Technical Reports of the Ins�tute of Cetacean Research (2020)

pp. 35–40

Technical Report (not peer reviewed)

Gene�c tagging technique: basic concept and a case study by theIns�tute of Cetacean Research
Mioko Taguchi*
Institute of Cetacean Research, 4–5 Toyomi-cho, Chuo-ku, Tokyo 104–0055, Japan*Contact e-mail: taguchi@cetacean.jp

ABSTRACTThe studies on biological conserva�on require contemporary demographic es�mates, e.g., migra�on rate,eﬀec�ve popula�on size and stock structure. Although these es�mates can be obtained through standard
popula�on gene�cs analyses, such studies could fail to infer them because of recent evolu�onary and biogeographical events. Alterna�ve approaches to obtain such parameters include individual iden�ﬁca�on andkinship inferences using gene�c data, also called ʻgene�c taggingʼ, which has become a common technique inthe ﬁelds of ecology and conserva�on during the last several decades. This paper presents the basic concept ofgene�c individual iden�ﬁca�on and kinship inference. It subsequently outlines the parentage analysis for thewestern North Paciﬁc common minke whale conducted by the Ins�tute of Cetacean Research as a case study.

INTRODUCTION
The studies on biological conserva�on in animal species
require informa�on on contemporary popula�on parameters, e.g., eﬀec�ve popula�on size, migra�on rate andpopula�on structure. The es�mates of these parameters
can be obtained through tradi�onal popula�on gene�cs
analyses. However, these analyses o�en fail to infer themin cases where the popula�ons have diverged recently,or if changes in gene ﬂow and eﬀec�ve popula�on size
have occurred. Therefore, such es�mates have been
mainly obtained by individual-based tagging researchusing conven�onal tagging or photo-iden�ﬁca�on (e.g.,
Calambokidis et al., 2001; Johnson et al., 2009; Mizroch
and Rice, 2013; Urian et al., 2015).
Individual animals can also be iden�ﬁed using gene�cdata, generally called ʻgene�c taggingʼ (Palsbøll, 1999).
This has become a common technique to obtain the contemporary es�mates in the ecological and conserva�onﬁelds during the last several decades (e.g., humpback
whales, Megaptera novaeangliae, Palsbøll et al., 1997;
North Paciﬁc right whales, Eubalaena japonica, Wadeet al., 2011). In addi�on, in recent years, the extended gene�c tagging incorpora�ng informa�on on kinship amongindividuals is being used to inves�gate contemporary
popula�on dynamics (e.g., Kanda et al., 2014; Bravington
et al., 2016; Ohashi et al., in-press). The methods forabundance es�mates based on two types of gene�c tags
(individual iden�ﬁca�on and kinship) are summarized by

Takahashi (this issue).
This paper presents the basic concept of gene�cindividual iden�ﬁca�on and kinship inference, with abrief summary of a case study by the Ins�tute of Cetacean Research (ICR), which is the parentage analysis for
the North Paciﬁc common minke whale (Balaenoptera
acutorostrata).

BASIC CONCEPT OF GENETIC TAGGING TECHNIQUES
Individual iden�ﬁca�on
Sexual reproduc�on guarantees that, in the main, eachindividual has a unique genotype. There are some excep�ons, however, such as iden�cal twins. This characteris�callows each individual to be tagged and, as a consequence, it can be determined if two gene�c samples
come from the same individual. Figure 1 shows an example of individual iden�ﬁca�on using microsatellite
genotype data. In this example, the four samples are derived from three whales. Samples A and B have the same
genotypes and therefore the two samples are assumed
from the same whale, while samples C and D have diﬀerent genotypes sugges�ng they are from diﬀerent whales.
Probability of identity
The probability of iden�ty (I) is the probability that two
unrelated individuals in the popula�on share the same
genotype. This es�mate requires informa�on on allele
frequencies in the popula�on, because common alleles
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Technical Reports of the Ins�tute of Cetacean Research (2020)

Figure 1.

Example data involving four microsatellite loci genotyped for each of four whales. Genotypes are represented by 6-digit codes, and each allele is coded by 3 digits. The results of individual iden�ﬁca�on are
shown in the column ʻMatching DNA proﬁleʼ.

Figure 2.

