A fast and noise-resilient approach to detect rare-variant associations with deep sequencing data for complex disorders.

ABSTRACT: Next generation sequencing technology has enabled the paradigm shift in genetic association studies from the common disease/common variant to common disease/rare-variant hypothesis. Analyzing individual rare variants is known to be underpowered; therefore association methods have been developed that aggregate variants across a genetic region, which for exome sequencing is usually a gene. The foreseeable widespread use of whole genome sequencing poses new challenges in statistical analysis. It calls for new rare-variant association methods that are statistically powerful, robust against high levels of noise due to inclusion of noncausal variants, and yet computationally efficient. We propose a simple and powerful statistic that combines the disease-associated P-values of individual variants

SUBMITTER: Cheung YH 

PROVIDER: S-EPMC6240912 | biostudies-literature | 2012 Nov

REPOSITORIES: biostudies-literature