| Full text | |
| Author(s): Show less - |
Martins, Felipe Bitencourt
[1]
;
Moraes, Aline Costa Lima
[1]
;
Aono, Alexandre Hild
[1]
;
Ferreira, Rebecca Caroline Ulbricht
[1]
;
Chiari, Lucimara
[2]
;
Simeao, Rosangela Maria
[2]
;
Barrios, Sanzio Carvalho Lima
[2]
;
Santos, Mateus Figueiredo
[2]
;
Jank, Liana
[2]
;
Do Valle, Cacilda Borges
[2]
;
Vigna, Bianca Baccili Zanotto
[3]
;
De Souza, Anete Pereira
[1, 4]
Total Authors: 12
|
| Affiliation: | [1] Univ Campinas UNICAMP, Ctr Mol Biol & Genet Engn CBMEG, Sao Paulo - Brazil
[2] Brazilian Agr Res Corp, Embrapa Gado Corte, Campo Grande, MS - Brazil
[3] Brazilian Agr Res Corp, Embrapa Pecuaria Sudeste, Sao Paulo - Brazil
[4] Univ Campinas UNICAMP, Biol Inst, Dept Plant Biol, Sao Paulo - Brazil
Total Affiliations: 4
|
| Document type: | Journal article |
| Source: | FRONTIERS IN PLANT SCIENCE; v. 12, OCT 22 2021. |
| Web of Science Citations: | 0 |
| Abstract | |
Artificial hybridization plays a fundamental role in plant breeding programs since it generates new genotypic combinations that can result in desirable phenotypes. Depending on the species and mode of reproduction, controlled crosses may be challenging, and contaminating individuals can be introduced accidentally. In this context, the identification of such contaminants is important to avoid compromising further selection cycles, as well as genetic and genomic studies. The main objective of this work was to propose an automated multivariate methodology for the detection and classification of putative contaminants, including apomictic clones (ACs), self-fertilized individuals, half-siblings (HSs), and full contaminants (FCs), in biparental polyploid progenies of tropical forage grasses. We established a pipeline to identify contaminants in genotyping-by-sequencing (GBS) data encoded as allele dosages of single nucleotide polymorphism (SNP) markers by integrating principal component analysis (PCA), genotypic analysis (GA) measures based on Mendelian segregation, and clustering analysis (CA). The combination of these methods allowed for the correct identification of all contaminants in all simulated progenies and the detection of putative contaminants in three real progenies of tropical forage grasses, providing an easy and promising methodology for the identification of contaminants in biparental progenies of tetraploid and hexaploid species. The proposed pipeline was made available through the polyCID Shiny app and can be easily coupled with traditional genetic approaches, such as linkage map construction, thereby increasing the efficiency of breeding programs.</p> (AU) | |
| FAPESP's process: | 18/19219-6 - Detection of genes of agronomic interest and involved in heterosis in Urochloa spp. |
| Grantee: | Rebecca Caroline Ulbricht Ferreira |
| Support Opportunities: | Scholarships in Brazil - Post-Doctorate |
| FAPESP's process: | 19/03232-6 - Genome wide selection in sugarcane using machine learning and complex networks for economically important traits |
| Grantee: | Alexandre Hild Aono |
| Support Opportunities: | Scholarships in Brazil - Doctorate (Direct) |