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Exploiting the GTEx resources to decipher the mechanisms at GWAS loci
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 gen...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7836161/ https://www.ncbi.nlm.nih.gov/pubmed/33499903 http://dx.doi.org/10.1186/s13059-020-02252-4 |
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author | 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, François Bastarache, Lisa Jordan, Daniel M. Verbanck, Marie Do, Ron Stephens, Matthew Ardlie, Kristin McCarthy, Mark Montgomery, Stephen B. Segrè, Ayellet V. Brown, Christopher D. Lappalainen, Tuuli Wen, Xiaoquan Im, Hae Kyung |
author_facet | 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, François Bastarache, Lisa Jordan, Daniel M. Verbanck, Marie Do, Ron Stephens, Matthew Ardlie, Kristin McCarthy, Mark Montgomery, Stephen B. Segrè, Ayellet V. Brown, Christopher D. Lappalainen, Tuuli Wen, Xiaoquan Im, Hae Kyung |
author_sort | Barbeira, Alvaro N. |
collection | PubMed |
description | 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at (10.1186/s13059-020-02252-4). |
format | Online Article Text |
id | pubmed-7836161 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-78361612021-01-26 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, François Bastarache, Lisa Jordan, Daniel M. Verbanck, Marie Do, Ron Stephens, Matthew Ardlie, Kristin McCarthy, Mark Montgomery, Stephen B. Segrè, Ayellet V. Brown, Christopher D. Lappalainen, Tuuli Wen, Xiaoquan Im, Hae Kyung Genome Biol Research 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at (10.1186/s13059-020-02252-4). BioMed Central 2021-01-26 /pmc/articles/PMC7836161/ /pubmed/33499903 http://dx.doi.org/10.1186/s13059-020-02252-4 Text en © The Author(s) 2021 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research 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, François Bastarache, Lisa Jordan, Daniel M. Verbanck, Marie Do, Ron Stephens, Matthew Ardlie, Kristin McCarthy, Mark Montgomery, Stephen B. Segrè, Ayellet V. Brown, Christopher D. Lappalainen, Tuuli Wen, Xiaoquan Im, Hae Kyung Exploiting the GTEx resources to decipher the mechanisms at GWAS loci |
title | Exploiting the GTEx resources to decipher the mechanisms at GWAS loci |
title_full | Exploiting the GTEx resources to decipher the mechanisms at GWAS loci |
title_fullStr | Exploiting the GTEx resources to decipher the mechanisms at GWAS loci |
title_full_unstemmed | Exploiting the GTEx resources to decipher the mechanisms at GWAS loci |
title_short | Exploiting the GTEx resources to decipher the mechanisms at GWAS loci |
title_sort | exploiting the gtex resources to decipher the mechanisms at gwas loci |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7836161/ https://www.ncbi.nlm.nih.gov/pubmed/33499903 http://dx.doi.org/10.1186/s13059-020-02252-4 |
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