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

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

*Corresponding author for this work

Research output: Contribution to journalLiterature reviewpeer-review

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.

Original languageEnglish
Pages (from-to)327-+
Number of pages25
JournalGenetics
Volume193
Issue number2
DOIs
Publication statusPublished - Feb 2013

Keywords

  • MARKER-ASSISTED SELECTION
  • BEEF-CATTLE
  • DAIRY-CATTLE
  • DENSE MOLECULAR MARKERS
  • LINEAR UNBIASED PREDICTION
  • SINGLE NUCLEOTIDE POLYMORPHISMS
  • GENETIC-RELATIONSHIP INFORMATION
  • QUANTITATIVE TRAIT LOCUS
  • VARIABLE SELECTION
  • REFERENCE POPULATION

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