Identifying QTLs and epistasis in structured plant populations using adaptive mixed LASSO

DOI10.1007/s13253-010-0046-2zbMath1306.62358OpenAlexW2166426981WikidataQ58896057 ScholiaQ58896057MaRDI QIDQ2260993

Dong Wang, Kent M. Eskridge, Jose Crossa

Publication date: 6 March 2015

Published in: Journal of Agricultural, Biological, and Environmental Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s13253-010-0046-2

zbMATH Keywords

shrinkage estimationpenalized regressionoracle propertyassociation analysisplant breeding

Mathematics Subject Classification ID

Applications of statistics to environmental and related topics (62P12)

Related Items (3)

Subset Selection for Linear Mixed Models ⋮ Variable selection for generalized linear mixed models by \(L_1\)-penalized estimation ⋮ Model selection in linear mixed models

Uses Software

• ElemStatLearn
• STRUCTURE
• Eigenstrat

Cites Work

• Unnamed Item
• The Adaptive Lasso and Its Oracle Properties
• Least angle regression. (With discussion)
• Incorporating LASSO effects into a mixed model for quantitative trait loci detection
• Pathwise coordinate optimization
• Fixed and Random Effects Selection in Mixed Effects Models
• Joint Variable Selection for Fixed and Random Effects in Linear Mixed-Effects Models
• Extended Bayesian information criteria for model selection with large model spaces
• The Bayesian Lasso
• Spatial smoothing and hot spot detection for CGH data using the fused lasso
• Genomic Control for Association Studies
• A Model Selection Approach for the Identification of Quantitative Trait Loci in Experimental Crosses
• Sparsity and Smoothness Via the Fused Lasso
• An Empirical Bayes Method for Estimating Epistatic Effects of Quantitative Trait Loci

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