Adaptation to new environments often occurs in the face of gene flow. Under these conditions, gene flow and recombination can impede adaptation by breaking down linkage disequilibrium between locally adapted alleles. Theory predicts that this decay can be halted or slowed if adaptive alleles are tightly linked in regions of low recombination, potentially favouring divergence and adaptive evolution in these regions over others. Here, we compiled a global genomic data set of over 1,300 individual threespine stickleback from 52 populations and compared the tendency for adaptive alleles to occur in regions of low recombination between populations that diverged with or without gene flow. In support of theory, we found that putatively adaptive alleles (F and d outliers) tend to occur more often in regions of low recombination in populations where divergent selection and gene flow have jointly occurred. This result remained significant when we employed different genomic window sizes, controlled for the effects of mutation rate and gene density, controlled for overall genetic differentiation, varied the genetic map used to estimate recombination and used a continuous (rather than discrete) measure of geographic distance as proxy for gene flow/shared ancestry. We argue that our study provides the first statistical evidence that the interaction of gene flow and selection biases divergence toward regions of low recombination.
Population genetic analyses often use summary statistics to describe patterns of genetic variation and provide insight into evolutionary processes. Among the most fundamental of these summary statistics are π and dXY, which are used to describe genetic diversity within and between populations, respectively. Here, we address a widespread issue in π and dXY calculation: systematic bias generated by missing data of various types. Many popular methods for calculating π and dXY operate on data encoded in the variant call format (VCF), which condenses genetic data by omitting invariant sites. When calculating π and dXY using a VCF, it is often implicitly assumed that missing genotypes (including those at sites not represented in the VCF) are homozygous for the reference allele. Here, we show how this assumption can result in substantial downward bias in estimates of π and dXY that is directly proportional to the amount of missing data. We discuss the pervasive nature and importance of this problem in population genetics, and introduce a user‐friendly UNIX command line utility, pixy, that solves this problem via an algorithm that generates unbiased estimates of π and dXY in the face of missing data. We compare pixy to existing methods using both simulated and empirical data, and show that pixy alone produces unbiased estimates of π and dXY regardless of the form or amount of missing data. In summary, our software solves a long‐standing problem in applied population genetics and highlights the importance of properly accounting for missing data in population genetic analyses.
1 2 Population genetic analyses often use summary statistics to describe patterns of genetic 3 variation and provide insight into evolutionary processes. Among the most fundamental 4 of these summary statistics are π and d XY , which are used to describe genetic diversity 5 within and between populations, respectively. Here, we address a widespread issue in π 6 and d XY calculation: systematic bias generated by missing data of various types. Many 7 popular methods for calculating π and d XY operate on data encoded in the Variant Call 8 Format (VCF), which condenses genetic data by omitting invariant sites. When 9 calculating π and d XY using a VCF, it is often implicitly assumed that missing genotypes 10 (including those at sites not represented in the VCF) are homozygous for the reference 11 allele. Here, we show how this assumption can result in substantial downward bias in 12 estimates of π and d XY that is directly proportional to the amount of missing data. We 13 discuss the pervasive nature and importance of this problem in population genetics, and 14 introduce a user-friendly UNIX command line utility, pixy, that solves this problem via 15 an algorithm that generates unbiased estimates of π and d XY in the face of missing data. 16 We compare pixy to existing methods using both simulated and empirical data, and 17 show that pixy alone produces unbiased estimates of π and d XY regardless of the form or 18 amount of missing data. In sum, our software solves a long-standing problem in applied 19 population genetics and highlights the importance of properly accounting for missing 20 data in population genetic analyses. 21 22 31 genetics. 32 33 Many summary statistics are based on the comparison of DNA sequences. Two 34 important summary statistics in this class are π, the average number of nucleotide 35 differences between genotypes drawn from the same population (Nei and Li 1979); and 36 d XY , the average number of nucleotide differences between genotypes drawn from two 37 different populations (Nei and Li 1979). These two summary statistics underlie a large 38 variety of descriptive and inferential procedures in population genetics. For example, π 39 is often used as an estimator of the central population genetic parameter (and is thus 40 sometimes styled as ). Similarly, d XY is a key statistic for exploring patterns of 41 divergence between populations, particularly in the context of divergence with gene 42 flow (Noor and Bennett 2009; Cruickshank and Hahn 2014; Burri 2017). 43 44 Calculation of π and d XY 45 46 For a single biallelic locus, π is usually calculated using one of three expressions shown 47 in Equation 1, all of which are exactly equivalent: 48 (Eq. 1) 49 50 51 52 (Nei and Li 1979; Gillespie 2004; Hahn 2019) 53 54 Where k ij corresponds to the count of allelic differences between the ith and jth haploid 55 genotypes, n is the number of samples, and c 0 and c 1 are the respective counts of the two 56 alleles at the locus. Note that the last expression is simply the sample-size correct...
While recombination is widely recognized to be a key modulator of numerous evolutionary phenomena, we have a poor understanding of how recombination rate itself varies and evolves within a species. Here, we performed a comprehensive study of recombination rate (rate of meiotic crossing over) in two natural populations of Drosophila pseudoobscura from Utah and Arizona, USA. We used an amplicon sequencing approach to obtain high-quality genotypes in approximately 8000 individual backcrossed offspring (17 mapping populations with roughly 530 individuals each), for which we then quantified crossovers. Interestingly, variation in recombination rate within and between populations largely manifested as differences in genome-wide recombination rate rather than remodeling of the local recombination landscape.Comparing populations, we discovered individuals from the Utah population displayed on average 8% higher crossover rates than the Arizona population, a statistically significant difference. Using a QST-FST analysis, we found that this difference in crossover rate was dramatically higher than expected under neutrality, indicating that this difference may have been driven by natural selection. Finally, using a combination of short and long read wholegenome sequencing, we found no significant association between crossover rate and structural variation at the 200-400kb scale. Our results demonstrate that (1) there is abundant variation in genome-wide crossover rate in natural populations (2) interpopulation differences in recombination rate may be the result of local adaptation, and (3) the observed variation among individuals in recombination rate is primarily driven by global regulators of crossover rate, with little detected variation in recombination rate among strains across specific tracts of individual chromosomes.
Uncovering factors that shape variation in brain morphology remains a major challenge in evolutionary biology. Recently, it has been shown that brain size is positively associated with level of parental care behavior in various taxa. One explanation for this pattern is that the cognitive demands of performing complex parental care may require increased brain size. This idea is known as the parental brain hypothesis (PBH). We set out to test the predictions of this hypothesis in wild populations of threespine stickleback (Gasterosteus aculeatus). These fish are commonly known to exhibit (1) uniparental male care and (2) sexual dimorphism in brain size (males>females). To test the PBH, we took advantage of the existence of closely related populations of stickleback that display variation in parental care behavior: common marine threespine sticklebacks (uniparental male care) and white threespine sticklebacks (no care). To begin, we quantified genetic differentiation among two common populations and three white populations from Nova Scotia. We found overall low differentiation among populations, although FST was increased in between-type comparisons. We then measured the brain weights of males and females from all five populations along with two additional common populations from British Columbia. We found that sexual dimorphism in brain size is reversed in white stickleback populations: males have smaller brains than females. Thus, while several alternatives need to be ruled out, the PBH appears to be a reasonable explanation for sexual dimorphism in brain size in threespine sticklebacks.
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