Gene set analysis of genome-wide association studies: Methodological issues and perspectives
Open accessAbstract
Recent studies have demonstrated that gene set analysis, which tests disease association with genetic variants in a group of functionally related genes, is a promising approach for analyzing and interpreting genome-wide association studies (GWAS) data. These approaches aim to increase power by combining association signals from multiple genes in the same gene set. In addition, gene set analysis can also shed more light on the biological processes underlying complex diseases. However, current approaches for gene set analysis are still in an early stage of development in that analysis results are often prone to sources of bias, including gene set size and gene length, linkage disequilibrium patterns and the presence of overlapping genes. In this paper, we provide an in-depth review of the gene set analysis procedures, along with parameter choices and the particular methodology challenges at each stage. In addition to providing a survey of recently developed tools, we also classify the analysis methods into larger categories and discuss their strengths and limitations. In the last section, we outline several important areas for improving the analytical strategies in gene set analysis.
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Longer genes are more likely to be implicated by CNVs and SNVs.
“For example, longer genes are more likely to be implicated by CNVs and SNVs87,184,185”
From Systems biology and gene networks in neurodevelopmental and neurodegenerative disorders (2015) · cites this paper as [185]
Studies have demonstrated biases in gene length, gene mutability, and other factors that can drive gene set enrichment instead of a true biological signal.
“However, studies have demonstrated biases in gene length, gene mutability and other factors that can drive gene set enrichment instead of a true biological signal.”
From Systems biology and gene networks in neurodevelopmental and neurodegenerative disorders (2015) · cites this paper as [185]
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- [14]Hong MG Pawitan Y Magnusson PK Prince JA Strategies and issues in the detection of pathway enrichment in genome-wide association studies Hum Genet 2009 126 289 301 19408013 PMC2865249
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- Authors
- Wang, L., Jia, P., Wolfinger, R. D., Chen, X., & Zhao, Z.
- Affiliations
- Vanderbilt University (US)
- SAS Institute (United States) (US)
- Corresponding author
- Wang, Lily
- Zhao, Zhongming
- Venue
- Genomics
- Publisher
- Elsevier BV
- Published
- 2011-07
- Volume
- 98
- Issue
- 1
- Pages
- 1-8
- ISSN
- 0888-7543 (ISSN-L)
- PMC
- PMC3852939
- OpenAlex
- W2162960415
- Cited by
- 207
- References
- 94
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 6 · 2023: 7 · 2022: 14 · 2021: 14 · 2020: 16 · 2019: 15 · 2018: 14 · 2017: 22 · 2016: 26 · 2015: 18 · 2014: 15 · 2013: 17 · 2012: 14
- FWCI
- 1.51
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Genomics (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- NICHD (5P30 HD015052-25)
- NIH (1P50 MH078028-01A1, R21AA017437, P20AA017828, R01MH083094)
- NCI (5P30CA068485-13)
- Vanderbilt-Ingram Cancer Center Core (P30CA68485)
- NARSAD
- 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 › Molecular Biology › Biomedical Text Mining and Ontologies
- Keywords
- gene set analysis, genome-wide association study, statistical power, linkage disequilibrium, gene length, overlapping genes, complex diseases
- MeSH terms
- Multigene Family, Humans, Polymorphism, Single Nucleotide, Genome-Wide Association Study
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