An Adaptive Association Test for Multiple Phenotypes with GWAS Summary Statistics

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


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.

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

Publication types

  • Research Support, N.I.H., Extramural

MeSH terms

  • Genetic Markers / genetics*
  • Genome-Wide Association Study / statistics & numerical data*
  • Genotype
  • Humans
  • Lipids / blood*
  • Lipids / genetics*
  • Models, Genetic*
  • Phenotype
  • Polymorphism, Single Nucleotide / genetics


  • Genetic Markers
  • Lipids