A Semi-Automated SNP-Based Approach for Contaminant Identification in Biparental Polyploid Populations of Tropical Forage Grasses

• DOI: 10.3389/fpls.2021.737919
• Publication Date: 2021-10-22T06:38:48Z
• Authors: Felipe Bitencourt...; Aline Costa Lima ...; Alexandre Hild Aono; Rebecca Caroline ...; Lucimara Chiari; Rosangela Maria S...; Sanzio Carvalho L...; Mateus Figueiredo...; Liana Jank; Cacilda Borges do...; Bianca Baccili Za...; Anete Pereira de ...
• Keywords: Polyploid; Apomixis; Mendelian inheritance

Artificial hybridization plays a fundamental role in plant breeding programs since it generates new genotypic combinations that can result desirable phenotypes. Depending on the species and mode of reproduction, controlled crosses may be challenging, contaminating individuals introduced accidentally. In this context, identification such contaminants is important to avoid compromising further selection cycles, as well genetic genomic studies. The main objective work was propose an automated multivariate methodology for detection classification putative contaminants, including apomictic clones (ACs), self-fertilized individuals, half-siblings (HSs), full (FCs), biparental polyploid progenies tropical forage grasses. We established pipeline identify genotyping-by-sequencing (GBS) data encoded allele dosages single nucleotide polymorphism (SNP) markers by integrating principal component analysis (PCA), (GA) measures based Mendelian segregation, clustering (CA). combination these methods allowed correct all simulated three real grasses, providing easy promising tetraploid hexaploid species. proposed made available through polyCID Shiny app easily coupled with traditional approaches, linkage map construction, thereby increasing efficiency programs.