title: Gene-based partial least-squares approaches for detecting rare variant associations with complex traits.
abstract: 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.
authors.affiliation: Department of Statistics, The Ohio State University, 1179 University Drive, Newark, OH 43055, USA. turkmen@stat.osu.edu.
authors.forename: Asuman S
authors.initials: AS
authors.lastname: Turkmen
authors.forename: Shili
authors.initials: S
authors.lastname: Lin