title: Identifying rare disease variants in the Genetic Analysis Workshop 17 simulated data: a comparison of several statistical approaches.
abstract: Genome-wide association studies have been successful at identifying common disease variants associated with complex diseases, but the common variants identified have small effect sizes and account for only a small fraction of the estimated heritability for common diseases. Theoretical and empirical studies suggest that rare variants, which are much less frequent in populations and are poorly captured by single-nucleotide polymorphism chips, could play a significant role in complex diseases. Several new statistical methods have been developed for the analysis of rare variants, for example, the combined multivariate and collapsing method, the weighted-sum method and a replication-based method. Here, we apply and compare these methods to the simulated data sets of Genetic Analysis Workshop 17 and thereby explore the contribution of rare variants to disease risk. In addition, we investigate the usefulness of extreme phenotypes in identifying rare risk variants when dealing with quantitative traits. Finally, we perform a pathway analysis and show the importance of the vascular endothelial growth factor pathway in explaining different phenotypes.
authors.affiliation: Department of Statistics, Columbia University, 1255 Amsterdam Avenue, MC 4690, New York, NY 10027, USA.
authors.forename: Ruixue
authors.initials: R
authors.lastname: Fan
authors.affiliation: Department of Statistics, Columbia University, 1255 Amsterdam Avenue, MC 4690, New York, NY 10027, USA.
authors.forename: Chien-Hsun
authors.initials: CH
authors.lastname: Huang
authors.affiliation: Department of Statistics, Columbia University, 1255 Amsterdam Avenue, MC 4690, New York, NY 10027, USA.
authors.forename: Shaw-Hwa
authors.initials: SH
authors.lastname: Lo
authors.affiliation: Department of Statistics, Columbia University, 1255 Amsterdam Avenue, MC 4690, New York, NY 10027, USA.
authors.forename: Tian
authors.initials: T
authors.lastname: Zheng
authors.affiliation: Department of Biostatistics, Mailman School of Public Health, Columbia University, 722 West 168th Street, New York, NY 10032, USA.
authors.forename: Iuliana
authors.initials: I
authors.lastname: Ionita-Laza