Integration of GWAS and multi-omic QTLs identifies uncharacterized COVID-19 gene-biotype and phenotype associations

Abstract

AbstractTo better understand COVID-19 pathobiology and to prioritize treatment targets, we sought to identify human genes influencing genetically driven disease risk and severity, and to identify additional organismal-level phenotypes impacted by pleiotropic COVID-19-associated genomic loci. To this end, we performed ancestry-aware, trans-layer, multi-omic analyses by integrating recent COVID-19 Host Genetics Initiative genome-wide association (GWAS) data from six ancestry endpoints - African, Amerindian, South Asian, East Asian, European and meta-ancestry - with quantitative trait loci (QTL) and GWAS endpoints by colocalization analyses. We identified colocalizations for 47 COVID-19 loci with 307 GWAS trait endpoints and observed a highly variable (1-435 endpoint colocalizations) degree of pleiotropy per COVID-19 locus but a high representation of pulmonary traits. For those, directionality of effect mapped to COVID-19 pathological alleles pinpoints to systematic protective effects for COPD, detrimental effects for lung adenocarcinoma, and locus-dependent effects for IPF. Among 64 QTL-COVID-19 colocalized loci, we identified associations with most reported (47/53) and half of unreported (19/38) COVID-19-associated loci, including 9 loci identified in non-European cohorts. We generated colocalization evidence metrics and visualization tools, and integrated pulmonary-specific QTL signal, to aid the identification of putative causal genes and pulmonary cells. For example, among likely causal genes not previously linked to COVID-19, we identified desmoplakin-driven IPF-shared genetic perturbations in alveolar cells. Altogether, we provide insights into COVID-19 biology by identifying molecular and phenotype links to the genetic architecture of COVID-19 risk and severity phenotypes; further characterizing previously reported loci and providing novel insights for uncharacterized loci.