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Genet Epidemiol. 2015 Dec;39(8):651-63. doi: 10.1002/gepi.21931. Epub 2015 Oct 22.

An Adaptive Association Test for Multiple Phenotypes with GWAS Summary Statistics.

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Division of Biostatistics, University of Minnesota, Minneapolis, Minnesota, United States of America.


We study the problem of testing for single marker-multiple phenotype associations based on genome-wide association study (GWAS) summary statistics without access to individual-level genotype and phenotype data. For most published GWASs, because obtaining summary data is substantially easier than accessing individual-level phenotype and genotype data, while often multiple correlated traits have been collected, the problem studied here has become increasingly important. We propose a powerful adaptive test and compare its performance with some existing tests. We illustrate its applications to analyses of a meta-analyzed GWAS dataset with three blood lipid traits and another with sex-stratified anthropometric traits, and further demonstrate its potential power gain over some existing methods through realistic simulation studies. We start from the situation with only one set of (possibly meta-analyzed) genome-wide summary statistics, then extend the method to meta-analysis of multiple sets of genome-wide summary statistics, each from one GWAS. We expect the proposed test to be useful in practice as more powerful than or complementary to existing methods.


GEE; adaptive sum of powered score test; meta analysis; multivariate trait; statistical power

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