Person:
Semagn, K.

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Semagn
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K.
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Semagn, K.
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0000-0001-6486-5685

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2010 - 20122
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Region: Global × Start date: 2010 × End date: 2012 ×

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Now showing 1 - 2 of 2
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    Effectiveness of Genomic Prediction of Maize Hybrid Performance in Different Breeding Populations and Environments
    (Genetics Society of America, 2012) Windhausen, V.S.; Atlin, G.; Hickey, J.; Crossa, J.; Jannink, J.L.; Sorrells, M.E.; Babu, R.; Cairns, J.E.; Tarekegne, A.T.; Semagn, K.; Beyene, Y.; Grudloyma, P.; Technow, F.; Riedelsheimer, C.; Melchinger, A.E.
    Genomic prediction is expected to considerably increase genetic gains by increasing selection intensity and accelerating the breeding cycle. In this study, marker effects estimated in 255 diverse maize (Zea mays L.) hybrids were used to predict grain yield, anthesis date, and anthesis-silking interval within the diversity panel and testcross progenies of 30 F2-derived lines from each of five populations. Although up to 25% of the genetic variance could be explained by cross validation within the diversity panel, the prediction of testcross performance of F2-derived lines using marker effects estimated in the diversity panel was on average zero. Hybrids in the diversity panel could be grouped into eight breeding populations differing in mean performance. When performance was predicted separately for each breeding population on the basis of marker effects estimated in the other populations, predictive ability was low (i.e., 0.12 for grain yield). These results suggest that prediction resulted mostly from differences in mean performance of the breeding populations and less from the relationship between the training and validation sets or linkage disequilibrium with causal variants underlying the predicted traits. Potential uses for genomic prediction in maize hybrid breeding are discussed emphasizing the need of (1) a clear definition of the breeding scenario in which genomic prediction should be applied (i.e., prediction among or within populations), (2) a detailed analysis of the population structure before performing cross validation, and (3) larger training sets with strong genetic relationship to the validation set.
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    The genetic dissection of quantitative traits in crops
    (Pontificia Universidad Católica de Valparaíso, 2010) Semagn, K.; Bjornstad, A.; Yunbi Xu
    Most traits of interest in plant breeding show quantitative inheritance, which complicate the breeding process since phenotypic performances only partially reflects the genetic values of individuals. The genetic variation of a quantitative trait is assumed to be controlled by the collective effects of quantitative trait loci (QTLs), epistasis (interaction between QTLs), the environment, and interaction between QTL and environment. Exploiting molecular markers in breeding involve finding a subset of markers associated with one or more QTLs that regulate the expression of complex traits. Many QTL mapping studies conducted in the last two decades identified QTLs that generally explained a significant proportion of the phenotypic variance, and therefore, gave rise to an optimistic assessment of the prospects of markers assisted selection. Linkage analysis and association mapping are the two most commonly used methods for QTL mapping. This review provides an overview of the two QTL mapping methods, including mapping population type and size, phenotypic evaluation of the population, molecular profiling of either the entire or a subset of the population, marker-trait association analysis using different statistical methods and software as well as the future prospects of using markers in crop improvement.
    Publication