Optimal design of preliminary yield trials with genome-wide markers

Authors: ENDELMAN, JEFFREY - Cornell University; ATLIN, GARY - Gates Foundation; BEYENE, YOSEPH - International Maize & Wheat Improvement Center (CIMMYT); FENTAYE, KASSA - International Maize & Wheat Improvement Center (CIMMYT); ZHANG, XUECAI - International Maize & Wheat Improvement Center (CIMMYT); SORRELLS, MARK - Cornell University; Jannink, Jean-Luc

Submitted to: Crop Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 7/19/2013
Publication Date: 1/30/2014
Citation: Endelman, J., Atlin, G., Beyene, Y., Fentaye, K., Zhang, X., Sorrells, M., Jannink, J. 2014. Optimal design of preliminary yield trials with genome-wide markers. Crop Science. 54:48-59.

Interpretive Summary: Genomic selection (GS) involves predicting future performance of new breeding lines on the basis of performance of related lines coupled to high density DNA marker data. Previous research on genomic selection (GS) has focused on predicting lines that have never been evaluated. GS can also improve the accuracy of line evaluation when the trait is associated with high error as is often the case in a preliminary yield trial (PYT). We estimated this improvement of evaluation within families, using multi-location yield data for barley and maize. We found that accuracy increased with training population size and was higher when family progeny were spread across multiple locations than when testing all progeny in one location. This result illustrates that when seed is limited, genome-wide markers enable broader sampling of environments. Our second objective was to explore the optimum allocation of resources at a fixed budget. When PYT selections are advanced for further testing, we propose a new metric for optimizing genetic gain: Rmax, the expected maximum genotypic value among selections. The optimal design did not involve genotyping more progeny than were phenotyped, even when the cost of creating and genotyping each line was only 0.25 the cost of one yield plot unit (YPU). At a genotyping cost of 0.25 YPU, GS offered up to a 5% increase in genetic gain compared to phenotypic selection for a budget of 200 YPU per family. To increase genetic gains further, the training population must be expanded beyond within-family selection, using close relatives of the parents as a source of prediction accuracy.

Technical Abstract: Previous research on genomic selection (GS) has focused on predicting unphenotyped lines. GS can also improve the accuracy of phenotyped lines at low heritability, e.g., in a preliminary yield trial (PYT). Our first objective was to estimate this effect within a biparental family, using multi-location yield data for barley and maize. We found that accuracy increased with training population size and was higher with an unbalanced design spread across multiple locations than when testing all entries in one location. The latter phenomenon illustrates that when seed is limited, genome-wide markers enable broader sampling from the target population of environments. Our second objective was to explore the optimum allocation of resources at a fixed budget. When PYT selections are advanced for further testing, we propose a new metric for optimizing genetic gain: Rmax, the expected maximum genotypic value of selections. The optimal design did not involve genotyping more progeny than were phenotyped, even when the cost of creating and genotyping each line was only 0.25 the cost of one yield plot unit (YPU). At a genotyping cost of 0.25 YPU, GS offered up to a 5% increase in genetic gain compared to phenotypic selection for a budget of 200 YPU per family. To increase genetic gains further, the training population must be expanded beyond the full-sib family under selection, using close relatives of the parents as a source of prediction accuracy.