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Strategies and issues in the detection of pathway enrichment in genome-wide association studies

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Abstract

A fundamental question in human genetics is the degree to which the polygenic character of complex traits derives from polymorphism in genes with similar or with dissimilar functions. The many genome-wide association studies now being performed offer an opportunity to investigate this, and although early attempts are emerging, new tools and modeling strategies still need to be developed and deployed. Towards this goal, we implemented a new algorithm to facilitate the transition from genetic marker lists (principally those generated by PLINK) to pathway analyses of representational gene sets in either threshold or threshold-free downstream applications (e.g. DAVID, GSEA-P, and Ingenuity Pathway Analysis). This was applied to several large genome-wide association studies covering diverse human traits that included type 2 diabetes, Crohn’s disease, and plasma lipid levels. Validation of this approach was obtained for plasma HDL levels, where functional categories related to lipid metabolism emerged as the most significant in two independent studies. From analyses of these samples, we highlight and address numerous issues related to this strategy, including appropriate gene based correction statistics, the utility of imputed versus non-imputed marker sets, and the apparent enrichment of pathways due solely to the positional clustering of functionally related genes. The latter in particular emphasizes the importance of studies that directly tie genetic variation to functional characteristics of specific genes. The software freely provided that we have called ProxyGeneLD may resolve an important bottleneck in pathway-based analyses of genome-wide association data. This has allowed us to identify at least one replicable case of pathway enrichment but also to highlight functional gene clustering as a potentially serious problem that may lead to spurious pathway findings if not corrected.

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Acknowledgments

We are greatly indebted to the scientists responsible for making their genome-wide data accessible. We are grateful for financial support from The Swedish Medical Research Council (grant 2007-2722) and the National Institutes of Health (grant AG028555).

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The authors declare no conflict of interest.

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  1. Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Nobels väg 12A, 171 77, Stockholm, Sweden

    Mun-Gwan Hong, Yudi Pawitan, Patrik K. E. Magnusson & Jonathan A. Prince

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  1. Mun-Gwan Hong
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  2. Yudi Pawitan
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Correspondence to Jonathan A. Prince.

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Hong, MG., Pawitan, Y., Magnusson, P.K.E. et al. Strategies and issues in the detection of pathway enrichment in genome-wide association studies. Hum Genet 126, 289–301 (2009). https://doi.org/10.1007/s00439-009-0676-z

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