title: Whole-genome regression and prediction methods applied to plant and animal breeding.
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.
authors: Department of Biostatistics, School of Public Health, University of Alabama, Birmingham, AL 35294, USA. gcampos@uab.edu
authors: Gustavo
authors: G
authors: de Los Campos
authors: John M
authors: JM
authors: Hickey
authors: Ricardo
authors: R
authors: Pong-Wong
authors: Hans D
authors: HD
authors: Daetwyler
authors: Mario P L
authors: MP
authors: Calus
mesh_terms: D000818:Animals; D001499:Bayes Theorem; D001947:Breeding / Q000379:methods*; D016678:Genome; D008957:Models, Genetic; D010641:Phenotype; D010944:Plants; D012044:Regression Analysis; D012641:Selection, Genetic