Abstract
To identify novel genetic variants associated with complex traits and to shed new insights on underlying biology, in addition to the most popular single SNP-single trait association analysis, it would be useful to explore multiple correlated (intermediate) traits at the gene- or pathway-level by mining existing single GWAS or meta-analyzed GWAS data. For this purpose, we present an adaptive gene-based test and a pathway-based test for association analysis of multiple traits with GWAS summary statistics. The proposed tests are adaptive at both the SNP- and trait-levels; that is, they account for possibly varying association patterns (e.g. signal sparsity levels) across SNPs and traits, thus maintaining high power across a wide range of situations. Furthermore, the proposed methods are general: they can be applied to mixed types of traits, and to Z-statistics or P-values as summary statistics obtained from either a single GWAS or a meta-analysis of multiple GWAS. Our numerical studies with simulated and real data demonstrated the promising performance of the proposed methods.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 64-71 |
| Number of pages | 8 |
| Journal | Bioinformatics |
| Volume | 33 |
| Issue number | 1 |
| DOIs | |
| State | Published - Jan 1 2017 |
Bibliographical note
Funding Information:The authors are grateful to the reviewers for constructive comments. This work was supported by National Institutes of Health [R01GM113250, R01HL105397 and R01HL116720], and the Minnesota Supercomputing Institute.
Publisher Copyright:
© The Author 2016. Published by Oxford University Press. All rights reserved.