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Statistical methods for association tests of multiple continuous traits in genome-wide association studies.

TitleStatistical methods for association tests of multiple continuous traits in genome-wide association studies.
Publication TypeJournal Article
Year of Publication2015
AuthorsWu B
Secondary AuthorsPankow JS
JournalAnn Hum Genet
Volume79
Issue4
Pagination282-93
Date Published2015 Jul
ISSN1469-1809
KeywordsAged, Atherosclerosis, Computer Simulation, Diabetes Mellitus, Female, Genome-Wide Association Study, Humans, Likelihood Functions, Male, Middle Aged, Multicenter Studies as Topic, Polymorphism, Single Nucleotide, Regression Analysis
Abstract

Multiple correlated traits are often collected in genetic studies. The joint analysis of multiple traits could have increased power by aggregating multiple weak effects and offer additional insights into the aetiology of complex human diseases by revealing pleiotropic variants. We propose to study multivariate test statistics to detect single nucleotide polymorphism (SNP) association with multiple correlated traits. Most existing methods have been based on the generalized estimating equation (GEE) approach without explicitly modelling the trait correlations. In this article, we explore an alternative likelihood-based framework to test the multiple trait associations. It is based on the familiar multinomial logistic regression modelling of genotypes, which can be readily implemented using widely available software, and offers very competitive performance. We demonstrate through extensive numerical studies that the proposed method has competitive performance. Its usefulness is further illustrated with application to association analysis of diabetes-related traits in the Atherosclerosis Risk in Communities (ARIC) Study.

DOI10.1111/ahg.12110
Alternate JournalAnn Hum Genet
PubMed ID25857693
PubMed Central IDPMC4474745
Grant ListHHSN268201100012C / HL / NHLBI NIH HHS / United States
UL1RR025005 / RR / NCRR NIH HHS / United States
HHSN268201100009I / HL / NHLBI NIH HHS / United States
R01HL59367 / HL / NHLBI NIH HHS / United States
HHSN268201100010C / HL / NHLBI NIH HHS / United States
UL1 RR025005 / RR / NCRR NIH HHS / United States
HHSN268201100008C / HL / NHLBI NIH HHS / United States
R01 CA134848 / CA / NCI NIH HHS / United States
HHSN268201100005G / HL / NHLBI NIH HHS / United States
HHSN268201100008I / HL / NHLBI NIH HHS / United States
HHSN268201100005C / / PHS HHS / United States
R01 HL059367 / HL / NHLBI NIH HHS / United States
HHSN268201100007C / HL / NHLBI NIH HHS / United States
HHSN268201100009C / / PHS HHS / United States
HHSN268201100011I / HL / NHLBI NIH HHS / United States
HHSN268201100011C / HL / NHLBI NIH HHS / United States
R01 HL086694 / HL / NHLBI NIH HHS / United States
HHSN268200625226C / / PHS HHS / United States
U01 HG004402 / HG / NHGRI NIH HHS / United States
GM083345 / GM / NIGMS NIH HHS / United States
HHSN268201100010C / / PHS HHS / United States
U01HG004402 / HG / NHGRI NIH HHS / United States
HHSN268201100006C / HL / NHLBI NIH HHS / United States
HHSN268201100008C / / PHS HHS / United States
HHSN268201100012C / / PHS HHS / United States
R01HL087641 / HL / NHLBI NIH HHS / United States
HHSN268201100005I / HL / NHLBI NIH HHS / United States
HHSN268201100007C / / PHS HHS / United States
HHSN268201100009C / HL / NHLBI NIH HHS / United States
HHSN268201100011C / / PHS HHS / United States
R01 GM083345 / GM / NIGMS NIH HHS / United States
HHSN268201100005C / HL / NHLBI NIH HHS / United States
HHSN268201100007I / HL / NHLBI NIH HHS / United States
HHSN268201100006C / / PHS HHS / United States
R01 HL087641 / HL / NHLBI NIH HHS / United States
R01HL086694 / HL / NHLBI NIH HHS / United States