentof zona penetrating
ability
was closely
similarto the developmentof overall
fertilizing
ability.
Recently
several groups have tried to relate sperm ability
to bind to the zona
pellucidato fertility(Fazeli
et al., 1993, 1995a,
1995b;
Coddington
et al., 1994;
Codde and Berger, 1995; Meyers et al., 1996),
with varying conclusions.
In ourassay of boar sperm function, in which we specificallydifferentiatedbetweensperm that had only bound to the zona surface and sperm that had penetrated
into
the zona matrix, there was little or no relationshipbetween zona binding and zona
penetration:
considerable
numbers of sperm bound to the zona pellucida
under nonfertilizing
conditions(see Figure 4). In general, assays of sperm-zona interaction
have not distinguishedbetween binding
and penetration;
our results
suggeststronglythat estimationof zona penetrating
abilitywould be a sensitive
andmeaningful quantitative
parameter of sperm fertilizing
capability.
An intriguing
if worrying conundrum in current sperm evaluationis theapparent discrepancy
between the numbers of sperm that after incubation
under
fertilizing
conditions
show cell characteristics
considered
as possible
indicatorsofcapacitation
stil valid)

and those that actuallyshow an ability
to fertilize.
The original(anddefinitionof the capacitatedstate in sperm is the ability
to initiate

Figure 4. Development of boar sperm ability to penetrate the pig zona pellucida.
Solidsymbols:"Capacitating
medium" (Hepes-buffered
Tyrode's-based
medium containing
15 mM bicarbonate/5%
CO2 and 2 mM caffeine);
open symbols:"Non-capacitating
medium" (Hepes-buffered
Tyrode's-based
medium without
supplementation).
Washedspermatozoa were preincubatedfor various periods of time in one or other of these two
media, and then coincubatedfor 10 min with groups of cumulus-free eggs in the same
medium (The eggs were members of a large cryopreserved
pool previously
preparedfrom ovaries taken from immature females). The groups of sperm-egg complexes werethen transferred
to fresh medium, after which some of each group were assessed
immediately
for total numbers of bound sperm while the rest of the group was
incubated
for a further 60 min before being assessed for numbers of zona-penetrated
sperm. Results of three independent
experiments are presented (means + SD). Further
details are given in Lynham and Harrison (1998).

fertilization
very soon after the start of gamete coincubation(see Harrison,
1996).
However, in recent times, more specific,"molecular"changes have begun to betaken as direct parameters of capacitation(e.g. changes in chlortetracycline
binding
pattern:
Fraser et al. 1995; ability
to undergo the acrosome reaction in response tozona pellucida
components:
Shi and Roldan,
1995).
Assays of such in-vitro
capacitation
depending(examplescharacteristics
detectratherhigh percentagesof altered
sperm:
on the parameter used, figures of between 10 and 50% are the norm
for boar sperm: Mattioli
et al., 1996; Harkema. et al., 1998).
However,

calculations
suggest that far fewer may be capable of fertilizing.
Table 2 shows
data derived from publishedreports of pig IVF. Relating
numbers of penetrated
sperm with those potentiallyable to make contact with eggs in an in-vitro
fertilization
system is very imprecise,since it must be based on very many, as-yetuntested assumptions.
However, in our studies (Lynham and Harrison,
1998), ofthe sperm that bound to the surface of stored pig eggs during brief gamete
coincubation,
only some 15% showed any degree of zona penetrating
abilityduringa subsequent3-hr incubation.
These low numbers were certainly
more in accord
with the fertilization
calculationsin Table 2. Data from other species
similarly
suggests very low rates of sperm fertilizing
success in vitro (rat: Nishimura
et al.,
1982; mouse: Siddiquey
and Cohen, 1982; rabbit:
Lambert et al., 1978; hamster:Stewart-Savage
and Bavister, 1988; human: Van der Ven et al., 1989).
One probablereason for the discrepancy
between numbers of sperm
deemed 'capacitated'
and those actuallycapableof zona penetrationis that theformer measures a single sperm parameter/function
whereas the latter depends onthe expressionof several
functions(Hammitt
et al., 1989;
Amann andHammerstedt,
1993; Amann et al., 1993).
For example, the acrosome reaction isclearly a necessary prerequisite
for zona penetration(see Yanagimachi,
1994), andtherefore
a number of studies
have evaluatedsperm ability
to undergotheacrosome.reaction(see Troup et al., 1994; Fenichel,
1995; Mortimer, 1995);
on theother hand, other investigations
have indicatedthat motility
also plays a crucialrole in zona penetration(Stauss et al1, 1995; Olds-Clarke,
1996). Obviously,
for agiven sperm to fertilize,
it must express several functions
in a carefully
controlled
temporalsequence (see Amann and Hammerstedt,
1993;
Amann et al., 1993).
Fulfilment
functionsof this criterion
is inevitably
of low likelihood
since the different
are by no means necessarily'coupled'or related. For accuracy of semenTableA u th o rs

2.

Calculation

F M e r et iliz
d iu amt io n

C o y e t a l. ,199 3

T C M ‑199
+ 1 2 % fo e ta l c a lf se ru m
+ 2 m M c a ff e i n e
a t p H 7 .4

M

B r a c k e t & O lip h a n t 's
+ 1% B S A+ 5 m M c a ff e in e
a t p H 7 .6

o r i e t a l. ,199 6

X u e t a l.,
19 9 6T C M ‑ 19 9
+ 1 0 % fo e t a l c a lf s e r u m+ 1 0 m M c a ff e in e
a t p H 7 .4of proportionsof fully competent

sperm in boar semen from in vitro fertilizationdV iso h lu (m m e l) p e r

p e S r p d e isr mh

p eO r o d c i ys hte s

C p oe rinioc du b ( ah t) io n

F e rr atit lize (a %t io) n2

6 x 10315

4

5 5 .3

x 104

10

2 .5

7 2 .10 .1

1 x lO4

10

4

9 7 .31

3 .1 2 5 x 1 0 4

10

0 .1

1

6

*: calculated
by multiplying
the number of penetrated
sperm per oocyte by the number of oocytes,
in the fertilization
volume, and multiplying
by 1000.

9 1 .5

dividing

P e pn ee rt roa to ec dy t se p e r m

1

8

data
C op em r p t eh teo un st a snp d e *r m

.4 40 .0 3 6

2 .2

2 .2

.8

8 .8

1 .4

0 .4 4 8

by the total number of spermevaluation,
it may well be necessary
simultaneously
in the individual
cells,

to monitor
a technicalseveralability

dynamic characteristics
which is as yet largely

beyondour capabilities.
The future of sperm evaluation
obviously
residesin the continued
developmentof "in vitro" assays. However, such development
risks overlooking
the very large overall
differences
between in-vitro
and in-vivo fertilization(Harrison,
1996). A particularly
importantissue is the need for sperm to survive inthe female tract for long periods of time (see Drobnis and Overstreet,
1992; Hunter,
1995).
Naturalor artificial
insemination
usually
takes place following
oestrusdetection,
which may be several (or many) hours in advance of actual ovulation;
once depositedin the female tract, the sperm must await the eggs' arrival. Strong
circumstantial
evidence suggests that ability
to survive this waiting period is a key
contributory
factor in the overall in-vivo fertilityof the sperm sample (Waberski
etal., 1994; Rota et al., 1997; Troedsson et al., 1998). In-vitro fertilizing
tests, on theother hand, traditionallydemand a rapid sperm response. Thus it seems pertinent toconsider including'survival'tests in the repertoireof sperm evaluation
procedures(e.g. Holt et al., 1997; Rota et al., 1997). Recent studies have indicatedthat in vivo
those sperm that will eventually
fertilize
the egg(s) populate
a localised
region, theoviductal
isthmus,where they form close relationships
with the epithelial
cells(Drobnisand Overstreet,
1992;
Hunter, 1995).
Considerable
evidence
has nowbeen gathered to show that oviductal
epithelial
cells prolong
sperm survival in a
specific manner, at the same time reducing sperm activity (Chian and Sirard, 1995;
Smith and Nothnick,
1997; Smith, 1998;
Suarez, 1998). The exact mechanism of
this process is as yet unknown. However, the attention
currently being paid to this
aspect of sperm physiology
is likely to provide novel ways of testing sperm fitness.
Tests of survival may be particularly
importantwith respect to the developmentofnew preservation
technology,
because considerable
number of recent studies havereported
that after coolingor cryopreservation
sperm exhibit
behaviour
and/orsurface characteristics
that appear to mimic capacitation(see Watson, 1995;
Maxwell and Watson, 1996; also Cormier et al., 1997;
Fuller and Whittingham,1997).
Capacitationis considered
a 'destabilizing'process which will eventuallylead to more rapid cell death (see Harrison,
1996), thus capacitation-likechanges
inducedsufficientby cryopreservation
are clearly counterproductive
numbers of sperm to survive in the femalein view of the need for
tract until the time ofovulation.
A further interesting
considerationis that capacitatedcells appear tobind less strongly
to oviductal
epithelial
cells(Smith,1998),
thus one canhypothesisethat prematurely"capacitated"
sperm may be less able to profit fromthe specific protecting
power of the isthmic oviduct.
At this point, it may be worth discussing
briefly
the concept that the female
tract in some way 'selects'
sperm, with the result that a high proportionof spermthat encounter the egg are capable of fertilization(Drobnisand Overstreet,
1992;
Hunter, 1995).
At first sight, this might seem to render unnecessary any concernthat only a very low proportionof sperm in the ejaculate
seem capable of fertilizing
eggs. However, it should be remembered that there is much evidence to indicatethat the likelihoodof fertilization
depends
on the numbers of 'fit' sperm
inseminated(see Amann and Hammerstedt, 1993). Thus semen evaluation must be
carried out on unselected
sperm samples because it is important
to know what
proportionof sperm are 'fit'. All other things being equal, use of AI will involve theinseminationof approximately
constant numbers of (live) unselected
sperm, and it
will be the sample which contains the largest proportionof 'fit' sperm that will
stand the greatest chance of fertilizing
successfully.
The approaches to semen evaluation describedabove have all focused on
fertilizing
ability.
However, the sperm play further roles in the developmentof liveyoung (Chaykin
and Watson, 1983; Setchell
et al., 1988; Janny and Menezo, 1994),
the subtletiesof which are still being uncovered. Full consideration
should be given
to finding means of testing relevant sperm capabilities
in these roles. Four generalareas of sperm activity
have so far been identified.
In order of involvementin
embryonic development,
they are:- the contributionof the sperm centrosomes tosyngamy and subsequentmitosis (Navarra et al., 1995);
the sperm's initiationofcorrect Ca2+ signalling
processes
within the zygote (Swann and Ozil, 1994);
thecontributionof the paternallyderivedgenome to correct
differentiationofembryonic
tissue (Norris
et al., 1990);
and the sperm's contributionof a full
haploidcomplement of potentiallyfunctional
genes to the diploidyof the embryo.
The importance of the first area is still to be defined (see Navarra et al., 1995), butnonethelessits potential
should not be ignored. The second area relates to spermborne egg-activating
factor(s)known as oscillogens;as these have not yet beenclearly
identified(see Swann et al., 1998; Tesarik,
1998), nothing can be proposedwith respect to testing their presence within the sperm cell or their potential
activity.

