Multi-locus test conditional on confirmed effects leads to increased power in genome-wide association studies.

Complex diseases or phenotypes may involve multiple genetic variants and interactions between genetic, environmental and other factors. Current genome-wide association studies (GWAS) mostly used single-locus analysis and had identified genetic effects with multiple confirmations. Such confirmed sing...

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Autores principales:	Li Ma, Shizhong Han, Jing Yang, Yang Da
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2010
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/9f42d6e9135f4ee39050aff23309320e
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spelling	oai:doaj.org-article:9f42d6e9135f4ee39050aff23309320e2021-11-18T07:36:47ZMulti-locus test conditional on confirmed effects leads to increased power in genome-wide association studies.1932-620310.1371/journal.pone.0015006https://doaj.org/article/9f42d6e9135f4ee39050aff23309320e2010-11-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/21103364/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203Complex diseases or phenotypes may involve multiple genetic variants and interactions between genetic, environmental and other factors. Current genome-wide association studies (GWAS) mostly used single-locus analysis and had identified genetic effects with multiple confirmations. Such confirmed single-nucleotide polymorphism (SNP) effects were likely to be true genetic effects and ignoring this information in testing new effects of the same phenotype results in decreased statistical power due to increased residual variance that has a component of the omitted effects. In this study, a multi-locus association test (MLT) was proposed for GWAS analysis conditional on SNPs with confirmed effects to improve statistical power. Analytical formulae for statistical power were derived and were verified by simulation for MLT accounting for confirmed SNPs and for single-locus test (SLT) without accounting for confirmed SNPs. Statistical power of the two methods was compared by case studies with simulated and the Framingham Heart Study (FHS) GWAS data. Results showed that the MLT method had increased statistical power over SLT. In the GWAS case study on four cholesterol phenotypes and serum metabolites, the MLT method improved statistical power by 5% to 38% depending on the number and effect sizes of the conditional SNPs. For the analysis of HDL cholesterol (HDL-C) and total cholesterol (TC) of the FHS data, the MLT method conditional on confirmed SNPs from GWAS catalog and NCBI had considerably more significant results than SLT.Li MaShizhong HanJing YangYang DaPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 5, Iss 11, p e15006 (2010)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Li Ma Shizhong Han Jing Yang Yang Da Multi-locus test conditional on confirmed effects leads to increased power in genome-wide association studies.
description	Complex diseases or phenotypes may involve multiple genetic variants and interactions between genetic, environmental and other factors. Current genome-wide association studies (GWAS) mostly used single-locus analysis and had identified genetic effects with multiple confirmations. Such confirmed single-nucleotide polymorphism (SNP) effects were likely to be true genetic effects and ignoring this information in testing new effects of the same phenotype results in decreased statistical power due to increased residual variance that has a component of the omitted effects. In this study, a multi-locus association test (MLT) was proposed for GWAS analysis conditional on SNPs with confirmed effects to improve statistical power. Analytical formulae for statistical power were derived and were verified by simulation for MLT accounting for confirmed SNPs and for single-locus test (SLT) without accounting for confirmed SNPs. Statistical power of the two methods was compared by case studies with simulated and the Framingham Heart Study (FHS) GWAS data. Results showed that the MLT method had increased statistical power over SLT. In the GWAS case study on four cholesterol phenotypes and serum metabolites, the MLT method improved statistical power by 5% to 38% depending on the number and effect sizes of the conditional SNPs. For the analysis of HDL cholesterol (HDL-C) and total cholesterol (TC) of the FHS data, the MLT method conditional on confirmed SNPs from GWAS catalog and NCBI had considerably more significant results than SLT.
format	article
author	Li Ma Shizhong Han Jing Yang Yang Da
author_facet	Li Ma Shizhong Han Jing Yang Yang Da
author_sort	Li Ma
title	Multi-locus test conditional on confirmed effects leads to increased power in genome-wide association studies.
title_short	Multi-locus test conditional on confirmed effects leads to increased power in genome-wide association studies.
title_full	Multi-locus test conditional on confirmed effects leads to increased power in genome-wide association studies.
title_fullStr	Multi-locus test conditional on confirmed effects leads to increased power in genome-wide association studies.
title_full_unstemmed	Multi-locus test conditional on confirmed effects leads to increased power in genome-wide association studies.
title_sort	multi-locus test conditional on confirmed effects leads to increased power in genome-wide association studies.
publisher	Public Library of Science (PLoS)
publishDate	2010
url	https://doaj.org/article/9f42d6e9135f4ee39050aff23309320e
work_keys_str_mv	AT lima multilocustestconditionalonconfirmedeffectsleadstoincreasedpoweringenomewideassociationstudies AT shizhonghan multilocustestconditionalonconfirmedeffectsleadstoincreasedpoweringenomewideassociationstudies AT jingyang multilocustestconditionalonconfirmedeffectsleadstoincreasedpoweringenomewideassociationstudies AT yangda multilocustestconditionalonconfirmedeffectsleadstoincreasedpoweringenomewideassociationstudies
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Multi-locus test conditional on confirmed effects leads to increased power in genome-wide association studies.

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