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

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.

A joint learning approach for genomic prediction in polyploid grasses

“…To provide an overview of the genotypic and phenotypic data, we performed different multivariate analyses. For FG populations, contaminating individuals might be introduced during crosses, which could be detected by such analyses 84 . As expected, we found putative contaminants in Pop2 though t-SNE analysis performed on genotypic data (Supplementary Fig.…”

Advances in genomic characterization ofUrochloa humidicola: exploring polyploid inheritance and apomixis

“…SNP calling was performed using the Tassel-GBS pipeline (Glaubitz et al, 2014) modified to obtain the read counts for each SNP allele (Pereira et al 2018b). A previous study (Martins et al 2021) revealed a few putative apomictic clones of the female parent in a biparental population. Therefore, 62 hybrids were removed, and the next analyses were performed using 217 hybrids.…”

“…Advances in large-scale genotyping technologies and genotypic software have allowed the identification of many high-quality single nucleotide polymorphisms (SNPs) with allele dosage information in tropical forage grasses (Bourke et al 2018a; Grandke et al 2017; Mollinari and Garcia 2019). In this context, robust genomic studies have been recently reported, including genome-wide association studies (Matias et al 2019a), genomic predictions (Aono et al 2022; de C Lara et al 2019; Martins et al 2021; Matias et al 2019b), and genetic maps (Deo et al 2020; Ferreira et al 2019; Worthington et al 2016, 2019, 2021). Additionally, the recent assembly of two diploid genomes of Urochloa ruziziensis (Pessoa-Filho et al 2019; Worthington et al 2021) provided an invaluable resource for progress in genomic studies and molecular breeding of Urochloa grasses (Ferreira et al 2021).…”

Genome-wide family prediction unveils molecular mechanisms underlying the regulation of agronomic traits inUrochloa ruziziensis

“…Recent advances in omics approaches and computational methods for polyploid species have enabled the emergence of studies in important Urochloa breeding areas. These include genome assembly (Pessoa-Filho et al, 2019; Worthington et al, 2021), contaminant identification (Martins et al, 2021), transcriptomics (Vigna et al, 2016a; Salgado et al, 2017; Worthington et al, 2021; Jones et al, 2021; Hanley et al, 2021), linkage and QTL mapping (Vigna et al, 2016b; Thaikua et al, 2016; Ferreira et al, 2016; Worthington et al, 2016; Worthington et al, 2019; Worthington et al, 2021), GWAS (Matias et al, 2019b), and GS/GP (Matias et al, 2019a; Aono et al, 2022). Even with the recent progress, there are no studies employing integrative methodologies in the genus.…”