Abstract
There are a lot of methods developed to predict untested phenotypes in schemes commonly used in genomic selection (GS) breeding. The use of GS for predicting disease resistance has its own particularities: (a) most populations shows additivity in quantitative adult plant resistance (APR); (b) resistance needs effective combinations of major and minor genes; and (c) phenotype is commonly expressed in ordinal categorical traits, whereas most parametric applications assume that the response variable is continuous and normally distributed. Machine learning methods (MLM) can take advantage of examples (data) that capture characteristics of interest from an unknown underlying probability distribution (i.e., data-driven). We introduce some state-of-the-art MLM capable to predict rust resistance in wheat. We also present two parametric R packages for the reader to be able to compare.
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1 Electronic Supplementary Materials
Supplementary Table 1
TrainingPopulation.csv. The file contains stem rust data of 90 lines derived from the cross PBW343 × Kingbird grown in the wet season in Kenya [10] and characterized with 1355 DArT markers. Rust severity for each plot (column 1356) was determined using the modified Cobb Scale. Phenotypic data were processed by square root transformation and standardized to mean zero and standard deviation of 1. (CSV 242 kb)
Supplementary Table 2
BreedingPopulation.csv. The file contains stem rust data of the same population but grown during the dry season in Kenya [10]. Rust severity for each plot was determined using the modified Cobb Scale. Phenotypic data were processed by square root transformation and standardized to mean zero and standard deviation of 1. (CSV 242 kb)
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Ornella, L., González-Camacho, J.M., Dreisigacker, S., Crossa, J. (2017). Applications of Genomic Selection in Breeding Wheat for Rust Resistance. In: Periyannan, S. (eds) Wheat Rust Diseases. Methods in Molecular Biology, vol 1659. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-7249-4_15
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DOI: https://doi.org/10.1007/978-1-4939-7249-4_15
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