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This article is part of the supplement: Genetic Analysis Workshop 17: Unraveling Human Exome Data

Open Access Proceedings

A pathway-based association analysis model using common and rare variants

Lu Cheng1, Pingzhao Hu2, Jenna Sykes1, Melania Pintilie13, Geoffrey Liu13 and Wei Xu13*

Author Affiliations

1 Department of Biostatistics, Princess Margaret Hospital, 610 University Ave., Toronto, ON M5G 2M9, Canada

2 Centre for Applied Genomics, Hospital for Sick Children Research Institute, 101 College Street, Toronto, ON M5G 1L7, Canada

3 Dalla Lana School of Public Health, University of Toronto, 155 College St., Toronto, ON M5T 3M7, Canada

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BMC Proceedings 2011, 5(Suppl 9):S85  doi:10.1186/1753-6561-5-S9-S85

Published: 29 November 2011


How various genetic effects in combination affect susceptibility to certain disease states continues to be a major area of methodological research. Various rare variant models have been proposed, in response to a common failure to either identify or validate biologically driven causal genetic variants in genome-wide association studies. Adopting the idea that multiple rare variants may effectively produce a combined effect equal to a single common variant effect through common linkage with this variant, we construct a pathway-based genetic association analysis model using both common and rare variants. This genetic model is applied to the disease status of unrelated individuals in replication 1 from Genetic Analysis Workshop 17. In this simulated example, we were able to identify several pathways that were potentially associated with the disease status and found that common variants showed stronger genetic effect than rare variants.