Whole-genome regression and prediction methods applied to plant and animal breeding

G. De Los Campos, J.M. Hickey, R. Pong-Wong, H.D. Daetwyler, M.P.L. Calus

Research output: Contribution to journalArticleAcademicpeer-review

406 Citations (Scopus)

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. Following the groundbreaking contribution of MEUWISSEN et al. (2001) several methods have been proposed and evaluated, and genome-enabled selection (GS) is being implemented in several plant and animal breeding programs. The list of methods is long, and the relationships between the available methods have not been fully addressed. In this article we provide an overview of available methods for implementing parametric WGR models, discuss selected topics which emerge in the application of these methods and present a general discussion of lessons learnt from simulation and empirical data analysis in the last decade
Original languageEnglish
Pages (from-to)327-345
JournalGenetics
Volume193
Issue number2
DOIs
Publication statusPublished - 2013

Keywords

  • marker-assisted selection
  • quantitative trait locus
  • genetic-relationship information
  • single nucleotide polymorphisms
  • linear unbiased prediction
  • dense molecular markers
  • dairy-cattle
  • variable selection
  • reference population
  • beef-cattle

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