Adaptive Fisher method detects dense and sparse signals in association analysis of SNV sets.

ABSTRACT:

Background

With the development of next generation sequencing (NGS) technology and genotype imputation methods, statistical methods have been proposed to test a set of genomic variants together to detect if any of them is associated with the phenotype or disease. In practice, within the set, there is an unknown proportion of variants truly causal or associated with the disease. There is a demand for statistical methods with high power in both dense and sparse scenarios, where the proportion of causal or associated variants is large or small respectively.

Results

We propose a new association test - weighted Adaptive Fisher (wAF) that can adapt to both dense and sparse scenarios by adding weights to the Adaptive Fisher (AF) method we developed before. Using simulation, we show

SUBMITTER: Cai X 

PROVIDER: S-EPMC7118831 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature