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Ried, J.S. ; Döring, A. ; Oexle, K.* ; Meisinger, C. ; Winkelmann, J. ; Klopp, N. ; Meitinger, T. ; Peters, A. ; Suhre, K. ; Wichmann, H.-E. ; Gieger, C.

PSEA: Phenotype Set Enrichment Analysis - a new method for analysis of multiple phenotypes.

Genet. Epidemiol. 36, 244-252 (2012)
DOI PMC
Open Access Green as soon as Postprint is submitted to ZB.
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.
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Publication type Article: Journal article
Document type Scientific Article
Corresponding Author
Keywords Genome-wide Association ; Pleiotropy ; Permutation Test; GENOME-WIDE ASSOCIATION; TRAITS
ISSN (print) / ISBN 0741-0395
e-ISSN 1098-2272
Journal Genetic Epidemiology
Quellenangaben Volume: 36, Issue: 3, Pages: 244-252 Article Number: , Supplement: ,
Publisher Wiley
Non-patent literature Publications
Reviewing status Peer reviewed
Institute(s) Institute of Genetic Epidemiology (IGE)
Institute of Epidemiology II (EPI2)
Research Unit Molecular Epidemiology (AME)
Institute of Epidemiology (EPI)
Institute of Human Genetics (IHG)
Institute of Bioinformatics and Systems Biology (IBIS)