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The effects of training population design on genomic prediction accuracy in wheat
Sunday, 2019/07/07 | 06:09:52

Stefan McKinnon Edwards, Jaap B. Buntjer, Robert Jackson, Alison R. Bentley, Jacob Lage, Ed Byrne, Chris Burt, Peter Jack, Simon Berry, Edward Flatman, Bruno Poupard, Stephen Smith, Charlotte Hayes, R. Chris Gaynor, Gregor Gorjanc, Phil Howell, Eric Ober, Ian J. Mackay, John M. Hickey

Theoretical and Applied Genetics; July 2019, Volume 132, Issue 7, pp 1943–1952

Abstract

Genomic selection offers several routes for increasing the genetic gain or efficiency of plant breeding programmes. In various species of livestock, there is empirical evidence of increased rates of genetic gain from the use of genomic selection to target different aspects of the breeder’s equation. Accurate predictions of genomic breeding value are central to this, and the design of training sets is in turn central to achieving sufficient levels of accuracy. In summary, small numbers of close relatives and very large numbers of distant relatives are expected to enable predictions with higher accuracy. To quantify the effect of some of the properties of training sets on the accuracy of genomic selection in crops, we performed an extensive field-based winter wheat trial. In summary, this trial involved the construction of 44 F2:4 bi- and tri-parental populations, from which 2992 lines were grown on four field locations and yield was measured. For each line, genotype data were generated for 25 K segregating SNP markers. The overall heritability of yield was estimated to 0.65, and estimates within individual families ranged between 0.10 and 0.85. Genomic prediction accuracies of yield BLUEs were 0.125–0.127 using two different cross-validation approaches and generally increased with training set size. Using related crosses in training and validation sets generally resulted in higher prediction accuracies than using unrelated crosses. The results of this study emphasise the importance of the training panel design in relation to the genetic material to which the resulting prediction model is to be applied.

 

See https://link.springer.com/article/10.1007/s00122-019-03327-y

 

Fig. 4 Prediction accuracies increased with increasing number of crosses or increasing number of lines per cross in training set. Right-hand numbers show number of lines per cross in training set.

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