TY - JOUR
T1 - The effects of training population design on genomic prediction accuracy in wheat
AU - Edwards, Stefan McKinnon
AU - Buntjer, Jacob
AU - Jackson, Robert
AU - Bentley, Alison R.
AU - Lage , Jacob
AU - Byrne, Ed
AU - Burt, Chris
AU - Jack , Peter
AU - Berry , Simon
AU - Flatman , Edward
AU - Poupard, Bruno
AU - Smith, Stephen
AU - Hayes , Charlotte
AU - Gaynor, Robert
AU - Gorjanc, Gregor
AU - Howell, Phil
AU - Ober, Eric
AU - Mackay, Ian J.
AU - Hickey, John
PY - 2019/3/19
Y1 - 2019/3/19
N2 - Genomic selection offers several routes for increasing the genetic gain or efficiency of plant breeding programs. 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 triparental populations, from which 2992 lines were grown on four field locations and yield was measured. For each line, genotype data were generated for 25K 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 emphasize the importance of the training panel design in relation to the genetic material to which the resulting prediction model is to be applied.
AB - Genomic selection offers several routes for increasing the genetic gain or efficiency of plant breeding programs. 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 triparental populations, from which 2992 lines were grown on four field locations and yield was measured. For each line, genotype data were generated for 25K 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 emphasize the importance of the training panel design in relation to the genetic material to which the resulting prediction model is to be applied.
UR - http://www.scopus.com/inward/record.url?scp=85063212585&partnerID=8YFLogxK
U2 - 10.1007/s00122-019-03327-y
DO - 10.1007/s00122-019-03327-y
M3 - Article
C2 - 30888431
AN - SCOPUS:85063212585
JO - TAG Theoretical and Applied Genetics
JF - TAG Theoretical and Applied Genetics
SN - 0040-5752
ER -