Beschreibung:
<jats:p>Most genome‐wide association studies (<jats:styled-content style="fixed-case">GWAS</jats:styled-content>) 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” (<jats:styled-content style="fixed-case">PSEA</jats:styled-content>), which uses ideas of gene set enrichment analysis for the investigation of phenotype sets. <jats:styled-content style="fixed-case">PSEA</jats:styled-content> 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 <jats:styled-content style="fixed-case">GWAS</jats:styled-content> summary statistics. <jats:styled-content style="fixed-case">PSEA</jats:styled-content> was applied to data from the population‐based cohort <jats:styled-content style="fixed-case">KORA F</jats:styled-content>4 (<jats:italic>N</jats:italic> = 1,814) using iron‐related and blood count traits. By confirming associations previously found in large meta‐analyses on these traits, <jats:styled-content style="fixed-case">PSEA</jats:styled-content> was shown to be a reliable tool. Many of these associations were not detectable by <jats:styled-content style="fixed-case">GWAS</jats:styled-content> on single phenotypes in <jats:styled-content style="fixed-case">KORA F</jats:styled-content>4. Therefore, the results suggest that <jats:styled-content style="fixed-case">PSEA</jats:styled-content> can be more powerful than a single phenotype <jats:styled-content style="fixed-case">GWAS</jats:styled-content> for the identification of association with multiple phenotypes. <jats:styled-content style="fixed-case">PSEA</jats:styled-content> 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 <jats:styled-content style="fixed-case">PSEA</jats:styled-content> based on <jats:styled-content style="fixed-case">GWAS</jats:styled-content> results is available upon request.</jats:p>