Genomic Selection in Plant Breeding: A Comparison of Models

By Nicolas Heslot, Hsiao-Pei Yang, M. Sorrells, Jean-Luc Jannink. 2012

650 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/7c748c1bea85b9d0112ce5f8504086cedc644c2e

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In Genetics

In Plant Science

Citations in the corpus

Adding Genetically Distant Individuals to Training Populations Reduces Genomic Prediction Accuracy in Barley (2015, methodology): “The RR- BLUP model, and its equivalent G-BLUP, is the most commonly used model in genomic prediction for plant breeding because of its simplicity and good performance relative to more complex, computationally intensive models (Heslot et al., 2012).”
Genomic Relationship Matrix for Correcting Pedigree Errors in Breeding Populations: Impact on Genetic Parameters and Genomic Selection Accuracy (2014, background): “However, for most quantitative traits there is not a clear advantage of any of the proposed prediction methods (Heslot et al., 2012; Resende et al., 2012b).”
Genomewide Selection versus Marker‐assisted Recurrent Selection to Improve Grain Yield and Stover‐quality Traits for Cellulosic Ethanol in Maize (2013, methodology): “…work in maize (Lorenzana and Bernardo, 2009; Guo et al., 2012), barley (Lorenzana and Bernardo, 2009; Iwata and Jannink, 2011; Heslot et al., 2012), wheat (Heff ner et al., 2011; Heslot et al., 2012), and oat (Asoro et al., 2011) have shown little to no improvement from Bayesian estimation.”
Whole-Genome Regression and Prediction Methods Applied to Plant and Animal Breeding (2013, background): “However, although limited, empirical evidence suggests somewhat lower prediction accuracy of these methods relative to the more frequently used Bayesian regressions (Solberg et al. 2009; Coster et al. 2010; Gredler et al. 2010; Pszczola et al. 2011; Heslot et al. 2012).”
Genomic Prediction of Hybrid Wheat Performance (2013, result): “Despite the context dependency of genomic selection, the observed prediction accuracy was comparable to the accuracy reached for predicting grain yield per se performance in wheat (e.g., Crossa et al., 2010; Heffner et al., 2011; Heslot et al., 2012).”
Relatedness and Genotype × Environment Interaction Affect Prediction Accuracies in Genomic Selection: A Study in Cassava (2013, positive): “We chose RKHS for this purpose because it fits the training set phenotypes with a high coefficient of determination (Heslot et al., 2012).”
Comparing genomic selection and marker-assisted selection for Fusarium head blight resistance in wheat (Triticum aestivum L.) (2016, background): “In GS, several statistical models are available for estimating marker effects, each model with its own assumptions and features (Heslot et al. 2012; Desta and Ortiz 2014).”