Academic Commons

Articles

Identifying rare disease variants in the Genetic Analysis Workshop 17 simulated data: a comparison of several statistical approaches

Fan, Ruixue; Huang, Chien-Hsun; Lo, Shaw-Hwa; Zheng, Tian; Ionita-Laza, Iuliana

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.

Subjects

Files

Also Published In

Title
BMC Proceedings
DOI
https://doi.org/10.1186/1753-6561-5-S9-S17

More About This Work

Academic Units
Statistics
Biostatistics
Publisher
BioMed Central
Published Here
March 27, 2015
Academic Commons provides global access to research and scholarship produced at Columbia University, Barnard College, Teachers College, Union Theological Seminary and Jewish Theological Seminary. Academic Commons is managed by the Columbia University Libraries.