An Adaptive Association Test for Multiple Phenotypes with GWAS Summary Statistics

Junghi Kim, Yun Bai, Wei Pan

Research output: Contribution to journalArticle

29 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)651-663
Number of pages13
JournalGenetic epidemiology
Volume39
Issue number8
DOIs
StatePublished - Dec 1 2015

Keywords

  • Adaptive sum of powered score test
  • GEE
  • Meta analysis
  • Multivariate trait
  • Statistical power

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