Ionita-Laza, I. | bioRxiv Science

bioRxiv2020

Multitrait genetic-phenotype associations to connect disease variants and biological mechanisms

BackgroundGenome-wide association studies (GWAS) uncovered a wealth of associations between common variants and human phenotypes. These results, widely shared across the scientific community as summary statistics, fostered a flurry of secondary analysis: heritability and genetic correlation assessment, pleiotropy characterization and multitrait association test. Amongst these secondary analyses, a rising new field is the decomposition of multitrait genetic effects into distinct profiles of pleiotropy. ResultsWe conducted an integrative analysis of GWAS summary statistics from 36 phenotypes to decipher multitrait genetic architecture and its link to biological mechanisms. We started by benchmarking multitrait association tests on a large panel of phenotype sets and established the Omnibus test as the most powerful in practice. We detected 322 new associations that were not previously reported by univariate screening. Using independent significant associations, we investigated the breakdown of genetic association into clusters of variants harboring similar multitrait association profile. Focusing on two subsets of immunity and metabolism phenotypes, we then demonstrate how SNPs within clusters can be mapped to biological pathways and disease mechanisms, providing a putative insight for numerous SNPs with unknown biological function. Finally, for the metabolism set, we investigate the link between gene cluster assignment and success of drug targets in random control trials. We report additional uninvestigated drug targets classified by clusters. ConclusionsMultitrait genetic signals can be decomposed into distinct pleiotropy profiles that reveal consistent with pathways databases and random control trials. We propose this method for the mapping of unannotated SNPs to putative pathways.

genetics↗

bioRxiv2020

Ancestry adjustment improves genome-wide estimates of regional intolerance

Genomic regions subject to purifying selection are more likely to carry disease causing mutations. Cross species conservation is often used to identify such regions but has limited resolution to detect selection on short evolutionary timescales such as that occurring in only one species. In contrast, intolerance looks for depletion of variation relative to expectation within a species, allowing species specific features to be identified. When estimating the intolerance of noncoding sequence methods strongly leverage variant frequency distributions. As the expected distributions depend on demography, if not properly controlled for, ancestral population source may obfuscate signals of selection. We demonstrate that properly incorporating demography in intolerance estimation greatly improved variant classification (13% increase in AUC relative to comparison constraint test, CDTS; and 9% relative to conservation). We provide a genome-wide intolerance map that is conditional on demographic history that is likely to be particularly valuable for variant prioritization.

genetics↗