Inferring polygenic negative selection underlying an individual trait as a distribution of fitness effects (DFEs) from GWAS summary statistics

There has been rising interest in exploiting data from genome-wide association studies (GWAS) to detect a genetic signature of natural selection acting on a given phenotype. However, current approaches are unable to directly estimate the distribution of fitness effects (DFE), an established property in population genetics that can elucidate genomic architecture pertaining to a particular focal trait. To this end, we introduce ASSESS, an inferential method that exploits the Poisson Random Field (PRF) to model selection coefficients from genome-wide allele count data, while jointly conditioning GWAS summary statistics on a latent distribution of phenotypic effect sizes. This probabilistic model is unified under the assumption of an explicit relationship between fitness and trait effect to yield a DFE. To gauge the performance of ASSESS, we enlisted various simulation experiments that covered a range of usage cases and model misspecifications, which revealed accurate recovery of the underlying selection signal. As a further proof-of-concept, ASSESS was applied to an array of publicly available human trait data, whereby we replicated previously published empirical findings from an alternative methodology. These demonstrations illustrate the potential of ASSESS to satisfy an increasing need for powerful yet convenient population genomic inference from GWAS summary statistics.