The third area relates to the concept of genetic imprinting,in which identicalgenes
passing through the male or female parent acquire differing
phenotypic
potentials
within foetal development;
the most obvious expressionof imprinting
is the global
role played by the male-derived
genome in the correct differentiationof embryonicand extra-embryonictissues (Norris et al., 1990),
though more specificexamplesof imprinting
effectsare continually
being uncovered (Reik,
1996).
Again, themolecular identificationof imprinting
has not yet progressed
to a point at which
any advance testingof the sperm genome for its imprinting
adequacy could be
made. The fourth area, the general contributionof the sperm's haploid
genomic
complement to the embryo/foetus,
is less easy to define;
one aspect would
obviously
be the developmentof male and female offspring
followingfertilization
by Y- and X-bearing sperm respectively.
A preliminary
approach to this would beto employ more widely the newly developed
micromethods
for examining the stateof sperm genetic material.
There is considerable
evidencethat abnormalitiesofchromatin structure are associatedwith poor reproductive
performance (see among
others Evenson et al., 1980; Rodriguez
et al., 1985; Engh et al., 1993; Evenson etal., 1994;
Sailer et al., 1995;
Sakkas et al., 1996),
though there is rather little
evidence
to suggest that cryopreservation
procedurescause such abnormalities(Royere et al., 1988; Evenson et al., 1994; Sailer et al., 1995).
Chromatin packing (Ward and Coffey, 1991),
and the degree of interaction
between sperm protamines and DNA, can be assessed fluorimetricallyusing either
acridineorange (Evenson et al., 1980;
Saileret al. 1995)
or chromomycin
A3(Bianchiet al., 1993; Sakkas et al., 1996).
Damage to DNA itself, in the form of
strand breakage,can be assessed either by TUNEL (Terminal
deoxynucleotidyltransferase-mediateddUTP-biotin'nick' end labelling)
assay or by 'comet' assay. In
the TUNEL assay, DNA strand ends are labelled
by attachingbiotinylated
deoxyuridine
nucleotide,and detecting
the presence of the biotinylated
nucleotide
by secondarylabellingwithstreptavidin
conjugatedwitheitheralkaline
phosphataseor fluorescein.
The amount of labelling,
quantified
either by image
analysisof sperm smears (Bianchi
et al., 1993; Sakkas et al., 1996; Twigg et al.,1998a)
or by flow cytometry (Sun et al., 1997),
correlateswith the degree of strand
breakage. In the 'comet' assay (Hughes et al., 1996),
sperm are immobilizedinagaroseuncoiled,(as a layer
after whichon a slide),
their
the layer is placed

chromatin
decondensed
and theirin an electricfield; electrophoresis

DNAof theDNA proceeds, when fragmented DNA migrates more rapidly
than intact DNA.
Visualized
by ethidium
bromide staining,the 'intact'
DNA appears as a compact
groupingwith the DNA fragments spread to one side (hence the adjective'comet'),and the degree of fragmentation
can be estimatedby image analysis.
Up till now,
TUNEL and "comet" assays have been almost exclusively
appliedto human sperm,
related to the concern that the rapid proliferationof ICSI (intracytoplasmic
sperminjection)
as a means of overcoming human male-factor infertility
might involvethe injectionof sperm whose DNA is severely compromised;even if successful
births were thereby achieved,
the individuals
might have enhanced predispositions
to subsequent medical problems.
While it might be argued that ICSI is unlikely
to become a widely usedanimal breeding
technology,
one reason for the recent intensified
attentiontohuman sperm DNA damage has been the demonstrationthat such damage can becaused by reactive oxygen species (ROS), to which human sperm seem particularly
vulnerable(Aitken,
1997; Twigg et al., 1998a;b).
Cellulartargets other than DNA(e.g. plasma membrane phospholipids)
have also been shown to be sensitivetooxidation(Hammerstedt,
1993;
Storey,1997; Twigg et al., 1998a),but overall,there is uncertainty
as to which targets (or potentialfunctions)
are most at risk
(Aitkenet al., 1998; Ellington
et al., 1998). The actual source and/or conditionsofgenerationof the ROS will clearly have an important influence.
For example, ROS
that are water-solubleor that are generated
extracellularly
will tend to affectdifferent
cellulartargets
from thosethat are lipid-solubleor generated
intracellularly,
and the protective
abilityof particularantioxidants
added to thesperm's environment will vary similarly(see for example Twigg et al., 1998a).
Althoughrather little evidence has been obtained so far to show that ROS species
are responsiblefor decline
in sperm function during semen preservation,
this lackof evidence may simply be due to a failure to pose the right questionsin the correct
way. For example, although
the sperm may be in some way protectedfrom orinsensitive
to ROS during preservation
or in the preservation
medium, thepreservation
process may render the sperm more vulnerable
to ROS once they arein a more "physiological"
environment.
Even in freshly
obtained
spermatozoa,
ROS-based damage appears to be enhanced by incubationin "fertilization"
media(c.f. Estop et al., 1993;
Gomez and Aitken,
1996;
Twigg et al., 1998b),
inenvironments depletedof the wide range of antioxidants
that abound in body fluids

(Halliwell
and Gutteridge,
1990; Rice-Evans
and Gopinathan,1995).
An aspectwhich appears to have received rather little attention
is the fact that sperm neverencounter in vivo the levels of oxygen to which they are usually exposed in vitro i.e. atmospheric:
20% (Zhil'tsov,
1970; Free et al., 1976; Fischer
and Bavister,1993).
Preservationand processingof sperm under lowered oxygen conditions
might therefore
be of great benefit (see Hammerstedt,
1993;
VishwanathandShannon, 1997; Griveau and Le Lannou, 1998).
Concluding

remarks

Semen evaluation
has been a subject of great attention
for more than 50
years. Despite (or maybe because of!) this long history,
advances have not in myopinionkept up with the forefrontof our knowledgeof sperm physiology,
andcurrent methods remain rather basic and therefore perhaps inadequate. In this paper,
rather than cataloguingthe many semen evaluation
proceduresthat have been
proposed,
I have tried to present
lines of thinking
that may encourage thedevelopmentof new and more focused procedures.
While the main aim during
such development
must be to keep the detailsof sperm physiology
firmly in theforefront,
there is an important concomitantneed for students to keep abreast of
new ideas and techniques
in other fields of cell biology. Although
it is a specialised
cell with its own peculiarities,
the spermatozoon
is none the less just anothermammalian cell type, with features and mechanisms in common with other cells. It
is a well-establishedfact in practicalscience that breakthroughs
in one field often
depend on advances in other, apparently
unrelated fields!
Acknowledgement
The author's research is supportedby the UK Biotechnology
and Biological
Sciences
Research Council. Grateful thanks are also due to Dr. W.V. Holt for permission
to publish
some ofour unpublishedcollaborative
research and to Prof. P.F. Watson for reading the manuscriptandcommenting helpfully
on it.
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Using CoordinatedComputer-Assisted
Motility Analysis(CASA) and Western Phosphoamino Acid Detection to
ElucidateSignificant
Signal Transduction
Pathways in Live
Sperm
JOSEPH S. TASH, Ph.D.
Department of Molecularand Integrative
Physiology,
and the Center
for Reproductive
Sciences, Universityof Kansas Medical Center,
Kansas City, KS 66160, USA

In animal reproduction,assessment of sperm motility is a fundamental method
for screening
breedersof potential
value in animal husbandry programs and for
detectionof readily identifiable
infertility
due to dismotility(Holt et al. , 1997; Garner,
1997; Working, 1988). In humans, infertility
affects 15-19% of the general population
and almost 50% of infertility
cases in couples are attributable
to the male (Pontonnierand Bujan, 1993).
Of the male factor patientsin whom a single abnormal semen
parameter is presented(37% of all male factor patients),
24 out of the 37% arecharacterized
by asthenospermia(i.e. reduced motility)that is not due to agglutination,accessory fiber abnormalities,
infection,
cold shock or prolonged exposure to seminal
plasma (Sigman et al.,1991;
McConnell, 1991).
A classic example of a known cause of congenital
infertility
that affects malesperm function is immotile cilia syndrome (ICS) and an associated subset, of these with
Kartagener's
syndrome (Kartagener,
1933).
In ICS, the dynein arms are missing or
abnormal (Eliasson
et al., 1977) resulting
in immotile sperm and infertility(Baccetti
etal., 1979).
It has been suggested
that ICS is caused by at least two different
genes(somatic
and testicular
ICS), since some male patientswith respiratory
tract ICS
produce normal sperm and have fatheredchildrenwithout Assisted
Reproductive
Technologies(ART: Phillips
et al., 1995; Conraads et al., 1992). It should be notedthat the incidence of patients with ICS affecting
sperm is rather rare at about 1:20,000.
Thus when technicaland analysis
artifacts
have been ruled out, the overwhelming
proportionof male factor patients has asthenospermia
of unproven cause. Some of the
proposed origins for these kinds of asthenospermia
include epididymal
dysfunction,
varicocele,
inhibitory
factors in seminal plasma, deficiencies
in seminal plasma anddefects
in the axoneme, ATP generation,(McConnell,1991).
Signaltransduction

