https://www.alice.cnptia.embrapa.br/alice/handle/item/902 Artigo em periódico indexado (SAPC) Tue, 21 Jul 2026 15:06:17 GMT 2026-07-21T15:06:17Z https://www.alice.cnptia.embrapa.br/alice/handle/doc/1187655 Título: Leaf-scale phenotypic plasticity of Coffea arabica progenies under seasonal variation in water availability. Autoria: SILVA, E. A. da; SANTOS, C. S. dos; MATOS, N. M. S. de; PENNACCHI, J. P.; ABRAHÃO, J. C. de R.; CARVALHO, M. A. de F.; TAVARES, M. C. dos S.; CARVALHO, S. P. de; GUIMARÃES, R. J. Conteúdo: Abstract: Climate variability poses major challenges to coffee production, particularly due to the increasing frequency and intensity of drought events. Understanding the physiological acclimation capacity of Coffea arabica genotypes to water deficit is critical for developing resilient cultivars. We hypothesized that progenies with higher multivariate phenotypic plasticity index (MVPi) values would exhibit coordinated morphophysiological traits associated with greater acclimation capacity to seasonal water availability. This study aimed to quantify leaf-scale phenotypic plasticity in 16 C. arabica progenies derived from a plant selected for its large leaves and fruits, which originated from a natural mutation of the Acaiá cultivar. Physiological, anatomical, and biochemical traits were assessed during the dry and rainy seasons, and plasticity was quantified using the MVPi. Principal component analysis revealed substantial variation in plastic responses among genotypes, with M11, L30, and L16 exhibiting the highest MVPi values. These genotypes showed coordinated adjustments in water use efficiency, chlorophyll content, and leaf tissue structure. Although MVPi proved effective in integrating multidimensional trait variation, its interpretation requires caution, as higher plasticity does not necessarily indicate an adaptive advantage. These findings support the integration of multivariate plasticity analysis into breeding programs as a strategy to identify genotypes with superior acclimation potential under water-limited conditions. Thu, 01 Jan 2026 00:00:00 GMT https://www.alice.cnptia.embrapa.br/alice/handle/doc/1187655 2026-01-01T00:00:00Z https://www.alice.cnptia.embrapa.br/alice/handle/doc/1187490 Título: Extreme learning machine for genomic prediction of rust disease resistance in Arabica coffee. Autoria: SILVA, J. T. da; BARRETO, C. A. V.; NASCIMENTO, A. C. C.; AZEVEDO, C. F.; ALMEIDA, D. P. de; CAIXETA, E. T.; TEIXEIRA, F. R. F.; NASCIMENTO, M. Conteúdo: ABSTRACT – The objective of this work was to investigate the use of Extreme Learning Machines (ELM) for the genomic prediction of rust resistance in Coffea arabica. With the objective of identifying an effective predictive model for the selection of resistant genotypes, ELM was compared to Artificial Neural Networks (ANN) and Bayesian Generalized Linear Regression (GBLR) in terms of accuracy measures and computational time. To this end, an F2 population of 245 C. arabica plants genotyped with 137 markers was used to evaluate the application of ELM for the genomic prediction of coffee rust resistance. The results indicate that ELM and ANN show a higher accuracy – on average 15% greater than that of GBLR – in predicting rust resistance. Additionally, ELM proves to be computationally more efficient, with a processing speed 5.5 and 19.45 times slower than that of ANN and BGLR, respectively, making it promising for large-scale analyses. RESUMO – O objetivo deste trabalho foi investigar o uso de Máquinas de Aprendizagem Extrema (ELM) para a predição genômica da resistência à ferrugem em Coffea arabica. Com o objetivo de identificar um modelo preditivo eficaz para a seleção de genótipos resistentes, o ELM foi comparado a Redes Neurais Artificiais (RNA) e Regressão Linear Generalizada Bayesiana (GBLR) em termos de medidas de acurácia e tempo computacional. Para tanto, uma população F2 de 245 plantas de C. arabica genotipadas com 137 marcadores foi utilizada de modo a avaliar a aplicação do ELM na predição genômica da resistência à ferrugem-do-café. Os resultados indicam que o ELM e a RNA apresentam maior acurácia – em média 15% superior ao GBLR – na predição da resistência à ferrugem. Adicionalmente, o ELM se mostra computacionalmente mais eficiente, com velocidades de processamento 5,5 e 19,45 vezes menores que a de RNA e o BGLR, respectivamente, tornando-o promissor para análises de larga escala. Thu, 01 Jan 2026 00:00:00 GMT https://www.alice.cnptia.embrapa.br/alice/handle/doc/1187490 2026-01-01T00:00:00Z