Random regression for modeling yield genetic trajectories in Jatropha curcas breeding.
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...
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doaj-2c83ea035df94be5922a3c6d69fb3ed92021-03-04T13:06:28ZengPublic Library of Science (PLoS)PLoS ONE1932-62032020-01-011512e024402110.1371/journal.pone.0244021Random regression for modeling yield genetic trajectories in Jatropha curcas breeding.Marco Antônio PeixotoRodrigo Silva AlvesIgor Ferreira CoelhoJeniffer Santana Pinto Coelho EvangelistaMarcos Deon Vilela de ResendeJoão Romero do Amaral Santos de Carvalho RochaFabyano Fonseca E SilvaBruno Gâlveas LaviolaLeonardo Lopes BheringRandom 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.https://doi.org/10.1371/journal.pone.0244021 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Marco Antônio Peixoto Rodrigo Silva Alves Igor Ferreira Coelho Jeniffer Santana Pinto Coelho Evangelista Marcos Deon Vilela de Resende João Romero do Amaral Santos de Carvalho Rocha Fabyano Fonseca E Silva Bruno Gâlveas Laviola Leonardo Lopes Bhering |
spellingShingle |
Marco Antônio Peixoto Rodrigo Silva Alves Igor Ferreira Coelho Jeniffer Santana Pinto Coelho Evangelista Marcos Deon Vilela de Resende João Romero do Amaral Santos de Carvalho Rocha Fabyano Fonseca E Silva Bruno Gâlveas Laviola Leonardo Lopes Bhering Random regression for modeling yield genetic trajectories in Jatropha curcas breeding. PLoS ONE |
author_facet |
Marco Antônio Peixoto Rodrigo Silva Alves Igor Ferreira Coelho Jeniffer Santana Pinto Coelho Evangelista Marcos Deon Vilela de Resende João Romero do Amaral Santos de Carvalho Rocha Fabyano Fonseca E Silva Bruno Gâlveas Laviola Leonardo Lopes Bhering |
author_sort |
Marco Antônio Peixoto |
title |
Random regression for modeling yield genetic trajectories in Jatropha curcas breeding. |
title_short |
Random regression for modeling yield genetic trajectories in Jatropha curcas breeding. |
title_full |
Random regression for modeling yield genetic trajectories in Jatropha curcas breeding. |
title_fullStr |
Random regression for modeling yield genetic trajectories in Jatropha curcas breeding. |
title_full_unstemmed |
Random regression for modeling yield genetic trajectories in Jatropha curcas breeding. |
title_sort |
random regression for modeling yield genetic trajectories in jatropha curcas breeding. |
publisher |
Public Library of Science (PLoS) |
series |
PLoS ONE |
issn |
1932-6203 |
publishDate |
2020-01-01 |
description |
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. |
url |
https://doi.org/10.1371/journal.pone.0244021 |
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