Abstract
Genomic selection (GS) and marker-assisted selection (MAS) rely on marker–trait associations and are both routinely used for breeding purposes. Although similar, these two approaches differ in their applications and how markers are used to estimate breeding values. In this study, GS and MAS were compared in their ability to predict six traits associated with resistance to a destructive wheat disease, Fusarium head blight (FHB). A panel consisting in 273 soft red winter wheat lines from the US Midwestern and Eastern regions was used in this study. The statistical models for MAS were built using Fhb-1, the best-studied quantitative trait loci (QTL) for FHB resistance, and two sets of QTL: one independently identified by other groups and a newer set identified “in house”. In contrast, genomic selection models relied on 19,992 SNPs distributed throughout the genome. For the MAS and GS models, marker effects were estimated with ordinary least square and ridge regression best unbiased linear prediction, respectively. Intermediate to high values of prediction accuracy (0.4–0.9) were observed for most GS models, with lower values (<0.3) found for MAS models. Treating QTL as fixed effects in GS models resulted in higher prediction accuracy when compared with a GS model with only random effects, but overestimated accuracies were obtained with in house QTL. For the same selection intensity, GS resulted in higher selection differentials than MAS for all traits. Our results indicate that GS is a more appropriate strategy than MAS for FHB resistance.
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Acknowledgments
This material is based upon work supported by the U.S. Department of Agriculture, under Agreement No. 59-0206-4-029. This is a cooperative project with the U.S. Wheat & Barley Scab Initiative. The first author is grateful to Monsanto Company for providing graduate studies fellowship.
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Arruda, M.P., Lipka, A.E., Brown, P.J. et al. Comparing genomic selection and marker-assisted selection for Fusarium head blight resistance in wheat (Triticum aestivum L.). Mol Breeding 36, 84 (2016). https://doi.org/10.1007/s11032-016-0508-5
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DOI: https://doi.org/10.1007/s11032-016-0508-5