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PSEA : Phenotype Set Enrichment Analysis—A New Method for Analysis of Multiple Phenotypes
Author(s) -
Ried Janina S.,
Döring Angela,
Oexle Konrad,
Meisinger Christa,
Winkelmann Juliane,
Klopp Norman,
Meitinger Thomas,
Peters Annette,
Suhre Karsten,
Wichmann H.Erich,
Gieger Christian
Publication year - 2012
Publication title -
genetic epidemiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.301
H-Index - 98
eISSN - 1098-2272
pISSN - 0741-0395
DOI - 10.1002/gepi.21617
Subject(s) - phenotype , genome wide association study , genetics , biology , population , genetic association , set (abstract data type) , computational biology , genotype , single nucleotide polymorphism , gene , computer science , medicine , programming language , environmental health
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 F 4 ( 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 F 4. 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.