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Pathway-based analysis using reduced gene subsets in genome-wide association studies

Jingyuan Zhao1, Simone Gupta2, Mark Seielstad3, Jianjun Liu1 and Anbupalam Thalamuthu1*

Author Affiliations

1 Human Genetics, 60 Biopolis Street 02-01, Genome Institute of Singapore, 138672, Singapore

2 McKusick - Nathans Institute of Genetic Medicine, School of Medicine, Johns Hopkins University, 733 N. Broadway St. Baltimore, MD 21205, USA

3 Institute for Human Genetics, 513 Parnassus Avenue, University of California, San Francisco, CA 94143 and Blood Systems Research Institute, 270 Masonic Avenue, San Francisco, CA 94118, USA

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BMC Bioinformatics 2011, 12:17  doi:10.1186/1471-2105-12-17

Published: 12 January 2011



Single Nucleotide Polymorphism (SNP) analysis only captures a small proportion of associated genetic variants in Genome-Wide Association Studies (GWAS) partly due to small marginal effects. Pathway level analysis incorporating prior biological information offers another way to analyze GWAS's of complex diseases, and promises to reveal the mechanisms leading to complex diseases. Biologically defined pathways are typically comprised of numerous genes. If only a subset of genes in the pathways is associated with disease then a joint analysis including all individual genes would result in a loss of power. To address this issue, we propose a pathway-based method that allows us to test for joint effects by using a pre-selected gene subset. In the proposed approach, each gene is considered as the basic unit, which reduces the number of genetic variants considered and hence reduces the degrees of freedom in the joint analysis. The proposed approach also can be used to investigate the joint effect of several genes in a candidate gene study.


We applied this new method to a published GWAS of psoriasis and identified 6 biologically plausible pathways, after adjustment for multiple testing. The pathways identified in our analysis overlap with those reported in previous studies. Further, using simulations across a range of gene numbers and effect sizes, we demonstrate that the proposed approach enjoys higher power than several other approaches to detect associated pathways.


The proposed method could increase the power to discover susceptibility pathways and to identify associated genes using GWAS. In our analysis of genome-wide psoriasis data, we have identified a number of relevant pathways for psoriasis.