Division of Population Genetics, National Institute of Genetics, Mishima, Japan
Correspondence: E-mail: nsaitou{at}genes.nig.ac.jp.
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Abstract |
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Key Words: Mus musculus recombination rate population structure population subdivision genetic hitchhiking background selection FST
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Introduction |
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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 mainly by the genetic hitchhiking of rapidly fixed advantageous mutations (Maynard Smith and Haigh 1974; Kaplan, Hudson, and Langley 1989) and/or by background selection against deleterious mutations (Charlesworth, 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, and Kindahl 1994; Langley et al. 2000; Andolfatto and Przeworski 2001). However, in a pooled sample of a 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 be taken into account. For example, a subdivided population model of genetic hitchhiking is proposed by Slatkin and Wiehe (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 with a 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 of recombination. 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 recombination on the X chromosome. Begun and Aquadro (1993) found elevated FST in three of seven genomic regions with a reduced recombination rate in populations of Drosophila melanogaster in Zimbabwe and other localities. Both of these studies invoked genetic hitchhiking events as the cause (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 the absence 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 three major subspecies of Mus musculus. (Liu, Takahashi, Kitano, Koide, Shiroishi, Moriwaki, and Saitou, unpublished data). This sequence data showed overall clustering of 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 as units of a subpopulation in this species, we categorized the strains into four geographically related groups. Here, we investigate (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 the relationships between genetic variation and rate of recombination in this structured population.
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Materials and Methods |
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Results and Discussion |
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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 to an equivalent scale of cM/cR (1 cR = 100 kb; Van Etten et al. 1999), and are also listed in table 2. These two estimates are highly correlated (r = 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 ( total) of each gene is plotted against the recombination rate of its region in fig. 1A. There was no correlation detected between these two variables (fig. 1A; r = 0.06, P = 0.83; Spearman's P = 0.47). A possible reason for the lack of correlation is that the regional variation in recombination rate for mouse seems to be much lower than that 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 this study, we focused on investigating the latter possibility. We first calculated
within subpopulations and d, and plotted them against the recombination rate (fig. 1B and 1C, respectively). A positive correlation was detected between recombination rate and
within subpopulations (fig. 1B; r = 0.46, P = 0.045; Spearman's P = 0.019), but not between recombination rate and d (fig. 1C; r = 0.01, P = 0.97; Spearman's P = 0.54). We then examined the correlation between the recombination rate and the level of nucleotide differentiation among subpopulations (GST). There was a signification negative correlation, as shown in figure 1D (r = 0.64, P = 0.0034; Spearman's P = 0.0061). This was the clearest pattern in terms of the significance 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 and Wiehe 1998). Hence, our analyses suggest that these forces are acting in this structured population.
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However, we should be cautious about interpreting our results. First of all, the recombination rate estimates used 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 to know 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 of base pairs and number of individuals is not comparable in scale to the study of Hellman et al. (2003), which found a weak correlation between divergence and recombination that can account for the relationship between nucleotide diversity and recombination in human. In contrast, even a large data set of 255 Drosophila melanogaster and D. simulans loci revealed no detectable relationship between divergence and recombination rate (Betancourt and Presgraves 2002). A larger-scale analysis is awaited to know which of these cases applies for the mouse data. However, all the above concerns would not weaken the evidence of the correlations actually detected in this study, if at most they might veil a weak existing relationship. In conclusion, our analyses of a structured population of mice showed that the effect of genetic hitchhiking or background selection may still play a dominant role in shaping the positive correlation between recombination rate and genetic variation in many genomic regions of this species.
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Acknowledgements |
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Footnotes |
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Wolfgang Stephan, Associate Editor
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