The evolution of skin pigmentation-associated variation in West Eurasia

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 atSLC24A5was 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.

Ancestral contributions to contemporary European complex traits

“…We applied CLUES 27 to the list of GWAS hits used above as index for our candidate regions to obtain per-SNP evidence of recent (up to 500 generations ago) natural selection, and to see which phenotypes show enrichment in SNPs with strong selection signals compared to a random set of GWAS hits. Out of the genomic regions responsible for ancestry/trait association shown in Figure 3, pigmentation-related SNPs (eye and hair color) showed extremely high CLUES logLR values (Figures 4a, S4) in accordance with previous results 5,8,28 , as well as SNPs related to BMI and cholesterol, pointing to ongoing or recent selection at these loci. Diastolic blood pressure (DBP) and sleep-related SNPs also showed the same extreme signature, but the candidate regions encompassing them did not reach significance in ancestry/trait association.…”

“…Previous research efforts have indeed characterized evolutionary events specific to these populations which putatively affected their phenotype and appearance, through the tracking of few highly characterized SNPs 4–6 or polygenic scores 7,8 . Nevertheless, while the first approach is limited in the number of variants analyzed and largely blind with regards to complex polygenic traits, the second builds on population-dependent effect sizes 9,10 estimated in Genome Wide Association Studies (GWAS).…”

Modelling the spatiotemporal spread of beneficial alleles using ancient genomes

“…The origins and spread dynamics of large-effect pigmentation-associated SNPs in ancient Eurasians have also been intensely studied (Ju & Mathieson, 2020). Major loci of large effect on skin, eye and hair pigmentation have been documented as having been under recent positive selection in Western Eurasian history (Voight et al, 2006;Sabeti et al, 2007;Pickrell et al, 2009;Lao et al, 2007;Mathieson et al, 2015;Alonso et al, 2008;Hudjashov et al, 2013).…”

Tracing the evolution of human gene regulation and its association with shifts in environment

“…We hypothesized that genes involved in complex phenotypes under selection in a population would exhibit systematic changes over time in their regulation. To test this, we focused on skin pigmentation, a trait that is known to have been under selection in humans in West Eurasia (Berg and Coop, 2014; Ju and Mathieson, 2020; Wilde et al , 2014) and for which many of the genes involved are well-understood (Sturm and Duffy, 2012). We trained new PrediXcan models using genetic variants and gene expression in melanocytes from a diverse population (Zhang et al , 2017).…”