|HEFFNER, ELLIOT - Cornell University|
|JANNINK, JEAN-LUC - Cornell University|
|IWATA, HIROYOSHI - University Of Tokyo|
|SORRELLS, MARK - Cornell University|
Submitted to: Crop Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 8/20/2011
Publication Date: 10/15/2011
Citation: Heffner, E.L., Jannink, J., Iwata, H., Souza, E.J., Sorrells, M.E. 2011. Genomic selection accuracy for grain quality traits in biparental wheat populations. Crop Science. 51:2597-2606.
Interpretive Summary: Researcher's abilities to match important traits, or phenotypes, with specific marker locations in a crop genome have increased greatly in the past two decades. Because measuring important traits on many individuals can be very expensive, breeders have adopted marker-assisted selection to identify individuals with desired traits within their plant populations. However, this approach is not as effective when the trait is complex or controlled by a number of quantitative trait loci (QTL) that each have only a small effect on the trait. Recently, genomic selection has emerged as a new tool for plant and animal breeding that uses genomewide molecular marker data to identify the best individuals. In this study, we compared marker-assisted selection and genome-wide selection to evaluate two soft winter wheat populations for nine different grain traits. We found that genome selection was better than marker assisted selection for all the traits tested, and that relatively few markers were required for accurate selection using the genome-wide approach. These results indicate that genome selection can allow breeders to make more improvement in crop traits in each generation, and at lower cost than selections based on traits or marker assisted selection.
Technical Abstract: Genomic selection (GS) is a promising tool for plant and animal breeding that uses genome wide molecular marker data to capture small and large effect quantitative trait loci and predict the genetic value of selection candidates. Genomic selection has been shown previously to have higher prediction accuracies than conventional marker-assisted selection (MAS) for quantitative traits. In this study, we compared phenotypic and marker-based prediction accuracy of genetic value for nine different grain quality traits within two biparental soft winter wheat (Triticum aestivum L.) populations. We used a cross-validation approach that trained and validated prediction accuracy across years to evaluate effects of model training population size, training population replication, and marker density. Results showed that prediction accuracy was significantly greater using GS versus MAS for all traits studied and that accuracy for GS reached a plateau at low marker densities (128–256).The average ratio of GS accuracy to phenotypic selection accuracy was 0.66, 0.54, and 0.42 for training population sizes of 96, 48, and 24, respectively. These results provide further empirical evidence that GS could produce greater genetic gain per unit time and cost than both phenotypic selection and conventional MAS in plant breeding with use of year-round nurseries and inexpensive, high-throughput genotyping technology.