An Adaptive Association Test for Multiple Phenotypes with GWAS Summary Statistics.
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.
Division of Biostatistics, University of Minnesota, Minneapolis, Minnesota, United States of America.
Junghi
J
Kim
Division of Biostatistics, University of Minnesota, Minneapolis, Minnesota, United States of America.
Yun
Y
Bai
Division of Biostatistics, University of Minnesota, Minneapolis, Minnesota, United States of America.
Wei
W
Pan
D005819:Genetic Markers / Q000235:genetics*; D055106:Genome-Wide Association Study / Q000706:statistics & numerical data*; D005838:Genotype; D006801:Humans; D008055:Lipids / Q000097:blood* / Q000235:genetics*; D008957:Models, Genetic; D010641:Phenotype; D020641:Polymorphism, Single Nucleotide / Q000235:genetics
D005819:Genetic Markers; D008055:Lipids
GEE; adaptive sum of powered score test; meta analysis; multivariate trait; statistical power