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

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

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.

Bibliographic Details
Main 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
Format: Article
Language:English
Published: Nature Publishing Group 2021-06-01
Series:Nature Communications
Online Access:https://doi.org/10.1038/s41467-021-23134-8

Internet

https://doi.org/10.1038/s41467-021-23134-8

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