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The Fisher?s infinitesimal model is traditionally used in quantitative genetics and genomic selection, and it attributes most genetic variance to additive variance. Recently, the dominance maximization model was proposed and it prioritizes the dominance variance based on alternative parameterizations. In this model, the additive effects at the locus level are introduced into the model after the dominance variance is maximized. In this study, the new parameterizations of additive and dominance effects on quantitative genetics and genomic selection were evaluated and compared with the parameterizations traditionally applied using the genomic best linear unbiased prediction method. As the parametric relative magnitude of the additive and dominance effects vary with allelic frequencies of populations, we considered different minor allele frequencies to compare the relative magnitudes. We also proposed and evaluated two indices that combine the additive and dominance variances estimated by both models. The dominance maximization model, along with the two indices, offers alternatives to improve the estimates of additive and dominance variances and their respective proportions and can be successfully used in genetic evaluation. Made available in DSpace on 2022-01-19T16:00:25Z (GMT). No. of bitstreams: 1 evaluation-of-a-new-additive.pdf: 725842 bytes, checksum: 2c26fbbce6a08e566a87f91bb64eca59 (MD5) Previous issue date: 2022

This study aimed to analyze the genetic variability of Coffea canephora population with 190 genotypes from cultivar ?ES8152?, based on morphoagronomic characteristics and vegetation index, to identify the most important characteristics for genetic divergence and compare them with commercial clones. The experiment was installed, in 2019, at the Bananal do Norte Experimental Farm/INCAPER, Cachoeiro de Itapemirim, ES, Brazil. The experiment was carried out in Federer?s augmented block design with three blocks, four common treatments (commercial clones A1, LB1, V8 and V12) and 190 regular treatments, genotypes from the seed production field of the conilon coffee cultivar ?ES8152?. At 24 months of age 14 morphoagronomic characteristics and vegetation index were evaluated. Descriptive analysis of the data, the estimation of the Standardized Euclidean Distance (ED) followed by the grouping by the methods of Tocher, UPGMA and principal coordinates, in addition to the relative importance of the characters estimated by the Singh methodology were performed. The most distant genotypes were 62 and 83 (ED=2.620) and the closest were 42 and 160 (ED=0.208). Genotype 83 stood out as the most distant among the others. The optimization and hierarchical groupings allowed the identification of genotypes 15, 81, 107 and 184 as similar to commercial clones. The discard analysis of variables recommended the elimination of the vegetation index and average internode length of the next diversity analysis. Principal coordinate analysis found phenotypic similarity of the genotypes 30, 81, 115, 141 and 163 with the clone V12, of the genotype 119 with the clone A1 and genotype 17 with clone LB1. The study, of morphoagronomic characters, allowed to detection the genetic diversity existing in the materials evaluated, indicating those with phenotypic similarity with the commercial clones, being possible the early identification of promising genotypes, agronomically superior, to start a breeding program for clonal selection, recurrent selection and controlled crosses to maximize heterosis. Made available in DSpace on 2022-08-25T13:20:45Z (GMT). No. of bitstreams: 1 Genetic-variability-of-conilon-coffee-population-from-cultivar.pdf: 1369713 bytes, checksum: d7b694376348a17e814b8f5f6eaab3ae (MD5) Previous issue date: 2022

This study aimed to evaluate the cup quality of Coffea arabica elite genotypes submitted to wet processing. C. arabica elite genotypes, which were grouped according to their genealogy: Bourbon, Paraíso Germplasm, and Resistant to Rust. Coffees were sent to wet processing to obtain fully washed coffee. After processing and drying the coffees were subjected to cup quality analysis according to the methodology of the Association of Special Coffees (SCA). To characterize and discriminate the genealogical groups the data were submitted to chemometric analysis, Principal Component Method (PCA) and Partial Least Squares Discriminant Analysis (PLS-DA). The PCA was effective in presenting an overview of the data, demonstrating the variables that most contributed to the analysis response. However, the PCA was not efficient to group genotypes according to their genealogical origin, based on chemometric data, as it is an unsupervised analysis. Even though most of the samples were classified correctly, the PLS-DA model created has not yet managed to correctly classify the genotypes of the Paraíso germplasm group. The C. arabica elite genotypes evaluated have the potential to produce special coffees, especially on the genotypes Paraíso 2, H493-1-2-10 and UFV-7158 with scores equal to or above 90 points. Made available in DSpace on 2021-08-19T17:00:40Z (GMT). No. of bitstreams: 1 Selection-of-Elite-Genotypes-of-Coffee.pdf: 1319544 bytes, checksum: 7a47985afeb7df14a293b569af64f0d6 (MD5) Previous issue date: 2021

The state of Espírito Santo is the major producer of Coffea canephora in Brazil. Knowledge of genetic reserves is fundamental to plant breeding. Therefore, the present study characterized and analyzed the genetic diversity of 600 C. canephora accessions from the germplasm bank of Incaper based on 38 traits evaluated in 24-30-month-old plants. Further, the predominant descriptors or traits were identified, and high phenotypic variability was determined. Genetic distances for the grouped (Gower), quantitative, and qualitative datasets were 0.48, 0.61, and 0.92, respectively, with accessions 76 (Conilon) and 407 (Robusta) being the most divergent ones at Incaper. In clustering using the Tocher optimization method, 30 groups were formed, with three accessions introduced from Epamig?s Robusta collection being the most dissimilar ones. Graphical dispersion analysis using the principal coordinate method revealed the predominance of three groups formed by the Robusta, Conilon, and hybrid Robusta × Conilon genotypes. Made available in DSpace on 2021-09-01T02:08:08Z (GMT). No. of bitstreams: 1 Characterization-and-genetic-diversity-of-Coffea-Crop-Breeding.pdf: 409681 bytes, checksum: 6241464af4981202090903c50cbfb0d2 (MD5) Previous issue date: 2021

This study assessed the efficiency of Genomic selection (GS) or genome‐wide selection (GWS), based on Regularized Quantile Regression (RQR), in the selection of genotypes to breed autogamous plant populations with oligogenic traits. To this end, simulated data of an F2 population were used, with traits with different heritability levels (0.10, 0.20 and 0.40), controlled by four genes. The generations were advanced (up to F6) at two selection intensities (10% and 20%). The genomic genetic value was computed by RQR for different quantiles (0.10, 0.50 and 0.90), and by the traditional GWS methods, specifically RR-BLUP and BLASSO. A second objective was to find the statistical methodology that allows the fastest fixation of favorable alleles. In general, the results of the RQR model were better than or equal to those of traditional GWS methodologies, achieving the fixation of favorable alleles in most of the evaluated scenarios. At a heritability level of 0.40 and a selection intensity of 10%, RQR (0.50) was the only methodology that fixed the alleles quickly, i.e., in the fourth generation. Thus, it was concluded that the application of RQR in plant breeding, to simulated autogamous plant populations with oligogenic traits, could reduce time and consequently costs, due to the reduction of selfing generations to fix alleles in the evaluated scenarios. Made available in DSpace on 2022-01-26T15:00:24Z (GMT). No. of bitstreams: 1 Quantile-regression-in-genomic.pdf: 1100652 bytes, checksum: 58ba57640b49b0a828b5d352e3d90293 (MD5) Previous issue date: 2021