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; The Biobank Japan Project; Francois Aguet; Kristin G. Ardlie; Daniel G. MacArthur; Hilary K. Finucane

doi:10.1038/s41467-021-23134-8

Nature Communications (Jun 2021)

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,
The Biobank Japan Project,
Francois Aguet,
Kristin G. Ardlie,
Daniel G. MacArthur,
Hilary K. Finucane

Affiliations

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: Department of Statistical Genetics, Osaka University Graduate School of Medicine
The Biobank Japan Project: Centre for Population Genomics, Garvan Institute of Medical Research
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

DOI: https://doi.org/10.1038/s41467-021-23134-8
Journal volume & issue: Vol. 12, no. 1
pp. 1 – 11

Abstract

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Finding causal variants and genes from GWAS loci results remains a challenge. Here, the authors train a model to predict if a variant affects nearby gene expression, and apply the method to identify new possible causal eQTLs and mechanisms of GWAS loci.

Published in Nature Communications

ISSN: 2041-1723 (Online)
Publisher: Nature Portfolio
Country of publisher: United Kingdom
LCC subjects: Science
Website: https://www.nature.com/ncomms/

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