Example of sex determina�on in common minke whales through mul�plex ﬂuorescent PCR using the SRY
locus (black) located on the Y chromosome and a microsatellite locus (blue) as the internal control: (a)failure to amplify PCR fragments; (b) male; (c) female.

are much more likely to be shared than rare ones. Thiscan be es�mated for a popula�on with random ma�ng
according to the formula derived by Paetkau and Strobeck(1994):

Iˆ =



pi4 +

i
k  i

∏


(2 pi p j ) 2 

j >i




where, pi and pj are the frequencies of the ith and jthalleles at the kth locus in the popula�on. In prac�ce, it
is desirable to aim for a probability of shared genotypeiden�ty between any two individuals (I) of <0.05.

36

Molecular sex determination
Sex informa�on is useful not only to assist in individual
iden�ﬁca�on but also for a biological interpreta�on of
the results of analyses.
The PCR-based technique has been proposed for sex
determina�on using the presence or absence of genes on
the sex chromosomes. The tes�s-determining SRY genes
are male-speciﬁc in mammals and can be detected by
PCR ampliﬁca�on using speciﬁc primers. The ICR gene�cteam developed the primer set that ampliﬁes the SRY
gene to determine the sex of cetaceans, using a microsatellite locus as the internal control (Abe et al., 2001).

Genetic tagging technique: Basic concept and a case study by the Institute of Cetacean Research

Figure 3.

Example of pedigree containing ten non-inbred individuals. The ﬁgure shows the kinship categories andthe values for three IBD coeﬃcients (κ 0, κ 1 and κ 2). The three IBD coeﬃcients can be also summarized by
the kinship coeﬃcients (Φ) which is the probability that a random allele from one individual is IBD to arandom allele from the other. The Φ is also equivalent to the inbreeding coeﬃcient of their oﬀspring.

Figure 2 shows an example of gene�c sex determina�onusing this method. In case of male, a PCR fragment of SRY
gene is observed between 150 and 155bps (Figure 2b),while the fragment is absent in case of female or PCR
failure. We can dis�nguish female (Figure 2c) from PCR
failure (Figure 2a) by the presence of the PCR fragment of
one microsatellite locus as an internal control.
Kinship inference
Individuals that are biologically related share genes thatare iden�cal-by-descent (IBD), i.e., iden�cal copies of agene segrega�ng from a common ancestor within thedeﬁned pedigree. IBD is a fundamental concept that
underlies kinship inference (Thompson, 2013). Here, thebasic concept of the IBD coeﬃcient is explained, which
provides an introduc�on to the ideas of IBD. It is followedby the simple principle of parentage analysis which is aunique applica�on of kinship inferences.
IBD coeﬃcients
Two non-inbred diploid individuals can share none,
one, or two alleles IBD. In case of the pedigree shown in
Figure 3, full sibling individuals 4 and 5 share one allele
IBD (A3 which is inherited from their father) while unrelated individuals 3 and 6 share no allele IBD. The monozygotic twins 9 and 10 share two alleles IBD (A3 and A6, which

Table 1Pa�erns of allele sharing IBD for a full sibling pair (individuals
4 and 5 in Figure 3).
Possiblegenotypes

A1A3

A1A4

A2A3

A2A4

A1A3
A1A4
A2A3
A2A4

2
1
1
0

1
2
0
1

1
0
2
1

0
1
1
2

are inherited from father and mother, respec�vely). The
probabili�es of each of the events are summarized by
the IBD coeﬃcients, i.e., κ 0, κ 1 and κ 2 (Cockerham, 1971),
which are shown in Figure 3.
Table 1 shows an example on how to determine the
IBD coeﬃcients for a category of full siblings. Consider
that the genotypes of the parents at a locus are A1A2 andA3A4 (individuals 1 and 2 in Figure 3). Each oﬀspring canhave one of the four following genotypes: A1A3, A1A4,
A2A3 or A2A4 (individuals 4 and 5 in Figure 3). Out of thesixteen ways to pair two oﬀsprings, the dyad can share 2
alleles that are IBD in four ways, 1 allele in eight ways and0 alleles in four ways. Thus, the IBD coeﬃcients, κ 0, κ 1 andκ 2, for full siblings are 0.25 (4/16), 0.50 (8/16) and 0.25
(4/16), respec�vely. The IBD coeﬃcients for other kinshipcategories can be calculated in the same manner.
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Technical Reports of the Ins�tute of Cetacean Research (2020)

Figure 4.

Principle of parentage analysis using the exclusion approach. The numbers under the peaks are the size of
PCR fragment in base pairs which can be interpreted as alleles.