https://www.alice.cnptia.embrapa.br/alice/handle/doc/1186547 Título: Comparison of machine learning methods for marker identification in GWAS. Autoria: COSTA, W. G. da; PEREIRA, H. D.; SILVA, G. N.; BORÉM, A.; CAIXETA, E. T.; OLIVEIRA, A. C. B. de; CRUZ, C. D; NASCIMENTO, M. Conteúdo: Genome-wide association studies (GWAS) are essential for identifying genomic regions associated with agronomic traits, but Linear Mixed Model (LMM)-based GWAS face challenges in capturing complex gene interactions. This study explores the potential of machine learning (ML) methodologies to enhance marker identification and association modeling in plant breeding. Unlike LMM-based GWAS, ML approaches do not require prior assumptions about marker–phenotype relationships, enabling the detection of epistatic effects and non-linear interactions. The research sought to assess and contrast approaches utilizing ML (Decision Tree—DT; Bagging—BA; Random Forest—RF; Boosting—BO; and Multivariate Adaptive Regression Splines—MARS) and LMM-based GWAS. A simulated F2 population comprising 1000 individuals was analyzed using 4010 SNP markers and ten traits modeled with epistatic interactions. The simulation included quantitative trait loci (QTL) counts varying between 8 and 240, with heritability levels set at 0.5 and 0.8. These characteristics simulate traits of candidate crops that represent a diverse range of agronomic species, including major cereal crops (e.g., maize and wheat) as well as leguminous crops (e.g., soybean), such as yield, with moderate heritability and a high number of QTLs, and plant height, with high heritability and an average number of QTLs, among others. To validate the simulation findings, the methodologies were further applied to a real Coffea arabica population (n = 195) to identify genomic regions associated with yield, a complex polygenic trait. Results demonstrated a fundamental trade-off between sensitivity and precision. Specifically, for the most complex trait evaluated (240 QTLs under epistatic control), Ensemble methods (Bagging and Random Forest) maintained a Detection Power (DP) exceeding 90%, significantly outperforming state-of-the-art GWAS methods (FarmCPU), which dropped to approximately 30%, and traditional Linear Mixed Models, which failed to detect signals (0%). However, this sensitivity resulted in lower precision for ensembles. In contrast, MARS (Degree 1) and BLINK achieved exceptional Specificity (>99%) and Precision (>90%), effectively minimizing false positives. The real data analysis corroborated these trends: while standard GWAS models failed to detect significant associations, the ML framework successfully prioritized consensus genomic regions harboring functional candidates, such as SWEET sugar transporters and NAC transcription factors. In conclusion, ML Ensembles are recommended for broad exploratory screening to recover missing heritability, while MARS and BLINK are the most effective methods for precise candidate gene validation. Thu, 01 Jan 2026 00:00:00 GMT https://www.alice.cnptia.embrapa.br/alice/handle/doc/1186547 2026-01-01T00:00:00Z https://www.alice.cnptia.embrapa.br/alice/handle/doc/1186548 Título: Low-density marker panels for genomic prediction in Coffea arabica L. Autoria: ARCANJO, E. S.; NASCIMENTO, M.; AZEVEDO, C. F.; CAIXETA, E. T.; OLIVEIRA, A. C. B. de; PEREIRA, A. A.; NASCIMENTO, A. C. C. Conteúdo: Developing new cultivars, particularly in perennial species like Coffea arabica, can be a time-consuming process. Employing molecular markers in genome-wide selection (GWS) for predicting genetic values offers an alternative to accelerate this process. However, implementing GWS typically involves genotyping many markers for both training and candidate individuals, which can increase the total genotyping cost for the breeding program. Therefore, this study aimed to assess the feasibility of using low-density marker panels to predict the genetic merit of C. arabica for a range of desirable agronomic traits. For this purpose, GWS analyses were performed using the G-BLUP method with panels of varying marker densities, selected based on marker effect magnitude. The results indicate that employing lower-density panels might be advantageous for this species' improvement. Models based on these panels yielded accurate predictions for various traits and demonstrated high agreement in terms of selected individuals compared to more complex models. Wed, 01 Jan 2025 00:00:00 GMT https://www.alice.cnptia.embrapa.br/alice/handle/doc/1186548 2025-01-01T00:00:00Z