Multiomic QTL mapping reveals phenotypic complexity of GWAS loci and prioritizes putative causal variants
Timothy D. Arthur, Jennifer Nguyen, Benjamin A. Henson, Agnieszka D’Antonio‐Chronowska, Jeffrey Jaureguy, Nayara Silva, Angelo D. Arias, Paola Benaglio, W. Travis Berggren, Victor Borja, Megan Cook, Christopher DeBoever, Kenneth E. Diffenderfer, Margaret K.R. Donovan, KathyJean Farnam, Kyohei Fujita, Melvin Garcia, Olivier Harismendy, David Jakubosky, Kristen Jepsen, Isaac Joshua, He Li, Hiroko Matsui, Angelina McCarron, Naoki Nariai, Daniel T. O’Connor, Jonathan Okubo, Fengwen Rao, Joaquin Reyna, Lana Ribeiro Aguiar, Bianca M. Salgado, Nayara Silva, Erin N. Smith, Josh Sohmer, Shawn Yost, William W. Young Greenwald, Athanasia D. Panopoulos, Juan Carlos Izpisua Belmonte, Matteo D’Antonio, Graham McVicker, Kelly A. Frazer
Abstract
Most GWAS loci are presumed to affect gene regulation; however, only ∼43% colocalize with expression quantitative trait loci (eQTLs). To address this colocalization gap, we map eQTLs, chromatin accessibility QTLs (caQTLs), and histone acetylation QTLs (haQTLs) using molecular samples from three early developmental-like tissues. Through colocalization, we annotate 10.4% (n = 540) of GWAS loci in 15 traits by QTL phenotype, temporal specificity, and complexity. We show that integration of chromatin QTLs results in a 2.3-fold higher annotation rate of GWAS loci because they capture distal GWAS loci missed by eQTLs, and that 5.4% (n = 13) of GWAS colocalizing eQTLs are early developmental specific. Finally, we utilize the iPSCORE multiomic QTLs to prioritize putative causal variants overlapping transcription factor motifs to elucidate the potential genetic underpinnings of 296 GWAS-QTL colocalizations.