Person:
Semagn, K.

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Semagn
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Semagn, K.

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  • Effect of trait heritability, training population size and marker density on genomic prediction accuracy estimation in 22 bi-parental tropical maize populations
    (Frontiers, 2017) Ao Zhang; Hongwu Wang; Beyene, Y.; Semagn, K.; Yubo Liu; Shiliang Cao; Zhenhai Cui; Yanye Ruan; Burgueño, J.; San Vicente Garcia, F.M.; Olsen, M.; Prasanna, B.M.; Crossa, J.; Haiqiu Yu; Xuecai Zhang
    Genomic selection is being used increasingly in plant breeding to accelerate genetic gain per unit time. One of the most important applications of genomic selection in maize breeding is to predict and select the best un-phenotyped lines in bi-parental populations based on genomic estimated breeding values. In the present study, 22 bi-parental tropical maize populations genotyped with low density SNPs were used to evaluate the genomic prediction accuracy (rMG) of the six trait-environment combinations under various levels of training population size (TPS) and marker density (MD), and assess the effect of trait heritability (h2), TPS and MD on rMG estimation. Our results showed that: (1) moderate rMG values were obtained for different trait-environment combinations, when 50% of the total genotypes was used as training population and ~200 SNPs were used for prediction; (2) rMG increased with an increase in h2, TPS and MD, both correlation and variance analyses showed that h2 is the most important factor and MD is the least important factor on rMG estimation for most of the trait-environment combinations; (3) predictions between pairwise half-sib populations showed that the rMG values for all the six trait-environment combinations were centered around zero, 49% predictions had rMG values above zero; (4) the trend observed in rMG differed with the trend observed in rMG/h, and h is the square root of heritability of the predicted trait, it indicated that both rMG and rMG/h values should be presented in GS study to show the accuracy of genomic selection and the relative accuracy of genomic selection compared with phenotypic selection, respectively. This study provides useful information to maize breeders to design genomic selection workflow in their breeding programs.
    Publication
  • Optimal design of preliminary yield trials with genome-wide markers
    (Crop Science Society of America (CSSA), 2014) Endelman, J.; Atlin, G.; Beyene, Y.; Semagn, K.; Xuecai Zhang; Sorrells, M.E.; Jannink, J.L.
    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: Rax, the expected maximum genotypic value of the 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.
    Publication