| Full text | |
| Author(s): |
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]
Total Authors: 10
|
| Affiliation: | [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
Total Affiliations: 3
|
| Document type: | Journal article |
| Source: | G3-GENES, GENOMES, GENETICS; v. 9, n. 8, p. 2463-2475, AUG 2019. |
| Web of Science Citations: | 0 |
| Abstract | |
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) | |
| FAPESP's process: | 16/01279-7 - Statistical models for genomic selection in Panicum maximum considering allelic dosage |
| Grantee: | Letícia Aparecida de Castro Lara |
| Support Opportunities: | Scholarships abroad - Research Internship - Doctorate |
| FAPESP's process: | 15/20659-2 - Statistical models for genomic selection in Panicum maximum considering allelic dosage |
| Grantee: | Letícia Aparecida de Castro Lara |
| Support Opportunities: | Scholarships in Brazil - Doctorate |
| FAPESP's process: | 17/03625-2 - Development of temporal genomic selection models via Bayesian Networks applied to sorghum bicolor |
| Grantee: | Jhonathan Pedroso Rigal dos Santos |
| Support Opportunities: | Scholarships in Brazil - Doctorate |
| FAPESP's process: | 17/25674-5 - Development of temporal Genomic Selection models via Dynamic Bayesian Networks, with application to Sorghum bicolor |
| Grantee: | Jhonathan Pedroso Rigal dos Santos |
| Support Opportunities: | Scholarships abroad - Research Internship - Doctorate |