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

de los Campos, Gustavo; Hickey, John; Pong-Wong, Ricardo; Daetwyler, Hans D; Calus, Mario P L

doi:10.1534/genetics.112.143313

Author(s)

de los Campos, Gustavo

Hickey, John

Pong-Wong, Ricardo

Daetwyler, Hans D

Calus, Mario P L

Publication Date

2013

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.

Citation

Genetics, 193(2), p. 327-345

ISSN

1943-2631

0016-6731

Link

https://hdl.handle.net/1959.11/15937

Language

en

Publisher

Genetics Society of America

Title

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

Type of document

Journal Article

Entity Type

Publication

Author(s)	de los Campos, Gustavo Hickey, John Pong-Wong, Ricardo Daetwyler, Hans D Calus, Mario P L
Publication Date	2013
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.
Citation	Genetics, 193(2), p. 327-345
ISSN	1943-2631 0016-6731
Link	https://hdl.handle.net/1959.11/15937
Language	en
Publisher	Genetics Society of America
Title	Whole-Genome Regression and Prediction Methods Applied to Plant and Animal Breeding
Type of document	Journal Article
Entity Type	Publication

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

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