Leveraging supervised learning for functionally informed fine-mapping of cis-eQTLs identifies an additional 20,913 putative causal eQTLs

Qingbo S. Wang (), David R. Kelley, Jacob Ulirsch, Masahiro Kanai, Shuvom Sadhuka, Ran Cui, Carlos Albors, Nathan Cheng, Yukinori Okada, Francois Aguet, Kristin G. Ardlie, Daniel G. MacArthur and Hilary K. Finucane ()
Additional contact information
Qingbo S. Wang: Broad Institute of MIT and Harvard
David R. Kelley: Calico Life Sciences
Jacob Ulirsch: Broad Institute of MIT and Harvard
Masahiro Kanai: Broad Institute of MIT and Harvard
Shuvom Sadhuka: Broad Institute of MIT and Harvard
Ran Cui: Broad Institute of MIT and Harvard
Carlos Albors: Broad Institute of MIT and Harvard
Nathan Cheng: Broad Institute of MIT and Harvard
Yukinori Okada: Osaka University Graduate School of Medicine
Francois Aguet: Broad Institute of MIT and Harvard
Kristin G. Ardlie: Broad Institute of MIT and Harvard
Daniel G. MacArthur: Centre for Population Genomics, Garvan Institute of Medical Research
Hilary K. Finucane: Broad Institute of MIT and Harvard

Nature Communications, 2021, vol. 12, issue 1, 1-11

Abstract: Abstract The large majority of variants identified by GWAS are non-coding, motivating detailed characterization of the function of non-coding variants. Experimental methods to assess variants’ effect on gene expressions in native chromatin context via direct perturbation are low-throughput. Existing high-throughput computational predictors thus have lacked large gold standard sets of regulatory variants for training and validation. Here, we leverage a set of 14,807 putative causal eQTLs in humans obtained through statistical fine-mapping, and we use 6121 features to directly train a predictor of whether a variant modifies nearby gene expression. We call the resulting prediction the expression modifier score (EMS). We validate EMS by comparing its ability to prioritize functional variants with other major scores. We then use EMS as a prior for statistical fine-mapping of eQTLs to identify an additional 20,913 putatively causal eQTLs, and we incorporate EMS into co-localization analysis to identify 310 additional candidate genes across UK Biobank phenotypes.

Date: 2021
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DOI: 10.1038/s41467-021-23134-8

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