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Review
. 2011 Jul;98(1):1-8.
doi: 10.1016/j.ygeno.2011.04.006. Epub 2011 Apr 30.

Gene set analysis of genome-wide association studies: methodological issues and perspectives

Lily Wang  1 , Peilin Jia, Russell D Wolfinger, Xi Chen, Zhongming Zhao
Affiliations
Review

Gene set analysis of genome-wide association studies: methodological issues and perspectives

Lily Wang et al. Genomics. 2011 Jul.

Abstract

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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Figures

Fig 1
Fig 1
Work flow for gene set analysis of GWAS datasets.
See this image and copyright information in PMC

References

    1. Manolio TA, Collins FS, Cox NJ, Goldstein DB, Hindorff LA, Hunter DJ, McCarthy MI, Ramos EM, Cardon LR, Chakravarti A, Cho JH, Guttmacher AE, Kong A, Kruglyak L, Mardis E, Rotimi CN, Slatkin M, Valle D, Whittemore AS, Boehnke M, Clark AG, Eichler EE, Gibson G, Haines JL, Mackay TF, McCarroll SA, Visscher PM. Finding the missing heritability of complex diseases. Nature. 2009;461:747–753. - PMC - PubMed
    1. Elbers CC, van der Schouw YT, Wijmenga C, Onland-Moret NC. Comment on: Perry et al. (2009) interrogating type 2 diabetes genome-wide association data using a biological pathway-based approach. Diabetes. 58:1463–1467. Diabetes 58 (2009) e9; author reply e10. - PMC - PubMed
    1. Elbers CC, van Eijk KR, Franke L, Mulder F, van der Schouw YT, Wijmenga C, Onland-Moret NC. Using genome-wide pathway analysis to unravel the etiology of complex diseases. Genet Epidemiol. 2009;33:419–431. - PubMed
    1. Jia P, Wang L, Meltzer HY, Zhao Z. Pathway-based analysis of GWAS datasets: effective but caution required. Int J Neuropsychopharmacol. 2011 Epub ahead of print December 16, 2010. - PubMed
    1. Wang K, Li M, Bucan M. Pathway-Based Approaches for Analysis of Genomewide Association Studies. Am. J. Hum. Genet. 2007;81:1278–1283. - PMC - PubMed

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