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Integrating genetic and gene expression evidence into genome-wide association analysis of gene sets.

Publication ,  Journal Article
Xiong, Q; Ancona, N; Hauser, ER; Mukherjee, S; Furey, TS
Published in: Genome Res
February 2012

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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Published In

Genome Res

DOI

EISSN

1549-5469

Publication Date

February 2012

Volume

22

Issue

2

Start / End Page

386 / 397

Location

United States

Related Subject Headings

  • Signal Transduction
  • Polymorphism, Single Nucleotide
  • Neoplasms
  • Models, Genetic
  • Humans
  • Genomics
  • Genome-Wide Association Study
  • Genetic Predisposition to Disease
  • Gene Expression Profiling
  • Crohn Disease
 

Citation

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Xiong, Q., Ancona, N., Hauser, E. R., Mukherjee, S., & Furey, T. S. (2012). Integrating genetic and gene expression evidence into genome-wide association analysis of gene sets. Genome Res, 22(2), 386–397. https://doi.org/10.1101/gr.124370.111
Xiong, Qing, Nicola Ancona, Elizabeth R. Hauser, Sayan Mukherjee, and Terrence S. Furey. “Integrating genetic and gene expression evidence into genome-wide association analysis of gene sets.Genome Res 22, no. 2 (February 2012): 386–97. https://doi.org/10.1101/gr.124370.111.
Xiong Q, Ancona N, Hauser ER, Mukherjee S, Furey TS. Integrating genetic and gene expression evidence into genome-wide association analysis of gene sets. Genome Res. 2012 Feb;22(2):386–97.
Xiong, Qing, et al. “Integrating genetic and gene expression evidence into genome-wide association analysis of gene sets.Genome Res, vol. 22, no. 2, Feb. 2012, pp. 386–97. Pubmed, doi:10.1101/gr.124370.111.
Xiong Q, Ancona N, Hauser ER, Mukherjee S, Furey TS. Integrating genetic and gene expression evidence into genome-wide association analysis of gene sets. Genome Res. 2012 Feb;22(2):386–397.

Published In

Genome Res

DOI

EISSN

1549-5469

Publication Date

February 2012

Volume

22

Issue

2

Start / End Page

386 / 397

Location

United States

Related Subject Headings

  • Signal Transduction
  • Polymorphism, Single Nucleotide
  • Neoplasms
  • Models, Genetic
  • Humans
  • Genomics
  • Genome-Wide Association Study
  • Genetic Predisposition to Disease
  • Gene Expression Profiling
  • Crohn Disease