Ca2+ regulation,and cAMP signalingin sperm motility

The incidence of male-factor cases due to reduced sperm motility of unknown
cause supports the need to understandthe signal transduction
pathways regulating
sperm motility.
Once specific targets in the signal transduction
cascade are identified,
it can be determined
whether these targets represent
lesions in the expressionofmotilityin cases of asthenospermia.
Conversely,
these targets could be employed as
potential
targetsin the developmentof sperm-directed
fertility
management. An
important role for cAMP in the initiationand maintenance of sperm motility has been
known for a long time (Tash and Mann, 1973; Garbers et al., 1973), reviewed by Tash
(1989)and Tash and Bracho (1994).
Proteinkinase activationand enhanced
phosphorylationof endogenous proteinsare concomitant
events associatedwithelevated CAMP(Tash et al., 1986; Tash and Means, 1982).
Identificationof important

targetsof regulation

Using reactivatedmodels, targets of protein phosphorylationthat have been
proposed to be involved in regulationof sperm motility
have been identified
in anumber of diverse species. In trout, for example, a 15 kDa protein was identified
whose phosphorylation
was increased
very rapidly
during activationof motility(Hayashiand Morisawa, 1988; Hayashi et al., 1987) in concert with an increase incAMP. However, analysis of the phosphoaminoacid residue in this protein revealed
phosphotyrosine(pY) rather than phosphoserine(pS) or phosphothreonine(pT). A
subsequent
paper (Jin et al., 1994b)
identified
a three-protein
complex that was
isolatedfrom trout testis that contained a 15 kDa protein that could be phosphorylated
in vitro in a cAMP-dependent
manner. Monoclonal antibodies
against the complex
recognized a larger protein at 38 kDa and not the 15 kDa protein (Jin et al., 1994a). In
a note added in proof in that paper, the authors stated that the antibodies
that weredeveloped to characterize
the 38 kDa protein, in fact, recognized a larger protein whose
size was not given. The major pY-containing
proteins of this size in sea urchin sperm
appear to be associatedwith the sperm head and do not change during activationofmotilityin the intact sperm (Bracho et al., 1998). In this connection,a slightly
largercAMP-dependent
phosphoprotein(18-20kDa) as well as a soluble
55 kDaphosphoprotein
were identified
in reactivatedCiona sperm correlatedwith motility(Dey and Brokaw, 1991). That study demonstrated
that a synthetic
tyrosinekinaseinhibitor
blocked motility
and 32P-incorporationin the reactivatedmodel, but had noeffect on pY levels when analyzed by anti-pY antibodies.
Several other studies haveidentified
phosphoprotein
targets in sperm that are either dynein subunits or tightly
bound to flagella(Bracho et al., 1998; Gingras et al, 1996; Si and Okuno, 1995;
Ashizawa et al., 1995; Stephensand Prior, 1995; Ashizawa et al., 1994). In bovine
sperm, a 55 kDa pY-containing
soluble protein was identified
whose phosphorylation
state is correlatedwith motility (Vijayaraghavan
et al., 1997b). This protein proved not
to be RII of PKA (CAMP-dependent
protein kinase),
another soluble protein that weand others have identifiedin sperm to be correlatedwith sperm motility(Tash, 1989;
Paupard et al., 1988; Noland et al., 1987). The phosphorylationof this protein was
stimulated
by cAMP suggestinga role for crosstalk between PKA and PKY signalingcascades. In mouse sperm, a flagellar
65 kDa cAMP-dependent phosphoprotein
wasidentified
whose microtubule-sliding
activity
was stimulated
in live sperm bybicarbonate
ions (Si and Okuno, 1995). When considering
all of the above data that has
been obtained in reactivatedmodels, no clear consistent picture can be made of what
are the key proteins involved in regulationof sperm flagellar
motility
because eachsystem varies, giving different
values for the size of phosphoproteins
as well as theirlocalization.
In contrast, using live intact sperm to examine phosphoproteins
coupled tosperm motility,
we have identified
an axonemal 130 kDa phosphoprotein(FP130)thatis the major flagellar
phosphoprotein
in sea urchin sperm. This protein
isphosphorylated
on pT and not pS or pY. Most importantly,
a functionallysimilar pTcontaining
protein of nearly identicalsize is the major pT protein present in mouse
sperm (Tash and Bracho, 1998). In addition,a major pT-modified
protein identicalinsize to the mouse motility-coupledprotein is present in live human sperm. The striking
similarityof the pT pattern in live human and sea urchin sperm combined with the factthat the field of likely
criticalregulatory
targets has been significantly
narrowed,
strengthens
the utility of such models to produce meaningful data.
In terms of the significanceto sperm fertility,
dynein is the primary flagellar
component that must be triggered
after sperm exit the testis in order for motility
to beexpressed. This is true for echinoderms to humans. Intact testicularsperm do not show
progressive
motility
when placed in a buffer in which mature sperm show normal
motility.
However under conditions
that stimulatecAMP or in reactivatedmodels inwhich phosphorylation
is increased,
the testicularsperm can display
forwardprogression
motility(Jaiswaland Majumder, 1996; Voglmayr and White, 1979;
Cascieri et al., 1976). In mammals, sperm must undergo additional
maturation
duringtransit through the epididymis
that allows them to be become ready to be fully motile
by the time the cauda epididymis
is reached. These maturation changes must allow
dynein ATPase activity
to be expressed(Mohri and Yanagimachi,1980). A link
between the acquisitionof the abilityof the sperm to display vigorous sperm motility
during epididymaltransit and successful
fertilizationof eggs has been known for along time (Brackett
et al., 1978; Hoppe, 1975). A positive
correlation
betweenstimulatedcAMP-dependent
pathwaysand stimulated
fertilization
has been
demonstrated
in a number of systems including
mouse, monkey, and humans (Fenichel
et al., 1996; Aitken et al., 1986; Monks et al., 1986; Boatman and Bavister,
1984;
Fraser,1981;
Fraser,1979).
We have recently
demonstrated
that FP130
phosphorylationis altered in response to the egg chemotactic
peptide,speract (Tashand Bracho, 1999).
One of the discernable
responses
to speract is a change inswimming pattern from circular
to straight
trajectories.
Thus, by altering
FP130
phosphorylation,
speract may contributeto the change in flagellar
mechanochemical
dynamics that changes circular to straight swimming patterns.
In studies that have focused on treatments that stimulate
sperm cAMP and
motility,
positiveoutcomes in the treatment of infertility
were achieved (Zheng andZhang, 1997; Sharma et al., 1996; Wolf et al., 1989). In such studies however, it is notpossibleto distinguishcAMP effects on motility from the known effects of cAMP on
the capacitation
process (GalantinoHomer et al., 1997; Leclerc et al., 1996a; Fraser,1990). Thus, in order to identify
important sperm components that couple motility
andsignal transduction
to successful
fertilization,
it is useful to also examine species inwhich sperm motility
pathways are not occurring in a backgroundthat includes
capacitation-associatedsignal transduction.
Sea urchin sperm have been a longstanding model for this type of analysis (for example, see Shapiro et al. , 1990; Christen
et al., 1986; Trimmer and Vacquier, 1986).
ReactivatedmodelsOur laboratory
was the first to utilize[y-32P]ATPto reactivate
detergentpermeabilized
sperm to identify
endogenous phosphoproteins(pPrs) associatedwithactivationof motility.
In all sperm that have been examined, elevated cAMP results inincreased phosphorylationof multiple proteins (reviewed
in Tash, 1989). A number ofstudies suggest that soluble factors are involved in this process (Leclerc and Gagnon,
1996b; Tash et al., 1986; Tash et al., 1984; Murofushi et al., 1982; Brandt and Hoskins,1980).
However, the degree to which soluble factors are requiredfor initiationofmotility
remains unresolved since all models examined thus far start with sperm thathave already undergone some degree of motility
initiation
prior to preparationof theimmotile model (Leclerc and Gagnon, 1996b; San Agustin and Witman, 1994; Dey
and Brokaw, 1991; Tash et al., 1986; Ishiguro
et al., 1982).
In the process ofcharacterizing
the mechanism of action of axokinin(RII),
we discoveredthat thepresence of detergent
alone had a major stimulatory
effecton reactivationofpermeabilized
dog sperm (Tash, 1989). This brought us to question whether detergent
treatment might allow interaction
between signal transduction
pathway enzymes that,in vivo, might not normally occur. However, in order to be able to add exogenous 32PATP for sperm activationand subsequent
analysisof phosphorylation,
membranecompartmentalization
is destroyed.
Furthermore, if protein phosphorylationand sperm
motility
are coupled, then any motility present in the sperm prior to stimulation
will beexpressed as a background phosphorylation.
This, combined with the destructionofcompartmentalization
makes more difficult
the identificationof primary regulatorycomponents. The problem is further magnified
by diversityof samples between
individuals
and collection
procedures that allow variable levels of motility
expression
prior to experimental
activation(Jones and Murdoch, 1996; Brokaw, 1984; Morton etal., 1978). Another possibility
is that some regulatory
proteins may undergo rapid
phosphorylationand that dephosphorylation
may be extremely slow or non-existent. In
this case, subsequent attempts to tag the targets with 32P in reactivation
experiments
would either fail or give highly variablelevels of incorporation,
dependingon theextent of 'background'motilityand phosphorylation
prior to preparationof the model.
Developmentof improved

methods

for collection

and storageof live sperm

Recent experiments that we conducted in microgravity
on the Space Shuttle
necessitatedthe development of improved methods to collect and store live sperm in
an immotile state (Tash and Bracho, 1999).
To solve the problem of motility
expression
prior to activation,
we sought to minimize sperm exposure to normal
seawater during spawning and activation
during storage.
This was achieved bymodificationof the conditions
outlinedby Christen
et al. (1986)
into MES sperm
storage buffer, MSSB (Table 1). This buffer contained higher potassium (50 mM),•Žlower pH (6.0) and 5 mM MES to buffer the lower pH (Bracho et al., 1997). Usingsuch buffers, sperm could be stored for up to 96 hr at 5with no significant
reduction in the motility
that was achieved by activating
the sperm by dilution
into seawater when compared to the level of motility
obtained in the freshly collected
sperm(Bracho et al., 1997). Using the same principles,a similar collection
method has beendeveloped
for mouse sperm (Tash and Bracho, 1998). Further testing is currently
underway for a similar method to apply to turkey sperm. Success in our endeavors todevelop a method for turkey sperm will greatly help productivity
in the turkey industry
since current technologies
yield sperm that are optimally
fertilefor only 3-6 hr(Donoghue et al., 1998; Brillard,1993).

Table 1. Composition
Activationof Immotile
given in mM.

Signaltransduction
Usingof Buffers for Collection,
Storage
Sperm. Note: All concentrations
A S W

H S W

M S S B

P H
K C l

8 .0
10

8 .3
0

6 .0
50

M E S
T A P S
N aC l
M g C l2

5
42 5
2 7

5
4 25
2 7

5
5
4 25
27

M g S O 4
C a C l2
N aH C O 3

2 9
10
2 .4

2 9
10
2 .4

29
10
2 .4

and
are

in live sperm

the new sperm collection
method, one could then ask whether theeliminationof 'background'motility
would
resultin lowerbackgroundphosphorylation
during initiationof motility.
Furthermore,
the experimentwasperformed with live sperm rather than permeabilized
sperm to eliminate the problem ofdestroyedcompartmentalization,
which might create fortuitous
kinase-substrateinteractions.
Since 32P-ATP could not be used with live sperm to examine internal
phosphorylation,
we used phosphoaminoacid
antibodies
to examinethephosphorylation
changes (GalantinoHomer et al., 1997; Berruti and Martegani, 1989).
Results showed that in immotile sperm, as expected, the levels of pS, pT and pY wereextremely low and limited predominantly
to a few low Mr proteins in the sperm head
(Bracho et al., 1998).
These results
were in sharp contrast to reactivatedspermexperiments
which showed numerous phosphoproteinsin which the identificationofcriticallyimportant proteins becomes uncertain (Ahmad et al., 1995; Tash et al., 1986;
Takahashi et al., 1985; Leclerc and Gagnon, 1996b; Dey and Brokaw, 1991; Tash andMeans, 1983). Initiationof motility
by dilutionof immotile
sperm into seawaterproduced the phosphorylationof two dominant sets of proteins within 20 sec. One setof proteins (32-29 kDa) contained predominantly
pS, appeared to be associatedwithflagella
and was extracted under conditions
that extract outer arm dynein (OAD). The
other major motility-related
phosphoprotein
contained
predominantly
pT, had amolecular weight of 130 kDa (named FP130),
and appeared to remain bound to theaxoneme after OAD extraction.
In these sperm, no phosphorylation
was detected at 56
kDa that would correspond to RII, either in the whole sperm, soluble material nor anyof the flagellar
fractions(Bracho et al., 1998). Another important new observation
yieldedby the live sperm model is that the phosphoproteins
revealed at 20 sec ofactivation
are the same after 20 min of activation.
This supports the hypothesisthat inthe intact sperm the signal transduction
kinases and phosphatases
are directed to a very
limited number of targets.
Role of signaltransduction

anchoringproteins

In this regard, a family of proteins, AKAP, (A kinase anchoring protein)thatanchor cAMP-dependent protein kinase to specific compartments of cells by binding to
RII, has been identified(reviewed by Coghlin et al.,1993).
In mammalian sperm, themajor fibrous sheath protein has been shown to a member of the AKAP family(Carrera et al., 1994). A role for AKAP in the regulationof sperm motility
has beensuggested by recent work of Vijayaraghavan
et al. (1997a).
This group showed that apermeable peptide that represents
the amphipathic
helical RII binding motif of AKAP
caused inhibitionof bovine sperm motility. A control peptide with identicalsequence,
except for a single proline substitutionthat renders the peptide inactive, did not inhibit
sperm motility.
They also demonstrated
that the reversible
inhibitionof motilityby theactive AKAP peptide was dependent on the presence of external Ca2+. In addition,theyfound that inhibitorsof PKA did not inhibit basal motilityor stimulationof motility
via
activatorsof the PKA pathway. This latter result is in contrast to work demonstrating
an inhibitory
effect of permeable inhibitorsof PKA on sperm motility(LeclercandGagnon, 1996b;
San Agustin and Witman, 1994; Brokaw, 1987; Tash et al, 1986;
Ishiguro
et al, 1982). It has been suggested
that the ability of inhibitorsof PKA toaffect sperm motility
depends on the timing of inhibitor
addition
relativeto activationof motility,
as well as the abilityof endogenous protein phosphatases
to reverse
activation-dependent
phosphorylations(Tash and Bracho, 1994; San Agustin andWitman, 1994).
In experiments
described
in the preliminary
results,
we havedemonstrated
that sea urchin sperm motility
is stimulatedby the same AKAP peptidethat inhibit
bovine sperm motility.
In addition,in studies performed in collaboration
with Dr. Stuart Moss (U. Pennsylvania)
we have identified
an 82 kDa AKAP in outerarm dynein containing
high salt extracts of the flagella as well as in the salt-extracted
axonemes. It should be noted that sea urchin sperm do not contain a fibrous sheath.
These new results emphasize
the need to study the role of AKAPs in signaltransductionin sperm from different
species. The fact that AKAP inhibitors
showedopposing effects in mammalian versus sea urchin sperm raise several questions:
1) Domammalian sperm also have AKAPs bound to the axoneme, and 2) do the axonemal
AKAPs help to target stimulatory
pathways for motility,
whereas the fibrous sheath
AKAPs target inhibitory
pathways for motility?
In interpreting
the results using these
AKAP inhibitors,
it must be noted that the protein sequence used to synthesize
theseinhibitors
is homologous to adrenal AKAPs rather than sperm AKAPs. Whether thisdifference
in sequence homology is of functionalsignificancealso remains to be
determined.
Finally,
it should also be mentioned that the AKAP inhibitors
mentioned
above had no effecton mouse sperm motility(Vijayaraghavan,
personal
communication).
One possible
explanation
for the divergent
results could be that the fibroussheathanchors predominantly
enzymes that inhibit
motilitysuch as protein
phosphatases.
On the other hand, the axonemal AKAPs may anchor stimulatory
enzymes such as cAMP-dependent protein kinase. AKAPs have been shown to bind
both kinases as well as phosphatases(Vo et al., 1998; Scott, 1997; Lester and Scott,1997; Klauck et al., 1996; Coghlan et al., 1995). The response to AKAP disruption
would be determinedby the prevailingenzyme released from its docking site. For
example, since sea urchin sperm do not contain a fibrous sheath, the predominant
enzyme would be stimulatory
to motility.
In sperm with a fibrous sheath, the amount ofphosphatasereleased may overwhelm the stimulatory
enzymes. This could also explain
why the stimulatory
effect of AKAP inhibitors
is very rapid, whereas the inhibitionofmotilityof mammalian sperm by these peptides
takes longer. In order to explorewhether such mechanisms indeed occur, it will be necessary to compare the molecular
structure of the axonemal and fibrous sheath AKAPs and directly
determine whether
these AKAPs differ with respect to the signal transduction
enzymes that they tether.
Evolutionary

significanceof important

regulatorsof sperm motility

Recent studies in Chlamydomonas have identified
a 138 kDa subunit of innerarm dynein 1 (IAD1)
whose phosphorylation
regulates
flagellar
beating(King and
Dutcher, 1997; Habermacher and Sale, 1997). Furthermore, the function of this subunitin the regulationof IAD function appears to be regulated
by interplay
between cAMP,
Ca2+, and PP1 (Habermacher
and Sale, 1996). A major aspect of this interplay
thatmust be mentioned is that cAMP inhibits
motilityin Chlamydomonas and Ca2+
stimulates
motility.
Thus, Chlamydomonas is not the best model to study regulationofflagellar
motilitythat may also occur in sperm. We identified
a pT-containing
phosphoprotein
in sea urchin sperm flagellawhose phosphorylation
was rapidly
increased during initiationof motility(Bracho et al., 1998). This protein remained
tightlybound to the sperm axoneme after flagellar
extraction
with 0.6 M NaCl.
Nonetheless,whether the 130 kDa sea urchin protein is related to the Chlamydomonas
138 kDa IAD subunit or not is a question that merits investigation.
Our recent studies
suggest that human sperm have a similar protein (Tash and Bracho, 1998). Thus, itappears that FP130 represents
a family of axonemal proteins of significanceto theregulationof flagellar
motility.
In order to be able to answer the ultimate question
regarding the physiologicalimportance of FP130 to sperm function, we have utilized asperm cell for which much has already been publishedregardingthe mechanisms
involved in expressionof flagellar
motility.
In addition,
many mechanochemical andregulatorycomponents of sea urchin sperm have been cloned and found to be relatives
to components in mammalian sperm or spermatogenic
cells (Neesen et al., 1997;
Milisav et al., 1996; Yuan et al., 1995). The sea urchin sperm also offers the ability toprovide investigatorswith large quantities
of homogenous cells, which are an excellent
starting point for the purificationof FP130. Once purified,
the protein may then be
used to generate peptide sequence and immunochemical probes that can be used togenerate reagents for obtaining molecular information
not only in sea urchins, but canbe used to generate immunochemical and molecular informationin other species as
well, including
the mouse and human.

Conclusion
In summary, lesions in sperm motility
remain a major component of male
factor infertility.
Althoughmany soluble and insoluble
sperm phosphoproteins
havebeen identified
that are correlatedwith changes in motility,
identificationof flagellar
components that are the primary link between signal transduction
pathways and theflagellar axoneme remained elusive. The development of new models for investigationof the initiationand modulationof motility
in live sperm that have never become
motileprior
to experimental
activation
will be a valuable
tool to identify
phosphoproteinsthat are part of the signal transduction
pathway of initiationofaxonemal function. Such new collection
and storage methods will prove useful indeveloping
similartechnologies
for species,
such as the turkey, for which suchmethods are not currently available.
A key and powerful aspect of the new model isthat phosphorylation
can be examined in live sperm where compartmentalizationisintact. Our recent studies have demonstrated
that initiationof motility
in sea urchin
sperm is associatedwith very rapid phosphorylationof pS and pT on very few proteins,
and that these proteins are flagellar, some of which are tightly bound to the axoneme.
In particular,
the evidence points to the axonemal protein, FP130, as an importantcomponent of the signal transduction
pathways that couple protein phosphorylation
toregulationof sperm motility.
In addition,evidence suggests that human sperm also
contain a FP130 homologue. The second messenger pathways that regulate FP130, thelocalizationof FP130 within the axoneme, the presence and functional
identificationofFP130-related
proteins in mouse and human sperm, how FP130 regulates
flagellarfunction, its developmental
significancewith regard to normal spermatogenesis
andultimately,
the expressionof motility
in the mature sperm, and how thephosphorylation
state and integrityof FP130 are altered in sperm with reduced motility
and fertility,
are all questions
that can now be asked with a degree of certainty
than hasbeen heretofore not possible
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Spermatogonial

Transplantation

TAKEHIKO OGAWA
Department of Urology, Yokohama City University
Yokohama 236-0004,
Japan

Schoolof Medicine

Abstract
The spermatogonial
stem cell is the foundationof spermatogenesis
and it continues
to dividethroughout
the lifetimeof a male. It divides
to renew itself as well as to produce daughter cells which
sequentiallydifferentiate
into spermatozoa. Transplantationof spermatogonial
stem cells from a fertile
mouse to the testis of sterile mouse has resulted in the developmentof donor cell derived spermatogenesis.
In successful
cases, the recipient
mice have become fertile. Furthermore, spermatogenesisof the rat andhamster has occurred in the immunodeficientmouse testis following
spermatogonialtransplantation.
It hasalso been shown that spermatogonial
stem cells can be cryopreserved
for later transplantation.
These newtechniques
will be useful for basic spermatogenesis
research and for future application
to reproductive
technology.

