ABSTRACT Genomic selection (GS) emphasizes the simultaneous prediction of the genetic effects of thousands of scattered markers over the genome. Several statistical methodologies have been used in GS for the prediction of genetic merit. In general, such methodologies require certain assumptions about the data, such as the normality of the distribution of phenotypic values. To circumvent the non-normality of phenotypic values, the literature suggests the use of Bayesian Generalized Linear Regression (GBLASSO). Another alternative is the models based on machine learning, represented by methodologies such as Artificial Neural Networks (ANN), Decision Trees (DT) and related possible refinements such as Bagging, Random Forest and Boosting. This study aimed to use DT and its refinements for predicting resistance to orange rust in Arabica coffee. Additionally, DT and its refinements were used to identify the importance of markers related to the characteristic of interest. The results were compared with those from GBLASSO and ANN. Data on coffee rust resistance of 245 Arabica coffee plants genotyped for 137 markers were used. The DT refinements presented equal or inferior values of Apparent Error Rate compared to those obtained by DT, GBLASSO, and ANN. Moreover, DT refinements were able to identify important markers for the characteristic of interest. Out of 14 of the most important markers analyzed in each methodology, 9.3 markers on average were in regions of quantitative trait loci (QTLs) related to resistance to disease listed in the literature.

O objetivo deste trabalho foi identificar cultivares geneticamente divergentes de mamoneira (Ricinus communis) com uso de marcadores RAPD. Um total de 58 iniciadores RAPD foi usado na genotipagem de 15 cultivares. A dissimilaridade genética entre as cultivares foi calculada a partir do índice de Jaccard, tendo-se utilizado o método da média aritmética não ponderada (UPGMA). Foram identificados 552 fragmentos, sendo 311 polimórficos (56,3%). As cultivares foram agrupadas em cinco grupos, evidência de que há divergência genética entre elas. Os marcadores moleculares do tipo RAPD são eficientes no estudo da dissimilaridade em mamoneira.

The objective of this work was to identify genetically different cultivars of castor bean (Ricinus communis) using RAPD markers. A total of 58 RAPD primers were used to genotype 15 cultivars. The genetic dissimilarity between cultivars was calculated by Jaccard's index, using the unweighted pair‑group method with arithmetic mean (UPGMA). Five hundred and fifty‑two fragments were identified, of which 311 were polymorphic (56.3%). The cultivars were clustered in five groups, evidence that there is genetic difference among them. RAPD markers are efficient in the study of genetic dissimilarity in castor bean.