Optimal Design of Preliminary Yield Trials with Genome-Wide Markers

By Jeffrey B. Endelman, G. Atlin, Y. Beyene, K. Semagn, Xuecai Zhang, M. Sorrells, Jean-Luc Jannink. 2014

112 citations recorded by the source corpus.

Previous research on genomic selection (GS) has focused on predicting unphenotyped lines. Genomic selection can also improve the accuracy of phenotyped lines at low heritability, e.g., in a preliminary yield trial (PYT). Our �耀 rst objective was to estimate this effect within a biparental family, using multilocation yield data for barley (Hordeum vulgare L.) and maize (Zea mays L.). 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 �耀 xed budget. When PYT selections are advanced for further testing, we propose a new metric for optimizing genetic gain: R max , the expected maximum genotypic value of the selections. For budgets up to 250 yield plot equivalents per family, 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 with phenotypic selection. 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.

Subfields: Genetics, Plant Science

View Paper: https://www.semanticscholar.org/paper/69b4921216b4dda732273a2d4d86413266f0d36b

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Citations in the corpus

Perspectives for Genomic Selection Applications and Research in Plants (2015, methodology): “Simulations could be used to compare multiple GS breeding strategies at a constant budget, for example (Endelman et al., 2014).”
Genomic Selection for Crop Improvement (2017, unknown): “The use of genomic prediction to select non-phenotyped individuals within a single family, however, needs to be carefully considered as studies on resource allocation have suggested little to no benefit to only phenotyping a subset of a single family in order to develop a model to predict the remain”