Genetic Variation Versus Recombination Rate in a Structured
Population of Mice
Aya Takahashi, Yu-Hua Liu,1 and Naruya Saitou
Division of Population Genetics, National Institute of Genetics, Mishima, JapanThe correlation between genetic variation and recombination rate was investigated in a structured mouse population.
Nucleotide sequence data from 19 autosomal DNA loci from eight inbred strains of mouse (Mus musculus) sampled fromthree major subspecies were analyzed. The recombination rate was estimated from the comparison of genetic andphysical map distances between markers flanking a 10-cM region of each locus. The strains were categorized into fourgroups (subpopulations) based on geography. By partitioning the genetic diversity into within-group and among-groupvariation, we detected a positive correlation between the recombination rate and nucleotide diversity within groups. The
level of nucleotide differentiation among groups (GST) showed a negative correlation with the rate of recombination.
There was no significant correlation between recombination rate and nucleotide diversity when data from different
subpopulations were pooled. No correlation was detected between recombination rate and nucleotide divergence ofM. musculus and M. spicilegus. These patterns deviate from the strict neutral expectation under the constant nucleotide
substitution rate, and they are likely to have been formed either by a hitchhiking effect of positively selected mutants orby background selection of deleterious mutants occurring in a subdivided population. Our series of comparisons showthat because a real population always has some structure, incorporation of its information is important in detecting nonneutral evolution.

Introduction
Detecting the effect of natural selection in a structured
population has thus far been a challenge. A number ofmethods have been proposed for detection of natural
selection in a ‘‘panmictic population.’’ One of the mostcommonly used approaches is to investigate whether thefrequency spectrum of silent polymorphic sites deviatesfrom the neutral expectation (Tajima 1989; Fu and Li
1993). An excess of high-frequency–derived variants may
be the strongest evidence for positive selection (Fay andWu 2000). However, this approach may be problematic;a real population usually has some structure, and unequal
sampling from a structured population can result in a
spurious Fay and Wu test (Przeworski 2002). Thus, ignoring the population structure is likely to obscure any pattern of selection, and this approach requires caution when
the neutral model is being tested (e.g., Wall 1999).
The effect of natural selection in a panmictic population can also be inferred from the presence of a positive
correlation between recombination rate and nucleotide
diversity. This correlation has been observed in Drosophila(Begun and Aquadro 1992; Aquadro, Begun, and Kindahl
1994; Andolfatto and Przeworski 2001), human (Nachman
et al. 1998; Przeworski, Hudson, and Di Rienzo 2000;
Nachman 2001, Lercher and Hurst 2002), and mouse(Nachman 1997). The pattern has been explained mainlyby the genetic hitchhiking of rapidly fixed advantageous
mutations (Maynard Smith and Haigh 1974; Kaplan,
Hudson, and Langley 1989) and/or by backgroundselection against deleterious mutations (Charlesworth,1
Present address: The Jackson Laboratory, Bar Harbor, ME 04609,
USA.
Key words: Mus musculus, recombination rate, population structure,
population subdivision, genetic hitchhiking, background selection, FST.

E-mail: nsaitou@genes.nig.ac.jp.
Mol. Biol. Evol. 21(2):404–409. 2004
DOI: 10.1093/molbev/msh030
Advance Access publication December 5, 2003
Molecular Biology and Evolution vol. 21 no. 2
Ó Society for Molecular Biology and Evolution 2004; all rights reserved.

