A Comparison Study of Fixed and Mixed Effect Models for Gene Level Association Studies of Complex Traits. Issue 8 (4th July 2016)

• Record Type: Journal Article
• Authors: Fan, Ruzong; Chiu, Chi‐yang; Jung, Jeesun; Weeks, Daniel E.; Wilson, Alexander F.; Bailey‐Wilson, Joan E.; Amos, Christopher I.; Chen, Zhen; Mills, James L.; Xiong, Momiao
• Is Part Of: Genetic epidemiology. Volume 40:Issue 8(2016)
• Page Start: 702
• Page End: 721
• Publication Date: 2016-07-04
• Subjects: rare variants -- common variants -- association mapping -- quantitative/dichotomous trait loci -- complex traits -- functional data analysis -- multivariate linear models -- logistic regressions; Genetic epidemiology -- Periodicals; Heredity -- Periodicals; Medical geography -- Periodicals; 614
• DOI: 10.1002/gepi.21984
• Languages: English
• ISSNs: 0741-0395

Abstract

ABSTRACT: In association studies of complex traits, fixed‐effect regression models are usually used to test for association between traits and major gene loci. In recent years, variance‐component tests based on mixed models were developed for region‐based genetic variant association tests. In the mixed models, the association is tested by a null hypothesis of zero variance via a sequence kernel association test (SKAT), its optimal unified test (SKAT‐O), and a combined sum test of rare and common variant effect (SKAT‐C). Although there are some comparison studies to evaluate the performance of mixed and fixed models, there is no systematic analysis to determine when the mixed models perform better and when the fixed models perform better. Here we evaluated, based on extensive simulations, the performance of the fixed and mixed model statistics, using genetic variants located in 3, 6, 9, 12, and 15 kb simulated regions. We compared the performance of three models: (i) mixed models that lead to SKAT, SKAT‐O, and SKAT‐C, (ii) traditional fixed‐effect additive models, and (iii) fixed‐effect functional regression models. To evaluate the type I error rates of the tests of fixed models, we generated genotype data by two methods: (i) using all variants, (ii) using only rare variants. We found that the fixed‐effect tests accurately control or have low false positive rates. We performed simulation analyses to compare power for two scenarios: (i) all causal variants are rare, (ii) some causal variants are rare and some are common. Either one or both of the fixed‐effect models performed better than or similar to the mixed models except when (1) the region sizes are 12 and 15 kb and (2) effect sizes are small. Therefore, the assumption of mixed models could be satisfied and SKAT/SKAT‐O/SKAT‐C could perform better if the number of causal variants is large and each causal variant contributes a small amount to the traits (i.e., polygenes). In major gene association studies, we argue that the fixed‐effect models perform better or similarly to mixed models in most cases because some variants should affect the traits relatively large. In practice, it makes sense to perform analysis by both the fixed and mixed effect models and to make a comparison, and this can be readily done using our R codes and the SKAT packages.