Widespread signatures of natural selection across human complex traits and functional genomic categories
Year: 2021
Type: article
Abstract: 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 ~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.
Source: Nature Communications
Language: English
Subfield: Genetics
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $5,560
Awards: The role of X-chromosome inactivation in quantitative trait variation, G3061990085, G3229869697, Complex trait genomics, Using genomics to understand psychiatric disorders, G4527525017, G453890003, G5026251150, G5834621717, Deciphering the genetic architecture of human complex traits, Methods and software tool for complex trait analyses using multi-omics data, Statistical methods and tools to integrate genetic and non-genetic data for risk prediction of common diseases, Neurogenetics and Statistical Genomics, The extent, causes and implications of pleiotropy among complex traits
OpenAlex ID: W3131856571