404

Morgan, and Charlesworth 1993; Charlesworth, Charlesworth, and Morgan 1995; Hudson and Kaplan 1995). The
hitchhiking hypothesis has been supported well by
empirical data in Drosophila (e.g., Aquadro, Begun, andKindahl 1994; Langley et al. 2000; Andolfatto andPrzeworski 2001). However, in a pooled sample ofa subdivided population, the correlation is likely to become
weak if the pattern of natural selection varies among
subpopulations. In this case, population structure has to betaken into account. For example, a subdivided population
model of genetic hitchhiking is proposed by Slatkin andWiehe (1998). They showed that under some conditions,
hitchhiking can lead to substantial population differentiation, as measured by Wright’s FST. They also suggested
that if subpopulations were completely isolated, greater
differentiation would be found in regions of a genome witha lower rate of recombination. Thus, in their model, genetic
hitchhiking is likely to form a negative correlation between
recombination rate and population divergence.
There are a few observations in Drosophila supporting a negative correlation between FST and the rate ofrecombination. For example, Stephan and Mitchell (1992)
found reduced variation within populations and increased
divergence between populations of Drosophila ananassae
in India and Burma in regions with reduced recombinationon the X chromosome. Begun and Aquadro (1993) foundelevated FST in three of seven genomic regions witha reduced recombination rate in populations of Drosophilamelanogaster in Zimbabwe and other localities. Both ofthese studies invoked genetic hitchhiking events as thecause (see also Stephan 1994). Nevertheless, it should be
noted that the background selection model also predicts
increased FST values in regions of reduced recombination(Charlesworth, Nordborg, and Charlesworth 1997), and it
is indistinguishable from the hitchhiking model in theabsence of a rigorous quantitative study (e.g., Stephan
et al. 1998).
In this study, we have analyzed the sequences of 21
nuclear genes in nine inbred mouse strains from threemajor subspecies of Mus musculus. (Liu, Takahashi,

Genetic Variation vs. Recombination Rate in Mice 405

Table 1
Mouse Strains Used in This Study
Species

Subspecies

StrainOriginMus musculus

domesticus

Pgn2
BFM/2
BLG2
NJL
MSM
SWN
CAST/Ei
HMI

CanadaFranceBulgaria
DenmarkJapan
Korea
ThailandTaiwanZBN

Bulgaria

musculus
molossinusa
castaneus
Mus spicilegus(outgroup)aOriginated from the hybrids of M. m. musculus and M. m. castaneus(Yonekawa et al. 1988).

Kitano, Koide, Shiroishi, Moriwaki, and Saitou, unpublished data). This sequence data showed overall clusteringof the strains within subspecies, with traces of genetic
exchange between them, suggesting a large ancient
population size and fairly vague subspecies level divergence in this species. Hence, these samples represent
a structured population. To define closely related groups asunits of a subpopulation in this species, we categorized thestrains into four geographically related groups. Here, weinvestigate (1) whether the phenomenon of increased
population divergence in regions of reduced recombination
exists in mouse and (2) whether the effect of natural
selection on linked neutral variation can explain therelationships between genetic variation and rate of recombination in this structured population.
Materials and Methods
Mouse Strains and Nucleotide Sequence Data
DNA sequences of 19 autosomal genes from eight
inbred strains of Mus musculus and one strain of Mus
spicilegus were analyzed. The mouse strains used in thisstudy are listed in table 1. All the sequence data used inthis study are available from the DDBJ/EMBL/GenBank
Database. The accession numbers of these sequences are
AB039045 through AB039223. The strains of Mus
musculus were categorized into four closely related groups
based on subpopulations found in this species (table 1).
We categorized Pgn2 and BFM/2 as M. m. domesticus
group, BLG2 and NJL as M. m. musculus group, CAST/Ei
and HMI as M. m. castaneus group, and MSM and SWN
as M. m. molossinus group. ZBN is an inbred strain of
M. spicilegus used as an outgroup. All of these inbred
strains are wild-caught and have been maintained at our
National Institute of Genetics (details described in Koide etal. 2000). Two genes on the sex chromosomes also in the
database were excluded from the analyses because they are
assumed to have smaller Nes than the rest of the loci.
Recombination Rate Estimates
The rates of recombination across the mouse genome
were estimated by comparing the genetic map and thehigh-density radiation hybrid (RH) map (physical map;
Van Etten et al. 1999). The map distance data were takenfrom Whitehead Institute/MIT Center for Genome Research, Mouse EST RH Mapping Project, Public Data
Release 10 (December 2001). The genetic map distance(cM) and the RH map distance (cR ¼ centiRay) between
the two markers flanking the 6 5 cM (10 cM) region of
the locus were used to calculate the recombination rate
(cM/cR). For each of the two loci, Fau and Fut4, whose
genetic map positions are both 3.0 cM from the distal endof their chromosomes, a marker at the distal end of thechromosome and another marker flanking the proximal
10 cM region from it were used. The markers and their
map positions used to calculate recombination rates arelisted in table 2.
Estimation of Genetic Variation
The level of genetic variation is estimated based on
pairwise sequence differences of synonymous sites andintrons. The sequence data of the Fut4 locus has suggested
an introgression from other distant taxa in the BLG2 strain(Liu, Takahashi, Kitano, Koide, Shiroishi, Moriwaki, andSaitou, unpublished data); thus this strain is excluded
from the analyses of this locus. Genetic variation within
subpopulations (p within subpopulation) was calculated
as the average number of pairwise nucleotide differences
per site (nucleotide diversity; Nei 1987) between thesequences of the four (three for Fut4) pairs of strains fromthe same subpopulations (pairs of Pgn2-BFM/2 for
subpopulation domesticus; BLG2-NJL, for musculus;
CAST/Ei-HMI, for castaneus; and MSM-SWN, formolossinus). Genetic variation between subpopulations(d ) is defined as the average number of pairwise nucleotide
differences per site between the rest of the 24 (18 for Fut4)
combinations of strains from different subpopulations. The
relative level of population divergence was calculated as
GST ¼ ðp total  p within subpopulation)=p total;
following Nei (1973). p total is the average number of
pairwise nucleotide differences per site calculated from all
28 (21 for Fut4) combinations of the strains. Because p
total of Hoxa2 was 0, GST of this locus was not calculated.
Results and Discussion
The mouse strains used in our study are wild-caught
inbred lines from different subspecies of Mus musculus.
This species is normally classified into four authentic
subspecies, M. m. domesticus, M. m. musculus, M. m.
castaneus, and M. m. bactrianus (Bonhomme and Guenet
1989; Sage, Atchley, and Ernesto 1993). We sampled two
strains each from the first three subspecies and two strainsfrom M. m. molossinus, which is a local population (ora subspecies) known to have originated from hybrids of M.
m. musculus and M. m. castaneus (Yonekawa et al. 1988;
table 1). Our definition of these four groups as subpopulations reflects the structure of the whole species;
however, migration rates cannot easily be determined. For
example, the discovery of a narrow hybrid zone between
M. m. domesticus and M. m. musculus suggests theexistence of a reproductive barrier (reviewed in Sage,
Atchley, and Ernesto 1993), although there is evidence406 Takahashi et al.

