Scientia Agrícola
URI permanente para esta coleçãohttps://sbicafe.ufv.br/handle/123456789/12094
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Item Multi-trait selection and the relationship between sensory analysis and post-harvest variables in Coffea arabica(Escola Superior de Agricultura "Luiz de Queiroz", 2025-11-21) Moreira, Daniele Birck; Silva, Sabrina Alves da; Costa, Weverton Gomes da; Caixeta, Eveline Teixeira; Nascimento, Moysés; Good God, Pedro Ivo VieiraWe aimed to estimate genetic parameters and multivariate selection in coffee related to quality and postharvest practices. In a population of 59 progeny of Coffea arabica, we analyzed the most important traits related to quality. Our analysis reveals significant genetic variance in all traits. We examined the effectiveness of direct selection and multivariate genetic gain using factor analysis. Our results suggest that genetic gains are achievable by all traits, although to varying degrees. In particular, selection based on the sensory quality factor alone can lead to gains opposite to those desired for traits such as ripening uniformity, bean size, and yield. This result may be due mainly to the way postharvest variables are processed for the standard Specialty Coffee Association of America (SCAA) beverage quality tests. Therefore, under this scenario, selection using factor analysis must be applied with care. These results provide a basis for future genetic selection strategies that will improve the quality of coffee.Item Receptor-Like Kinase (RLK) as a candidate gene conferring resistance to Hemileia vastatrix in coffee(Escola Superior de Agricultura "Luiz de Queiroz", 2021) Almeida, Dênia Pires de; Castro, Isabel Samila Lima; Mendes, Tiago Antônio de Oliveira; Alves, Danúbia Rodrigues; Barka, Geleta Dugassa; Barreiros, Pedro Ricardo Rossi Marques; Zambolim, Laércio; Sakiyama, Ney Sussumu; Caixeta, Eveline TeixeiraThe 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 (SH1-9). 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.Item Genomic prediction of leaf rust resistance to Arabica coffee using machine learning algorithms(Escola Superior de Agricultura "Luiz de Queiroz", 2021) Sousa, Ithalo Coelho de; Nascimento, Moysés; Silva, Gabi Nunes; Nascimento, Ana Carolina Campana; Cruz, Cosme Damião; Silva, Fabyano Fonseca e; Almeida, Dênia Pires de; Pestana, Kátia Nogueira; Azevedo, Camila Ferreira; Zambolim, Laércio; Caixeta, Eveline TeixeiraGenomic 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.