Doutorado em Genética e Melhoramento de Plantas (EA)
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Navegando Doutorado em Genética e Melhoramento de Plantas (EA) por Por Orientador "Brondani, Claudio"
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Item Estudo de associação genômica ampla para produtividade em arroz (Oryza sativa L.)(Universidade Federal de Goiás, 2016-04-06) Pantalião, Gabriel Feresin; Brondani, Claudio; http://lattes.cnpq.br/4775600104554147; Brondani, Claudio; Coelho, Alexandre Siqueira Guedes; Souza, Thiago Lívio Pessoa Oliveira de; Borba, Tereza Cristina de Oliveira; Vianello, Rosana PereiraCultivated rice (Oryza sativa L.) is one of the most important cereal for feeding. It is estimated that the demand for rice grains increases considerably in a reduction scenario of cultivable area and scarcity of water resources, which will require an increase in production compared to current levels. To solve this problem, a viable alternative would be the exploitation of genetic diversity available in rice germplasm banks. Rice breeding programs should prioritize the search for new strategies to increase yield in a variety of environmental conditions. The exploitation of genetic diversity allowed the identification of favorable alleles not present in the germplasm of rice varieties used in breeding programs, as well as obtaining new allelic combinations of genes related to important agronomic traits and that could significantly contribute to the achievement of more productive cultivars. In this context, genome-wide association studies (GWAS) are designed to analyze variations in the DNA sequence of the entire genome in an effort to identify associations with phenotypic traits of interest. It is expected, therefore, that the results of the GWAS analysis, together with the improvements obtained with the next generation sequencing technologies (NGS) in search of a large number of SNPs, such as genotyping by sequencing (GBS), be used to investigate the genetic control of traits related to yield. This study aimed to identify genomic regions of rice related to yield from the GWAS methodology using genotypes of Embrapa Rice Core Collection (ERiCC). The GWAS analysis was conducted from a panel of 550 accessions of the ERiCC, and after the imputation of raw data, were accounted 445,589 SNPs distributed along the 12 rice chromosomes. The molecular information was integrated with phenotypic data derived from yield evaluation experiments conducted in nine essays, divided into two cultivation systems (irrigated and rainfed) and three agricultural years (2004/2005, 2005/2006 and 2006/2007). From the joint analysis in all experiments, 31 SNPs were significantly associated with yield, but only three had the lowest frequency allele with positive effect. The joint analysis of irrigated experiments identified three SNPs associated with yield, of which one with lower frequency allele with a positive effect, whereas in the rainfed experiments was identified only one SNP with lower frequency allele associated to positive effect. Subsequently, a stepwise regression analysis was performed to keep in the model only SNPs without overlapping effects, so being selected 15 SNPs markers. After in silico analysis, it was found that the most productive accessions showed 80 to 100% of favorable alleles while the less productive showed 27 to 33% of favorable alleles. For this set of markers to be used in an assisted selection routine, they should also be validated in the laboratory. In the total joint analysis, from 44 genes identified, 14 had no particular function, while from the joint analysis of experiments irrigated and rainfed, from the six genes, only one had no particular function. The search for Arabidopsis homologues genes in the 15 unknown function rice genes resulted in four genes with known function. The expressed products of the set of genes were related to metabolic processes, response to biotic, abiotic, endogenous and external stimulus, post- embryonic multicellular development, growth and morphogenesis, which influence the number of grains, grains weight and photosynthetic capacity, all related to rice yield and be useful in indicating candidate genes to cloning and transformation, enabling the development of genetically superior rice cultivars. Among the genes identified as associated to productivity, nine were previously described in the literature, and of these, six were related proteins that influence the number and seed weight, and photosynthetic capacity: LOC_Os02g44290.1, LOC_Os04g35370.1, LOC_Os02g44260.1, LOC_Os02g44280 .1 LOC_Os09g36230.1 and LOC_Os01g66160.1. These genes are considered as candidates for