Background: Gene-and pathway-based analyses offer a useful alternative and complement to the usual single SNP-based analysis for GWAS. On the other hand, most existing gene-and pathway-based tests are not highly adaptive, and/or require the availability of individual-level genotype and phenotype data. It would be desirable to have highly adaptive tests applicable to summary statistics for single SNPs. This has become increasingly important given the popularity of large-scale meta-analyses of multiple GWASs and the practical availability of either single GWAS or meta-analyzed GWAS summary statistics for single SNPs. Results: We extend two adaptive tests for gene-and pathway-level association with a univariate trait to the case with GWAS summary statistics without individual-level genotype and phenotype data. We use the WTCCC GWAS data to evaluate and compare the proposed methods and several existing methods. We further illustrate their applications to a meta-analyzed dataset to identify genes and pathways associated with blood pressure, demonstrating the potential usefulness of the proposed methods. The methods are implemented in R package aSPU, freely and publicly available.
Bibliographical noteFunding Information:
This research was supported by NIH grants R01GM113250, R01HL105397 and R01HL116720, and by the Minnesota Supercomputing Institute at University of Minnesota.
© 2015 The Author. All rights reserved.