PSEA: Phenotype Set Enrichment Analysis—A New Method for Analysis of Multiple Phenotypes
Open accessAbstract
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.
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From Few shot learning for phenotype-driven diagnosis of patients with rare genetic diseases (2025) · cites this paper as [76]
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Few shot learning for phenotype-driven diagnosis of patients with rare genetic diseases
Alsentzer, E., Li, M. M., Kobren, S. N., Noori, A., Undiagnosed Diseases Network, Kohane, I. S., & Zitnik, M. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01749-1 · Cited by 54 · see citing works
- Authors
- Ried, J. S., Döring, A., Oexle, K., Meisinger, C., Winkelmann, J., Klopp, N., Meitinger, T., Peters, A., Suhre, K., Wichmann, H. ‐., & Gieger, C.
- Affiliations
- Helmholtz Munich (DE)
- Technical University of Munich (DE)
- Corresponding author
- Gieger, Christian
- ORCID
- Venue
- Genetic Epidemiology
- Publisher
- Wiley
- Published
- 2012-04
- Volume
- 36
- Issue
- 3
- Pages
- 244-252
- ISSN
- 0741-0395 (ISSN-L)
- 1098-2272
- OpenAlex
- W1594811851
- Cited by
- 21
- References
- 26
- Citations by year
- 2025: 1 · 2023: 1 · 2021: 2 · 2019: 2 · 2018: 2 · 2016: 4 · 2015: 4 · 2014: 4 · 2012: 1
- FWCI
- 1.16
- Type
- journal article
- Language
- en
- Open access
- green
- zenodo.org
- Licence
- Locations
- Genetic Epidemiology (publishedVersion)
- PubMed (publishedVersion)
- mediaTUM – the media and publications repository of the Technical University Munich (Technical University Munich) (submittedVersion)
- Site cant be reached (submittedVersion)
- OPUS (Augsburg University) (submittedVersion)
- Zenodo (CERN European Organization for Nuclear Research) (submittedVersion, other-oa)
- Funders
- Bundesministerium für Bildung und Forschung
- Helmholtz Zentrum München
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Mapping and Diversity in Plants and Animals
- Keywords
- multiple phenotypes, gene set enrichment analysis, genome-wide association study, GWAS summary statistics, statistical power
- MeSH terms
- Blood Proteins, Erythrocytes, Genotype, Humans, Models, Genetic, Phenotype, Software, Cohort Studies, Polymorphism, Single Nucleotide, Genome-Wide Association Study
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