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Analyze multivariate phenotypes in genetic association studies by combining univariate association tests
Author(s) -
Yang Qiong,
Wu Hongsheng,
Guo ChaoYu,
Fox Caroline S.
Publication year - 2010
Publication title -
genetic epidemiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.301
H-Index - 98
eISSN - 1098-2272
pISSN - 0741-0395
DOI - 10.1002/gepi.20497
Subject(s) - univariate , multivariate statistics , categorical variable , genome wide association study , multivariate analysis , type i and type ii errors , association test , statistics , phenotype , genetic association , framingham heart study , computational biology , single nucleotide polymorphism , biology , genetics , mathematics , medicine , genotype , framingham risk score , disease , gene
Multivariate phenotypes are frequently encountered in genome‐wide association studies (GWAS). Such phenotypes contain more information than univariate phenotypes, but how to best exploit the information to increase the chance of detecting genetic variant of pleiotropic effect is not always clear. Moreover, when multivariate phenotypes contain a mixture of quantitative and qualitative measures, limited methods are applicable. In this paper, we first evaluated the approach originally proposed by O'Brien and by Wei and Johnson that combines the univariate test statistics and then we proposed two extensions to that approach. The original and proposed approaches are applicable to a multivariate phenotype containing any type of components including continuous, categorical and survival phenotypes, and applicable to samples consisting of families or unrelated samples. Simulation results suggested that all methods had valid type I error rates. Our extensions had a better power than O'Brien's method with heterogeneous means among univariate test statistics, but were less powerful than O'Brien's with homogeneous means among individual test statistics. All approaches have shown considerable increase in power compared to testing each component of a multivariate phenotype individually in some cases. We apply all the methods to GWAS of serum uric acid levels and gout with 550,000 single nucleotide polymorphisms in the Framingham Heart Study. Genet. Epidemiol . 34:444–454, 2010. © 2010 Wiley‐Liss, Inc.