Genomic Selection in Multi-environment Crop Trials

  title={Genomic Selection in Multi-environment Crop Trials},
  author={Helena Oakey and Brian R. Cullis and Robin Thompson and Jordi Comadran and Claire Halpin and Robbie Waugh},
Genomic selection in crop breeding introduces modeling challenges not found in animal studies. These include the need to accommodate replicate plants for each line, consider spatial variation in field trials, address line by environment interactions, and capture nonadditive effects. Here, we propose a flexible single-stage genomic selection approach that resolves these issues. Our linear mixed model incorporates spatial variation through environment-specific terms, and also randomization-based… CONTINUE READING
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New look at statistical-model identification

  • H. Akaike
  • IEEE Transactions on Automatic Control
  • 1974
Highly Influential
11 Excerpts

who found no improvement in predictability of a MET over a single trial analysis, but support the findings

  • Guo
  • Burgueno et al
  • 2012
Highly Influential
4 Excerpts

Burgueno et al. 2012) have included pedigree information, which captures a polygenic effect as a way of accounting for nonadditive effects, and more recent studies

  • Crossa
  • Previous studies (Solberg et al
  • 2010
Highly Influential
7 Excerpts

On the design of early generation variety trials with correlated data

  • B. Cullis, A. Smith, N. Coombes
  • J. Agric. Biol. Environ. Stat
  • 2006
Highly Influential
4 Excerpts

STY, single trial year (note the DIAG model is equivalent to analyzing each trial year separately); MET, multi-environment trial

  • MET Smith
  • 2001
Highly Influential
5 Excerpts

Spatial analysis of multi-environment early generation trials

  • B. Cullis, B. Gogel, A. Verbyla, R. Thompson
  • Biometrics
  • 1998
Highly Influential
4 Excerpts

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