Supporting Information is available in the online issue at http://wileyonlinelibrary.com.
PSEA: Phenotype Set Enrichment Analysis—A New Method for Analysis of Multiple Phenotypes
Article first published online: 19 APR 2012
© 2012 Wiley Periodicals, Inc.
Volume 36, Issue 3, pages 244–252, April 2012
How to Cite
Ried, J. S., Döring, A., Oexle, K., Meisinger, C., Winkelmann, J., Klopp, N., Meitinger, T., Peters, A., Suhre, K., Wichmann, H.-E. and Gieger, C. (2012), PSEA: Phenotype Set Enrichment Analysis—A New Method for Analysis of Multiple Phenotypes. Genet. Epidemiol., 36: 244–252. doi: 10.1002/gepi.21617
- Issue published online: 19 APR 2012
- Article first published online: 19 APR 2012
- Manuscript Accepted: 2 JAN 2012
- Manuscript Revised: 8 NOV 2011
- Manuscript Received: 2 SEP 2011
- EU FP6. Grant Number: RI-031513
- FP7. Grant Number: RI-222919
- German Federal Ministry of Education and Research (BMBF)
- German National Genome Research Network (NGFN)
- Munich Center of Health Sciences (MC Health)
- genome-wide association;
- permutation test
Most genome-wide association studies (GWAS) are restricted to one phenotype, even if multiple related or unrelated phenotypes are available. However, an integrated analysis of multiple phenotypes can provide insight into their shared genetic basis and may improve the power of association studies. We present a new method, called “phenotype set enrichment analysis” (PSEA), which uses ideas of gene set enrichment analysis for the investigation of phenotype sets. PSEA combines statistics of univariate phenotype analyses and tests by permutation. It does not only allow analyzing predefined phenotype sets, but also to identify new phenotype sets. Apart from the application to situations where phenotypes and genotypes are available for each person, the method was adjusted to the analysis of GWAS summary statistics. PSEA was applied to data from the population-based cohort KORA F4 (N = 1,814) using iron-related and blood count traits. By confirming associations previously found in large meta-analyses on these traits, PSEA was shown to be a reliable tool. Many of these associations were not detectable by GWAS on single phenotypes in KORA F4. Therefore, the results suggest that PSEA can be more powerful than a single phenotype GWAS for the identification of association with multiple phenotypes. PSEA is a valuable method for analysis of multiple phenotypes, which can help to understand phenotype networks. Its flexible design enables both the use of prior knowledge and the generation of new knowledge on connection of multiple phenotypes. A software program for PSEA based on GWAS results is available upon request.