# Association Analysis of Common and Rare SNVs using Adaptive Fisher Method to Detect Dense and Sparse Signals

## TL;DR

This paper introduces a weighted Adaptive Fisher (wAF) method for genetic association analysis that effectively detects both dense and sparse signals in GWAS data, outperforming or matching existing methods.

## Contribution

The paper proposes a novel weighted Adaptive Fisher (wAF) test that adapts to different signal densities, improving power in GWAS set-based association testing.

## Key findings

- wAF shows comparable or superior power to SKAT, SKAT-O, and aSPU.
- The method performs well in both simulated and real GWAS data.
- It effectively detects both dense and sparse genetic signals.

## Abstract

The development of next generation sequencing (NGS) technology and genotype imputation methods enabled researchers to measure both common and rare variants in genome-wide association studies (GWAS). 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 of variants, there is an unknown proportion of variants truly causal or associated with the disease. Because most developed methods are sensitive to either the dense scenario, where a large proportion of the variants are associated, or the sparse scenario, where only a small proportion of the variants are associated, there is a demand of statistical methods with high power in both scenarios. In this paper, we propose a new association test (weighted Adaptive Fisher, wAF) that can adapt to both the dense and sparse scenario by adding weights to the Adaptive Fisher (AF) method we developed before. Using both simulation and the Genetic Analysis Workshop 16 (GAW16) data, we have shown that the new method enjoys comparable or better power to popular methods such as sequence kernel association test (SKAT and SKAT-O) and adaptive SPU (aSPU) test.