Introduction
Spermatogenesis
is a complex
process
includingseveraldistinctbut
continuous steps of cell proliferationand differentiation
which ultimately
lead to theformation of spermatozoa. It can be divided
into three phases. First, expansion of
spermatogonia
occurs on the basement membrane of the seminiferous
tubule. The
spermatogonial
stem cells commit to differentiation
division
followedby sequential
divisionsof progeny cells, which give rise to numerous differentiatedspermatogonia
inthe basal compartment of the seminiferous
tubules. The second phase consistsofmeiotic divisionof spermatocytes
which move into the adluminal
compartment of theseminiferous
tubules.
During the meiotic
division,
homologous
recombinationofchromosomes takes place. The third phase, spermiogenesis,
is characterizedbydramatic morphologicalchanges of spermatids
into spermatozoa. All these processes
are supported
by surrounding
somatic cells, especially
Sertolicells. Due to thiscomplexity,
it has not been possible
to reproduce this process in vitro even though anumber of trials have been done.
The foundation of spermatogensis
resides in the spermatogonial
stem cells thatare locatedon the basement membrane of seminiferous
tubules as a subset ofspermatogonia.
They have an enormous potential
to keep renewing and to give rise todifferentiateddaughter cells throughout
the life of a male. One spermatogonial
stem

cell

theoretically

can produce

4,096

spermatozoa(Russell

et al.,

1990),

althoughmorethan half of them will disappear
mainly through apoptotic
cell death (Tegelenboschand de Rooij, 1993; Barratt,1995). Nonetheless,the number of spermatozoa produced
from a single stem cell is considerable.
While a single spermatozoon transmits
only adefined set of genetic information
to the offspringthrough fertilizationof an oocyte,
the spermatogonial
stem cell carries
the entire genetic
code of the male. Throughmeiotic recombination,
resultant
haploid
cells acquire genomic diversity.
Therefore,
any two spermatozoa from a single animal could be different
in terms of their genetic
composition.
A spermatogonialtransplantation
techniquehas been developedin rodents by
Brinsterand colleagues(Brinsterand Zimmermann, 1994; Brinster
and Avarbock,1994). Through this technique,spermatogonial
stem cells can be collected,
maintainedin vitro, then transplanted
to recipient
testes to establish
spermatogenesis.
The recipientmouse became fertile and produced offspring
carrying the donor genotype.
Development of Spermatogonial
Transplantation
The spermatogonia
of a fertile mouse can be transplanted
to the testis of asterile mouse (Brinster
and Zimmerman, 1994). The testis cells of a donor mouse werecollectedusing a two-step enzymatic digestion
protocol to make a cell suspension. The
cells were then introduced
into the seminiferous
tubules of a recipientmouse testis.
The donor cells migrated
to the basement membrane of the recipient
seminiferous
tubulesand formed colonies
that developed
spermatogenesis.
Recipientmice weretreated with busulfan to eliminate
endogenous germ cells prior to the transplantation.
Mice carrying
the white spotting
mutation (W) were also available
as recipients
because they lack germ cells due to a mutation in the c-kit proto-oncogene
receptor
which is essential
for germ cell development
during embryogenesis.
In successful
cases of spermatogonialtransplantation,
the recipient
mice became fertileand hadoffspring
through mating with females. Some offspring
showed the donor haplotype
which indicatedthat donor derived spermatogenesis
was complete to form functional
sperm in the recipient

testis(Brinsterand Avarbock,1994).

Procedure of Spermatogonial
Transplantation
Transgenic
mice or rats that carry the lacZ gene as a marker were used as

•Ž
•Ž).

donor animals

in most cases to help distinguishthe donor cell derived

spermatogenesis

from residual endogenous spermatogenesisof a recipient.
In an experiment where thehamster was used as donor animal, histologicalexamination
was performed to
distinguishmorhpologicaldifferences
betweenhamster
spermatogenesis
andspermatogenesisof the recipientmouse (Ogawa et al., 1999)
because transgenic
hamsters carrying a lacZ marker gene are not available.
The testis
cells of donor animals were collectedby a two-step enzymatic
digestion(Bellve
et al., 1977).
Briefly,
decapsulated
testis tissue was treated withcollagenase
to remove interstitial
tissues,followedby trypsin and EDTA digestionformaking single cell suspensions
from seminiferous
tubules. The cells were suspended in
DMEM+10% FBS at concentrations
between 20 - 200x106 /ml. This cell suspension
includesdifferent stages of germ cells and some somatic cells.
One of the key preparations
for spermatogonialtransplantation
is to render
recipientmice devoid of germ cells. Busulfan treatment proved to be one of the best
methods to eliminate
germ cells includingstem cells. Busulfan at a dose of 40 mg/kg
or more was given intraperitoneallyto the recipientmouse 40 to 60 days prior to thetransplantation.
W mutant mice are also available
as recipient
mice because they lackgerm cells due to a mutation in the c-kit receptor (Brinster
and Zimmermann, 1994).
The procedure of donor cell injection
into the seminiferous
tubulesof arecipientmouse can be performed throughdifferentroutes:directinjection
intoseminiferous
tubules, injection
into the rete cavity, or injection
through the efferent
duct (Ogawa et al., 1997).
In case of xenotranplantation,
immunodeficient
mice, nude or SCID, were used
as recipients
to avoid immunologicalrejection:
Several months after transplantation,
the recipientmouse testes were taken out for lacZ staining.
The seminiferous
tubules
containing
donor spermatogenesis
expressing the lacZ transgene will stain blue whichmakes it possible
to unequivocally
identify donor-derived
spermatogenesis.
Donor testes cells can also be frozen in the same way as cultured somatic cells
for later use in transplantation.
Briefly,
collected
testis cells were suspended in DMEM
+10% FBS followedby drop-wise additionof freezing medium (FBS, DMEM+10%
FBS, DMSO in a ratio of 1:3:1)
in a volume equal to the original cell suspension.
Cells
were aliquoted
1.0 ml per freezing vial, placed in an insulated
container at -70
more than 12 hours and then moved into liquid nitrogen(-196
The thawed

for
cells

were resuspended
in DMEM+10% FBS and introduced
way as freshly collected
cells.

into recipient

Xenogeneic

andSpermatogonial

Transplantation

testesin the same

Cryopreservationof

Spermatogonial
Stem Cells.
In 1996,
Brinsterand colleagues
reported
that rat spermatogenesis
hadproceeded in the immunodeficientmouse testis (Clouthieret al., 1996). The success of
xenogeneic
spermatogonialtransplantation
has expanded
the possibility
forapplicationsof this techniqueto animals other than mice. In addition,
they also
reported that spermatogonial
stem cells could be cryopreservedin the same manner assomatic cells (Avarbock
et al., 1996).
Considering
the species
specific
protocols
necessary for sperm cryopreservation,
the comparatively
easy preservationof stemcells
will
be beneficial
for animals
for which reliable
proceduresof sperm
cryopreservation
have not been developed.
Spermatogenesisof each species has a unique defined duration from the onsetof stem cell differentiation
to the completionof spermatozoa formation. It is also
known that the length of each step of spermatogenesis
is rigidly
determined.
Based onnumerous observations
and experimental
results on the relationshipbetween germ cells
and Sertoli
cells, it has been supposed that Sertoli
cells are regulating
almost every
detailof spermatogenesis
includingthe lengthof the spermatogenic
processes.
Combining
the classic[3H]-thymidine
incorporation
method and the new xenogeneic
spermatogonialtransplantation
technique,it has been shown that rat spermatogenesis
in the mouse seminiferous
tubules is supported
by mouse Sertoli cells and proceededwith the timing typical for rat spermatogenesis.
This result demonstrated
that the celldifferentiation
process of spermatogenesis
is regulated
exclusively
by the germ cells.
Sertolicells are indispensable
but they do not regulate the duration of spermatogenesis(Franca et al., 1998).
Very recently, Brinster and colleagues
have successfullytransplanted
hamster
testis cells that resulted in spermatogenesis
in the recipientmouse testis (Ogawa et al.,1999).
Considering
the phylogenetic
distance
between mouse and hamster, which 1.6
times longer than that of mouse and rat, the results might suggest that xenogeneic
spermatogonialtransplantationis also possible
in other animals for productionofdonor spermatozoa. In addition,
frozen hamster testis cells transplanted
to mouse testes

also resulted
in spermatogenesis.
Taken together,it might be possible
to freeze thetestis cells of a particularspecies,
and at a later time the cells can be thawed andtransplanted
into the testis of not only the same but also a different species.
Conclusion
The techniqueof spermatogonialtransplantation
both in the same species andinter-species
has tremendous
potential
for developmentof future applications
toreproductive
technologies.
I expect that cryopreservationof diploid
spermatogonial
stem cells can be achieved in a broad, if not universal, range of species. Therefore, thespermatogonialtransplantation
techniquetogetherwithcryopreservationofspermatogonial
stem cells would be a powerful tool in conservationof genetic
resources, such as endangered species and valuable agricultural
animals. Now we canpreserve spermatogonial
stem cells from those animals for future transplantation
when
the recipientanimal systems become available
to support donor spermatogenesis
andsubsequent transmissionof genetic material.
One of the next important steps is the developmentof a culture system for
spermatogonia
which will allow expansion of spermatogonia,
leading to their genetic
modification.
Then it will be possible
to make geneticallymodifiedanimals throughspermatogonialtransplantationin combination
with other assisted
reproductive
technologies.
Acknowledgements
I would like to express my gratitude to Prof. Ralph L. Brinster
along with precious suggestions
and instructions
through all experiments
acknowledge
helpful
discussions
and suggestions
from my colleagues,
Makoto Nagano.

for his heartful encouragement
includedin this review. I also
especially,
Ina Dobrinski
andReferences
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Proc
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Clouthier

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I, Avorbock
M, Brinster
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LD, Ettlin RA, Hikim AP, Clegg ED. 1990. Mammalian spermatogenesis,
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Spermatogenic

Cells as Male Gametes

ATSUO OGURA
Department of Veterinary
Tokyo 162-8640, Japan

Science,National

Instituteof Infectious

Diseases

Abstract
Spermatogenic

cells are male germ cells that undergo mitosis,

meiosis,and spermiogenesis

in the seminiferous
tubules. Due to recent advances in micro fertilization
techniques, spermatogenic cells
at certain stages have been used to construct diploid zygotes that subsequently
develop to term. Using
round spermatids, the youngest haploid germ cells, normal births have been reported in the mouse,rabbit, and human. Secondary and primary spermatocytes have also been demonstrated to support fullterm development after incorporation
into mature or immature homologous oocytes. The techniques not
only provides oppotunitiesof treating
infertile
males, but also provides valuable informationon
gametogenesis

includingspermatogenesis,

meiosis,and genomic imprinting.

Introduction
In mammalian seminiferous
tubules, there are five types of germ cell(spermatogenic
cells):
spermatogonia,
primary and secondary spermatocytes,
spermatids
and spermatozoa. Although the testicularspermatozoa have alreadycompleted the morphologicalchanges that are needed to carry paternal genomes safely,
they acquire the ability to fertilize
oocytes as they pass through the epididymis
andmature. Due to advances in micro fertilization
techniques, however, these days the term
'fertilizing
ability ' means the ability of male germ cells to form normal diploid zygotes
after transfer into oocytes. In this context, elongated and round spermatids,
andsecondary and primary spermatocytes have fertilizing
ability, as demonstrated by recent
micro fertilization
studies. The clinical and biologicalsignificancesof microfertilization
with spermatogenic
cells have been publishedelsewhere (Ogura and Yanagimachi,
1995; Tesarik et al., 1998; Ogura and Yanagimachi, 1999). In this paper, I review
micro fertilization
with spermatogenic
cells in mammals, with special emphasis on its
technical aspects that differs remarkably from those of micro fertilization
with mature
spermatozoa.

