Coffee leaf rust (CLR) caused by Hemileia vastatrix Berk. et Br. is one of the major Coffea arabica diseases worldwide. CLR resistance has been attributed to at least nine dominant genes, as single or in combination. We present an inheritance study and mapping loci involved in the Híbrido de Timor (HDT) UFV 443-03 resistance to race I, race II, and pathotype 001 of H. vastatrix. Molecular markers were used to ascertain the phenotypic results and to map the putative resistance loci. For all tree isolates, the inheritance study indicated that the resistance of HDT UFV 443-03 is conditioned by two independent dominant loci or by three independent loci (two dominant and one recessive). Molecular marker analyses ascertained that the resistance of HDT UFV 443-03 to race II is conditioned by at least two independent dominant loci, while the resistance to race I and pathotype 001 is conditioned by at least four independent dominant loci. Gene pyramiding might result in a cultivar with durable resistance; however, it is difficult to distinguish between plants with one or more resistance genes due to epistatic effects. Molecular markers linked to these genes were indicated for marker-assisted selection, as it is an efficient breeding alternative for CLR resistance with no such epistatic effects.

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 ApparentError 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.

-The objective of this work was to evaluate the use of artificial neural networks in comparison with Bayesian generalized linear regression to predict leaf rust resistance in Arabica coffee (Coffea arabica). This study used 245 individuals of a F 2 population derived from the self-fertilization of the F 1 H511-1 hybrid, resulting from a crossing between the susceptible cultivar Catuaí Amarelo IAC 64 (UFV 2148-57) and the resistant parent Híbrido de Timor (UFV 443-03). The 245 individuals were genotyped with 137 markers. Artificial neural networks and Bayesian generalized linear regression analyses were performed. The artificial neural networks were able to identify four important markers belonging to linkage groups that have been recently mapped, while the Bayesian generalized model identified only two markers belonging to these groups. Lower prediction error rates (1.60%) were observed for predicting leaf rust resistance in Arabica coffee when artificial neural networks were used instead of Bayesian generalized linear regression (2.4%). The results showed that artificial neural networks are a promising approach for predicting leaf rust resistance in Arabica coffee.Index terms: Coffea arabica, Hemileia vastatrix, artificial intelligence, molecular markers, prediction. Redes neurais artificiais comparadas com modelos lineares generalizados sob o enfoque bayesiano para predição de resistência à ferrugem em café arábicaResumo -O objetivo deste trabalho foi avaliar o uso de redes neurais artificiais em comparação à modelagem por meio de modelos lineares generalizados na predição de resistência à ferrugem em café arábica (Coffea arabica). Foram utilizados 245 indivíduos provenientes de uma população F 2 , oriundos da autofecundação do híbrido F 1 H511-1, resultante do cruzamento da cultivar suscetível Catuaí Amarelo IAC 64 (UFV 2148-57) e do genitor resistente Híbrido de Timor (UFV 443-03). Os 245 indivíduos foram genotipados com 137 marcadores. Realizaram-se análises com redes neurais artificiais e com modelos lineares generalizados sob o enfoque bayesiano. As redes neurais identificaram quatro marcadores importantes pertencentes a grupos de ligação que foram recentemente mapeados, enquanto o modelo generalizado bayesiano identificou somente dois marcadores pertencentes a esses grupos. Foram observadas taxas de erro de predição inferiores (1,60%) para predizer a resistência à ferrugem em café arábica, quando foram utilizadas as redes neurais artificiais em vez de modelos lineares generalizados sob o enfoque bayesiano (2,4%). Os resultados mostraram que as redes neurais artificiais são uma abordagem promissora para predizer a resistência à ferrugem em café arábica.Termos para indexação: Coffea arabica, Hemileia vastatrix, inteligência artificial, marcadores moleculares, predição.

The biotrophic fungus Hemileia vastatrix causes coffee leaf rust (CLR), one of the most devastating diseases in Coffea arabica. Coffee, like other plants, has developed effective mechanisms to recognize and respond to infections caused by pathogens. Plant resistance gene analogs (RGAs) have been identified in certain plants as candidates for resistance (R) genes or membrane receptors that activate the R genes. The RGAs identified in different plants possess conserved domains that play specific roles in the fight against pathogens. Despite the importance of RGAs, in coffee plants these genes and other molecular mechanisms of disease resistance are still unknown. This study aimed to sequence and characterize candidate genes from coffee plants with the potential for involvement in resistance to H. vastatrix. Sequencing was performed based on a library of bacterial artificial chromosomes (BAC) of the coffee clone 'Híbrido de Timor' (HdT) CIFC 832/2 and screened using a functional marker. Two RGAs, HdT_ LRR_RLK1 and HdT_LRR_RLK2, containing the motif of leucine-rich repeat-like kinase (LRR-RLK) were identified. Based on the presence or absence of the HdT_LRR_RLK2 RGA in a number of differential coffee clones containing different combinations of the rust resistance gene, these RGAs did not correspond to any resistance gene already characterized (S H 1-9). These genes were also analyzed using qPCR and demonstrated a major expression peak at 24 h after inoculation in both the compatible and incompatible interactions between coffee and H. vastatrix. These results are valuable information for breeding programs aimed at developing CLR-resistant cultivars, in addition to enabling a better understanding of the interactions between coffee and H. vastatrix.

The use of resistant cultivars is the most effective strategy for controlling coffee leaf rust caused by the fungus Hemileia vastatrix. To assist the development of such cultivars, amplified fragment-length polymorphism (AFLP) markers linked to two loci of coffee resistance to races I and II as well as pathotype 001 of H. vastatrix were converted to sequence-characterized amplified region (SCAR) and cleaved amplified polymorphic site (CAPS) markers. In total, 2 SCAR markers and 1 CAPS marker were validated in resistant and susceptible parents as well as in 247 individuals from the F2 population. The efficiency of these markers for marker-assisted selection (MAS) was evaluated in F2:3 and backcross (BCrs2) populations genotyped with the developed markers and phenotyped with race II of H. vastatrix. The markers showed 90% efficiency in MAS. Therefore, the developed markers, together with molecular markers associated with other rust resistance genes, were used for F3:4 and BCrs3 coffee selection. The selected plants were analyzed using two markers associated with coffee berry disease (CBD) resistance, aiming for preventive breeding. MAS of F3:4 and BCrs3 individuals with all resistance loci was feasible. Our phenotypic and genotypic approaches are useful for the development of coffee genotypes with multiple genes conferring resistance to coffee leaf rust and CBD.