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Article: Multiple phenotype association tests using summary statistics in genome-wide association studies
Title | Multiple phenotype association tests using summary statistics in genome-wide association studies |
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Authors | |
Keywords | Pleiotropy Correlated phenotypes Fisher method Linear mixed models Summary statistics Variance component test |
Issue Date | 2018 |
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 Identifier | http://hdl.handle.net/10722/260265 |
ISSN | 2023 Impact Factor: 1.4 2023 SCImago Journal Rankings: 1.480 |
PubMed Central ID | |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Liu, Zhonghua | - |
dc.contributor.author | Lin, Xihong | - |
dc.date.accessioned | 2018-09-12T02:00:57Z | - |
dc.date.available | 2018-09-12T02:00:57Z | - |
dc.date.issued | 2018 | - |
dc.identifier.citation | Biometrics, 2018, v. 74, n. 1, p. 165-175 | - |
dc.identifier.issn | 0006-341X | - |
dc.identifier.uri | http://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.language | eng | - |
dc.relation.ispartof | Biometrics | - |
dc.subject | Pleiotropy | - |
dc.subject | Correlated phenotypes | - |
dc.subject | Fisher method | - |
dc.subject | Linear mixed models | - |
dc.subject | Summary statistics | - |
dc.subject | Variance component test | - |
dc.title | Multiple phenotype association tests using summary statistics in genome-wide association studies | - |
dc.type | Article | - |
dc.description.nature | link_to_OA_fulltext | - |
dc.identifier.doi | 10.1111/biom.12735 | - |
dc.identifier.pmcid | PMC5743780 | - |
dc.identifier.scopus | eid_2-s2.0-85021374562 | - |
dc.identifier.hkuros | 294006 | - |
dc.identifier.volume | 74 | - |
dc.identifier.issue | 1 | - |
dc.identifier.spage | 165 | - |
dc.identifier.epage | 175 | - |
dc.identifier.eissn | 1541-0420 | - |
dc.identifier.isi | WOS:000427562400018 | - |
dc.identifier.issnl | 0006-341X | - |