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. 2021 Jan 5;118(1):e2009227118.
doi: 10.1073/pnas.2009227118.

The evolution of skin pigmentation-associated variation in West Eurasia

Affiliations

The evolution of skin pigmentation-associated variation in West Eurasia

Dan Ju et al. Proc Natl Acad Sci U S A. .

Abstract

Skin pigmentation is a classic example of a polygenic trait that has experienced directional selection in humans. Genome-wide association studies have identified well over a hundred pigmentation-associated loci, and genomic scans in present-day and ancient populations have identified selective sweeps for a small number of light pigmentation-associated alleles in Europeans. It is unclear whether selection has operated on all of the genetic variation associated with skin pigmentation as opposed to just a small number of large-effect variants. Here, we address this question using ancient DNA from 1,158 individuals from West Eurasia covering a period of 40,000 y combined with genome-wide association summary statistics from the UK Biobank. We find a robust signal of directional selection in ancient West Eurasians on 170 skin pigmentation-associated variants ascertained in the UK Biobank. However, we also show that this signal is driven by a limited number of large-effect variants. Consistent with this observation, we find that a polygenic selection test in present-day populations fails to detect selection with the full set of variants. Our data allow us to disentangle the effects of admixture and selection. Most notably, a large-effect variant at SLC24A5 was introduced to Western Europe by migrations of Neolithic farming populations but continued to be under selection post-admixture. This study shows that the response to selection for light skin pigmentation in West Eurasia was driven by a relatively small proportion of the variants that are associated with present-day phenotypic variation.

Keywords: ancient DNA; complex traits; evolution; polygenic selection; skin pigmentation.

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Conflict of interest statement

The authors declare no competing interest.

Figures

Fig. 1.
Fig. 1.
Genetic scores for skin pigmentation over time. The solid lines indicate fitted mean scores with gray 95% confidence intervals. Weighted scores based on UK Biobank SNPs and unweighted scores based on manually curated SNPs for samples in the (A and D) shotgun dataset, (B and E) capture-shotgun dataset, and (C and F) capture-shotgun dataset dated within the past 15,000 y. Scores of labeled samples are plotted but not included in regressions. Averages for 1000 Genomes superpopulations are plotted at time 0. Area of points scale with number of SNPs genotyped in the individual.
Fig. 2.
Fig. 2.
Plot of P values for ancestry and time for individual skin pigmentation SNPs. The dashed lines indicate fifth percentile of P values. UK Biobank SNPs using the shotgun dataset are plotted in A and capture-shotgun dataset in B and C. Manually curated SNPs using the shotgun dataset are plotted in D and capture-shotgun dataset in E and F. SNPs are colored according to whether the light allele is increasing over time (red) or decreasing (blue), with saturation determined by the magnitude of change.
Fig. 3.
Fig. 3.
Joint PBS distributions for 20-SNP windows across the genome for GBR-CHB-YRI on the x axis and X-CHB-YRI on the y axis, with X being (A) hunter-gatherer, (B) Early Farmer, and (C) Steppe. The boxes represent windows centered around UK Biobank skin pigmentation SNPs and are colored redder according to how much higher the light allele frequency is in X compared to YRI. Nearest genes are labeled. The orange and red lines represent the top 1 and 0.1 percentiles.
Fig. 4.
Fig. 4.
(A) Regressions of genetic score based on UK Biobank SNPs using the capture-shotgun dataset over date, with scores using all SNPs and iteratively removing top GWAS effect size SNPs. (B) Qx empirical −log10(P values) using UK Biobank skin pigmentation-associated variants with all 1000 Genomes populations. Different numbers of SNPs were used to calculate Qx, which were ordered by GWAS-estimated effect size. (C) Variance explained by UK Biobank SNPs with nearest gene labeled between UK and YRI populations (y axis) and within the UK population (x axis).

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