Integrating genetic and gene expression evidence into genome-wide association analysis of gene sets
Open accessAbstract
Single variant or single gene analyses generally account for only a small proportion of the phenotypic variation in complex traits. Alternatively, gene set or pathway association analyses are playing an increasingly important role in uncovering genetic architectures of complex traits through the identification of systematic genetic interactions. Two dominant paradigms for gene set analyses are association analyses based on SNP genotypes and those based on gene expression profiles. However, gene–disease association can manifest in many ways, such as alterations of gene expression, genotype, and copy number; thus, an integrative approach combining multiple forms of evidence can more accurately and comprehensively capture pathway associations. We have developed a single statistical framework, Gene Set Association Analysis (GSAA), that simultaneously measures genome-wide patterns of genetic variation and gene expression variation to identify sets of genes enriched for differential expression and/or trait-associated genetic markers. Simulation studies illustrate that joint analyses of genomic data increase the power to detect real associations when compared with gene set methods that use only one genomic data type. The analysis of two human diseases, glioblastoma and Crohn's disease, detected abnormalities in previously identified disease-associated pathways, such as pathways related to PI3K signaling, DNA damage response, and the activation of NFKB. In addition, GSAA predicted novel pathway associations, for example, differential genetic and expression characteristics in genes from the ABC transporter family in glioblastoma and from the HLA system in Crohn's disease. These demonstrate that GSAA can help uncover biological pathways underlying human diseases and complex traits.
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Xiong et al. (2012) only focused on integrating gene expression and SNP genotype data.
“Although Xiong et al. (2012) only focused on integrating the gene expression and SNP genotype data”
From Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases (2016) · cites this paper as Xiong et al. (2012)
Xiong et al. (2012) developed GSAA, a statistical framework that aggregates genetic and gene expression evidence as association scores at the gene set or pathway level for genome-wide association analysis.
“Xiong et al. (2012) developed a statistical framework, called Gene Set Association Analysis (GSAA), that aggregates genetic and gene expression evidence in terms of “association scores” at the level of gene sets or pathways for genome-wide association analysis of gene sets or pathways.”
From Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases (2016) · cites this paper as Xiong et al. (2012)
The flexibility of the Xiong et al. framework allows integration of other omic data such as DNA methylation, proteomics, and metabolomics.
“the flexibility of this framework allows integration of other omic data such as DNA methylation, proteomics, and metabolomics data (Figure 5)”
From Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases (2016) · cites this paper as Xiong et al. (2012)
A phenotype-based permutation procedure that preserves LD structure in SNP data and gene-gene correlation structure in gene expression data can calculate FDR or FWER and declare significant gene sets controlling FDR or FWER below a threshold.
“Based on a phenotype-based permutation procedure that preserves LD structure in SNP data and gene-gene correlation structure in gene expression data, the false discovery rate (FDR) or the family-wise error rate (FWER) can be calculated and the significant gene sets are declared controlling for FDR or FWER below a certain threshold.”
From Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases (2016) · cites this paper as Xiong et al. (2012)
The weighted Kolmogorov-Smirnov statistic (gene set association score) determines whether genes in a gene set are preferentially near the top of a ranked list based on gene association scores.
“Third, the gene set is evaluated by a weighted Kolmogorov-Smirnov (K-S) statistic (i.e., gene set association score) to determine whether the genes belonging to this gene set are preferentially near the top of the ranked ordered list based on gene association scores.”
From Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases (2016) · cites this paper as Xiong et al. (2012)
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References 91 · Cited by 103
- Authors
- Xiong, Q., Ancona, N., Hauser, E. R., Mukherjee, S., & Furey, T. S.
- Affiliations
- University of North Carolina at Chapel Hill (US)
- Institute of Intelligent Systems for Automation (IT)
- Duke University (US)
- Venue
- Genome Research
- Publisher
- Cold Spring Harbor Laboratory
- Published
- 2011-09-22
- Volume
- 22
- Issue
- 2
- Pages
- 386-397
- ISSN
- 1088-9051 (ISSN-L)
- PMC
- PMC3266045
- OpenAlex
- W4298085301
- Cited by
- 103
- References
- 91
- Citations by year
- 2026: 2 · 2025: 4 · 2024: 3 · 2023: 4 · 2022: 5 · 2021: 7 · 2020: 5 · 2019: 9 · 2018: 2 · 2017: 8 · 2016: 18 · 2015: 10 · 2014: 10 · 2013: 13 · 2012: 3
- Type
- posted content
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- other-oa
- Locations
- Genome Research (publishedVersion, other-oa)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Carolina Digital Repository (University of North Carolina at Chapel Hill) (submittedVersion)
- HighWire Press Open Archive (submittedVersion)
- UNC Libraries
- Funders
- National Science Foundation (1049290, 0732260)
- University of Chicago
- Wellcome Trust
- Broad Institute
- National Institutes of Health
- University of North Carolina at Chapel Hill
- National Institute on Aging (P30AG028716)
- National Institute of Mental Health (MH059528, R01MH059528)
- National Cancer Institute (R01CA123175, R01CA125618, CA123175-01A1, RC1CA146849)
- 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
- integrative genomic analysis, gene expression, genetic variation, complex traits, glioblastoma, Crohn's disease, PI3K signaling, DNA damage response, NF-κB activation, ABC transporters, HLA system, differential gene expression
- MeSH terms
- Algorithms, Computer Simulation, Crohn Disease, Humans, Models, Genetic, Neoplasms, Signal Transduction, Computational Biology, Genetic Predisposition to Disease, Polymorphism, Single Nucleotide, Gene Expression Profiling, Genomics, Genome-Wide Association Study
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