Genomic Selection (GS) has allowed the maximization of genetic gains per unit time in several annual and perennial plant species. However, no GS studies have addressed Coffea arabica, the most economically important species of the genus Coffea. Therefore, this study aimed (i) to evaluate the applicability and accuracy of GS in the prediction of the genomic estimated breeding value (GEBV); (ii) to estimate the genetic parameters; and (iii) to evaluate the time reduction of the selection cycle by GS in Arabica coffee breeding. A total of 195 Arabica coffee individuals, belonging to 13 families in generation of F₂, susceptible backcross and resistant backcross, were phenotyped for 18 agronomic traits, and genotyped with 21,211 SNP molecular markers. Phenotypic data, measured in 2014, 2015, and 2016, were analyzed by mixed models. GS analyses were performed by the G-BLUP method, using the RKHS (Reproducing Kernel Hilbert Spaces) procedure, with a Bayesian algorithm. Heritabilities and selective accuracies were estimated, revealing moderate to high magnitude for most of the traits evaluated. Results of GS analyses showed the possibility of reducing the cycle time by 50%, maximizing selection gains per unit time. The effect of marker density on GS analyses was evaluated. Genomic selection proved to be promising for C. arabica breeding. The agronomic traits presented high complexity for they are controlled by several QTL and showed low genomic heritabilities, evidencing the need to incorporate genomic selection methodologies to the breeding programs of this species.

We provide a transcriptional profile of coffee rust interaction and identified putative up regulated resistant genes

Acrocomia aculeata (Arecaceae), a palm endemic to South and Central America, is a potential oil crop. Knowledge of the mating system of this species is limited to its reproductive biology and to studies using molecular markers. The present study analyzed genetic diversity between its developmental stages and determined its prevailing mating system in order to support genetic conservation and breeding programs. We tested 9 microsatellite markers in 27 mother trees (adult plants) and 157 offspring (juvenile plants) from the southeastern region of Brazil. Heterozygosity levels differed between the 2 studied life stages, as indicated by the fixation index of adult and juvenile trees, suggesting that selection against homozygotes occurs during the plant life cycle. The mating system parameters analyzed indicate that A. aculeata is predominantly outcrossing (allogamous). However, its low levels of selfing suggest that there is individual variation with regard to self-incompatibility, which can be a survival strategy in isolated or fragmented habitats. Deviations in variance effective size were detected because of high mating rates among relatives and correlated matings. These findings indicate that the main source of inbreeding results from biparental inbreeding in the population and that the progenies are predominantly composed of full-sibs. The information provided by this study on the ecology and reproduction dynamics of A. aculeata should be useful to both breeding and genetic conservation programs, allowing the development of more precise mathematical models and the estimation of the appropriate number of mother trees for seed collection.

The most important disease of Coffea arabica is coffee leaf rust caused by the fungus Hemileia vastatrix. The purpose of this study was to characterize the inheritance of coffee resistance gene(s) to race II of this pathogen and to identify and map molecular markers linked to this trait. Different populations were used: F₂ (160 plants), BCr (20), and BCs (135), derived from a cross between the resistant genotype Híbrido de Timor UFV 427-15 and the susceptible cultivar Catuaí Amarelo UFV 2143-236 (IAC 30). The segregation analysis showed that the resistance of Híbrido de Timor to race II of the H. vastatrix is conferred by a single dominant gene. The amplification of 176 AFLP (Amplified fragment length polymorphism) primer combinations using bulked segregant analysis (BSA) allowed the identification of three molecular markers linked to the resistance gene. Genetic mapping of these three markers in the F₂ population indicated that they are distributed on both sides, flanking the resistance gene. The markers E.CTC/M.TTT405 and E.CGT/M.TGT300 were found linked to the resistance gene at 8.69 cM (LOD 18.91) and 25.10 cM (LOD 5.37), respectively, while E.CCT/M.TTC230 was localized on the other side of the gene, at 20.50 cM (LOD 6.15). These markers are the first rust resistance markers identified in Híbrido de Timor and can be useful for marker assisted selection in coffee breeding programs.

Selecting superior genotypes is facilitated by marker-assisted selection (MAS), which is particularly suitable for transferring disease resistance alleles because it nullifies environmental effects and allows selection of resistant individuals in the absence of the pathogen or race, enabling preventive breeding. Molecular markers linked to two major genes (S_H3 and S_H?), conferring resistance to coffee rust, and those linked to the Ck-1 gene, conferring resistance to coffee berry disease (CBD), have previously been identified. These markers were validated and used in a progeny of crosses between Indian selections with Coffea arabica cultivars. Eleven resistant individuals homozygous for S_H3 were identified by MAS. Of these, seven carry S_H? from Híbrido de Timor and the gene introduced from Coffea liberica (S_H3). S_H? was characterized as derived from Coffea canephora. Thus, it was possible to identify C. arabica genotypes carrying important genes for rust resistance introgressed from other coffee species. MAS also allowed identification of sources of CBD resistance for use in preventive breeding for resistance to this serious disease. Using two validated molecular markers, two coffee plants carrying Ck-1 were identified: the UFV 328-60 genotype (F₂) was resistant and homozygous based on both molecular markers but exhibited no markers related to S_H3 and S_H?, and the UFV 317-12 genotype (F₁) was resistant and homozygous but resistant and heterozygous based on CBD-Sat207 and CBD-Sat235, respectively. Along with possessing Ck-1, the latter carries S_H?. Overall, plants carrying different genes for resistance to rust and CBD were identified. These plants are important sources for gene pyramiding in breeding programs aimed at multiple and durable resistance.

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.