ArticleGenetic epidemiology2021
REHE: Fast variance components estimation for linear mixed models.
Article in Genetic epidemiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- A biobank-scale method for learning modulators of gene-environment interaction underlying human complex traits from multiple environmental exposures.bioRxiv : the preprint server for biology · 2026Article
- Estimating the heritability of longitudinal rate-of-change: genetic insights into PSA velocity in prostate cancer-free individuals.Biostatistics (Oxford, England) · 2026Article
- netgsa: Fast computation and interactive visualization for topology-based pathway enrichment analysis.PLoS computational biology · 2021Article
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Authors and funding
4 authors.
Funding
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.
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Registered trials
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