Whole-Genome Regression and Prediction Methods Applied to Plant and Animal Breeding
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TLDR
An overview of available methods for implementing parametric WGR models is provided, selected topics that emerge in applications are discussed, and a general discussion of lessons learned from simulation and empirical data analysis in the last decade are presented.Abstract:
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.read more
Citations
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Efficient Bayesian mixed-model analysis increases association power in large cohorts
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Genome-Wide Regression and Prediction with the BGLR Statistical Package
TL;DR: The BGLR R-package implements a large collection of Bayesian regression models, including parametric variable selection and shrinkage methods and semiparametric procedures, which allows integrating various parametric and nonparametric shrinkage and variable selection procedures in a unified and consistent manner.
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Genomic Selection in Plant Breeding: Methods, Models, and Perspectives
José Crossa,Paulino Pérez-Rodríguez,Jaime Cuevas,Osval A. Montesinos-López,Diego Jarquin,Gustavo de los Campos,Juan Burgueño,Juan Manuel González-Camacho,Sergio Pérez-Elizalde,Yoseph Beyene,Susanne Dreisigacker,Ravi P. Singh,Xuecai Zhang,Manje Gowda,Manish Roorkiwal,Jessica Rutkoski,Rajeev K. Varshney +16 more
TL;DR: Based on GP results, it is speculated how GS in germplasm enhancement programs could accelerate the flow of genes from gene bank accessions to elite lines and recent advances in hyperspectral image technology could be combined with GS and pedigree-assisted breeding.
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Polygenic modeling with bayesian sparse linear mixed models.
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Pitfalls of predicting complex traits from SNPs
Naomi R. Wray,Jian Yang,Ben J. Hayes,Ben J. Hayes,Alkes L. Price,Michael E. Goddard,Peter M. Visscher +6 more
TL;DR: Some of the limitations and pitfalls of prediction analysis are discussed and how naive implementations can lead to severe bias and misinterpretation of results are shown.
References
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