Vinícius Ribeiro Faria

Parentesco na seleção para produtividade e teores de óleo e proteína em soja via modelos mistos

The objective of this work was to evaluate the influence of relationship information for selecting soybean progenies as to their productivity, and oil and protein contents, using mixed models for the prediction of breeding values. Nine hundred F4:6 and 200 F4:7 soybean progenies were evaluated in the seasons 2010/2011 and 2011/2012, respectively. The progenies were obtained from multiple crosses from 57 parents. Data were analyzed using random models (least squares) and mixed models BLUP/REML (best linear unbiased prediction/restricted maximum likelihood). The highest values of predicted gains were obtained by BLUP/REML. The breeding values predicted with the use of BLUP/REML without relationship information were highly correlated with the ones obtained with the random model, and the selected progenies were rather coincident. The inclusion of the relationship matrix resulted in the selection of different progenies and in higher accuracy of breeding values.

BLUP for genetic evaluation of plants in non-inbred families of annual crops

Best Linear Unbiased Prediction (BLUP) has become the most widely used method for genetic assessment of animal and perennial species, and it is potentially relevant for annual crops. The objective of this study was to assess this method for selection within non-inbred families in recurrent breeding programs. The ‘animal model’ was fitted. The data were expansion volume (EV) and grain yield of plants in recombination plots of two to three selection cycles in the popcorn population Viçosa, with half- and full-sib progenies. The ASReml program was used to perform the analyses. For both EV and yield the breeding values predicted from the additive and additive-dominant models were highly correlated. Multi-generation BLUP was, in general, more accurate than single-generation analysis. These two methods resulted in highly correlated predicted breeding values. The dominance genetic values predicted from the single- and multi-generation analysis were also highly correlated. The pedigree information reduced the percentage of coincidences among the selected individuals relative to phenotypic selection mainly in the population structured in half-sib families. Based on breeding values predicted by BLUP analysis, the most efficient selection procedure was mass selection.

Bayesian inference of mixed models in quantitative genetics of crop species

The objectives of this study were to implement a Bayesian framework for mixed models analysis in crop species breeding and to exploit alternatives for informative prior elicitation. Bayesian inference for genetic evaluation in annual crop breeding was illustrated with the first two half-sib selection cycles in a popcorn population. The Bayesian framework was based on the Just Another Gibbs Sampler software and the R2jags package. For the first cycle, a non-informative prior for the inverse of the variance components and an informative prior based on meta-analysis were used. For the second cycle, a non-informative prior and an informative prior defined as the posterior from the non-informative and informative analyses of the first cycle were used. Regarding the first cycle, the use of an informative prior from the meta-analysis provided clearly distinct results relative to the analysis with a non-informative prior only for the grain yield. Regarding the second cycle, the results for the expansion volume and grain yield showed differences among the three analyses. The differences between the non-informative and informative prior analyses were restricted to variance components and heritability. The correlations between the predicted breeding values from these analyses were almost perfect.

Genetic evaluation of inbred plants based on BLUP of breeding value and general combining ability

The testcross method is considered efficient for identifying inbred families with superior general combining ability. The objective of the present study was to assess the relative importance of the performance per se and in crossing in the selection of inbred progenies using bi-trait best linear unbiased prediction. We analysed data for expansion volume (EV) and grain yield from three tests of popcorn (Zea mays L. ssp. everta) S3 families and seven testcross trials, using the ASRemL software. Four selection strategies were assessed based on: breeding value (strategy 1), general combining ability effect (GCA) (strategy 2), additive value and GCA from strategies 1 and 2 (strategy 3), and breeding value and GCA predicted by bi-trait analyses considering EV and yield of the families and testcrosses as different traits (strategy 4). The bi-trait analyses of the same characteristic assessed in S3 families and topcrosses were generally more accurate and had greater heritabilites. The greatest predicted gains in EV were obtained using strategy 4, which was inferior to the other strategies for the yield predicted gains. Strategies 1 and 2 differed most for the families selected. Selection based on GCA maximised heterosis. All of the strategies resulted in comparable realised gains, especially the strategies 3 and 4 based on breeding value and GCA. Selection on S3 based on the additive value and GCA (strategies 3 and 4) resulted in inbred lines superior in number of favourable genes and in general combining ability.

Bias in the prediction of genetic gain due to mass and half-sib selection in random mating populations

The prediction of gains from selection allows the comparison of breeding methods and selection strategies, although these estimates may be biased. The objective of this study was to investigate the extent of such bias in predicting genetic gain. For this, we simulated 10 cycles of a hypothetical breeding program that involved seven traits, three population classes, three experimental conditions and two breeding methods (mass and half-sib selection). Each combination of trait, population, heritability, method and cycle was repeated 10 times. The predicted gains were biased, even when the genetic parameters were estimated without error. Gain from selection in both genders is twice the gain from selection in a single gender only in the absence of dominance. The use of genotypic variance or broad sense heritability in the predictions represented an additional source of bias. Predictions based on additive variance and narrow sense heritability were equivalent, as were predictions based on genotypic variance and broad sense heritability. The predictions based on mass and family selection were suitable for comparing selection strategies, whereas those based on selection within progenies showed the largest bias and lower association with the realized gain.

Adaptabilidade e estabilidade de populações de milho-pipoca relacionadas por ciclos de seleção

The objectives of this work were to estimate the adaptability and stability parameters of popcorn populations related through cycles of selection, to infer about the efficiency of the selection methods by which the populations were obtained and to assess the effects of selection on the parameters of adaptability and stability. Twenty-five populations and three commercial controls were evaluated in 14 trials carried out in the 2003/2004, 2004/2005, 2006/2007, 2008/2009 and 2009/2010 growing seasons, in seven locations. The experimental design used was randomized complete blocks with four replicates. The expansion volume, evaluated in a microwave oven and in the Metric Weight Volume Tester (MWVT), as well as grain yield were analyzed. The Eberhart & Russell adaptability and stability method was used. In general the initial and improved populations showed predictable performance in response to changes in the environment. Selection can cause changes in the adaptability and stability patterns, and the different selection strategies performed in order to obtain the populations showed similar efficiencies