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

Authors :
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
Source :
Nature Communications, Vol 12, Iss 1, Pp 1-11 (2021)
Publication Year :
2021
Publisher :
Nature Portfolio, 2021.

Abstract

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.

Subjects

Subjects :
Science

Details

Language :
English
ISSN :
20411723
Volume :
12
Issue :
1
Database :
Directory of Open Access Journals
Journal :
Nature Communications
Publication Type :
Academic Journal
Accession number :
edsdoj.f492bfb02a464d72ae49db0b2d0594f8
Document Type :
article
Full Text :
https://doi.org/10.1038/s41467-021-23134-8