Efficient penalized generalized linear mixed models for variable selection and genetic risk prediction in high-dimensional data

MOTIVATION: Sparse regularized regression methods are now widely used in genome-wide association studies (GWAS) to address the multiple testing burden that limits discovery of potentially important predictors. Linear mixed models (LMMs) have become an attractive alternative to principal components (...

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Detalles Bibliográficos
Autores principales: St-Pierre, Julien, Oualkacha, Karim, Bhatnagar, Sahir Rai
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2023
Materias:
Original Paper
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9907224/
https://www.ncbi.nlm.nih.gov/pubmed/36708013
http://dx.doi.org/10.1093/bioinformatics/btad063

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9907224/
https://www.ncbi.nlm.nih.gov/pubmed/36708013
http://dx.doi.org/10.1093/bioinformatics/btad063

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