This article is part of the supplement: Genetic Analysis Workshop 17: Unraveling Human Exome Data
Gene-based partial least-squares approaches for detecting rare variant associations with complex traits
1 Department of Statistics, The Ohio State University, 1179 University Drive, Newark, OH 43055, USA
2 Department of Statistics, The Ohio State University, 1958 Neil Avenue, 404 Cockins Columbus, OH 43210, USA
BMC Proceedings 2011, 5(Suppl 9):S19 doi:10.1186/1753-6561-5-S9-S19Published: 29 November 2011
Genome-wide association studies are largely based on single-nucleotide polymorphisms and rest on the common disease/common variants (single-nucleotide polymorphisms) hypothesis. However, it has been argued in the last few years and is well accepted now that rare variants are valuable for studying common diseases. Although current genome-wide association studies have successfully discovered many genetic variants that are associated with common diseases, detecting associated rare variants remains a great challenge. Here, we propose two partial least-squares approaches to aggregate the signals of many single-nucleotide polymorphisms (SNPs) within a gene to reveal possible genetic effects related to rare variants. The availability of the 1000 Genomes Project offers us the opportunity to evaluate the effectiveness of these two gene-based approaches. Compared to results from a SNP-based analysis, the proposed methods were able to identify some (rare) SNPs that were missed by the SNP-based analysis.