Optimal Design of Preliminary Yield Trials with Genome-Wide Markers
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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 first 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 fixed 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.
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