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Activity Number: 535 - Contributed Poster Presentations: Section on Statistics in Genomics and Genetics
Type: Contributed
Date/Time: Wednesday, August 1, 2018 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistics in Genomics and Genetics
Abstract #328929
Title: An Improved Estimator for Variance Components in Linear Mixed Model
Author(s): Kun Yue* and Jing Ma and Ali Shojaie
Companies: University of Washington and Fred Hutch Cancer Research Center and University of Washington
Keywords: linear mixed model; variance component; Restricted Haseman-Elston; genome-wise association mapping; network enrichment analysis
Abstract:

Linear mixed models have been widely used in ecological and biological context, in particular genetic studies. Estimation of variance components is usually a necessary part of statistical inference. Common variance component estimation methods, including ANOVA and restricted maximum likelihood estimation (REML), have important limitations such as negative estimators and/or expensive computation. We propose a new estimator based on the Restricted Haseman-Elston method (REHE), which guarantees nonnegative consistent variance component estimates and efficient computation for large dataset. We further introduce subsampling technique applied to REHE to further improve computation speed. The performance of REHE is compared with ANOVA and REML estimators using simulation studies. We also illustrate the proposed method in two case studies, one on network-based pathway enrichment analysis and the other on genome-wise association mapping.


Authors who are presenting talks have a * after their name.

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