Technicalaspects
1. Collectionof spermatogenic

cells

Spermatogenic
cells should be collectedfrom testicular
tissues,althoughsometimes in humans, round spermatids could be collected from the ejaculates(Tesarik
et al., 1995). In general, we can readily obtain suspension of testicular
cells containing>80% spermatogenic
cells by either enzyme treatment or mechanical separation withpipetting(Ogura and Yanagimachi,
1993). The remaining nucleatedcells in thesuspension include Sertoli cells, Leydig cells, and leukocytes. Although there are several
ways to prepare spermatogenic
cell suspensions
with more purity, we can correctly
identify
spermatogenic
cells before micro fertilizationby their morphologyorchromosome analyses, as described
below.
2. Identificationof spermatogenic
cells
Most spermatogenic
cells can be identified
by their size and morphology, butsome experience is needed. For more precise identification,
presumptive cell types are
injected into Met II-arrested oocytes (the mouse oocyte is most suitable)
and then thechromosome ploidy is determined(Kimura and Yanagimachi,
1995a).
3. Method of nuclear transfer
Unlike mature spermatozoa, most spermatogenic
cells are round cells with adecondensed
nucleus. Therefore,
they can be introduced
into oocytes by either
intracytoplasmic
injection or electrofusion.
Electrofusion
is thought to be less traumaticthan intracytoplasmic
injection,
but generally less efficient(compare Ogura et al. 1994
with Kimura and Yanagimachi,
1995a). Each nuclear transfer method has inherent
advantages and disadvantages(Table 1).
4. Oocyte activationand the timing of nuclear transfer
Round spermatids and younger spermatogenic cells in mouse and hamster havelittle or no capacity to activate oocytes (Ogura and Yanagimachi,
1993; Kimura andYanagimachi,
1995a). Therefore, oocytes should be activated artificiallyat or aroundthe time of transfer of spermatogenic
cell nuclei. Mammalian oocytes can be activatedby chemical treatment or electric stimulus. The method of oocyte activation,
as well as
its timing,
may affect subsequent
embryo development.
The combinationofspermatogenic
cells and oocytes that would result in the formation of diploidzygotesis shown (Table 2). These protocols workbest in mouse experiments because prematurechromosome condensationof the spermatogenic
cell nuclei can readily be induced inmouse oocytes. This character allows the chromosome pairs to segregate correctly and,

Table 1. Comparison of electrofusion
spermatogenic
cell neuclei into oocytes
M e th o d

E ffic ie n c y

E le c tro fu s io n

L o w to m o d e ra te

In tra c y to p la sticin je c tio n

H ig hTable 2. Combinationand intracytoplastic

injectionfor introductionof theC e ll v ia b ility

In c o rp o ra tio nd oInn teo rsrm a inn dg lino o gc yotef s

H ig hIn ta c t c e lls

S lo wru
Isop tula ter edd nc ue clls
le i o r

Q u ic k

M o d e ra te to h ig hof spermatogenic

cells and oocytes for microfertilization

S p e rm a to g e n ic c e lls

S ta g e o f o o c y te s

Sh pa ve ec ieb se einn re
w hp ico rte
h ndo rm a l b irth

R o u n d s p e rm a tid

M e ta p h a s e II
M e ta p h a s e II (P C C )T e lo p h a se II

H u m a n , R a b b it
M ou seM ouseS e c o n d a ry
sp e rm a to c y te

M e ta p h a se II (P C C )M o u seP rim a ry
s p e rm a to c y te

M e ta p h a se II (P C C )M e ta p h as e I (P C C )M o u seM o u se

PCC: Premature chromosome condensation

very recently,

we demonstrated

that the spermatid

nuclei

could

support

full

term

development following premature condensation of their chromosomes in Met II-arrested
oocytes (Ogura et al., 1999).
In the case of round spermatidsfrom human andcynomolgus monkeys, oocytes are activated at the time of spermatid injection
becausethey probably
contain oocyte-activating
factor(s)(Sousa et al., 1996; Ogura et al.,1997a).

Historical

overview
Since the first attempt to fertilize
hamster oocytes with round spermatids(Ogura and Yanagimachi, 1993), micro fertilization
with spermatogenic cells has become
an important technique for studying the mechanisms of mammalian fertilizationand fortreating male factor infertility
in humans (Tesarik et al. 1998) and animals (Ogura etal., 1996a; Tanemura et al., 1997). Its history is summarized in Table 3. Very recently,
normal mice were born after microinsemination
with primary spermatocytes,
the malegerm cells before the first meiotic division (Ogura et al., 1998). This indicates
that, atleast in some male germ cells, the epigenetic
modifications
needed for normal embryo
development are completed before meiosis I. Cryopreservationof spermatogenic
cells
has become possible in some mammalian species including
humans (Ogura et al.,1996b; Ogura et al., 1997a; Antinori et al., 1997). This avoids the repeated collectionof testicularsamples and may increase the chance of rescuing endangered species.
Recently we have succeeded in obtaining fertilized
oocytes in mastomys, an
African rodent, using round spermatids as male gametes (unpublished).
Althoughinductionof superovulationand embryo culuture in vitro is relatively
easy in thisspecies (Ogura et al., 1997b),
it is extremely
difficultto fertilize
oocytes in vitro(Nohara et al. , 1998) or to inject spermatozoa into oocytes due to technical problems(e.g., large sperm head) and the character of their oocytes. After round spermatid
injection,
however, about 90% of oocytes survived the injection
procedure and most(>90%) of them formed the male pronucleus. These oocytes fertilizedwith roundspermatids
developed
to the blastocyst
stage, although
so far no pups have beenobtained after embryo transfer.
Future prospects
The fact that round spermatids
and spermatocytes
can be used to fertilize
oocytes has several implications
for basic research as well as for human clinicalTable3. Microinsemination

with spermatogenic

cells by intracytoplasmic

injection(ICI)

or electrofusion(EF)

S p e rm a to g e n ic c e lls u s e d

M e th o d

D e v e lo p m e n t

g o ld e n h a m ste r

ro u n d s p e rm a tid

IC I

2 ‑c e ll

h a m ste r, m o u s ero u n d s p e rm a tid

EF

2 ‑c e ll

O g u ra e t a l.

m o usera b b it

ro u n d s p e rm a tid

E F

te rm

O g u ra e t a l.

ro u n d s p e r m atid

IC I

te r m

S o fik itis e t a l.

IC I

te rm

K im u ra a n d Y a n a g im a c h i

1 99 5m o u sehum anro u n d sp e rm a tid
e lo n g a te d sp e rm a tid

IC I

te rm

F isa h e l e t a l.

19 9 5

hum anro u n d s p e rm a tid

IC I

te rm

T e s a rik e t a l.

1 99 5bm o useb o v in es e c o n d a ry s p e rm a to c y te

IC I

te rm

K im u ra a n d Y a n a g im a c h i

c u ltu re ‑d e riv e d sp e rm a tid

IC I

b la s tc y st

G o to e t a l.

m o usehu m anfro z e n ‑th a w e d sp e rm a tid

IC I

te rm

O g u ra e t a l.

199 7

f ro z e n ‑th a w e d s p e rm a tid

IC I

A n tin o ri e t a l.

199 7cm o u sep r im a ry s p e rm a to c y te

E F

o n g o in g p re g .
b la sto c y stO g u ra e t a l.

te rm *

S a sa g a w a e t a l.

Y ea rS p e c ie s

19 93
19 93
19 94
19 94
19 95 a1 99 6
199 6b199 8

m o usep rim a ry s p e rm a to c y te

IC I

199 8

R e fe re n c e
O g u ra a n d Y a n a g im a c h i

m o usep rim a ry s p e rm a to c y te

EF

te rm

O g u ra e t a l.

199 8

m o useM e t I sp e rm a to c y te

IC I

te rm

K im u ra e t a l.

199 9m ousero u n d sp e rm a tid * *

IC I

te rm

O g u ra e t a l.

p o rc in ero u n d s p e rm a tid

IC I

b la sto c y st

K im e t a l.

m a stm y s

ro u n d s p e rm a tid

IC I

b la s to c y st

199 9

Pups died before

wearing.

** After prematurechromosome condensation.

practice. Its application
for generating transgenic animals is an example. The offspring
derived from spermatogenic cells with exogenous DNAmust carry the expected genes.
The culture of spermatogenic
cells in vitro, which is still extremely difficult,would
facilitate
the use of spermatogenic
cells as male gametes in future studies.
References
Antinori,

S., Versaci,C, Dani, G., Antinori,pregnancy

after

Reproduction

injectionS., Green, S., Bishop,

Goto,

K., Kinoshita,M., Thornton,

intracytoplasmic

injectionof spermatid.

LancetY., Ogawa,K. 1996.

injection

Reproduction

11: 824-829.

Kimura,Y., Yanagimachi,

Kimura,Y.,

Kimura,Y., Tateno,

spermatids

can develop

Successful

and
Human

with

Pregnancy

formation

followingbovinetesticularof normal

oocytes.

spermatozoa

121:

Human
or round2397-2405.

mice from oocytes

injectedwithand

53: 855-862.

R. 1998.

spermatocyte

Factors

affectingmeiotic

injected

intomouse oocytes.

Fertilizationand in vitro

nucleiKim, C, Jun, S.H., Lee, H.T., Chung,nuclei.

oocytesfollowinga new species

injectionof round spermatidor(in press).

M., Araki,

of a successful

K.S. 1999.

intracytopiasmic

Theriogenology

T., Ogura, A., Hiroi,

and development

intoof Reproductionof primaryS. 1997.

59: 871-877.

of porcine

round spermatid

Blastocyst

spermatids

M. A., Yanagimachi,competence

Nohara, M., Hirayama,

BiologyS., Al-Hassan,

Development

Development

nuclei.

of Reproduction

development

coucha):fertilization

into human oocytes.

345: 1641-1642.

injected

into normal offspring.

H., Handel,

J.S.,

derivedMouse oocytes

R. 1995b.

spermatocyte

developmental
Kim, N.-H., Shin,of in-vitro

R. 1995a.

Yanagimachi,

secondaryS., Hunter, A., Fleming,A., Nakanishi,

intracytoplasmic

Biology

H. A. 1997.

round spermatids

12: 554-556.

Fishel,after

M., Selman,of frozen-thawedin vitro

for reproductive

Y. 1998. Partial

fertilization
biologycharacterization

procedure
research.

of the gametes

in the mastomys(Praomysof Reproduction

58: 226-

Biology

233.
Ogura,A.,

Yanagimachi,

pronuclei

Ogura, A., Yanagimachi,
incorporated
Ogura, A., Matsuda,

Ogura,

R. 1993.

and participate

Roundspermatidin syngamy.

BiologyR., Usui, N. 1993.
into mature oocytes

J., Yanagimachi,with round spermatids

Science,

U.S.A. 91: 7460-7462.

A. and Yanagimachi,

R. 1995.

Development
7: 155-159.
Ogura, A., Yamamoto, Y., Suzuki,

Behavior

0., Takano,

into

hamsteroocytesand mouse round spermatid

Zygoteform

48: 219-225.
nuclei1: 1-8.

of normal young following

by electrofusion.
Spermatids

injectedof hamster

by electrofusion.

