Approximate conditional phenotype analysis based on genome wide association summary statistics

Abstract Because single genetic variants may have pleiotropic effects, one trait can be a confounder in a genome-wide association study (GWAS) that aims to identify loci associated with another trait. A typical approach to address this issue is to perform an additional analysis adjusting for the con...

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Autores principales:	Peitao Wu, Biqi Wang, Steven A. Lubitz, Emelia J. Benjamin, James B. Meigs, Josée Dupuis
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/13ad1023b2e04c5caf175a7085c14354
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spelling	oai:doaj.org-article:13ad1023b2e04c5caf175a7085c143542021-12-02T13:24:17ZApproximate conditional phenotype analysis based on genome wide association summary statistics10.1038/s41598-021-82000-12045-2322https://doaj.org/article/13ad1023b2e04c5caf175a7085c143542021-01-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-82000-1https://doaj.org/toc/2045-2322Abstract Because single genetic variants may have pleiotropic effects, one trait can be a confounder in a genome-wide association study (GWAS) that aims to identify loci associated with another trait. A typical approach to address this issue is to perform an additional analysis adjusting for the confounder. However, obtaining conditional results can be time-consuming. We propose an approximate conditional phenotype analysis based on GWAS summary statistics, the covariance between outcome and confounder, and the variant minor allele frequency (MAF). GWAS summary statistics and MAF are taken from GWAS meta-analysis results while the traits covariance may be estimated by two strategies: (i) estimates from a subset of the phenotypic data; or (ii) estimates from published studies. We compare our two strategies with estimates using individual level data from the full GWAS sample (gold standard). A simulation study for both binary and continuous traits demonstrates that our approximate approach is accurate. We apply our method to the Framingham Heart Study (FHS) GWAS and to large-scale cardiometabolic GWAS results. We observed a high consistency of genetic effect size estimates between our method and individual level data analysis. Our approach leads to an efficient way to perform approximate conditional analysis using large-scale GWAS summary statistics.Peitao WuBiqi WangSteven A. LubitzEmelia J. BenjaminJames B. MeigsJosée DupuisNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Peitao Wu Biqi Wang Steven A. Lubitz Emelia J. Benjamin James B. Meigs Josée Dupuis Approximate conditional phenotype analysis based on genome wide association summary statistics
description	Abstract Because single genetic variants may have pleiotropic effects, one trait can be a confounder in a genome-wide association study (GWAS) that aims to identify loci associated with another trait. A typical approach to address this issue is to perform an additional analysis adjusting for the confounder. However, obtaining conditional results can be time-consuming. We propose an approximate conditional phenotype analysis based on GWAS summary statistics, the covariance between outcome and confounder, and the variant minor allele frequency (MAF). GWAS summary statistics and MAF are taken from GWAS meta-analysis results while the traits covariance may be estimated by two strategies: (i) estimates from a subset of the phenotypic data; or (ii) estimates from published studies. We compare our two strategies with estimates using individual level data from the full GWAS sample (gold standard). A simulation study for both binary and continuous traits demonstrates that our approximate approach is accurate. We apply our method to the Framingham Heart Study (FHS) GWAS and to large-scale cardiometabolic GWAS results. We observed a high consistency of genetic effect size estimates between our method and individual level data analysis. Our approach leads to an efficient way to perform approximate conditional analysis using large-scale GWAS summary statistics.
format	article
author	Peitao Wu Biqi Wang Steven A. Lubitz Emelia J. Benjamin James B. Meigs Josée Dupuis
author_facet	Peitao Wu Biqi Wang Steven A. Lubitz Emelia J. Benjamin James B. Meigs Josée Dupuis
author_sort	Peitao Wu
title	Approximate conditional phenotype analysis based on genome wide association summary statistics
title_short	Approximate conditional phenotype analysis based on genome wide association summary statistics
title_full	Approximate conditional phenotype analysis based on genome wide association summary statistics
title_fullStr	Approximate conditional phenotype analysis based on genome wide association summary statistics
title_full_unstemmed	Approximate conditional phenotype analysis based on genome wide association summary statistics
title_sort	approximate conditional phenotype analysis based on genome wide association summary statistics
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/13ad1023b2e04c5caf175a7085c14354
work_keys_str_mv	AT peitaowu approximateconditionalphenotypeanalysisbasedongenomewideassociationsummarystatistics AT biqiwang approximateconditionalphenotypeanalysisbasedongenomewideassociationsummarystatistics AT stevenalubitz approximateconditionalphenotypeanalysisbasedongenomewideassociationsummarystatistics AT emeliajbenjamin approximateconditionalphenotypeanalysisbasedongenomewideassociationsummarystatistics AT jamesbmeigs approximateconditionalphenotypeanalysisbasedongenomewideassociationsummarystatistics AT joseedupuis approximateconditionalphenotypeanalysisbasedongenomewideassociationsummarystatistics
_version_	1718393077183283200

Approximate conditional phenotype analysis based on genome wide association summary statistics

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