REHE: Fast variance components estimation for linear mixed models. Issue 8 (17th October 2021)

• Record Type: Journal Article
• Authors: Yue, Kun; Ma, Jing; Thornton, Timothy; Shojaie, Ali
• Is Part Of: Genetic epidemiology. Volume 45:Issue 8(2021)
• Page Start: 891
• Page End: 905
• Publication Date: 2021-10-17
• Subjects: genome‐wide association study -- heritability study -- linear mixed model -- restricted Haseman–Elston regression -- variance component; Genetic epidemiology -- Periodicals; Heredity -- Periodicals; Medical geography -- Periodicals; 614
• DOI: 10.1002/gepi.22432
• Languages: English
• ISSNs: 0741-0395

Abstract

Abstract: Linear mixed models are widely used in ecological and biological applications, especially in genetic studies. Reliable estimation of variance components is crucial for using linear mixed models. However, standard methods, such as the restricted maximum likelihood (REML), are computationally inefficient in large samples and may be unstable with small samples. Other commonly used methods, such as the Haseman–Elston (HE) regression, may yield negative estimates of variances. Utilizing regularized estimation strategies, we propose the restricted Haseman–Elston (REHE) regression and REHE with resampling (reREHE) estimators, along with an inference framework for REHE, as fast and robust alternatives that provide nonnegative estimates with comparable accuracy to REML. The merits of REHE are illustrated using real data and benchmark simulation studies.