Leonardo de Azevedo Peixoto

Bayesian Multi-Trait Analysis Reveals a Useful Tool to Increase Oil Concentration and to Decrease Toxicity in Jatropha curcas L.

The biggest challenge for jatropha breeding is to identify superior genotypes that present high seed yield and seed oil content with reduced toxicity levels. Therefore, the objective of this study was to estimate genetic parameters for three important traits (weight of 100 seed, oil seed content, and phorbol ester concentration), and to select superior genotypes to be used as progenitors in jatropha breeding. Additionally, the genotypic values and the genetic parameters estimated under the Bayesian multi-trait approach were used to evaluate different selection indices scenarios of 179 half-sib families. Three different scenarios and economic weights were considered. It was possible to simultaneously reduce toxicity and increase seed oil content and weight of 100 seed by using index selection based on genotypic value estimated by the Bayesian multi-trait approach. Indeed, we identified two families that present these characteristics by evaluating genetic diversity using the Ward clustering method, which suggested nine homogenous clusters. Future researches must integrate the Bayesian multi-trait methods with realized relationship matrix, aiming to build accurate selection indices models.

Breeding Jatropha curcas by genomic selection: A pilot assessment of the accuracy of predictive models

Genomic wide selection is a promising approach for improving the selection accuracy in plant breeding, particularly in species with long life cycles, such as Jatropha. Therefore, the objectives of this study were to estimate the genetic parameters for grain yield (GY) and the weight of 100 seeds (W100S) using restricted maximum likelihood (REML); to compare the performance of GWS methods to predict GY and W100S; and to estimate how many markers are needed to train the GWS model to obtain the maximum accuracy. Eight GWS models were compared in terms of predictive ability. The impact that the marker density had on the predictive ability was investigated using a varying number of markers, from 2 to 1,248. Because the genetic variance between evaluated genotypes was significant, it was possible to obtain selection gain. All of the GWS methods tested in this study can be used to predict GY and W100S in Jatropha. A training model fitted using 1,000 and 800 markers is sufficient to capture the maximum genetic variance and, consequently, maximum prediction ability of GY and W100S, respectively. This study demonstrated the applicability of genome-wide prediction to identify useful genetic sources of GY and W100S for Jatropha breeding. Further research is needed to confirm the applicability of the proposed approach to other complex traits.

Comparison of methods used to identify superior individuals in genomic selection in plant breeding.

The aim of this study was to evaluate different methods used in genomic selection, and to verify those that select a higher proportion of individuals with superior genotypes. Thus, F2 populations of different sizes were simulated (100, 200, 500, and 1000 individuals) with 10 replications each. These consisted of 10 linkage groups (LG) of 100 cM each, containing 100 equally spaced markers per linkage group, of which 200 controlled the characteristics, defined as the 20 initials of each LG. Genetic and phenotypic values were simulated assuming binomial distribution of effects for each LG, and the absence of dominance. For phenotypic values, heritabilities of 20, 50, and 80% were considered. To compare methodologies, the analysis processing time, coefficient of coincidence (selection of 5, 10, and 20% of superior individuals), and Spearman correlation between true genetic values, and the genomic values predicted by each methodology were determined. Considering the processing time, the three methodologies were statistically different, rrBLUP was the fastest, and Bayesian LASSO was the slowest. Spearman correlation revealed that the rrBLUP and GBLUP methodologies were equivalent, and Bayesian LASSO provided the lowest correlation values. Similar results were obtained in coincidence variables among the individuals selected, in which Bayesian LASSO differed statistically and presented a lower value than the other methodologies. Therefore, for the scenarios evaluated, rrBLUP is the best methodology for the selection of genetically superior individuals.

Leveraging genomic prediction to scan germplasm collection for crop improvement

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.

Determination of the optimal number of markers and individuals in a training population necessary for maximum prediction accuracy in F2 populations by using genomic selection models.

Genomic selection is a useful technique to assist breeders in selecting the best genotypes accurately. Phenotypic selection in the F2 generation presents with low accuracy as each genotype is represented by one individual; thus, genomic selection can increase selection accuracy at this stage of the breeding program. This study aimed to establish the optimal number of individuals required to compose the training population and to establish the amount of markers necessary to obtain the maximum accuracy by genomic selection methods in F2 populations. F2 populations with 1000 individuals were simulated, and six traits were simulated with different heritability values (5, 20, 40, 60, 80 and 99%). Ridge regression best linear unbiased prediction was used in all analyses. Genomic selection models were set by varying the number of individuals in the training population (2 to 1000 individuals) and markers (2 to 3060 markers). Phenotypic accuracy, genotypic accuracy, genetic variance, residual variance, and heritability were evaluated. Greater the number of individuals in the training population, higher was the accuracy; the values of genotypic and residual variances and heritability were close to the optimum value. Higher the heritability of the trait, higher is the number of markers necessary to obtain maximum accuracy, ranging from 200 for the trait with 5% heritability to 900 for the trait with 99% heritability. Therefore, genomic selection models for prediction in F2 populations must consist of 200 to 900 markers of major effect on the trait and more than 600 individuals in the training population.

