303V Poster Online - Virtual Posters
Tuesday June 07, 11:00 AM - 3:00 PM

Authors:
Vasili Pankratov ¹; Milyausha Yunusbaeva ²; Sergei Ryakhovsky ²; Maksym Zarodniuk ³; Bayazit Yunusbayev ^1,2; Estonian Biobank Research Team

Affiliations:
1) Institute of Genomics University of Tartu; 2) SCAMT Institute, ITMO University, Saint-Petersburg, Russia; 3) Institute of Bio- and Translational Medicine, University of Tartu, Tartu, Estonia

Keywords:
Complex traits

Causal variants for inflammatory diseases might have been under pathogen-driven natural selection. Such variants are promising for functional experiments since they likely strongly affect the immune response. While this hypothesis has important implications for biomedicine, its application in practice has been hindered by challenges with pinpointing the targets of selection (mutations). We attempted to approach this challenge using Biobank-scale sequence data and a new class of methods based on local tree inference. We focused on 593 risk loci associated with 21 autoimmune disorders. Altogether, 4838 candidate SNPs were analyzed across these loci using Relate-inferred local trees and likelihood-based selection tests using CLUES. We found that 204 out of 593 risk loci contain at least one candidate SNP with evidence for natural selection (logLR>1.59). Inferred selection coefficients suggest that these SNPs were likely under weak and moderate selective sweep. Such sweeps can leave some flanking variation, making it possible to fine-map the target of selection. We were able to fine-map likely targets of natural selection among candidate risk SNPs (57 loci out of 204), distinguish neutral hitchhikers and identify more complex scenarios. Risk SNPs with adaptive history are promising targets for functional analyses since natural selection picks mutations with a tangible effect on the phenotype.