Parentage analyses
Parentage analysis is a unique applica�on in which
the most likely parents of a target oﬀspring are sought
amongst eligible candidates. The basic concepts of theparentage analysis are grouped into six categories according to Jones et al. (2010): (1) Exclusion, (2) Categorical
Alloca�on, (3) Frac�onal Alloca�on, (4) Full Probability
Parentage Analysis, (5) Parental Reconstruc�on, (6) Sibship Reconstruc�on. In this sec�on, categories (1) (theconceptually simplest approach) and (2) (the most commonly used approach), are described.
Exclusion approachThis approach uses rules of Mendelian inheritance for
diploid organisms wherein an oﬀspring inherits oneof two alleles at each locus from each of its parents. A
simple exercise for the exclusion approach using a single
microsatellite locus is shown in Figure 4.
In this exercise, the mother shows the genotype of
200/204, and the genotypes of her three oﬀspring 1, 2
and 3 were 196/200, 200/208 and 204/210, respec�vely.
Here, a male with genotype of 196/208 can be excluded
as a father candidate for oﬀspring 3 since no allele fromthe male is observed in oﬀspring 3. On the other hand,
the male is s�ll a father candidate for oﬀspring 1 because
the alleles 196 and 200 can be found from the male andmother, respec�vely. The same is true for oﬀspring 2.
This approach is powerful when there are few candidate parents and mul�ple highly polymorphic markers
are available. In prac�ce, most of the studies based on
the exclusion approach actually require at least two mismatching loci between the candidate and the oﬀspring to38

account for typing errors or muta�ons.
Categorical Alloca�on approach
As in the case of exclusion method, this approach requires at least one focal oﬀspring and a set of candidate
parents. The Categorical Alloca�on approach was developed to resolve situa�ons in which complete exclusion
may not be feasible (Meagher and Thompson, 1986). The
main beneﬁt of this approach is to choose the single most
likely parent from a group of non-excluded puta�ve parents. The logic stems from the observa�on that diﬀerent
genotypes of parent may diﬀer in their probability of having produced the genotype of the focal oﬀspring (Meagher and Thompson, 1986). This approach also has theadvantage of handling genotyping errors or muta�ons.
Currently, most of the Categorical Alloca�on approaches use a likelihood approach (e.g., Marshall et al., 1998).
However, a Bayesian approach (e.g., Nielsen et al., 2001)
can also be used (see also Takahashi, this issue).
Genotyping errors and their eﬀects
Microsatellite DNA marker is one of the most common
gene�c markers not only for individual iden�ﬁca�onand kinship analyses, but also for the standard analyses
on popula�on gene�cs. However, microsatellite DNA is
known as the error-prone marker in its genotyping, whichis recognized to have a serious impact on gene�c individual iden�ﬁca�on as well as kinship inferences (reviewedin Hoﬀman and Amos, 2005).
The genotyping of a microsatellite allele depends onthe microsatellite proﬁle, and requires strict rules to bedeﬁned in advance. Typical microsatellite proﬁles are

Genetic tagging technique: Basic concept and a case study by the Institute of Cetacean Researchcharacterized by a succession of peaks with growing intensity due to stu�er band. Researchers o�en suﬀer fromseveral types of ambiguous microsatellite proﬁles in genotyping, e.g., null alleles, overlapping of stu�er bands,
split peak, large allele dropout or short allele dominanceand false alleles. Some factors are known to be involvedin the ambiguous proﬁles, e.g., varia�on in DNA sequences, low quality or quan�ty of template DNA, biochemicalar�fact and human error (Hoﬀman and Amos, 2005).
Figure 5 illustrates two types of genotyping error thatare likely to induce false paternity and consequently bias
the biological conclusions. Consider in the example of
Figure 5 that the male is the real father of the oﬀspring.
The real genotype for male is 196/208 without error: theoﬀspring inherited allele 200 from his mother and allele
208 from his father. In the cases with genotyping errors,

Figure 5.

Simple example of parentage analysis by the
Exclusion approach. Arrows show the genotyping
errors due to false allele (red) and large allele dropout (blue).

Figure 6.

the male would incorrectly be excluded as the father ofthe oﬀspring because allele 208 is mistyped as allele 202.
In order to avoid this type of incorrect paternity exclusion, it is be�er to allow one or several gene�c mismatches in the parentage analysis depending on the probabilityof iden�ty and the calculated error rate.

