Population genetic inference from genomic sequence variation

Abstract

Population genetics has evolved from a theory-driven field with little empirical data into a data-driven discipline in which genome-scale data sets test the limits of available models and computational analysis methods. In humans and a few model organisms, analyses of whole-genome sequence polymorphism data are currently under way. And in light of the falling costs of next-generation sequencing technologies, such studies will soon become common in many other organisms as well. Here, we assess the challenges to analyzing whole-genome sequence polymorphism data, and we discuss the potential of these data to yield new insights concerning population history and the genomic prevalence of natural selection.

Figure 1.

Reads from different individuals are aligned to a reference genome, and SNPs have been called. In this toy example there are seven true segregating sites (black dots) and four false ones (red dots). Also note that segments differ in sample size. In segment 4, for example, five reads from three individuals were sampled; thus, there were at least three chromosomes and at most five sampled in this segment.

Figure 3.

Lengths of haplotypes shared by pairs of alleles within African (green) and European (blue) human populations, and between populations (red), in HapMap SNP data (open boxes) and simulations (×).

Figure 2.

The relationship between recombination rate and nucleotide diversity in data from D. melanogaster and humans. The data points shown represent average nucleotide diversity for recombination rate bins. The D. melanogaster data (Shapiro et al. 2007) are from 349 loci with ≥50 synonymous sites sequenced in up to 15 African lines (as analyzed by Sella et al. 2009). The human data are from the whole-genome shotgun sequence data analyzed by Hellmann et al. (2008), analyzed in 100-kb windows and processed as described in that study. Human nucleotide diversity is corrected for differences in interspecific divergence (to account for differences in mutation rate) (Hellmann et al. 2003). Drosophila diversity is not corrected for divergence—the correlation between recombination and divergence was weakly negative for this data set (Sella et al. 2009). For both data sets, recombination rate bins were defined manually based on data availability and log-scale bin width.