Widespread signatures of natural selection across human complex traits and functional genomic categories

Understanding how natural selection has shaped genetic architecture of complex traits is of importance in medical and evolutionary genetics. Bayesian methods have been developed using individual-level GWAS data to estimate multiple genetic architecture parameters including selection signature. Here, we present a method (SBayesS) that only requires GWAS summary statistics. We analyse data for 155 complex traits (n = 27k-547k) and project the estimates onto those obtained from evolutionary simulations. We estimate that, on average across traits, about 1% of human genome sequence are mutational targets with a mean selection coefficient of similar to 0.001. Common diseases, on average, show a smaller number of mutational targets and have been under stronger selection, compared to other traits. SBayesS analyses incorporating functional annotations reveal that selection signatures vary across genomic regions, among which coding regions have the strongest selection signature and are enriched for both the number of associated variants and the magnitude of effect sizes.

Automated summary

The study reveals that approximately 1% of the human genome is mutational targets with a mean selection coefficient of 0.001. Common diseases tend to have fewer mutational targets and have undergone stronger selection pressure compared to other traits. The analyses show that selection signatures vary across genomic regions, with coding regions having the strongest selection signature.