Predicting Quantitative Traits With Regression Models for Dense Molecular Markers and Pedigree

By G. de los Campos, Hugo Naya, D. Gianola, Jose Crossa, Andrés Legarra, E. Manfredi, Kent A. Weigel, J. M. Cotes. Genetics, 2009

599 citations recorded by the source corpus.

Subfields: Genetics, Plant Science

View Paper: https://www.semanticscholar.org/paper/3c8e37924ef94861970b4f87e81500a397e36590

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Citations in the corpus

Genomic Selection in Plant Breeding: A Comparison of Models (2012, background): “In the case of the BL (Park and Casella, 2008; Yi and Xu, 2008; de los Campos et al., 2009), the shrinkage is marker specifi c and dependent on a regularization parameter λ.”
Genomic Selection and Prediction in Plant Breeding (2011, methodology): “In recent articles, de los Campos et al. (2009a), de los Campos (2010), Pérez et al. (2010), and Crossa et al. (2010) validated GS in plant breeding using genomic regression and showed that models using MM were more accurate in predicting grain yield in wheat and maize than those based on pedigree…”
Whole-Genome Regression and Prediction Methods Applied to Plant and Animal Breeding (2013, background): “Some studies have shown benefits of extending the standard models of GS by adding a random effect representing a regression on pedigree information (de los Campos et al. 2009; Crossa et al. 2010); however, the benefits of jointly modeling pedigree and marker data relative to a markersonly model…”
Accuracy and Training Population Design for Genomic Selection on Quantitative Traits in Elite North American Oats (2011, methodology): “…narrow-based biparental populations (Lorenzana and Bernardo, 2009) and in broad-based populations such as multilines of barley ( Hordeum vulgare L.), wheat ( Triticum aestivum L.), and maize ( Zea mays L.) (Zhong et al., 2009; Heff ner et al., 2011; de los Campos et al., 2009; Crossa et al., 2010).”
Genomic Prediction of Genetic Values for Resistance to Wheat Rusts (2012, methodology): “…and Casella, 2008) are well-established parametric regression methods for GS. Bayesian LASSO has the advantage of being robust regarding prior distributions of the λ parameter controlling shrinkage of regression coei cients (Crossa et al., 2010; de los Campos et al., 2009; Long et al., 2011b).”
Prediction of Genetic Values of Quantitative Traits in Plant Breeding Using Pedigree and Molecular Markers (2010, methodology): “The effect of the prior choice for l2 in the BL has been addressed in de los Campos et al. (2009a).”
Polygenic prediction via Bayesian regression and continuous shrinkage priors (2018, methodology): “Several special cases of continuous shrinkage priors have been applied to quantitative trait prediction or gene mapping [De Los Campos et al., 2009; Hoggart et al., 2008; Makowsky et al., 2011; Xu, 2003; Yi and Xu, 2008].”