NDLI: Developing a parsimonius predictor for binary traits in sugar beet (Beta vulgaris)

Content Provider	Springer Nature Link
Author	Biscarini, Filippo Marini, Simone Stevanato, Piergiorgio Broccanello, Chiara Bellazzi, Riccardo Nazzicari, Nelson
Copyright Year	2015
Abstract	Binary traits are often encountered in plant (and animal) breeding. These include resistance/susceptibility to diseases or the presence/absence of a given characteristic. Root vigor in sugar beet is related to nutrient uptake from the soil and sugar yield and can be classified as either high or low, thus providing an example of binary trait of agronomic importance. Genomic data may be used for early prediction of seedlings root vigor in sugar beet breeding programmes. In this context, it may be of theoretical and practical interest to determine the minimum set of data needed for accurate predictions. A panel of 175 SNP markers was used to genotype 123 sugar beet individual plants. SNPs were ranked based on their predictive ability in a model selection algorithm. Starting from the bottom (least relevant SNP), one SNP at a time was removed and the predictive ability of the remaining SNPs assessed. The accuracy of prediction was in general very high, close to 100 %. Only starting from $$\le$$ 30 SNPs in the model, the prediction accuracy became less stable and began to decrease. Based on results, a set of 30–50 SNPs can be recommended for accurate prediction of root vigor in sugar beet populations. The described procedure is in principle applicable to any binary trait in any plant (or animal) species of agricultural interest.
Starting Page	1
Ending Page	12
Page Count	12
File Format	PDF
ISSN	13803743
Journal	Molecular Breeding
Volume Number	35
Issue Number	1
e-ISSN	15729788
Language	English
Publisher	Springer Netherlands
Publisher Date	2015-01-15
Publisher Place	Dordrecht
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Binary traits Genomic predictions SNP Dataset reduction Parsimonious predictor Sugar beet Plant Sciences Plant Genetics & Genomics Plant Pathology Plant Physiology Biotechnology
Content Type	Text
Resource Type	Article
Subject	Genetics Plant Science Molecular Biology Biotechnology Agronomy and Crop Science

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