A CASE STUDY ON PARENTAGE BY THE INSTITUTEOF CETACEAN RESEARCH
Parent-oﬀspring inference in North Paciﬁc common
minke whales
The parent-oﬀspring pair analysis was carried out based ona type of Categorical Alloca�on approach, using a total of
4,707 common minke whales in the western North Paciﬁc(see Tiedemann et al. (2017) for the details of the analy�cal procedures). Each sample was genotyped at 16 microsatellite loci. The mitochondrial DNA control region haplotypes as well as biological informa�on, e.g., sampling dateand posi�on, sex, sexual maturity and body length, wereused, if available, to assist the interpreta�on of the results.
The parent-oﬀspring analyses inferred a total of 40
and 13 parent-oﬀspring pairs for the O and J stock (Gotoet al., 2017), respec�vely . The forty parent-oﬀspring pairsof O stocks were widely distributed through the Paciﬁcside of Japan (Figure 6). A total of 17 pairs of O stock animals were found between coastal and oﬀshore waters. In
most of the pairs, the oﬀspring were found near the coast
while their parents were in oﬀshore waters. This is consistent with the pa�ern of migra�on with sexual segrega�ondescribed previously for western North Paciﬁc common
minke whales (Hatanaka and Miyashita, 1997).
The thirteen parent-oﬀspring pairs of J stocks were
distributed within and between the Sea of Japan and the
Paciﬁc side of Japan (Figure 6). Four of them were foundbetween the Sea of Japan side and the Paciﬁc side. The

Distribu�on of parent-oﬀspring pairs of O (le�) and J (right) stocks in western North Paciﬁc common minke
whales. Blue square: parent; orange circle: mature oﬀspring; red cross: immature or unknown maturity
stage oﬀspring.
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Technical Reports of the Ins�tute of Cetacean Research (2020)

results implied that the J stock individuals occurring in
the Sea of Japan and the Paciﬁc side of Japan were derived from the same stock.
Future work
The development of the new gene�c marker which is lesserror-prone, that is, Single Nucleo�de Polymorphisms(SNPs) marker is on-going at the ICR. The ICR gene�cteam will perform analyses using SNPs genotypes, notonly for western North Paciﬁc common minke whales,
but also for other whale species such as Brydeʼs, ﬁn, rightand blue whales. The results of these analyses will aid in
obtaining the contemporary demographic es�mates ofthese whale species using gene�c data.

REFERENCES
Abe, H., Goto, M., Pastene, L.A., Dewa, K. and Naito, E. 2001.
Prac�cal use of mul�plex ﬂuorescent PCR for cetacean sex
iden�ﬁca�on. Mar. Mam. Sci. 17(3): 657–664.
Bravington, M.V., Skaug, H.J. and Anderson, E.C. 2016. Close-kin
mark-recapture. Statist. Sci. 31(2): 259–274.
Calambokidis, J., Steiger, G.H., Straley, J.M., Herman, L.M.,
Cerchio, S., Salden, D.R., Jorge, U.R., Jacobsen, J.K., Ziegesar,
O.V., Balcomb, K.C., Gabriele, C.M., Dahlheim, M.E., Uchida,S., Ellis, G., Mlyamura, Y., de guevara Paloma Ladrón,P., Yamaguchi, M., Sato, F., Mizroch, S.A., Schlender, L.,
Rasmussen, K., Barlow, J. and Ii, T.J.Q. 2001. Movements
and popula�on structure of humpback whales in the North
Paciﬁc. Mar. Mam. Sci. 17: 769–794.
Cockerham, C.C. 1971. Higher order probability func�ons ofiden�ty of alleles by descent. Genetics 69(2): 235–246.
Goto, M., Taguchi, M. and Pastene, L.A. 2017. Distribu�onand movement of ʻOʼ and ʻJʼ stock common minke whales
in waters around Japan based on gene�c assignment methods. Technical Reports of the Institute of Cetacean Research(TEREP-ICR) No.1: 37–43.
Hatanaka, H. and Miyashita, T. 1997. On the feeding migra�on of the Okhotsk Sea-West Paciﬁc stock of minke whales,es�mates based on length composi�on data. Rep. int. Whal.
Commn 47: 557–564.
Hoﬀman, J.I. and Amos, W. 2005. Microsatellite genotyping
errors: detec�on approaches, common sources and consequences for paternal exclusion. Mol. Ecol. 14: 599–612.
Johnson, M., Aguilar, de Soto N. and Madsen, P.T. 2009. Studying
the behaviour and sensory ecology of marine mammals using
acous�c recording tags: a review. Mar. Ecol. Prog. Ser. 395:
55–73.
Jones, A.G., Small, C.M., Paczolt, K.A. and Ra�erman, N.L. 2010.
A prac�cal guide to methods of parentage analysis. Mol. Ecol.
Resour. 10: 6–30.
Kanda, N., Goto, M. and Pastene, L.A. 2014. Paternity analysis
on Antarc�c minke whales using JARPA and JARPAII samples.

