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