Journal
GENETICS
Volume 193, Issue 2, Pages 327-+Publisher
GENETICS SOCIETY AMERICA
DOI: 10.1534/genetics.112.143313
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Funding
- Dutch Ministry of Economic Affairs, Agriculture, and Innovation (Programme Kennisbasis Research) [KB-17-003.02-006]
- Cooperative Research Centre for Sheep Industry Innovation
- Australian Research Council [LP100100880]
- Genus Plc
- Aviagen LTD
- Pfizer
- Australian Research Council [LP100100880] Funding Source: Australian Research Council
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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.
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