R. 1994. Birthoocytes

nucleiof Reproduction

fertilizationof mouse

Proceedingsof the National

Academy ofas male gametes.

Reproduction,

FertilityK., Wakayama, T., Mochida,K., Kimura, H. 1996a.

andInvitro fertilizationand microinsemination

genes in mice. Theriogenology
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0., Yanagimachi,

R. 1996b.

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normalReproduction

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Ogura, A., Matsuda,

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with

offspring.

Journalof Assisted
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K., Nakayama,K., Naiki,

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and Development

J., Suzuki,

coucha)43: 65-71.

O., Sankai,T., Ogonuki,N., Yoshikawa,of mammalianspermatids.

JournalY., Yanagimachi,

of Reproduction

R. 1997a.

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103-104.

Ogura, A., Wakayama, T., Suzuki,
of mouse primary
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Ogura, A., Suzuki,

0., Shin, T.-Y., Matsuda,

spermatocytes

immature oocytes.

undergoZygotethe National

Ogura, A. and Yanagimachi,
Sterility

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Chromosomes

incorporation

into

fertilizedY., Matsuda,

J. 1998. Developmentofwith primary spermatocytes.

Proceedings

of

U.S.A. 95: 5611-5615.

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Motility

Disorders(ed.

Hamamah,S.),

Heidelberg,

Germany:

using

spermatogenic

Spermatid

nucleican support

full

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Ogura, A., Inoue, K., Matsuda,prematureY. 1997c.

divisionsR. 1999.
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K., Kobayashi,

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Academy of Science,J., Kobayashi,

meiotic

5: 177-182.

O., Tanemura, K., Mochida,normal mice from metaphaseMale

injected0., Takimoto,

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K., Suzuki,

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Ogura, A., Matsuda,of nephrotic

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J., Asano, T., Suzuki,

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J. 1999.

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14: 1294-

1298.
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I., Kuretake,complete

S., Eppig,

two meioticJ., Yanagimachi,

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the oocyte

Mouse primarycytoplasm.

Biology

spermatocytes

canof Reproduction

58:248-254.
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Sousa,N. V., Miyagawa,I., Agapitos,

H. 1994.

Reproductive

meiosis.

JournalE., Pasyianos,

capacityof Assisted

Reproduction

M., Mendoza, C., Barros, A., Tesarik,
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injection

Human ReproductionP., Toda, T., Hellstrom,of the nucleus

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Tesarik,of

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Y., Sato, E., Ogura, A. 1997.

withazoospermic

mice. Laboratory

Animal ScienceJ., Mendoza,C, Testart,

New England

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Molecular

2: 853-857.

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oocytes.

Calcium

spermatocytes

Tanemura, K., Wakayama, T., Kuramoto, K., Hayashi,

Tesarik,W. J. G., Kawamura,of the male gameteJ. 1995.
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indicationsandUse of Freeze-Thaw and Freeze-Dry Spermatozoa
Intra-Cytoplasmic
Sperm Injection

for

T.WAKAYAMAand R.YANAGIMACHI
Department of Anatomy and Reproductive
Biology,
Universityof Hawaii Medical School,
1951 East-West Road. Honolulu, Hawaii 96822, USA

Introduction
Geneticallyengineered animals could be the subjects for study of humandiseases and therapy (Sharp and Mobraaten, 1997). Thus, the number of geneticallyengineered animals, especially
mice, has increased to the point where it became aserious financial
burden to maintain them. Maintaining
all geneticallyengineered
animals in breeding colonies has potential disadvantages:
the loss of animals due to(a) impaired
reproductive
efficiency,(b) disease or catastrophic
accidents and (c)genetic drift. The most commonly used alternative
to the maintenance of numerous
mouse strains and mutants is to cryopreserve
preimplantationembryos and later
used them when they became needed. Many potentially
important strains of mouse,
as well as geneticallyengineered
mice, have been "banked" using embryo
cryopreservation
technique.
While embryo cryopreservation
is an alternativeto
maintain breedingcolonies,
it still requires
significant
physicalfacilities
andfinancial
backup. Many females are required to obtain sufficientnumbers ofembryos to be frozen. Thus, many laboratories
are looking for more cost effectivemeans of preserving important mouse germplasm.
To maintain a particularstrain of mouse, approximately
100 females are
required to provide sufficientnumbers of embryos. In contrast, only one or two
males would be enough to preserve that strain. Although sperm cryopreservationiswell advanced in animal husbandry
and human reproductive
medicine,
cryopreservationof mouse spermatozoa has not been very successful.
Thiscontrasts
to the highly
successful
cryopreservationof mouse oocytes andpreimplantationembryos. Poor sperm survival after freeze-thawing
is due, at leastin part, to the fact that mouse spermatozoa are extremely sensitive
to osmotic(Willoughby
et al., 1996) and mechanicalshock damages (Wakayama et al.,unpublishdata). At present, the rate of sperm survival after freeze-thawing
is ratherlow, yet it is possible
to use these spermatozoa for in vitro fertilization
to obtain

•Ž,

ƒÊl)
•Ž
•Ž).
•Ž
ƒÊl

ƒÊl)

•Ž)

normal offspring(Nakagata et al., 1996; Songsasen et al., 1997).
Wereport that mouse spermatozoa that had been frozen or freeze-dried
without cryoprotectants
are all dead (or almost all are dead) in the conventional
sense, yet they are capable of supporting
normal embryo development when they
are injected
microsurgicallyinto oocytes (Wakayama et al., 1998; Wakayama andYanagimachi, 1998).
Materials
and methods
Sperm collection
For each experiment, two caudae epididymidesof a mature B6D2F1 male
were used. While applying
finger pressure to each epididymis,
its distal portion
was punctured with sharp forceps. A dense sperm mass oozing out of theepididymis
was transferred
into a 1.5 ml polypropylene
tube containing
1 ml of atest medium. After incubationfor 30 min at 37.5
the upper 0.3-0.5 ml of the
sperm suspension was removed from the tube and used for each experiment. Over
90% of spermatozoa in this suspension (approximately
3-10x106 /ml) were actively
motile.
Freeze-thaw experiment
Sperm suspensions were placed in CZB, PBS or isotonic NaCl with orwithout raffinose.
Aliquot (50of each suspension were dispensed
into 1 ml
cyrotubes and place directly into a -15
or -50freezer or liquid nitrogen (-196
All samples were stored for 1 day to 18 months. Just before experiment, tube
were removed from freezer or liquid nitrogen and placed in water or air at 24-26
for about 10 min. Then 5of sperm suspension was thoroughly mixed with 50
Hepes-CZB containing
12% (w/v) polyvinylpyrrolidone(PVP, average Mol. wt.
360,000;
Sigma).
Freeze-drying
experiment
Two major test solutions
were CZB medium (Chatot
et al., 1990)
without
EDTA and Dulbecco's
modified
Eagle'smedium (DMEM; Sigma
Chemical Co, St. Louis, MO) supplemented
with 10% (v/v) fetal bovine serum(Hyclone;
Logan, UT). An aliquot (100of the sperm suspension was put in a 2
ml ampul (Wheaton Scientific,
Millville,
NJ; cat. no. 651506)
which was plunged
directly
into liquid
nitrogen.
Ten minutes later, ampules were placed in a precooled (-50
freeze-flask
attached to a freeze-dry system (Model 10-020, VirTis

•Ž.
•Ž)
ƒÊl
ƒÊl
ƒÊl

•Ž

•Ž

Co., Gardner, NY). Inlet pressure was approximately
1 milli torr. About 12 h later,
the flask was removed from the system after it had been filled with argon suppliedby way of a gas-drying jar (Fisher
Scientific,
Pittsburgh,
PA; cat. no. 09-204). Each
ampul was connected to a vacuum pump and frame-sealed after >99% of the gas
was pumped out it. Ampules were individuallywrapped with aluminum foil andstored at room temperature (about 25
or at 4All samples were stored for 1
day to 3 months. Just before experiment, the ampul was broken and 100
ofdistilledwater was added to the dried spermatozoa. Then 5of sperm suspension
was thoroughly mixed with 50
Hepes-CZB containing 12% (w/v) PVP.
Sperm viability
test
Sperm viability
was assessed using a commercially available cell viability
test kit (Live/deadFertiLight;
Molecular
Probes,Eugene, Oregon)
whichdifferentiate
between plasma membrane-intact (live) and -damaged (dead) cells
according to fluorescencestaining pattern under a UV microscope. The nuclei of"live" spermatozoa with intact plasma membranes fluoresced green whereas thoseof "dead" spermatozoa fluoresced bright orange-red.
Oocyte preparationand sperm injection
Mature B6D2F1 females were induced to superovulate
by consecutive
injectionsof pregnant
mare serum gonadotropin
and human chorionicgonadotropin(hCG) 48 h apart. Fourteen hours after hCG injection,
cumulusoocyte complexes were collected from oviducts and treated with bovine testicular
hyaluronidase(300 USP units/ml)
in Hepes-buffered
CZB medium (Hepes-CZB)
for 3 min to disperse cumulus cells. Before sperm injection
the oocytes were rinsed
and stored in CZB medium for up to 4 h at 37.5
in an atmosphere of 5% CO2 inair. A single spermatozoon was aspirated
into an injection
pipette attachedto aPiezo electric
pipette-driving
unit (Kimura and Yanagimachi,
1995). After the
sperm head and tails were separated by applying
a single or a few Piezo pulses tothe neck region, each head alone was injected into each oocyte. All injections
wereperformed in Hepes-CZB at room temperature within 1 h of sperm rehydration.
Sperm-injected
oocytes were incubated in CZB at 37.5
under 5% CO2 in air andexamined 5-6 h later. Those with two distinct
pronuclei and a second polar body
were considered normally fertilized.
Embryo transfer

•Ž
•Ž,

•Ž
•Ž

•Ž;
•Ž),

Normal fertilized
eggs were cultured for 4 days and those reaching themorulae or blastocyst
stages were transferred
into the uterine horns of recipient
CD-I females (albino)which had been mated with vasectomized
CD-1 males threedays previously.
A mean number of eight morulae/blastocysts
was transferred into
each horn. Females were allowed to deliver and raise their surrogate offspring(with black, brown or gray coats). Some mature male and female offspringwere
randomly selected and mated to examine their fertility.
Results

and Discussion
After thawing or rehydrating,almost all (frozen)
or all (freeze-dried)
spermatozoa were motionless and judged to be dead by live/deadcell staining.
Electron microscopy revealed that sperm plasma membranes and the acrosome
were extensively
disrupted.
The majority of the oocytes fertilized
by the spermatozoa frozen in CZB
medium developed to blastocysts(about 90%, Fig.1). Embryonic development waspoor when the oocytes were fertilizedby spermatozoa frozen in PBS and isotonic
saline, especiallywhen they were frozen in the absence of raffinose.
When
spermatozoa were suspend and frozen in CZB medium and stored for 1 month at-15-50
or -196then injected
into oocytes, almost all of the oocytes
developed
to the blastocyst
stage (Fig.2).
The proportionof two-cell embryos
developed
into live offspring
after transfer to foster mothers are summarized(Figs.3 and 4). Normal offspring were obtained after all treatments. The best results
were obtained when spermatozoa were suspended and frozen at -50
or -196in the absence of raffinose(Fig.3).
Spermatozoa
could participate
in thedevelopment of normal offspring after 18 months storage at -15(Fig.4).
Freeze-dried
spermatozoa were rehydrated
and their heads were injectedindividually
into oocytes. Most oocytes were fertilized
normally regardlessof thetype of sperm-suspension
medium (CZB, Fig.5; DMEM, Fig.6), and the storage
temperature(room temperature
24-26
4
and periodsof freeze-dried
spermatozoa (1 day to 3 weeks). The majority of fertilized
eggs could develop intomorulae/blastocysts
in vitro. When transferred
to foster mothers, many embryos
developed
into normal offspringwhich grew normally. The effects of storage
temperature and period of freeze-dried
spermatozoa on the embryonic development
to term are shown (Figs.7
and 8). Embryonicdevelopment(from morulae

•Ž,
•Ž

Fig. 1. Effect of sperm freezing
media on embryo development.
Spermatozoa werestored in LN2 for 1 month. Sperm were frozen without raffinose(• )or with raffinose(•¡).