40

Paper SC/F14/J7 presented to the IWC/SC Review Workshopof the Japanese Whale Research Program under Special
Permit in the Antarc�c-Phase II (JARPAII), February 2014
(unpublished). 4 pp. [Available from the IWC Secretariat].
Marshall, T.C., Slate, J., Kruuk, L.E.B. and Pemberton, J.M. 1998.
Sta�s�cal conﬁdence for likelihood-based paternity inferencein natural popula�ons. Mol. Ecol. 7: 639–655.
Meagher, T.R. and Thompson, E. 1986. The rela�onship between single parent and parent pair gene�c likelihoods ingenealogy reconstruc�on. Theor. Popul. Biol. 29(1): 87–106.
Mizroch, S.A. and Rice, D.W. 2013. Ocean nomads: Distribu�onand movements of sperm whales in the North Paciﬁc shownby whaling data and Discovery marks. Mar. Mam. Sci. 29:
E136–E165.
Nielsen, R., Ma�la, D.K., Clapham, P.J. and Palsbøll, P.J. 2001.
Sta�s�cal approaches to paternity analysis in natural popula�ons and applica�ons to the north Atlan�c humpback whale.
Genetics 157: 1673–1682.
Ohashi, Y., Goto, M., Taguchi, M., Pastene, L.A. and Kitakado,
T. Evalua�on of a paternity method based on microsatellite
DNA genotypes for es�ma�ng the abundance of the Antarc�cminke whales, Balaenoptera bonaerensis, in the Indo-Paciﬁcregion of the Antarc�c. Cetacean Population Studies (inpress).
Palsbøll, P.J. 1999. Gene�c tagging: contemporary molecular
ecology. Biol. J. Linn. Soc. Lond. 68: 3–22.
Palsbøll, P.J., Allen, J., Berube, M., Clapham, P.J., Feddersen,
T.P., Hammond, P.S., Hudson, R.R., Jorgensen, H., Katona, S.,
Larsen, A.H., Larsen, F., Lien, J., Ma�la, D.K., Sigurjonsson, J.,
Sears, R., Smith, T., Sponer, R., Stevick, P. and Oien, N. 1997.
Gene�c tagging of humpback whales. Nature 388(6644):
767–769.
Paetkau, D. and Strobeck, C. 1994. Microsatellite analysis ofgene�c varia�on in black bear popula�ons. Mol. Ecol. 3:
489–495.
Thompson, E.A. 2013. Iden�ty by Descent: Varia�on in Meiosis,
Across Genomes, and in Popula�ons. Genetics 194(2):301–326.
Tiedemann, R., Tiedemann, M.R., Goto, M., Taguchi, M. andPastene, L.A. 2017. Finding parent-oﬀspring pairs among
western North Paciﬁc common minke whales. Paper SC/67a/
SDDNA1 presented to the IWC Scien�ﬁc Commi�ee,
May 2017 (unpublished). 18 pp. [Available from the IWC
Secretariat].
Urian, K., Gorgone, A., Read, A., Balmer, B., Wells, R.S., Berggren,P., Durban, J., Eguchi, T., Rayment, W. and Hammond, P.S.
2015. Recommenda�ons for photo-iden�ﬁca�on methods
used in capture-recapture models with cetaceans. Mar.
Mam. Sci. 31: 298–321.
Wade, P.R., Kennedy, A., LeDuc, R., Barlow, J., Carre�a, J.,
Shelden, K., Perryman, W., Pitman, R., Robertson, K., Rone, B.,
Salinas, J.C., Zerbini, A., Brownell, R.L. and Clapham, P.J. 2011.
The world's smallest whale popula�on? Biol. Lett. 7: 83–85.