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Tree-based Rare Variants Analyses

Banff International Research Station for Mathematical Innovation and Discovery

Chi Song and Heping Zhang Since the development of next generation sequencing (NGS) technology, researchers have been extending their efforts on genome-wide association studies (GWAS) from common variants to rare variants to find the missing inheritance. Although various statistical methods have been proposed to analyze rare variants data, they generally face difficulties for complex disease models involving multiple genes. In this paper, we propose a tree-based method that adopts a non-parametric disease model and is capable of exploring gene-gene interactions. We found that our method outperforms the sequence kernel association test (SKAT) in most of our simulation scenarios, and by notable margins in some cases. By applying the tree- based method to the Study of Addiction: Genetics and Environment (SAGE) data, we successfully detected gene CTNNA2 and its 44 specific variants that increase the risk of alcoholism in women. This gene has not been detected in the SAGE data. Post hoc literature search also supports the role of CTNNA2 as a likely risk gene for alcohol addiction. This finding suggests that our tree-based method can be effective in dissecting genetic variants for complex diseases using rare variants data.

Mathematics; Statistics; Biology and other natural sciences; Applied statistics

video/mp4

Author affiliation: Yale University

2014-10-20

Attribution-NonCommercial-NoDerivs 2.5 Canada

http://hdl.handle.net/2429/50797

Unreviewed

http://creativecommons.org/licenses/by-nc-nd/2.5/ca/