Whole-Genome Regression and Prediction Methods Applied to Plant and Animal Breeding

By G. de los Campos, John M. Hickey, R. Pong-Wong, Hans D. Daetwyler, M. Calus. Genetics, 2013

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Genomic-enabled prediction is becoming increasingly important in animal and plant breeding and is also receiving attention in human genetics. Deriving accurate predictions of complex traits requires implementing whole-genome regression (WGR) models where phenotypes are regressed on thousands of markers concurrently. Methods exist that allow implementing these large-p with small-n regressions, and genome-enabled selection (GS) is being implemented in several plant and animal breeding programs. The list of available methods is long, and the relationships between them have not been fully addressed. In this article we provide an overview of available methods for implementing parametric WGR models, discuss selected topics that emerge in applications, and present a general discussion of lessons learned from simulation and empirical data analysis in the last decade.

Subfields: Genetics, Plant Science

View Paper: https://www.semanticscholar.org/paper/1c8aa5b42e0945eb7fc12b7ed976ad909887fb62

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Citations in the corpus

Computationally efficient whole-genome regression for quantitative and binary traits (2020, methodology): “This was also observed in more extreme relatedness scenarios, where the use of the Step 1 predictions in REGENIE led to good calibration of the test unlike the PCA method, which was inflated (Supplementary Table 10).”
Genomic Selection in Plant Breeding: Methods, Models, and Perspectives. (2017, background): “A large body of GP research has focused on developing efficient parametric and nonparametric statistical and computational models with increased accuracy for predicting nonphenotyped genotypes [13].”
Evaluation of genomic selection training population designs and genotyping strategies in plant breeding programs using simulation (2014, methodology): “More complex statistical methods (e.g., BayesB; Meuwissen et al., 2001) or other types of mixture models (de los Campos et al., 2013) could be used, which make use of statistical or biological prior information, and these may reduce the effect of overfitting, but the benefit would be marginal.”
Genome-Wide Regression and Prediction with the BGLR Statistical Package (2014, methodology): “Next we brieﬂy describe the built-in rules implemented in BGLR; these are based on formulas similar to those described by de los Campos et al. (2013) implemented using the prior mode instead of the prior mean.”