Abstract
Key messageThe optimization of training populations and the use of diagnostic markers as fixed effects increase the predictive ability of genomic prediction models in a cooperative wheat breeding panel.Plant breeding programs often have access to a large amount of historical data that is highly unbalanced, particularly across years. This study examined approaches to utilize these data sets as training populations to integrate genomic selection into existing pipelines. We used cross-validation to evaluate predictive ability in an unbalanced data set of 467 winter wheat (Triticum aestivum L.) genotypes evaluated in the Gulf Atlantic Wheat Nursery from 2008 to 2016. We evaluated the impact of different training population sizes and training population selection methods (Random, Clustering, PEVmean and PEVmean1) on predictive ability. We also evaluated inclusion of markers associated with major genes as fixed effects in prediction models for heading date, plant height, and resistance to powdery mildew (caused by Blumeria graminis f. sp. tritici). Increases in predictive ability as the size of the training population increased were more evident for Random and Clustering training population selection methods than for PEVmean and PEVmean1. The selection methods based on minimization of the prediction error variance (PEV) outperformed the Random and Clustering methods across all the population sizes. Major genes added as fixed effects always improved model predictive ability, with the greatest gains coming from combinations of multiple genes. Maximum predictabilities among all prediction methods were 0.64 for grain yield, 0.56 for test weight, 0.71 for heading date, 0.73 for plant height, and 0.60 for powdery mildew resistance. Our results demonstrate the utility of combining unbalanced phenotypic records with genome-wide SNP marker data for predicting the performance of untested genotypes.
Highlights
Historical sets of phenotypic data generated by plant breeding programs allow for potential integration of existing phenotypic information into genomic selection approaches that increase genetic gain through reduced breeding cycle times (Hayes et al 2009; Dawson et al 2013; Crossa et al 2013)
As selection of the appropriate training population is critical to obtaining accurate genomic predictions, we examined the effects of selecting training populations of different sizes using a strategy that considered population structure based on cluster analysis and strategies utilizing the PEVmean algorithm
Entries developed at Clemson University (CU) were evaluated in 2008 through 2012, the field evaluation was not conducted in CU
Summary
Historical sets of phenotypic data generated by plant breeding programs allow for potential integration of existing phenotypic information into genomic selection approaches that increase genetic gain through reduced breeding cycle times (Hayes et al 2009; Dawson et al 2013; Crossa et al 2013). Extended author information available on the last page of the article (GEBVs) for genotypes with limited or no available phenotypic data is central to genomic selection approaches. GEBVs are estimated using genome-wide marker profiles and prediction models based on training populations containing both phenotypic and genotypic information on a set of individuals (Meuwissen et al 2001). Many cultivars of wheat grown in the USA are developed by breeding programs located at land grant universities. There is interest in incorporating the historical data derived from these breeding programs into genomic selection. Public programs have a long tradition of germplasm exchange and collaborative testing of experimental lines prior to cultivar release. Breeders routinely enter advanced lines into collaborative testing programs grown in multiple environments throughout targeted wheat growing regions within the USA.
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