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Theor Appl Genet. 2017 Dec;130(12):2543-2555. doi: 10.1007/s00122-017-2975-4. Epub 2017 Sep 8.

Increased genomic prediction accuracy in wheat breeding using a large Australian panel.

Author information

1
School of Agriculture, Food and Wine, University of Adelaide, Waite Campus, Glen Osmond, SA, Australia. adam.norman@agtbreeding.com.au.
2
Australian Grain Technologies Pty Ltd, Perkins Building, Roseworthy Campus, Roseworthy, SA, Australia. adam.norman@agtbreeding.com.au.
3
School of Agriculture, Food and Wine, University of Adelaide, Waite Campus, Glen Osmond, SA, Australia.
4
National Institute for Applied Statistics Research Australia (NIASRA), School of Mathematics and Applied Statistics, University of Wollongong, Wollongong, NSW, Australia.
5
Australian Grain Technologies Pty Ltd, Perkins Building, Roseworthy Campus, Roseworthy, SA, Australia.
6
Centre of Research, Limagrain Field Seeds Pty Ltd, Chappes, France.

Abstract

Genomic prediction accuracy within a large panel was found to be substantially higher than that previously observed in smaller populations, and also higher than QTL-based prediction. In recent years, genomic selection for wheat breeding has been widely studied, but this has typically been restricted to population sizes under 1000 individuals. To assess its efficacy in germplasm representative of commercial breeding programmes, we used a panel of 10,375 Australian wheat breeding lines to investigate the accuracy of genomic prediction for grain yield, physical grain quality and other physiological traits. To achieve this, the complete panel was phenotyped in a dedicated field trial and genotyped using a custom AxiomTM Affymetrix SNP array. A high-quality consensus map was also constructed, allowing the linkage disequilibrium present in the germplasm to be investigated. Using the complete SNP array, genomic prediction accuracies were found to be substantially higher than those previously observed in smaller populations and also more accurate compared to prediction approaches using a finite number of selected quantitative trait loci. Multi-trait genetic correlations were also assessed at an additive and residual genetic level, identifying a negative genetic correlation between grain yield and protein as well as a positive genetic correlation between grain size and test weight.

PMID:
28887586
PMCID:
PMC5668360
DOI:
10.1007/s00122-017-2975-4
[Indexed for MEDLINE]
Free PMC Article

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