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.

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Detalles Bibliográficos
Autores principales: 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
Formato: article
Lenguaje:EN
Publicado: Nature Portfolio 2021
Materias:
Science
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Acceso en línea:https://doaj.org/article/f492bfb02a464d72ae49db0b2d0594f8
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Descripción
Sumario: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.

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