Identification of SNP Markers and Candidate Genes Associated with Major Agronomic Traits in Coffea arabica.

Genome-wide association studies (GWASs) allow for inferences about the relationships between genomic variants and phenotypic traits in natural or breeding populations. However, few have used this methodology in Coffea arabica. We aimed to identify chromosomal regions with significant associations between SNP markers and agronomic traits in C. arabica. We used a coffee panel consisting of 195 plants derived from 13 families in F2 generations and backcrosses of crosses between leaf rust-susceptible and -resistant genotypes. The plants were phenotyped for 18 agronomic markers and genotyped for 21,211 SNP markers. A GWAS enabled the identification of 110 SNPs with significant associations (p < 0.05) for several agronomic traits in C. arabica: plant height, plagiotropic branch length, number of vegetative nodes, canopy diameter, fruit size, cercosporiosis incidence, and rust incidence. The effects of each SNP marker associated with the traits were analyzed, such that they can be used for molecular marker-assisted selection. For the first time, a GWAS was used for these important agronomic traits in C. arabica, enabling applications in accelerated coffee breeding through marker-assisted selection and ensuring greater efficiency and time reduction. Furthermore, our findings provide preliminary knowledge to further confirm the genomic loci and potential candidate genes contributing to various structural and disease-related traits of C. arabica.

Marker-Assisted Recurrent Selection for Pyramiding Leaf Rust and Coffee Berry Disease Resistance Alleles in Coffea arabica L.

In this study, marker-assisted recurrent selection was evaluated for pyramiding resistance gene alleles against coffee leaf rust (CLR) and coffee berry diseases (CBD) in Coffea arabica. A total of 144 genotypes corresponding to 12 hybrid populations from crosses between eight parent plants with desired morphological and agronomic traits were evaluated. Molecular data were used for cross-certification, diversity study and resistance allele marker-assisted selection (MAS) against the causal agent of coffee leaf rust (Hemileia vastatrix) and coffee berry disease (Colletotrichum kahawae). In addition, nine morphological and agronomic traits were evaluated to determine the components of variance, select superior hybrids, and estimate genetic gain. From the genotypes evaluated, 134 were confirmed as hybrids. The genetic diversity between and within populations was 75.5% and 24.5%, respectively, and the cluster analysis revealed three primary groups. Pyramiding of CLR and CBD resistance genes was conducted in 11 genotypes using MAS. A selection intensity of 30% resulted in a gain of over 50% compared to the original population. Selected hybrids with increased gain also showed greater genetic divergence in addition to the pyramided resistance alleles. The strategies used were, therefore, efficient to select superior coffee hybrids for recurrent selection programs and could be used as a source of resistance in various crosses.

Machine learning and statistics to qualify environments through multi-traits in Coffea arabica.

Several factors such as genotype, environment, and post-harvest processing can affect the responses of important traits in the coffee production chain. Determining the influence of these factors is of great relevance, as they can be indicators of the characteristics of the coffee produced. The most efficient models choice to be applied should take into account the variety of information and the particularities of each biological material. This study was developed to evaluate statistical and machine learning models that would better discriminate environments through multi-traits of coffee genotypes and identify the main agronomic and beverage quality traits responsible for the variation of the environments. For that, 31 morpho-agronomic and post-harvest traits were evaluated, from field experiments installed in three municipalities in the Matas de Minas region, in the State of Minas Gerais, Brazil. Two types of post-harvest processing were evaluated: natural and pulped. The apparent error rate was estimated for each method. The Multilayer Perceptron and Radial Basis Function networks were able to discriminate the coffee samples in multi-environment more efficiently than the other methods, identifying differences in multi-traits responses according to the production sites and type of post-harvest processing. The local factors did not present specific traits that favored the severity of diseases and differentiated vegetative vigor. Sensory traits acidity and fragrance/aroma score also made little contribution to the discrimination process, indicating that acidity and fragrance/aroma are characteristic of coffee produced and all coffee samples evaluated are of the special type in the Mata of Minas region. The main traits responsible for the differentiation of production sites are plant height, fruit size, and bean production. The sensory trait "Body" is the main one to discriminate the form of post-harvest processing.

Early Selection Enabled by the Implementation of Genomic Selection in Coffea arabica Breeding.

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.

Selection of arabica coffee progenies tolerant to heat stress / Seleção de progênies de café arábica tolerantes ao calor

Due to high temperatures, practically all coffee farms in the state of Rondonia are of the C. canephora species. Thus, importing arabica coffee from other states becomes necessary for composition of blends, as well as for the specialty or gourmet coffee market. The purpose of this study was to select arabica coffee genotypes that exhibit satisfactory agronomic performance under high temperature conditions. The experiment was conducted in OuroPreto do Oeste, RO, Brazil, with mean annual temperature of 25.8°C and mean annual rainfall of 2300mm year-1. The experiment was composed of 114 arabica coffee genotypes, with 103 progenies and eleven control cultivars, provided by EPAMIG. A randomized block experimental design was used with three replications, spacing of 3.0x1.0 meters and five plants per plot. All the crop seasons showed significant difference for the green coffee yield trait. In joint analysis, significant differences were detected among progenies and control cultivars. In the average of the four harvests, green coffee yield was 32.38 bags ha-1. The cultivars 'CatuaíVermelho IAC 15', 'Obatã IAC 1669-20' and 'Catucaí Amarelo 2SLCAK' stood out, achieving yields greater than 40 bags ha-1. The gain obtained from selection was 14.33 bags ha-1, which is equivalent to an increase of 44.04% in production of green coffee. The progeny H514-7-10-6-2-3-9 stood out with an average yield of 51.20 bags ha-1. In regard to maturation cycle, 56% of the progenies were classified as early maturity and 44% as medium maturity. Late maturity genotypes were not observed.

Devido às temperaturas elevadas, basicamente, todas as lavouras de café no estado de Rondônia são da espécie C. canephora. Desse modo, a importação de café arábica de outros estados faz-se necessária para a composição dos blends, além do mercado de cafés especiais ou gourmets. O objetivo deste trabalho foi selecionar genótipos de café arábica que apresentam desempenho agronômico satisfatório sob condições de temperaturas elevadas. O experimento foi instalado em Ouro Preto do Oeste-RO, com temperaturas médias anuais de 25,8°C e precipitação pluvial média de 2.300mm ano-1. O experimento foi composto por 114 genótipos de café arábica, sendo 103 progênies e onze cultivares testemunhas, fornecidas pela EPAMIG. O delineamento foi blocos casualizados com três repetições, espaçamento de 3,0 x 1,0 metros, com cinco plantas por parcela. Todas as safras demonstraram diferença significativa para a característica produtividade de café beneficiado. Na análise conjunta, foram detectadas diferenças significativas entre progênies e entre cultivares testemunhas. Na média das quatro colheitas, a produtividade de café beneficiado foi de 32,38 sacas ha-1. Destaques para as cultivares 'Catuaí Vermelho IAC 15', 'Obatã IAC 1669-20' e 'Catucaí Amarelo 2SLCAK' que alcançaram produtividades acima de 40 sacas ha-1. O ganho de seleção obtido foi de 14,33 sacas ha-1, que equivale a um aumento de 44,04% na produção de café beneficiado. Destaque para a progênie H514-7-10-6-2-3-9, com produtividade média de 51,20 sacas ha-1. Quanto ao ciclo de maturação, 56% das progênies foram classificadas como ciclo precoce e 44% de ciclo intermediário. Não foi observado genótipo de ciclo tardio.