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Article: Multiple phenotype association tests using summary statistics in genome-wide association studies

TitleMultiple phenotype association tests using summary statistics in genome-wide association studies
Authors
KeywordsPleiotropy
Correlated phenotypes
Fisher method
Linear mixed models
Summary statistics
Variance component test
Issue Date2018
Citation
Biometrics, 2018, v. 74, n. 1, p. 165-175 How to Cite?
Abstract© 2017, The International Biometric Society We study in this article jointly testing the associations of a genetic variant with correlated multiple phenotypes using the summary statistics of individual phenotype analysis from Genome-Wide Association Studies (GWASs). We estimated the between-phenotype correlation matrix using the summary statistics of individual phenotype GWAS analyses, and developed genetic association tests for multiple phenotypes by accounting for between-phenotype correlation without the need to access individual-level data. Since genetic variants often affect multiple phenotypes differently across the genome and the between-phenotype correlation can be arbitrary, we proposed robust and powerful multiple phenotype testing procedures by jointly testing a common mean and a variance component in linear mixed models for summary statistics. We computed the p-values of the proposed tests analytically. This computational advantage makes our methods practically appealing in large-scale GWASs. We performed simulation studies to show that the proposed tests maintained correct type I error rates, and to compare their powers in various settings with the existing methods. We applied the proposed tests to a GWAS Global Lipids Genetics Consortium summary statistics data set and identified additional genetic variants that were missed by the original single-trait analysis.
Persistent Identifierhttp://hdl.handle.net/10722/260265
ISSN
2015 Impact Factor: 1.36
2015 SCImago Journal Rankings: 1.906

 

DC FieldValueLanguage
dc.contributor.authorLiu, Zhonghua-
dc.contributor.authorLin, Xihong-
dc.date.accessioned2018-09-12T02:00:57Z-
dc.date.available2018-09-12T02:00:57Z-
dc.date.issued2018-
dc.identifier.citationBiometrics, 2018, v. 74, n. 1, p. 165-175-
dc.identifier.issn0006-341X-
dc.identifier.urihttp://hdl.handle.net/10722/260265-
dc.description.abstract© 2017, The International Biometric Society We study in this article jointly testing the associations of a genetic variant with correlated multiple phenotypes using the summary statistics of individual phenotype analysis from Genome-Wide Association Studies (GWASs). We estimated the between-phenotype correlation matrix using the summary statistics of individual phenotype GWAS analyses, and developed genetic association tests for multiple phenotypes by accounting for between-phenotype correlation without the need to access individual-level data. Since genetic variants often affect multiple phenotypes differently across the genome and the between-phenotype correlation can be arbitrary, we proposed robust and powerful multiple phenotype testing procedures by jointly testing a common mean and a variance component in linear mixed models for summary statistics. We computed the p-values of the proposed tests analytically. This computational advantage makes our methods practically appealing in large-scale GWASs. We performed simulation studies to show that the proposed tests maintained correct type I error rates, and to compare their powers in various settings with the existing methods. We applied the proposed tests to a GWAS Global Lipids Genetics Consortium summary statistics data set and identified additional genetic variants that were missed by the original single-trait analysis.-
dc.languageeng-
dc.relation.ispartofBiometrics-
dc.subjectPleiotropy-
dc.subjectCorrelated phenotypes-
dc.subjectFisher method-
dc.subjectLinear mixed models-
dc.subjectSummary statistics-
dc.subjectVariance component test-
dc.titleMultiple phenotype association tests using summary statistics in genome-wide association studies-
dc.typeArticle-
dc.description.natureLink_to_subscribed_fulltext-
dc.identifier.doi10.1111/biom.12735-
dc.identifier.scopuseid_2-s2.0-85021374562-
dc.identifier.volume74-
dc.identifier.issue1-
dc.identifier.spage165-
dc.identifier.epage175-
dc.identifier.eissn1541-0420-

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