TIGAR-V2: Efficient TWAS tool with nonparametric Bayesian eQTL weights of 49 tissue types from GTEx V8

Summary: Standard transcriptome-wide association study (TWAS) methods first train gene expression prediction models using reference transcriptomic data and then test the association between the predicted genetically regulated gene expression and phenotype of interest. Most existing TWAS tools requir...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Randy L. Parrish, Greg C. Gibson, Michael P. Epstein, Jingjing Yang
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2022
Materias:	transcriptome-wide association study TWAS GWAS cis-eQTL nonparametric Bayesian DPR Elastic-Net Genetics QH426-470
Acceso en línea:	https://doaj.org/article/0991e4914aff475099fea18687efd5c4
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:0991e4914aff475099fea18687efd5c4
record_format	dspace
spelling	oai:doaj.org-article:0991e4914aff475099fea18687efd5c42021-11-20T05:14:07ZTIGAR-V2: Efficient TWAS tool with nonparametric Bayesian eQTL weights of 49 tissue types from GTEx V82666-247710.1016/j.xhgg.2021.100068https://doaj.org/article/0991e4914aff475099fea18687efd5c42022-01-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S266624772100049Xhttps://doaj.org/toc/2666-2477Summary: Standard transcriptome-wide association study (TWAS) methods first train gene expression prediction models using reference transcriptomic data and then test the association between the predicted genetically regulated gene expression and phenotype of interest. Most existing TWAS tools require cumbersome preparation of genotype input files and extra coding to enable parallel computation. To improve the efficiency of TWAS tools, we developed Transcriptome-Integrated Genetic Association Resource V2 (TIGAR-V2), which directly reads Variant Call Format (VCF) files, enables parallel computation, and reduces up to 90% of computation cost (mainly due to loading genotype data) compared to the original version. TIGAR-V2 can train gene expression imputation models using either nonparametric Bayesian Dirichlet process regression (DPR) or Elastic-Net (as used by PrediXcan), perform TWASs using either individual-level or summary-level genome-wide association study (GWAS) data, and implement both burden and variance-component statistics for gene-based association tests. We trained gene expression prediction models by DPR for 49 tissues using Genotype-Tissue Expression (GTEx) V8 by TIGAR-V2 and illustrated the usefulness of these Bayesian cis-expression quantitative trait locus (eQTL) weights through TWASs of breast and ovarian cancer utilizing public GWAS summary statistics. We identified 88 and 37 risk genes, respectively, for breast and ovarian cancer, most of which are either known or near previously identified GWAS (∼95%) or TWAS (∼40%) risk genes and three novel independent TWAS risk genes with known functions in carcinogenesis. These findings suggest that TWASs can provide biological insight into the transcriptional regulation of complex diseases. The TIGAR-V2 tool, trained Bayesian cis-eQTL weights, and linkage disequilibrium (LD) information from GTEx V8 are publicly available, providing a useful resource for mapping risk genes of complex diseases.Randy L. ParrishGreg C. GibsonMichael P. EpsteinJingjing YangElsevierarticletranscriptome-wide association studyTWASGWAScis-eQTLnonparametric Bayesian DPRElastic-NetGeneticsQH426-470ENHGG Advances, Vol 3, Iss 1, Pp 100068- (2022)
institution	DOAJ
collection	DOAJ
language	EN
topic	transcriptome-wide association study TWAS GWAS cis-eQTL nonparametric Bayesian DPR Elastic-Net Genetics QH426-470
spellingShingle	transcriptome-wide association study TWAS GWAS cis-eQTL nonparametric Bayesian DPR Elastic-Net Genetics QH426-470 Randy L. Parrish Greg C. Gibson Michael P. Epstein Jingjing Yang TIGAR-V2: Efficient TWAS tool with nonparametric Bayesian eQTL weights of 49 tissue types from GTEx V8
description	Summary: Standard transcriptome-wide association study (TWAS) methods first train gene expression prediction models using reference transcriptomic data and then test the association between the predicted genetically regulated gene expression and phenotype of interest. Most existing TWAS tools require cumbersome preparation of genotype input files and extra coding to enable parallel computation. To improve the efficiency of TWAS tools, we developed Transcriptome-Integrated Genetic Association Resource V2 (TIGAR-V2), which directly reads Variant Call Format (VCF) files, enables parallel computation, and reduces up to 90% of computation cost (mainly due to loading genotype data) compared to the original version. TIGAR-V2 can train gene expression imputation models using either nonparametric Bayesian Dirichlet process regression (DPR) or Elastic-Net (as used by PrediXcan), perform TWASs using either individual-level or summary-level genome-wide association study (GWAS) data, and implement both burden and variance-component statistics for gene-based association tests. We trained gene expression prediction models by DPR for 49 tissues using Genotype-Tissue Expression (GTEx) V8 by TIGAR-V2 and illustrated the usefulness of these Bayesian cis-expression quantitative trait locus (eQTL) weights through TWASs of breast and ovarian cancer utilizing public GWAS summary statistics. We identified 88 and 37 risk genes, respectively, for breast and ovarian cancer, most of which are either known or near previously identified GWAS (∼95%) or TWAS (∼40%) risk genes and three novel independent TWAS risk genes with known functions in carcinogenesis. These findings suggest that TWASs can provide biological insight into the transcriptional regulation of complex diseases. The TIGAR-V2 tool, trained Bayesian cis-eQTL weights, and linkage disequilibrium (LD) information from GTEx V8 are publicly available, providing a useful resource for mapping risk genes of complex diseases.
format	article
author	Randy L. Parrish Greg C. Gibson Michael P. Epstein Jingjing Yang
author_facet	Randy L. Parrish Greg C. Gibson Michael P. Epstein Jingjing Yang
author_sort	Randy L. Parrish
title	TIGAR-V2: Efficient TWAS tool with nonparametric Bayesian eQTL weights of 49 tissue types from GTEx V8
title_short	TIGAR-V2: Efficient TWAS tool with nonparametric Bayesian eQTL weights of 49 tissue types from GTEx V8
title_full	TIGAR-V2: Efficient TWAS tool with nonparametric Bayesian eQTL weights of 49 tissue types from GTEx V8
title_fullStr	TIGAR-V2: Efficient TWAS tool with nonparametric Bayesian eQTL weights of 49 tissue types from GTEx V8
title_full_unstemmed	TIGAR-V2: Efficient TWAS tool with nonparametric Bayesian eQTL weights of 49 tissue types from GTEx V8
title_sort	tigar-v2: efficient twas tool with nonparametric bayesian eqtl weights of 49 tissue types from gtex v8
publisher	Elsevier
publishDate	2022
url	https://doaj.org/article/0991e4914aff475099fea18687efd5c4
work_keys_str_mv	AT randylparrish tigarv2efficienttwastoolwithnonparametricbayesianeqtlweightsof49tissuetypesfromgtexv8 AT gregcgibson tigarv2efficienttwastoolwithnonparametricbayesianeqtlweightsof49tissuetypesfromgtexv8 AT michaelpepstein tigarv2efficienttwastoolwithnonparametricbayesianeqtlweightsof49tissuetypesfromgtexv8 AT jingjingyang tigarv2efficienttwastoolwithnonparametricbayesianeqtlweightsof49tissuetypesfromgtexv8
_version_	1718419463400849408

TIGAR-V2: Efficient TWAS tool with nonparametric Bayesian eQTL weights of 49 tissue types from GTEx V8

Ejemplares similares