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IGREX for quantifying the impact of genetically regulated expression on phenotypes

By leveraging existing GWAS and eQTL resources, transcriptome-wide association studies (TWAS) have achieved many successes in identifying trait-associations of genetically regulated expression (GREX) levels. TWAS analysis relies on the shared GREX variation across GWAS and the reference eQTL data, w...

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Detalles Bibliográficos
Autores principales: Cai, Mingxuan, Chen, Lin S, Liu, Jin, Yang, Can
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7034630/
https://www.ncbi.nlm.nih.gov/pubmed/32118202
http://dx.doi.org/10.1093/nargab/lqaa010
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author Cai, Mingxuan
Chen, Lin S
Liu, Jin
Yang, Can
author_facet Cai, Mingxuan
Chen, Lin S
Liu, Jin
Yang, Can
author_sort Cai, Mingxuan
collection PubMed
description By leveraging existing GWAS and eQTL resources, transcriptome-wide association studies (TWAS) have achieved many successes in identifying trait-associations of genetically regulated expression (GREX) levels. TWAS analysis relies on the shared GREX variation across GWAS and the reference eQTL data, which depends on the cellular conditions of the eQTL data. Considering the increasing availability of eQTL data from different conditions and the often unknown trait-relevant cell/tissue-types, we propose a method and tool, IGREX, for precisely quantifying the proportion of phenotypic variation attributed to the GREX component. IGREX takes as input a reference eQTL panel and individual-level or summary-level GWAS data. Using eQTL data of 48 tissue types from the GTEx project as a reference panel, we evaluated the tissue-specific IGREX impact on a wide spectrum of phenotypes. We observed strong GREX effects on immune-related protein biomarkers. By incorporating trans-eQTLs and analyzing genetically regulated alternative splicing events, we evaluated new potential directions for TWAS analysis.
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spelling pubmed-70346302020-02-26 IGREX for quantifying the impact of genetically regulated expression on phenotypes Cai, Mingxuan Chen, Lin S Liu, Jin Yang, Can NAR Genom Bioinform Standard Article By leveraging existing GWAS and eQTL resources, transcriptome-wide association studies (TWAS) have achieved many successes in identifying trait-associations of genetically regulated expression (GREX) levels. TWAS analysis relies on the shared GREX variation across GWAS and the reference eQTL data, which depends on the cellular conditions of the eQTL data. Considering the increasing availability of eQTL data from different conditions and the often unknown trait-relevant cell/tissue-types, we propose a method and tool, IGREX, for precisely quantifying the proportion of phenotypic variation attributed to the GREX component. IGREX takes as input a reference eQTL panel and individual-level or summary-level GWAS data. Using eQTL data of 48 tissue types from the GTEx project as a reference panel, we evaluated the tissue-specific IGREX impact on a wide spectrum of phenotypes. We observed strong GREX effects on immune-related protein biomarkers. By incorporating trans-eQTLs and analyzing genetically regulated alternative splicing events, we evaluated new potential directions for TWAS analysis. Oxford University Press 2020-02-19 /pmc/articles/PMC7034630/ /pubmed/32118202 http://dx.doi.org/10.1093/nargab/lqaa010 Text en © The Author(s) 2019. Published by Oxford University Press on behalf of NAR Genomics and Bioinformatics. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Standard Article
Cai, Mingxuan
Chen, Lin S
Liu, Jin
Yang, Can
IGREX for quantifying the impact of genetically regulated expression on phenotypes
title IGREX for quantifying the impact of genetically regulated expression on phenotypes
title_full IGREX for quantifying the impact of genetically regulated expression on phenotypes
title_fullStr IGREX for quantifying the impact of genetically regulated expression on phenotypes
title_full_unstemmed IGREX for quantifying the impact of genetically regulated expression on phenotypes
title_short IGREX for quantifying the impact of genetically regulated expression on phenotypes
title_sort igrex for quantifying the impact of genetically regulated expression on phenotypes
topic Standard Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7034630/
https://www.ncbi.nlm.nih.gov/pubmed/32118202
http://dx.doi.org/10.1093/nargab/lqaa010
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