Genomic Selection with Allele Dosage in Panicum maximum Jacq.

Autor(es)

Lara, Leticia A. de C. ^[1] ; Santos, Mateus F. ^[2] ; Jank, Liana ^[2] ; Chiari, Lucimara ^[2] ; Vilela, Mariane de M. ^[2] ; Amadeu, Rodrigo R. ^[1] ; dos Santos, Jhonathan P. R. ^[1] ; Pereira, Guilherme da S. ^[3] ; Zeng, Zhao-Bang ^[3] ; Garcia, Antonio Augusto F. ^[1]

Afiliação do(s) autor(es)

^[1] Univ Sao Paulo, ESALQ, Luiz de Queiroz Coll Agr, Piracicaba, SP - Brazil

^[2] Embrapa Beef Cattle, Campo Grande, MS - Brazil

^[3] NCSU, Raleigh, NC - USA

• Tipo de documento: Artigo Científico
• Fonte: G3-GENES, GENOMES, GENETICS; v. 9, n. 8, p. 2463-2475, AUG 2019.
• Citações Web of Science: 0

Resumo

Genomic selection is an efficient approach to get shorter breeding cycles in recurrent selection programs and greater genetic gains with selection of superior individuals. Despite advances in genotyping techniques, genetic studies for polyploid species have been limited to a rough approximation of studies in diploid species. The major challenge is to distinguish the different types of heterozygotes present in polyploid populations. In this work, we evaluated different genomic prediction models applied to a recurrent selection population of 530 genotypes of Panicum maximum, an autotetraploid forage grass. We also investigated the effect of the allele dosage in the prediction, i.e., considering tetraploid (GS-TD) or diploid (GS-DD) allele dosage. A longitudinal linear mixed model was fitted for each one of the six phenotypic traits, considering different covariance matrices for genetic and residual effects. A total of 41,424 genotyping-by-sequencing markers were obtained using 96-plex and Pst1 restriction enzyme, and quantitative genotype calling was performed. Six predictive models were generalized to tetraploid species and predictive ability was estimated by a replicated fivefold cross-validation process. GS-TD and GS-DD models were performed considering 1,223 informative markers. Overall, GS-TD data yielded higher predictive abilities than with GS-DD data. However, different predictive models had similar predictive ability performance. In this work, we provide bioinformatic and modeling guidelines to consider tetraploid dosage and observed that genomic selection may lead to additional gains in recurrent selection program of P. maximum. (AU)

• Processo FAPESP: 16/01279-7 - Métodos genético-estatísticos para seleção genômica em Panicum maximum com informação de dosagem alélica
• Beneficiário: Letícia Aparecida de Castro Lara
• Modalidade de apoio: Bolsas no Exterior - Estágio de Pesquisa - Doutorado
• Processo FAPESP: 15/20659-2 - Métodos genético-estatísticos para seleção genômica em Panicum maximum com informação de dosagem alélica
• Beneficiário: Letícia Aparecida de Castro Lara
• Modalidade de apoio: Bolsas no Brasil - Doutorado
• Processo FAPESP: 17/03625-2 - Desenvolvimento de modelos genético-estatísticos temporais de seleção genômica via redes bayesianas: uma aplicação em sorgo
• Beneficiário: Jhonathan Pedroso Rigal dos Santos
• Modalidade de apoio: Bolsas no Brasil - Doutorado
• Processo FAPESP: 17/25674-5 - Desenvolvimento de modelos temporais de Seleção Genômica via Redes Bayesianas Dinâmicas, com uma aplicação em Sorghum bicolor
• Beneficiário: Jhonathan Pedroso Rigal dos Santos
• Modalidade de apoio: Bolsas no Exterior - Estágio de Pesquisa - Doutorado