Linear mixed models for association analysis of quantitative traits with next‐generation sequencing data. Issue 2 (9th December 2018)

• Record Type: Journal Article
• Authors: Chiu, Chi‐yang; Yuan, Fang; Zhang, Bing‐song; Yuan, Ao; Li, Xin; Fang, Hong‐Bin; Lange, Kenneth; Weeks, Daniel E.; Wilson, Alexander F.; Bailey‐Wilson, Joan E.; Musolf, Anthony M.; Stambolian, Dwight; Lakhal‐Chaieb, M'Hamed Lajmi; Cook, Richard J.; McMahon, Francis J.; Amos, Christopher I.; Xiong, Momiao; Fan, Ruzong
• Is Part Of: Genetic epidemiology. Volume 43:Issue 2(2019)
• Page Start: 189
• Page End: 206
• Publication Date: 2018-12-09
• Subjects: common variants -- complex diseases -- functional data analysis -- functional linear mixed models -- linear mixed models -- rare variants; Genetic epidemiology -- Periodicals; Heredity -- Periodicals; Medical geography -- Periodicals; 614
• DOI: 10.1002/gepi.22177
• Languages: English
• ISSNs: 0741-0395

Abstract

Abstract: We develop linear mixed models (LMMs) and functional linear mixed models (FLMMs) for gene‐based tests of association between a quantitative trait and genetic variants on pedigrees. The effects of a major gene are modeled as a fixed effect, the contributions of polygenes are modeled as a random effect, and the correlations of pedigree members are modeled via inbreeding/kinship coefficients. F ‐statistics and χ 2 likelihood ratio test (LRT) statistics based on the LMMs and FLMMs are constructed to test for association. We show empirically that the F ‐distributed statistics provide a good control of the type I error rate. The F ‐test statistics of the LMMs have similar or higher power than the FLMMs, kernel‐based famSKAT (family‐based sequence kernel association test), and burden test famBT (family‐based burden test). The F ‐statistics of the FLMMs perform well when analyzing a combination of rare and common variants. For small samples, the LRT statistics of the FLMMs control the type I error rate well at the nominal levels α = 0.01 and 0.05 . For moderate/large samples, the LRT statistics of the FLMMs control the type I error rates well. The LRT statistics of the LMMs can lead to inflated type I error rates. The proposed models are useful in whole genome and whole exome association studies of complex traits.