Random regression for modeling yield genetic trajectories in Jatropha curcas breeding

Abstract

Random regression models (RRM) are a powerful tool to evaluate genotypic plasticity over time. However, to date, RRM remains unexplored for the analysis of repeated measures in Jatropha curcas breeding. Thus, the present work aimed to apply the random regression technique and study its possibilities for the analysis of repeated measures in Jatropha curcas breeding. To this end, the grain yield (GY) trait of 730 individuals of 73 half-sib families was evaluated over six years. Variance components were estimated by restricted maximum likelihood, genetic values were predicted by best linear unbiased prediction and RRM were fitted through Legendre polynomials. The best RRM was selected by Bayesian information criterion. According to the likelihood ratio test, there was genetic variability among the Jatropha curcas progenies; also, the plot and permanent environmental effects were statistically significant. The variance components and heritability estimates increased over time. Non-uniform trajectories were estimated for each progeny throughout the measures, and the area under the trajectories distinguished the progenies with higher performance. High accuracies were found for GY in all harvests, which indicates the high reliability of the results. Moderate to strong genetic correlation was observed across pairs of harvests. The genetic trajectories indicated the existence of genotype × measurement interaction, once the trajectories crossed, which implies a different ranking in each year. Our results suggest that RRM can be efficiently applied for genetic selection in Jatropha curcas breeding programs.

Genetic evaluation and selection in Jatropha curcas through Frequentist and Bayesian inferences

“…Therefore, the predicted selection gain (≈ 14%) highlighted the great potential of the evaluated progenies for recombination and for development of a commercial variety. Similar predicted selection gains were reported by Alves et al (2019), Peixoto et al (2020 and Peixoto et al (2021). Few studies exist in the literature evaluating repeated measures in J. curcas breeding.…”

“…In the analysis of repeated measures, it is very important to evaluate different residual variance structures (homogeneous, heterogeneous), since the prediction of the genetic values directly depends on the precision of the estimates of variance components (Peixoto et al 2020;Peixoto et al 2021).…”

Assessing genotype adaptability and stability in perennial forage breeding trials using random regression models for longitudinal dry matter yield data

“…4A, 8A and 12A). This autoregressive pattern is very common in longitudinal data in perennial species [3, 7, 19, 44, 45, 46] and have a satisfactory biological explanation, indicating that genes are expressing differently according to the environmental conditions and genotypes’ age [2, 47].…”

“…Each eigenfunction is a continuous function that represents a possible evolutionary deformation of the mean yield trajectory [23]. When the eigenfunction is nearly constant, it means that the eigenfunction captured a gene pool that was equally expressed over time [23, 18, 45]. On trial T1, the first eigenfunction had a constant behavior and it is explaining the general adaptability gene pool equally expressed over time and the positive genetic correlation (Fig.…”

Métodos estatísticos na avaliação da repetibilidade genotípica em lima ácida ‘Tahiti’

“…The calculation of the AUGT enables the ranking of genotypes considering all crops simultaneously and considering the genotypic plasticity of genotypes over time (Peixoto et al 2020). For the productive traits, FY and NF, the greater the area under the curve, the greater the productive capacity of the genotype.…”