Optimizing a sweet corn breeding program: implementing genomic selection and doubled haploid technology

• Autor(a) principal: Coelho, Igor Ferreira
• Data de Publicação: 2022
• Tipo de documento: Tese
• Idioma: eng
• Título da fonte: LOCUS Repositório Institucional da UFV
• Texto Completo: https://locus.ufv.br//handle/123456789/30135; https://doi.org/10.47328/ufvbbt.2022.374
• topic: Milho-doce - Melhoramento genético; Genética quantitativa; Biometria; Interação genótipo-ambiente; Custo-benefício; Genética Quantitativa

Several tools have been adopted to optimize the breeding programs performance, in terms of breeding cycle length and number of field plots. In this way, tools as genomic selection (GS) and doubled haploid technologies (DH) have been adopted because they shorten the breeding cycle, by predicting the best materials without needing for trying in field (GS) or by the generation of lines faster (DH); diminish the number of field plots, by bringing just the most potential materials (GS) or skipping successive cycles of autopollination (DH); and others. Moreover, many models and packages were developed to simulate breeding programs following the advancement of computational efficiency. With reliability of biological process and robust statistical principles. This fact enables the researchers to investigate the breeding methods and strategies, avoiding the need to implement everything in field, which would take long time and have high cost, to choose the most potential strategy(ies) to be adopted in the program. This work adopted the AlphaSimR package with the goal of optimize a sweet corn breeding program, by including the GS and DH tools, through the evaluation of the genetic parameters and general costs. It was observed that the adoption of these technologies inflates the budget of the program and increase the number of field plots. However, these strategies bring higher genetic gains of the programs and reduce the breeding cycle length. As conclusion, the financial/genetic recompense of adopting these technologies is given by the generation of lines/hybrids faster, which is an intangible gain, but it is very important in a long-term commercial breeding program. Keywords: Plant Breeding. Quantitative Genetics. Biometric Analyses. Genotype-by- Environment Interaction. Cost Efficiency.