Genome-wide insights into the patterns and determinants of fine-scale population structure in humans

Abstract

Studying genomic patterns of human population structure provides important insights into human evolutionary history and the relationship among populations, and it has significant practical implications for disease-gene mapping. Here we describe a principal component (PC)-based approach to studying intracontinental population structure in humans, identify the underlying markers mediating the observed patterns of fine-scale population structure, and infer the predominating evolutionary forces shaping local population structure. We applied this methodology to a data set of 650K SNPs genotyped in 944 unrelated individuals from 52 populations and demonstrate that, although typical PC analyses focus on the top axes of variation, substantial information about population structure is contained in lower-ranked PCs. We identified 18 significant PCs, some of which distinguish individual populations. In addition to visually representing sample clusters in PC biplots, we estimated the set of all SNPs significantly correlated with each of the most informative axes of variation. These polymorphisms, unlike ancestry-informative markers (AIMs), constitute a much larger set of loci that drive genomic signatures of population structure. The genome-wide distribution of these significantly correlated markers can largely be accounted for by the stochastic effects of genetic drift, although significant clustering does occur in genomic regions that have been previously implicated as targets of recent adaptive evolution.

Figure 1

Change in Scale of Differentiation from Global to Local along PCs The top panel shows a biplot of PC1 versus PC2, and the bottom panel is a biplot of PC10 versus PC11. Filled circles represent all 944 samples and are colored according to the continent of origin (indicated in the legend). In the bottom panel, brown filled circles are used for highlighting the Kalash population.

Figure 2

Intracontinental Population Stratification in Africa The 102 African samples are represented as filled circles, and the color legend for the predefined population labels is indicated within each plot.

Figure 3

Genomic Distribution of PC1-Correlated SNPs The genome was divided into nonoverlapping 500 kb bins (x axis), and each bin was tested for whether it contained more PC1-correlated SNPs than expected by chance (y axis). p values are plotted as −log₁₀ (p). Panels represent the seven continents, and the dashed red line corresponds to a p value of 1.8 × 10⁻⁶.