Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/15937
Title: Whole-Genome Regression and Prediction Methods Applied to Plant and Animal Breeding
Contributor(s): de los Campos, Gustavo (author); Hickey, John (author); Pong-Wong, Ricardo (author); Daetwyler, Hans D (author); Calus, Mario P L (author)
Publication Date: 2013
Open Access: Yes
DOI: 10.1534/genetics.112.143313Open Access Link
Handle Link: https://hdl.handle.net/1959.11/15937
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.
Publication Type: Journal Article
Source of Publication: Genetics, 193(2), p. 327-345
Publisher: Genetics Society of America
Place of Publication: United States of America
ISSN: 1943-2631
0016-6731
Fields of Research (FoR) 2008: 070301 Agro-ecosystem Function and Prediction
070201 Animal Breeding
060408 Genomics
Fields of Research (FoR) 2020: 300402 Agro-ecosystem function and prediction
300305 Animal reproduction and breeding
310509 Genomics
Socio-Economic Objective (SEO) 2008: 970107 Expanding Knowledge in the Agricultural and Veterinary Sciences
Socio-Economic Objective (SEO) 2020: 280101 Expanding knowledge in the agricultural, food and veterinary sciences
Peer Reviewed: Yes
HERDC Category Description: C1 Refereed Article in a Scholarly Journal
Appears in Collections:Journal Article

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