Seleção recorrente genômica como estratégia para aceleração de ganhos genéticos em arroz
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2016-12-15
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Universidade Federal de Goiás
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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.
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MORAIS JÚNIOR, O. P. Seleção recorrente genômica como estratégia para aceleração de ganhos genéticos em arroz. 2016. 172 f. Tese (Doutorado em Genética e Melhoramento de Plantas) - Universidade Federal de Goiás, Goiânia, 2016.