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|Title:||A semi-automated SNP-based approach for contaminant identification in biparental Polyploid Populations of tropical forage grasses.|
|Authors:||MARTINS, F. B.|
SOUZA, A. P. DE
VIGNA, B. B. Z.
VALLE, C. B. do
SANTOS, M. F.
BARRIOS, S. C. L.
SIMEÃO, R. M.
FERREIRA, R. C. U.
AONO, A. H.
MORAES, A. C. L.
|Affiliation:||CACILDA BORGES DO VALLE, CNPGC|
BIANCA BACCILI ZANOTTO VIGNA, CPPSE
ANETE PEREIRA DE SOUZA, Center for Molecular Biology and Genetic Engineering
ALEXANDRE HILD AONO, Center for Molecular Biology and Genetic Engineering
FELIPE BITENCOURT MARTINS, Center for Molecular Biology and Genetic Engineering
ALINE COSTA LIMA MORAES, Center for Molecular Biology and Genetic Engineering
REBECCA CAROLINE ULBRICHT FERREIRA, Center for Molecular Biology and Genetic Engineering
LUCIMARA CHIARI, CNPGC
ROSANGELA MARIA SIMEAO, CNPGC
SANZIO CARVALHO LIMA BARRIOS, CNPGC
MATEUS FIGUEIREDO SANTOS, CNPGC
LIANA JANK, CNPGC
|Citation:||Frontiers in Plant Science, v.12, article 737919, 2021.|
|Description:||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.|
|NAL Thesaurus:||Principal component analysis|
|Type of Material:||Artigo de periódico|
|Appears in Collections:||Artigo em periódico indexado (CPPSE)|