Table 2
Recombination Rate Estimates of the 19 Mouse Genes Analyzed in This Study
Gene Symbol

Chromosome: Genetic Map
Position (cM)aMarkersb: Genetic Map
Position (cM)

B3galt1 (b3GT1)

2: 38eB3galt2 (b3GT2)

1: 78eB3galt3 (b3GT3)

3: 37eB3galt4 (b3GT4)

17: 18.4

Camp (Cramp)

9: 61.0

Cd14

18: 31.0

Ctgf (fisp-12)

10: 17.0

Dfy

1: 94.0Fau

19: 3.0

Fut1

7: 23.2

Fut2

7: 23.2

Fut4

9: 3.0

Hoxa2 (Hox-1.11)

6: 26.29

Nppb (BNP)

4: 76.5

Psmb10 (MECL1)

8: 51.0

Sec1-pending (sec1)

7: 23.2

Sox15

11: 39.0

Tnf

17: 19.06

Wnt1

15: 56.8

D2Mit324 (32.8)
D2Mit245 (43.7)
D1Mit193 (71.0)
D1Mit107 (83.1)
D3Mit282 (30.6)
D3Mit348 (42.6)
D17Mit104 (10.9)
D17Mit216 (25.1)
D9Mit115 (54.6)
D9Mit82 (67.8)
D18Mit206 (25.1)
D18Mit143 (36.1)
D10Mit106 (12.0)
D10Mit196 (23.0)
D1Mit370 (86.3)
D1Mit166 (99.5)
D19Mit29 (1.1)
D19Mit16 (13.1)
D7Mit80 (15.3)
D7Mit260 (28.4)
D7Mit80 (15.3)
D7Mit260 (28.4)
D9Mit43 (2.2)
D9Mit205 (13.1)
D6Mit186 (20.8)
D6Mit177 (31.7)
D4Mit49 (71.0)
D4Mit256 (82.0)
D8Mit182 (44.8)
D8Mit314 (56.8)
D7Mit80 (15.3)
D7Mit26 (28.4)
D11Mit276 (33.9)
D11Mit356 (48.1)
D17Mit104 (10.9)
D17Mit216 (25.1)
D15Mit73 (50.3)
D15Mit160 (62.3)

Recombination Rate(cM/cR)c(101 3 cM/Mb)d

0.058

0.088

0.036

0.037

0.023

0.033

0.192

0.089

0.096

0.098

0.074

0.081

0.026

0.030

0.158

0.097

0.078

0.083

0.051

0.048

0.051

0.048

0.043

0.039

0.048

0.048

0.131

0.130

0.093

0.044

0.051

0.054

0.050

0.038

0.192

0.086

0.120

0.072

a

Genetic map position from Mouse Genome Resources of the National Center for Biotechnology Information (USA; NCBI) unless indicated.
Markers used to estimate recombination rate. Data from Van Etten et al. (1999).
c
Recombination rate estimated using data from Van Etten et al. (1999).
d
Recombination rate from Nachman and Churchill (1996), converted approximately to an equivalent scale of cM/cR in Van Etten et al. (1999).
e
Genetic map position estimated through the use of the Celera Discovery System and Celera Genomics associated databases.
bof past hybridization between M. m. musculus and M. m.
castaneus (Yonekawa et al. 1988). Nevertheless, a subdivided population model with a low migration rate
probably fits the data best, and thus provides a good
opportunity to study the effects of population subdivision.
The estimated local recombination rate (cM/cR) of
each locus is shown in table 2. There is another independent estimate of rates of recombination by Nachman
and Churchill (1996) calculated from the density of markers
on the genetic map. They are converted approximately toan equivalent scale of cM/cR (1 cR ¼ 100 kb; Van Etten
et al. 1999), and are also listed in table 2. These two
estimates are highly correlated (r ¼ 0.72, P , 0.001,
Spearman’s P , 0.001), but because our estimate in cM/
cR uses more recent information on the mouse genome, we
decided to use it for the following analyses.

The number of silent sites used for the analyses,
nucleotide diversities, GST, and divergence of each locus
are listed in table 3. The nucleotide diversity of the pooled
sample (p total) of each gene is plotted against therecombination rate of its region in fig. 1A. There was nocorrelation detected between these two variables (fig. 1A;
r ¼ 0.06, P ¼ 0.83; Spearman’s P ¼ 0.47). A possible reason
for the lack of correlation is that the regional variation inrecombination rate for mouse seems to be much lower thanthat for Drosophila or for human (data from Nachman and
Churchill 1996; Payseur and Nachman 2000). Alternatively, the population structure of mice could have
prevented advantageous alleles from spreading throughout
the range of all subdivided populations, assuming genetic
hitchhiking as the primary cause of the correlation. In thisstudy, we focused on investigating the latter possibility.

Genetic Variation vs. Recombination Rate in Mice 407

Table 3
Nucleotide Diversity of the 19 Loci Analyzed in This Study
Number of Silent Sites
Symbol
B3galt1B3galt2B3galt3
B3galt4
Camp
Cd14
Ctgf
Dfy
Fau
Fut1
Fut2
Fut4
Hoxa2
Nppb
Psmb10Sec1-pending
Sox15
Tnf
Wnt1
a
b

Length (bp)

Synonymous

Intron (bp)p Total (bp)p Totala

WithinSub-populationb

dc

GSTd

Divergencee

917
1228
996
1112
987
1254
1156
1509
1241
1134
963
1170
907
1112
652
1107
1340
1201
1044

208
282
228
292
81
254
82
252
102
277
234
309
280
87
174
39
292
63
86

0
0
0
0
672
0
371
462
826
0
0
0
0
672
0
478
553
926
714

208
282
228
292
753
254
453
714
928
277
234
309
280
759
174
517
845
989
800

0.01800.00390.01240.00700.00220.00890.00380.01460.00670.01010.00210.00470.00000.00940.00280.01620.00240.00450.00610.00720.00090.00550.00770.00100.00590.00000.00810.00590.00810.00210.00000.00000.00790.00240.00630.00060.00510.00220.01980.00440.01350.00680.00240.00940.00440.01560.00680.01040.00210.00580.00000.00960.00290.01790.00270.00440.0061

0.60
0.77
0.56
0.11
0.55
0.33
1.00
0.45
0.11
0.19
0.00
1.00
NA
0.16
0.15
0.61
0.75
0.12
0.64

0.00000.01420.00440.01710.01730.01580.01100.00560.00750.0036
0.03360.00970.00000.01050.00390.00360.00240.00910.0075

Average number of pairwise nucleotide differences per site calculated from all 28 (21 for Fut4, see text) combinations of strains.
Average number of pairwise nucleotide differences per site between the sequences of the four (three for Fut4, see text) pairs of strains from the same subpopula-tions.
c
Average number of pairwise nucleotide differences per site between the sequences of the 24 (18 for Fut4, see text) combinations of strains from different subpopulations.
dNei’s (1973) estimation of FST (GST).
e
Divergence between M. musculus and M. spicilegus.

