Seleção recorrente genômica como estratégia para aceleração de ganhos genéticos em arroz

• Ano de defesa: 2016
• Autor(a) principal: Morais Júnior, Odilon Peixoto de
• Orientador(a): Duarte, João Batista
• Banca de defesa: Duarte, João Batista, Breseghello, Flávio, Coelho, Alexandre Siqueira Guedes, Silva Filho, João Luis da, Resende, Marcela Pedroso Mendes
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Goiás
• Programa de Pós-Graduação: Programa de Pós-graduação em Genética e Melhoramento de Plantas (EAEA)
• Departamento: Escola de Agronomia e Engenharia de Alimentos - EAEA (RG)
• País: Brasil
• Palavras-chave em Português: Oryza sativa; C variabilidade genética; Desequilíbrio de ligação; Predição genômica; Covariáveis ambientais; Norma de reação; Progresso genético
• Palavras-chave em Inglês: Oryza sativa; Linkage disequilibrium; Genetic progress; Genetic variability; Genomic prediction; Environmental covariates; Reaction norm
• Área do conhecimento CNPq: FITOTECNIA::MELHORAMENTO VEGETAL
• Link de acesso: http://repositorio.bc.ufg.br/tede/handle/tede/7168

Genetic gains for quantitative traits associated with the maintenance of genetic variability are important factors in recurrent selection programs. With advances in the area of statistical genomics, selection strategies potentially faster to achieve genetic gains are being developed, such as genomic selection. Using a subtropical population of irrigated rice (CNA12S), conducted during three cycles of recurrent selection, this study had as general objective to evaluate the potential of use of genomic recurrent selection (GRS) in a rice breeding program. Three specific studies were developed. In the first chapter, the efficiency of the genotypic recurrent selection (RS) used in the Embrapa’s rice breeding program was evaluated, in order to obtain genetic gains and maintain the population genetic variability. Ten yield trials of S1:3 progenies were used in the analyses. The evaluated traits were grain yield, plant height and days-to-flowering. Variance and covariance components were obtained using Bayesian approach. Using single nucleotide polymorphisms (SNP) markers, the population diversity and genetic structure also were estimated. Adjusted means of progenies in each cycle were computed and, genetic progress was estimated by generalized linear regression using frequentist approach. The magnitudes of effective population size and genetic variance indicated maintenance of genetic variability over selection cycles. The genetic progress achieved for grain yield was 760 kg ha-1 per cycle (1.95% per year), and for days-to-flowering, it was -6.3 days per cycle (-1.28% per year). It was concluded that the genetic progress already achieved and the genetic variability available in the population demonstrate the efficiency of RS in the improvement of rice populations. In the second chapter, in the context of genomic selection, the relative efficiency of GRS on RS was assessed, as well as the accuracy of different models of genomic prediction, in order to propose a GRS scheme for population breeding of self-pollinating species such as rice. In this study, the genetic material was the S1:3 progenies yield trial of the third selection cycle. From a group of 196 progenies that were phenotyped for eight traits with different heritabilities and genetic architectures, a group of 174 progenies was genotyped for SNP markers. Ten predictive models were fitted to the data set. The proposed GRS scheme, when compared to the RS method, showed higher efficiency, especially in genetic gain per unit of time. From the predictive models assessed, HBLUP (hybrid best linear unbiased prediction, using hybrid relationship matrix based in pedigree and SNP markers) and RForest (random forest) have greater potential for genomic prediction in irrigated rice, given the high accuracy of their predictions for a number of traits. The HBLUP model was notoriously superior for more complex traits, such as grain yield, while RForest stood out for less complex traits. The high extent of linkage disequilibrium in the population suggests that the marker density employed (approximately one SNP per 60 kb) is enough for the practice of genomic selection in populations with similar genetic structure. In the third chapter, the objective was to extend a class of HBLUP models based on reaction norm, in context of multi-environmental trials with genotype x environment interaction, for accommodation of hybrid genetic relationship and information of the assessed environments. The accuracy of alternative models for multi-environmental predictions was evaluated, as well as the relative importance of structures of additive and multiplicative components, using genetic relationship information and environmental covariates. This strategy allowed to evaluate the influence of different approaches to group the genetic-environmental information on the accuracy of models for prediction of breeding value of progenies for agronomic traits. The data consisted of the same ten trial of S1:3 progenies, carried out during three recurrent selection cycles. Six predictive HBLUP models of reaction norm were considered, using genetic and environmental covariates, as well as interactions between these effects. Genomic information was derived from SNP markers obtained for the 174 progenies of the third selection cycle. The 401 environmental covariates, the genetic information (hybrid genetic relationship) and the interactions among these effects explained an important portion of the phenotypic variance, allowing an increase in the predictive accuracy of models. The use of genetic information and environmental covariates only from the respective selection cycle is enough for accurate predictions of unphenotyped progenies, even in non-sampled environments. This is the first study to take into account simultaneously hybrid genetic relationship, stemming from pedigree information plus SNP markers, and environmental covariates in multi-environmental models based on reaction norm for breeding value prediction in target environments of a recurrent selection program.