New methods and algorithms are being developed for predicting untested phenotypes in schemes commonly used in genomic selection (GS). The prediction of disease resistance in GS has its own peculiarities: a) there is consensus about the additive nature of quantitative adult plant resistance (APR) genes, although epistasis has been found in some populations; b) rust resistance requires effective combinations of major and minor genes; and c) disease resistance is commonly measured based on ordinal scales (e.g., scales from 1–5, 1–9, etc.). Machine learning (ML) is a field of computer science that uses algorithms and existing samples to capture characteristics of target patterns. In this paper we discuss several state‐of‐the‐art ML methods that could be applied in GS. Many of them have already been used to predict rust resistance in wheat. Others are very appealing, given their performance for predicting other wheat traits with similar characteristics. We briefly describe the proposed methods in the Appendix.

Durable resistance to the rust diseases of wheat ( Triticum aestivum L.) can be achieved by developing lines that have race‐nonspecific adult plant resistance conferred by multiple minor slow‐rusting genes. Genomic selection (GS) is a promising tool for accumulating favorable alleles of slow‐rusting genes. In this study, five CIMMYT wheat populations evaluated for resistance were used to predict resistance to stem rust ( Puccinia graminis ) and yellow rust ( Puccinia striiformis ) using Bayesian least absolute shrinkage and selection operator (LASSO) (BL), ridge regression (RR), and support vector regression with linear or radial basis function kernel models. All parents and populations were genotyped using 1400 Diversity Arrays Technology markers and different prediction problems were assessed. Results show that prediction ability for yellow rust was lower than for stem rust, probably due to differences in the conditions of infection of both diseases. For within population and environment, the correlation between predicted and observed values (Pearson's correlation [ρ]) was greater than 0.50 in 90% of the evaluations whereas for yellow rust, ρ ranged from 0.0637 to 0.6253. The BL and RR models have similar prediction ability, with a slight superiority of the BL confirming reports about the additive nature of rust resistance. When making predictions between environments and/or between populations, including information from another environment or environments or another population or populations improved prediction.

Multi-layer perceptron (MLP) and radial basis function neural networks (RBFNN) have been shown to be effective in genome-enabled prediction. Here, we evaluated and compared the classification performance of an MLP classifier versus that of a probabilistic neural network (PNN), to predict the probability of membership of one individual in a phenotypic class of interest, using genomic and phenotypic data as input variables. We used 16 maize and 17 wheat genomic and phenotypic datasets with different trait-environment combinations (sample sizes ranged from 290 to 300 individuals) with 1.4 k and 55 k SNP chips. Classifiers were tested using continuous traits that were categorized into three classes (upper, middle and lower) based on the empirical distribution of each trait, constructed on the basis of two percentiles (15–85 % and 30–70 %). We focused on the 15 and 30 % percentiles for the upper and lower classes for selecting the best individuals, as commonly done in genomic selection. Wheat datasets were also used with two classes. The criteria for assessing the predictive accuracy of the two classifiers were the area under the receiver operating characteristic curve (AUC) and the area under the precision-recall curve (AUCpr). Parameters of both classifiers were estimated by optimizing the AUC for a specific class of interest. The AUC and AUCpr criteria provided enough evidence to conclude that PNN was more accurate than MLP for assigning maize and wheat lines to the correct upper, middle or lower class for the complex traits analyzed. Results for the wheat datasets with continuous traits split into two and three classes showed that the performance of PNN with three classes was higher than with two classes when classifying individuals into the upper and lower (15 or 30 %) categories. The PNN classifier outperformed the MLP classifier in all 33 (maize and wheat) datasets when using AUC and AUCpr for selecting individuals of a specific class. Use of PNN with Gaussian radial basis functions seems promising in genomic selection for identifying the best individuals. Categorizing continuous traits into three classes generally provided better classification than when using two classes, because classification accuracy improved when classes were balanced.

Citrus is an economically important fruit crop that is severely afflicted by Asiatic citrus bacterial canker (CBC), a disease caused by the phytopathogen Xanthomonas citri subsp. citri (X. citri). To gain insight into the molecular epidemiology of CBC, 42 Xanthomonas isolates were collected from a range of Citrus spp. across 17 different orchards in Tucumán, Argentina and subjected to molecular, biochemical, and pathogenicity tests. Analysis of genome-specific X. citri markers and DNA polymorphisms based on repetitive elements-based polymerase chain reaction showed that all 42 isolates belonged to X. citri. Interestingly, pathogenicity tests showed that one isolate, which shares >90% genetic similarity to the reference strain X. citri T, has host range specificity. This new variant of X. citri subsp. citri, named X. citri A^T, which is deficient in xanthan production, induces an atypical, noncankerous chlorotic phenotype in Citrus limon and C. paradisi and weak cankerous lesions in C. aurantifolia and C. clementina leaves. In C. limon, suppression of canker development is concomitant with an oxidative burst; xanthan is not implicated in the phenotype induced by this interaction, suggesting that other bacterial factors would be involved in triggering the defense response.