cloning and transformation of rice, in order, through its overexpression, enable the development of higher yielding rice cultivars.Item Análise comparativa dos métodos de avanço por Bulk e SSD na identificação de QTLs para produtividade de grão de arroz no cruzamento Epagri 108 X Irati 122(Universidade Federal de Goiás, 2019-11-12) Ramos, Mariana Rodrigues Feitosa; Brondani, Claudio; http://lattes.cnpq.br/4775600104554147; Brondani, Claudio; Vianello, Rosana Pereira; Borba, Tereza Cristina de Oliveira; Coelho, Gesimária Ribeiro Costa; Ramalho, IvanildoA relevant aspect of all rice breeding programs is the extensive genetic variability available and stored in germplasm banks. A major challenge is precisely how to select the most appropriate genotypes to meet the objectives of these programs. An interesting alternative is the assembly of core collections. Besides the characterization per se, the accessions that stood out for their genetic variability or productive performance were crossed in a diallel scheme. The resulting hybrids were self-fertilized to obtain generation F2, which was advanced by Bulk and SSD until F7. Among the most productive crosses, one in particular was interesting due to the genetic distance between the parents (RW = 0.91), and the high value of specific combining ability - Epagri 108 (Oryza sativa spp. Indica) x Irat 122 (Oryza sativa spp. Japonica). This study aimed to perform QTL analysis for plant yield and height using two populations of Epagri 108 x Irat 122 cross, advanced by SSD (generation F8) and Bulk (generation F7:8) methods. The 158 recombinant inbred lines of each method (SSD and Bulk) were evaluated for two years (2016/2017 and 2017/2018 seasons), in a 18x18 double lattice design with two replications, consisting of four-line plots of three meters in Palmital Farm (Goianira, GO). The RILs were genotyped by the DArTseq® methodology, which generated about 6,000 SNPs. The statistical model adopted for the grain yield data analysis was mixed linear model (MLM) through the R program. For the first and second year evaluations (2016/2017 and 2017/2018 seasons) and joint analysis (two years/seasons), the RILs-Bulk group presented higher grain yield averages when compared to the RILs-SSD and testers group. However, regarding the genetic variance component, the SSD group presented the highest estimate followed by Bulk and testers. Bulk-RIL yields ranged from 4,010.75 kg ha-1 to 5,815.42 kg ha-1, while SSD-RILs ranged from 3,321.76 kg ha-1 to 8,096.27 kg ha-1, both exceeding the testers group, which ranged from 2,754.30 kg ha-1 to 3,643.73 kg ha-1.For the plant height trait (ALT), in the first year, the plants ranged from 116 cm to 165 cm for RILs-Bulk. On the other hand, RILsSSD ranged from 91 cm to 177 cm, while the testers ranged from 100 cm to 104 cm. In the second year, RILs-Bulk ranged from 101 cm to 130 cm, while RILs-SSD ranged from 81 cm to 132 cm, while the testers presented heights from 96 cm to 117 cm. In the joint analysis, the testers presented the lowest heights. For QTL analysis, multiple interval mapping was used, with a total of 2,115 SNPs, and 3 QTLs were identified in the SSIL-RILs for the grain yield (PG) traitr, of which 2 QTLs were located on chromosome 6 (qGYLD6.1 and qGYLD6.2), one for the second year of experiment, with a phenotypic variation of 23.56%, and the other for the joint analysis, explaining 9.45% of the phenotypic variation. The other QTL was identified on chromosome 9 (qGYLD9) for the second year, with a phenotypic variation of 7.45%. For the trait height (ALT) a QTL on chromosome 1 (qPTHT1) was identified, with a phenotypic variation of 14.01%. For RILs-Bulk, with a total of 2,354 markers, 3 QTLs were identified for the PG character, two QTLs mapped on chromosomes 6 and 9 (qGYLD6 and qGYLD9), referring to the second year of evaluation, presenting a phenotypic variation of 21.65. % and 3.71%, respectively. In the joint analysis a QTL was mapped on chromosome 7 (qGYLD7), with phenotypic variation of 12.9%. For ALT no QTL was found in the RILs-Bulk. From the identification of these QTLs in haplotypic blocks, the next step will be the validation of markers in Embrapa germplasm bank accesses before being incorporated into the assisted selection routine, in order to identify materials with higher grain yield potential. For the Epagri 108 x Irat 122 cross, the SSD method was the most efficient in the generation of superior rice lines for grain yield, but at a higher operating cost than the Bulk method. RILs derived from both Bulk and SSD identified QTLs for the PG character; however, SSD identified a higher number of QTLs with greater effect on trait variation.