Whole-Genome Regression and Prediction Methods Applied to Plant and Animal Breeding
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- Gustavo de los Campos
- Department of Biostatistics, School of Public Health, University of Alabama, Birmingham, Alabama 35294
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- John M Hickey
- School of Environmental and Rural Science, University of New England, Armidale 2351, New South Wales, Australia
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- Ricardo Pong-Wong
- The Roslin Institute, Royal (Dick) School of Veterinary Studies, University of Edinburgh, Easter Bush, Midlothian, EH25 9RG, Scotland
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- Hans D Daetwyler
- Biosciences Research Division, Department of Primary Industries, Bundoora 3083, Victoria, Australia
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- Mario P L Calus
- Animal Breeding and Genomics Centre, Wageningen UR Livestock Research, 8200 AB Lelystad, The Netherlands
説明
<jats:title>Abstract</jats:title><jats:p>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.</jats:p>
収録刊行物
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- Genetics
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Genetics 193 (2), 327-345, 2013-02-01
Oxford University Press (OUP)
