ArticleBMC proceedings2014
Penalized multivariate linear mixed model for longitudinal genome-wide association studies.
Article in BMC proceedings, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The trial behind it
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Who cites it
3 citing papers in PubMed.
- HighDimMixedModels.jl: Robust high-dimensional mixed-effects models across omics data.PLoS computational biology · 2025Article
- Regularized multi-trait multi-locus linear mixed models for genome-wide association studies and genomic selection in crops.BMC bioinformatics · 2023Article
- Chromosome 2 Fragment Substitutions in Dahl Salt-Sensitive Rats and RNA Sequencing IdentifiedHypertension (Dallas, Tex. : 1979) · 2021Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
We consider analysis of Genetic Analysis Workshop 18 data, which involves multiple longitudinal traits and dense genome-wide single-nucleotide polymorphism (SNP) markers. We use a multivariate linear mixed model to account for the covariance of random effects and multivariate residuals. We divide the SNPs into groups according to the genes they belong to and score them using weighted sum statistics. We propose a penalized approach for genetic variant selection at the gene level. The overall modeling and penalized selection method is referred to as the penalized multivariate linear mixed model. Cross-validation is used for tuning parameter selection. A resampling approach is adopted to evaluate the relative stability of the identified genes. Application to the Genetic Analysis Workshop 18 data shows that the proposed approach can effectively select markers associated with phenotypes at gene level.
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Registered trials
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