248W Poster - Population Genetics
Wednesday June 08, 8:30 PM - 9:15 PM

Authors:
Alexander Xue ¹; Yi-Fei Huang ²; Adam Siepel ¹

Affiliations:
1) Cold Spring Harbor Laboratory; 2) Pennsylvania State University

Keywords:
Natural selection

Current approaches for detecting selection from GWAS data are unable to directly estimate the distribution of fitness effects (DFE). 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 from genotypes. The likelihood function, which is unified under the assumption of an explicit relationship between fitness and trait effect, is optimized using an EM algorithm to yield a trait’s DFE. To validate the performance of ASSESS, we conducted several simulation experiments under various data configurations, demographic scenarios, and genomic architectures. We find consistent behavior in accurately recovering the underlying selection history, as well as a high degree of robustness to a range of assumption violations of our conceptual framework. Additionally, we applied ASSESS to publicly available data for an array of human traits in both European and non-European populations. We discover a pattern of polygenicity estimates much higher than in previous investigations, which we attribute to the PRF’s sensitivity to weaker selection coefficients. Our in silico demonstration as well as the empirical insight gained here illustrate the potential of ASSESS to satisfy an increasing need for powerful yet convenient population genomic inference from GWAS summary statistics.