A powerful association test of multiple genetic variants using a random-effects model

Statistics in Medicine
K F ChengC Li

Abstract

There is an emerging interest in sequencing-based association studies of multiple rare variants. Most association tests suggested in the literature involve collapsing rare variants with or without weighting. Recently, a variance-component score test [sequence kernel association test (SKAT)] was proposed to address the limitations of collapsing method. Although SKAT was shown to outperform most of the alternative tests, its applications and power might be restricted and influenced by missing genotypes. In this paper, we suggest a new method based on testing whether the fraction of causal variants in a region is zero. The new association test, T REM , is derived from a random-effects model and allows for missing genotypes, and the choice of weighting function is not required when common and rare variants are analyzed simultaneously. We performed simulations to study the type I error rates and power of four competing tests under various conditions on the sample size, genotype missing rate, variant frequency, effect directionality, and the number of non-causal rare variant and/or causal common variant. The simulation results showed that T REM was a valid test and less sensitive to the inclusion of non-causal rare variants and/or lo...Continue Reading

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Citations

Aug 19, 2015·Genetic Epidemiology·Erin AustinWei Pan
Mar 4, 2015·Statistical Methods in Medical Research·K F Cheng, J Y Lee
Aug 15, 2018·Statistical Methods in Medical Research·J Y LeeK F Cheng
Sep 21, 2021·G3 : Genes - Genomes - Genetics·Xiaoqin Jin, Gang Shi

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