Artificial neural networks reveal efficiency in genetic value prediction

The objective of this study was to evaluate the efficiency of artificial neural networks (ANNs) for predicting genetic value in experiments carried out in randomized blocks. Sixteen scenarios were simulated with different values of heritability (10, 20, 30, and 40%), coefficient of variation (5 and 10%), and the number of genotypes per block (150 and 200 for validation, and 5000 for neural network training). One hundred validation populations were used in each scenario. Accuracy of ANNs was evaluated by comparing the correlation of network value with genetic value, and of phenotypic value with genetic value. Neural networks were efficient in predicting genetic value with a 0.64 to 10.3% gain compared to the phenotypic value, regardless the simulated population size, heritability, or coefficient of variation. Thus, the artificial neural network is a promising technique for predicting genetic value in balanced experiments.

Multivariate diallel analysis allows multiple gains in segregating populations for agronomic traits in Jatropha.

Jatropha is research target worldwide aimed at large-scale oil production for biodiesel and bio-kerosene. Its production potential is among 1200 and 1500 kg/ha of oil after the 4th year. This study aimed to estimate combining ability of Jatropha genotypes by multivariate diallel analysis to select parents and crosses that allow gains in important agronomic traits. We performed crosses in diallel complete genetic design (3 x 3) arranged in blocks with five replications and three plants per plot. The following traits were evaluated: plant height, stem diameter, canopy projection between rows, canopy projection on the line, number of branches, mass of hundred grains, and grain yield. Data were submitted to univariate and multivariate diallel analysis. Genotypes 107 and 190 can be used in crosses for establishing a base population of Jatropha, since it has favorable alleles for increasing the mass of hundred grains and grain yield and reducing the plant height. The cross 190 x 107 is the most promising to perform the selection of superior genotypes for the simultaneous breeding of these traits.

Correction: The number of measurements needed to obtain high reliability for traits related to enzymatic activities and photosynthetic compounds in soybean plants infected with Phakopsora pachyrhizi

[This corrects the article DOI: 10.1371/journal.pone.0192189.].

Jatropha half-sib family selection with high adaptability and genotypic stability

Jatropha (Jatropha curcas) has become one of the most important species for producing biofuels. Currently, Genotype x Environment (GxE) interaction is the biggest challenge that breeders should solve to increase the section accuracy in the plant breeding. Therefore, the objectives in this study were to estimate the parameters in the 180 half-sib families in Jatropha evaluated for five production years, to verify the significance of the GxE interaction variance, to evaluate the adaptability and stability for each family based on three prediction methods, to select superior half-sib families based on the adaptability and stability analyses, and to predict the accuracy for the sixth production year. Jatropha half-sib families were classified and selected using the follow adaptability and stability methods: linear regression, bi-segmented linear regression and mixed models concepts called harmonic mean of the relative performance of genetic values (HMRPGV). The prediction accuracy was estimated by the Pearson correlation between the predicted genetic values by adaptability and stability methods and the phenotypic value in the sixth production year. In result, most half-sib families were classified as general adaptability and general stability for the evaluated traits. The selection gain obtained via HMRPGV was higher than other methods. The prediction accuracy for the sixth production year was 0.45. Therefore, HMRPGV is efficient to maximize the genetic gain, and it can be a useful strategy to select genotype with high adaptability and stability in Jatropha breeding as well as other species that should be evaluated for many years to take a suitable selection accuracy.

Parental selection in diallel crosses of Jatropha curcas using mixed models

Diallel crosses in an unbalanced scheme were carried out in Jatropha to (i) evaluate the additive and non-additive genetic components; (ii) select parents through the general combining ability; (iii) estimate the specific combining ability used in the crosses; and (iv) verify the existence of the maternal effect and inbreeding depression. The experiment was carried out in a complete diallel scheme with four progenitors, unbalanced for the number of crosses. The experimental design consisted of a randomized block, with 5 replications and 3 plants per plot. The following characteristics were evaluated: stem diameter (SD), number of branches (NB), plant height (PH), canopy projection on the row (CPR), canopy projection between rows (CPB), mass of hundred grains (MHG) and grain yield (GY). Estimates of variances were obtained using the method of restricted maximum likelihood, while breeding values were estimated by the best linear unbiased prediction. It was concluded that the additive effect was predominant in the genetic control for SD, CPR, and CPB; the dominance effect was predominant for PH, NB, and GY; there was a cytoplasmic effect and nuclear genes of the female parent for all evaluated traits; parents 107 and 190 are promising for reducing the size and increasing the grain yield; there was inbreeding depression for SD and GY; and the favorable crosses for increasing GY were 190x107 and 190x190.