Fig. 2. Effect of storage
temperatureon embryo development.
Spermatozoa
frozeN in CZB medium and stored for 1 month. Sperm were frozen without raffinose
or with raffinose (•¡),
and stored at -15-50
or LN2.

were
(• )

•Ž
•Ž

Fig. 3. Effect of sperm freezing media and storage temperature on the development oflive young. Spermatozoa were frozen without raffinose (• ) or with raffinose(•¡) andstored for 1 month.

Fig. 4.
Effectof storage period
at -15on the developmentof live young.
Spermatozoa were frozen in CZB medium and stored at -15
up to 18 months. Spermwere frozen without raffinose(• ) or with raffinose
(•¡).

•Ž

Fig. 5. Effect of the storage period of freeze-dried
sperm on embryo development.
Spermatozoa were suspended and freeze-dried
in CZB medium. Freeze-dried sperm werestored at room temperature (• ) or at 4
(•¡).

•Ž

Fig. 6.
Effectof storage
period of freeze-dried
sperm on embryo development.
Spermatozoa were suspended and freeze-driedin DMEM medium. Sperm were stored atroom temperature (• ) or at 4
(•¡).

•Ž

Fig. 7. Effect of storage period of freeze-dried
sperm on embryonic development to term.
Sperm were suspended and freeze-dried
in CZB medium, and stored up to 3 month ateither room temperature (• ) or 4
(•¡).

•Ž

Fig. 8. Effect of storage period of freeze-dried sperm on embryonic development to term.
Sperm were suspended and freeze-dried in DMEM medium, and stored up to 3 month ateither room temperature (• ) or 4
(•¡).

•Ž

/blastocysts
to live young) was better when freeze-dried
spermatozoa were stored
at 4
than at room temperature, regardlessof the type of medium used for thesuspension of spermatozoa (CZB, Fig.7; DMEM, Fig.8).
Althoughthe techniques
require further improvement,
the resultsofabove experiments suggest that spermatozoa
do not need to be alive, in theconventional
sense, to support normal embryonic development when injected into
oocytes microsurgically.
Conventional
sperm preservation
is expensive in the longrun because of the need for the constant supply of liquid nitrogen. If we can prefectthe techniquesof sperm freeze-drying
and are able to store spermatozoa at ambient
temperature
or in ordinaryrefrigerators,
the reductionin the cost of themaintenance and shipment costs of spermatozoa would be immense.
References
Chatot, C.L., Lewis, J.L., Torres, I., and Xiomek, C.A. 1990. Developmentof 1-cell embryos fromdifferent
strains of mice in CZB medium. Biol. Reprod. 42: 432-440.
Kimura,Y. and R.Yanagimachi,
1995. Intracytoplasmic
sperm injectionin the mouse. Biol. Reprod.
52:709-720
Kuretake,S.,Y.Kimura,K.Hoshi,
and R.Yanagimachi,
1996. Fertilizationand developmentof mouse
oocytes injectedwith isolated
sperm heads. Biol. Reprod. 55:789-793
Nakagata, N. 1996. Use of cryopreservation
techniquesof embryos and spermatozoa for productionof transgenic(Tg) mice and for maintenance of Tg mouse lines. Lab. Anim. Sci. 46: 236238.
Sharp, J.J. and Monbraaten, L.E. 1997. In: Transgenic
Animals:
Generationand Use. Harwood Pub.
Amsterdam. Pp 525-532.
Songsasen,
N., Betteridge,
K.J. and Leibo, S.P. 1997. Birth of live mice resulting
from oocytes
fertilized
in vitro with cryopreserved
spermatozoa. Biol. Reprod. 56: 143-152
Wakayama,T. and R.Yanagimachi,
1998. Developmentof normal mice from oocytes injectedwithfreeze-dried
spermatozoa. Nat. biotechnol.
16:639-641
Wakayama,T., D.G.Whittingham,
and R.Yanagimachi,
1998. Productionof normal offspringfrommouse oocytes injectedwith spermatozoa cryopreservedwith or without cryoprotection.
J.
Reprod. Fert. 112: 11-17
Willoughby,
C. Mazur, P. Peter, A. and Critser, J.K. 1996. Osmotic tolerance
limits and properties
ofmurine spermatozoa. Biol. Reprod. 55: 715-727.

Closing

remarks

SHOJI MIYAZAKI
Genetic Resources Coordinator,
National Instituteof AgrobiologicalKannondai 2-1-2, Tsukuba, Ibaraki 305-8602,
Japan

Resources

I wish we could have more time to continue this interesting
discussion.
But, since we have already used up time, we would like to close this workshop
now.On behalf of the organizing committee of the workshop, I should deliver a
few words. I heard that worldwide conservation of native breeds and land-races ofanimals related to agriculture
is at a relatively
early stage of development
compared with plants and microorganisms.
But during this workshop, a large
amount of data and impressive results from many years of research have beenpresented by invited speakers.
I am very conscious that in Asia meat consumption is increasing
rapidly
as people across the region have greater spending
power. Rapid economic
development is resulting
in a rapid increase in demand for livestock
production.
The research on the conservation and improvement of domestic animals are really
important subjects. It will require international
efforts not only to conserve animal
genetic resources but also to conserve the rural culture, since local breed hasstrong relationshipto the conservationof rural culture. Sometimes modern
farming resulted
in the environmental
problem. We often hear the words'Sustainableagriculture'.
Local breeds can help maintain sustainableagriculture
systems.
The MAFF International
Workshop on Genetic
Resources aims
promotingresearch
exchange and collaborationon the developmentoftechnologies
and global strategies
for conservation and use of genetic resources innational programs and international
research institutions.
This workshop has beenof a small scale, but I anticipate
that by continuingthese efforts in the future, wewill contributeto the promotion of research and collaborationin conserving andevaluating
animal genetic resources. As with previous workshop in this series, theproceedingsof this workshop, including all papers, will be compiled into a book.
This will enable a wider audience to benefit from what has been presented here
over the past two days.
I have learned much over the last two days of intensive discussions.
I
thank everyone for your wholehearted
participation.
I thank my colleagues here in
Japan for their hard work in preparing
papers for this meeting. I would like tothank all the cooperating
institutes
in Japan and the Agriculture
Forestry and
Fisheries
Research Council for their support.
To friends who have come from abroad it has been marvelous to haveyou with us. We look forward to collaborationand interactionin the years tocome.Wewish you a safe journey home when you leave at the end of the week.
It is my honor to close the 6th MAFF workshop.
Thank you very much.

The

6th

MAFF

International

Workshopon Genetic

Resources,

November

4-5,1998,

Tsukuba,Japan

Photographof Workshop

Front row left to right:
Sekikawa
K (NIAH),
MiyazakiMatsukawa T (Shirakawa
Inst.

Participants

S (NIAR),
Kanai
Animal Genetics),

Y (Tsukuba
Univ.),
Thuy LT (NIAH,

Vietnam),Komiyama T (Nippon Agricultural
Research Inst.), Katsura N(NIAR), Tanabe Y (Obirin Univ.), Hicks C (NIAI), Harrison R (Babraham
Inst., UK), Tash J (Univ. Kansas Med.Center),
Xu S (California
Univ.),
Mrode R (Animal Data Centre, UK).
Second row:
Kaneko H (NIAR), Kikuchi
K (NIAR),
Shimizu M (NIAH),
Univ.), Muramatsu S (Utsunomiya
Univ.), Obata T (NIAR),

Ito S (Gifu
Nomura T(Kyoto Sangyo Univ.), Takahashi
H (Gunma Pig Farms, Inc.), Nakanishi
Y(Kanazawa Univ.), Ogawa T (Yokohama City Univ.), Ogura A (NIID),
Oono K (NIAR).
Third

row:
Nakatsukasa E (Azabu Univ.), Yokoyama M (Mitsubishi
Kasei Inst. of Life
Sciences),
Matsubara
Y (NIAI),
Takeda K (NIAI),
Naito M (NIAI),
Nagamine Y (NIAI), Hanada H (NIAI), Oishi T (NIAI), Naito K (Tokyo
Univ.), Saito H (AFFRC),
Ohkawa M (AFFRC).

Forth

row:
Kunihiro Y (NIAR), Shirata K (NIAR), Shimada A (NIAR), Kosugiyama M(Ibaraki
Univ.),
Dr., Nagai T (TNAES),
Ueda H (NIAH), Hammond K(FAO), Roberts M (Missouri
Univ.), Noguchi J (NIAR).

Fifth

row:
Takahashi
R (YS New Technology
Inst.), Kashiwazaki
N (Azabu Univ.),
Izaike Y (NIAI), Gotoh H (NIAH), Nambo Y (JRA Equine Research Inst.),Hasegawa T (JRA Equine Research Inst.), Takeda H (NIAR), Yamauchi N(TNAES),
Kato K (NIAR),
Onishi A (NIAI).

Nirasawa

K (NLBC),

KojimaT (NLBC),

Editors
Editor in chief
Managing editors

Consulting

February,
Publishededitors

Oono, Kiyoharu
Vaughan, Duncan A.
Nagamine, Yoshitaka
Kaneko, Hiroyuki
Noguchi, Junko
Shirata, Kazuto
Miyazaki,
ShojiKato, Kunihiko

2000
by

Research Council Secretariatof MAFF
National Instituteof Agrobiological
Resources
Kannondai

2-1-2, Tsukuba,

ISBN4-931511-02-3

Ibaraki

305-8602,

Japan