High-Dimensional Kernel Linear Mixed-Effects Models with Applications in Estimating Genetic Heritability

Qing Lu

Abstract: Restricted maximum likelihood (REML) estimators are commonly used to obtain unbiased estimators for the variance components in linear mixed models. In modern applications, particularly in genomic studies, the dimension of the design matrix with respect to the random effects can be high. Motivated by this, we first introduce high-dimensional kernel linear mixed models, derive the REML equations, and establish theoretical results on the consistency of REML estimators for several commonly used kernel matrices. The validity of the theories is demonstrated via simulation studies. Our results provide rigorous justification for the consistency of REML estimators in high-dimensional kernel linear mixed models and offer insights into the application of estimating genetic heritability. Finally, we apply the kernel linear mixed models to estimate genetic heritability in a real-world data application.