Rbio is a free software for data processing. It is compatible and integrated with the free R software, which is globally accepted for statistical analysis. Thus, Rbio takes advantage of all processing potential of the R software. However, this new software allows the user to perform all the analyses without knowing the programming in R language. Rbio is available in Portuguese; it can be downloaded from the Internet (http://www.biometria.ufv.br), and requires the Windows operating system. It has a set of sample files, making it easy to be used. Currently, it supplies the user with all scripts internally used to process the analyses. Rbio can perform descriptive statistics, analysis of variance, estimation of genetic parameters and means tests, multivariate analysis, nonparametric tests, regressions, correlations, biometrics, bioinformatics, and simulation.

O objetivo deste trabalho foi estimar os parâmetros genéticos e determinar a eficiência da seleção simultânea de clones de eucalipto baseada em produtividade, estabilidade e adaptabilidade. Foram utilizados 21 clones, com 36 meses de idade, pertencentes ao programa de melhoramento genético da empresa Cenibra. O experimento foi instalado em delineamento de blocos ao acaso, em quatro ambientes, com 21 repetições de uma planta por parcela. Os clones foram avaliados quanto às variáveis: diâmetro à altura do peito, altura da planta e volume total com casca. Os parâmetros genéticos foram estimados pela metodologia de modelos mistos (REML/BLUP), e a seleção baseou-se no método da média harmônica do desempenho relativo dos valores genéticos (MHPRVG), em três estratégias: seleção com base no valor genético predito, tendo-se considerado o desempenho médio dos genótipos em todos os ambientes (sem efeito de interação) ou o desempenho em cada ambiente (com efeito da interação); e seleção simultânea quanto à produção, estabilidade e adaptabilidade. As particularidades dos ambientes influenciaram a expressão fenotípica dos clones. As estimativas de herdabilidade indicaram boas perspectivas para a seleção de clones com alta produtividade, estabilidade e adaptabilidade. A seleção simultânea otimiza a seleção de clones e pode ser usada na construção de populações de melhoramento e na recomendação de materiais genéticos para plantios comerciais.

The objective of this work was to estimate genetic gains in physic nut (Jatropha curcas) using selection indexes and to establish the best selection strategy for the species. Direct and indirect selection was carried out using different selection indexes, totalizing 14 strategies. One hundred and seventy five families from the active germplasm bank of Embrapa Agroenergy, Brasília, Brazil, were analyzed in a randomized complete block design with two replicates. The evaluated traits were: grain yield; seeds per fruit; endosperm/seed ratio; seed weight, length, width, and thickness; branches per plant at 0.5, 1.0, and 1.5 m; plant height; stem diameter; canopy projection on rows and between lines; canopy volume; juvenility (days to the first flowering); and height of the first inflorescence. Evaluations were done during the second year of cultivation. The use of selection indexes is relevant to maximize the genetic gains in physic nut, favoring a better distribution of desirable traits. The multiplicative and restrictive indexes are considered the most promising for selection.

The objective of this study was to explore the potential of genomic prediction (GP) for soybean resistance against Sclerotinia sclerotiorum (Lib.) de Bary, the causal agent of white mold (WM). A diverse panel of 465 soybean plant introduction accessions was phenotyped for WM resistance in replicated field and greenhouse tests. All plant accessions were previously genotyped using the SoySNP50K BeadChip. The predictive ability of six GP models were compared, and the impact of marker density and training population size on the predictive ability was investigated. Cross-prediction among environments was tested to determine the effectiveness of the prediction models. GP models had similar prediction accuracies for all experiments. Predictive ability did not improve significantly by using more than 5k SNPs, or by increasing the training population size (from 50% to 90% of the total of individuals). The GP model effectively predicted WM resistance across field and greenhouse experiments when each was used as either the training or validation population. The GP model was able to identify WM-resistant accessions in the USDA soybean germplasm collection that had previously been reported and were not included in the study panel. This study demonstrated the applicability of GP to identify useful genetic sources of WM resistance for soybean breeding. Further research will confirm the applicability of the proposed approach to other complex disease resistance traits and in other crops.