We first calculated p within subpopulations and d, andplotted them against the recombination rate (fig. 1B and
1C, respectively). A positive correlation was detected
between recombination rate and p within subpopulations(fig. 1B; r ¼ 0.46, P ¼ 0.045; Spearman’s P ¼ 0.019), but
not between recombination rate and d (fig. 1C; r ¼ 0.01,
P ¼ 0.97; Spearman’s P ¼ 0.54). We then examined thecorrelation between the recombination rate and the level of
nucleotide differentiation among subpopulations (GST).
There was a signification negative correlation, as shown infigure 1D (r ¼ 0.64, P ¼ 0.0034; Spearman’s P ¼
0.0061). This was the clearest pattern in terms of thesignificance level in our analyses. The hitchhiking or
background selection in a subdivided population is
expected to increase the genetic differentiation between
subpopulations in regions of low recombination (Charlesworth, Nordborg, and Charlesworth 1997; Slatkin andWiehe 1998). Hence, our analyses suggest that these forces
are acting in this structured population.
Recently, it has been suggested that an additional
factor unrelated to natural selection contributes to thecorrelation between recombination rate and nucleotide
variation. Lercher and Hurst (2002) claimed that thecorrelation observed in the human genome was due, atleast in part, to a higher mutation rate in regions of highrecombination because it holds for single-nucleotide
polymorphisms (SNPs) across the entire human genome,
the great majority of which are not near exons or control
elements. If this increased mutation rate is a major factor,
a positive correlation is expected between recombination
rate and divergence among closely related species. Hellman et al. (2003) showed a positive correlation between

recombination rate and human-chimp as well as humanbaboon divergence. Their findings support the neutral
explanation for the phenomenon in humans. In contrast,
our data showed no correlation between recombination
rate and divergence between M. musculus and M.
spicilegus (fig. 1E; r ¼ 0.11, P ¼ 0.65; Spearman’s P ¼
0.38), which diverged within several million years (Sheet al. 1990; Moriwaki, Shiroishi, and Yonekawa 1994).
The exclusion of the outlier (Fut2; 11) in figure 1B, which
has extremely high levels of divergence, would not reveal
any significant pattern (r ¼ 0.32, P ¼ 0.20; Spearman’s P ¼
0.28). Genetic variation between subpopulations (d),which must have diverged much later after the M.
musculus and M. spicilegus divergence, also had nocorrelation with the recombination rate (fig. 1C). Thus, ourdata indicate that any determinants of mutation and/or
substitution rates in mice do not have a comparable effecton diversity and divergence.
However, we should be cautious about interpreting
our results. First of all, the recombination rate estimatesused in our analyses are only from M. m. domesticus.
There is not yet sufficient information on the map
distances of other subspecies or closely related species toknow whether they are consistent. Second, the sequence
data we used are located in close proximity to functional
genes, and the whole non-functional region of the genome
has not been analyzed. Finally, the sample size in terms ofbase pairs and number of individuals is not comparable inscale to the study of Hellman et al. (2003), which founda weak correlation between divergence and recombinationthat can account for the relationship between nucleotide
diversity and recombination in human. In contrast, even

408 Takahashi et al.

ground selection may still play a dominant role in shaping
the positive correlation between recombination rate andgenetic variation in many genomic regions of this species.
Acknowledgments
We thank Michael Nachman for providing us with theexact numerals of recombination rates for all the generegions. We also thank Toshiyuki Takano-Shimizu, Jeff
Wall, and Hiroshi Akashi for valuable discussions. We are
grateful to Justin Fay for his suggestions to improve themanuscript. This study was supported by grants-in-aid forscientific studies from the Ministry of Education, Science,Sports, and Culture, Japan, to N.S., and a Japan Society for
the Promotion of Science Research Fellowship for Young
Scientists to A.T.
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FIG. 1.—Scatterplots of recombination rate versus nucleotide
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Wolfgang Stephan, Associate Editor
Accepted September 29, 2003