Ridge Regression and Extensions for Genomewide Selection in Maize

By Hans-Peter Piepho. 2009

279 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/aac2336e3f45af3de998ab6ae0eff954b4b3fb95

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In Genetics

In Plant Science

Citations in the corpus

Genomic Selection and Prediction in Plant Breeding (2011, background): “Selection in plant breeding is usually based on estimates of breeding values obtained with pedigree-based mixed models (Crossa et al. 2006; Piepho et al. 2007; Burgueño et al. 2007; Piepho 2009).”
Genomic selection in plant breeding: from theory to practice. (2010, result): “Similar equivalencies have been shown by Piepho [55], who compared GS to”
REVIEW: PART OF A HIGHLIGHT ON BREEDING STRATEGIES FOR FORAGE AND GRASS IMPROVEMENT Will genomic selection be a practical method for plant breeding? (2012, methodology): “Piepho (2009) compared the performance of nine models using a series of experiments with DHLs derived from a single cross conducted in five environments, and suggested the need to appropriately model genotype–environment interactions and to employ an independent estimate of error.”
Polygenic Modeling with Bayesian Sparse Linear Mixed Models (2012, background): “Indeed, models like these have been used in breeding value prediction to assist genomic selection in animal and plant breeding programs [30–35], gene selection in expression analysis while controlling for batch eﬀects [36], phenotype prediction of complex traits in model organisms and dairy cattle…”
Evaluation of Genomic Prediction Methods for Fusarium Head Blight Resistance in Wheat (2012, background): “For a more through description of RR refer to Whittaker et al. (2000) and Piepho (2009).”
Genomic Selection in Plant Breeding (2011, background): “(b), where var(e) is the residual variance and var(b) is the common marker effect variance (Piepho, 2009).”
Extension of the bayesian alphabet for genomic selection (2011, unknown): “, [2-6]).”
Genomic selection using regularized linear regression models: ridge regression, lasso, elastic net and their extensions (2012, methodology): “The RR and RR-BLUP penalties admit all markers into the model, resulting in a very large number of non-zero coefficients.”
Genomic Prediction of Breeding Values when Modeling Genotype × Environment Interaction using Pedigree and Dense Molecular Markers (2012, background): “…interaction can be incorporated into additive infi nitesimal models (e.g., Fisher, 1918; Wright, 1921) by means of genetic and environmental covariances (e.g., Piepho, 1997, 1998, 2009; Smith et al., 2001; Crossa et al., 2004, 2006; Oakey et al., 2006; Burgueño et al., 2007, 2008, 2011).”
Genomic Selection Accuracy using Multifamily Prediction Models in a Wheat Breeding Program (2011, methodology): “A two-stage analysis was used to calculate line best linear unbiased predictions (BLUPs) because it is less computationally demanding than a one-stage analysis and has been shown to generate similar results (Mohring and Piepho, 2009).”