Statistical models for genomic selection in Panicum maximum considering allelic dosage

• Ano de defesa: 2017
• Autor(a) principal: Lara, Letícia Aparecida de Castro
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: eng
• Instituição de defesa: Biblioteca Digitais de Teses e Dissertações da USP
• Palavras-chave em Português: Autotetraploides; Autotetraploids; Forage; Forrageira; Linear mixed models; Melhoramento de plantas; Modelos lineares mistos; Plant breeding; Predição; Prediction
• Link de acesso: http://www.teses.usp.br/teses/disponiveis/11/11137/tde-15032018-101505/

Several species of economic interest are autotetraploid, such as the forage Panicum maximum, which is responsible for high productivity and quality of tropical pastures. The main accessions in nature are autotetraploid apomictic plants, on the other hand, diploid sexual plants may also be found. Although apomixis is advantageous because it fixes hybrid vigor, sexual reproduction is fundamental to allow genetic recombination by crossing among superior genotypes. Thus, genetic breeding consists of crossing apomictic plants with tetraploidized sexual plants. In these crosses, the use of superior sexual parents allows to increase the frequency of favorable alleles in the progeny. Therefore, recurrent selection programs in tetraploid sexual populations are fundamental to P. maximum breeding programs and strategies such as genomic selection can increase the accuracy of selection, allowing shorter breeding cycles and release cultivars in the market in the short term when compared to conventional programs. As P. maximum is a perennial crop, genotypes are evaluated in sucessive harvests. Thus, the study goals are to evaluate nutritional, structural, and yield traits in a sexual tetraploid population of P. maximum, investigating different classes of linear mixed models applied to longitudinal data, as well as to develop genomic selection models which consider tetraploid allelic dosage. This work was split into two chapters. In the first chapter, three classes of models were analyzed: i) Class A consists in modeling the interaction of genotypes and harvests with homogeneous correlations, genotypes were assumed not correlated, and residual effects were assumed homocedastic and not correlated; ii) Class B consists of groups of models in which genetic and residual effects were fitted with different variance and covariance (VCOV) structures and genotypes were not correlated; and iii) Class C is similar to Class B, however genotypes were correlated by an additive relationship matrix based on pedigree values. For all traits, Class C models performed better based on goodness of fit of the models. Therefore, we recommend to incorporate additive relationship matrix besides to model harvests with different levels of correlations over time. In the second chapter, SNP markers, obtained by genotyping-by-sequencing (GBS) technique, were used to develop Bayesian and GBLUP models that consider tetraploid allelic dosage. Bayesian models accuracies did not differ from the accuracy of GBLUP model and, we recommend the latter because it requires less computational time. The accuracy of genomic selection models reinforces the advantage of implementing this strategy in P. maximum breeding programs.