Exploiting the GTEx resources to decipher the mechanisms at GWAS loci

Barbeira, Alvaro N.; Bonazzola, Rodrigo; Gamazon, Eric R.; Liang, Yanyu; Park, YoSon; Kim-Hellmuth, Sarah; Wang, Gao; Jiang, Zhuoxun; Zhou, Dan; Hormozdiari, Farhad; Liu, Boxiang; Rao, Abhiram; Hamel, Andrew R.; Pividori, Milton D.; Aguet, Francois; Bastarache, Lisa; Jordan, Daniel M.; Verbanck, Marie; Do, Ron; Stephens, Matthew; Ardlie, Kristin; McCarthy, Mark; Montgomery, Stephen B.; Segre, Ayellet V.; Brown, Christopher D.; Lappalainen, Tuuli; Wen, Xiaoquan; Im, Hae Kyung

doi:10.1186/s13059-020-02252-4

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Exploiting the GTEx resources to decipher the mechanisms at GWAS loci

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Kim-Hellmuth, Sarah
RG Statistical Genetics, Max Planck Institute of Psychiatry, Max Planck Society;

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Barbeira, A. N., Bonazzola, R., Gamazon, E. R., Liang, Y., Park, Y., Kim-Hellmuth, S., et al. (2021). Exploiting the GTEx resources to decipher the mechanisms at GWAS loci. GENOME BIOLOGY, 22(1): 49. doi:10.1186/s13059-020-02252-4.

Cite as: https://hdl.handle.net/21.11116/0000-0008-EA13-B

Abstract

The resources generated by the GTEx consortium offer unprecedented opportunities to advance our understanding of the biology of human diseases. Here, we present an in-depth examination of the phenotypic consequences of transcriptome regulation and a blueprint for the functional interpretation of genome-wide association study-discovered loci. Across a broad set of complex traits and diseases, we demonstrate widespread dose-dependent effects of RNA expression and splicing. We develop a data-driven framework to benchmark methods that prioritize causal genes and find no single approach outperforms the combination of multiple approaches. Using colocalization and association approaches that take into account the observed allelic heterogeneity of gene expression, we propose potential target genes for 47% (2519 out of 5385) of the GWAS loci examined.