Estimation of Molecular Pairwise Relatedness in Autopolyploid Crops

Amadeu, Rodrigo R.; Lara, Letícia A. C.; Muñoz, Patricio; Garcia, Antônio Augusto Franco

doi:10.1534/g3.120.401669

Cited by 12 publications

(12 citation statements)

References 61 publications

(65 reference statements)

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“…These results are explained by the similarity between the genomic relationship matrices computed using diploid and autotetraploid parametrizations. Recently, we presented empirical evidence on this topic by showing that the estimation of molecular pairwise relatedness in both scenarios are highly correlated, in particular, under low-to-middle rates of heterozygosity (Amadeu et al, 2020b ).…”

Section: Resultsmentioning

confidence: 99%

Genomic Selection in an Outcrossing Autotetraploid Fruit Crop: Lessons From Blueberry Breeding

et al. 2021

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Blueberry (Vaccinium corymbosum and hybrids) is a specialty crop with expanding production and consumption worldwide. The blueberry breeding program at the University of Florida (UF) has greatly contributed to expanding production areas by developing low-chilling cultivars better adapted to subtropical and Mediterranean climates of the globe. The breeding program has historically focused on recurrent phenotypic selection. As an autopolyploid, outcrossing, perennial, long juvenile phase crop, blueberry breeding cycles are costly and time consuming, which results in low genetic gains per unit of time. Motivated by applying molecular markers for a more accurate selection in the early stages of breeding, we performed pioneering genomic selection studies and optimization for its implementation in the blueberry breeding program. We have also addressed some complexities of sequence-based genotyping and model parametrization for an autopolyploid crop, providing empirical contributions that can be extended to other polyploid species. We herein revisited some of our previous genomic selection studies and showed for the first time its application in an independent validation set. In this paper, our contribution is three-fold: (i) summarize previous results on the relevance of model parametrizations, such as diploid or polyploid methods, and inclusion of dominance effects; (ii) assess the importance of sequence depth of coverage and genotype dosage calling steps; (iii) demonstrate the real impact of genomic selection on leveraging breeding decisions by using an independent validation set. Altogether, we propose a strategy for using genomic selection in blueberry, with the potential to be applied to other polyploid species of a similar background.

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Section: Resultsmentioning

confidence: 99%

Genomic Selection in an Outcrossing Autotetraploid Fruit Crop: Lessons From Blueberry Breeding

et al. 2021

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“…For polyploids, the few resources available are limited to microsatellite data (Spielmann et al, 2015;Zwart et al, 2016). Another common approach in polyploids is to estimate pairwise relatedness (r) (Huang et al, 2015;Amadeu et al, 2020), for example, to assess the relationships between parents, offspring, full-sibs and half-sibs in progenies. In addition, identity-by-descent (IBD) has been used to assess the probabilities of inheritance of particular combinations of parental haplotypes (Zheng et al, 2016), which are also quite difficult to evaluate in polyploid progenies.…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2021

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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.

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“…For polyploids, the few resources available are limited to microsatellite data (Spielmann et al, 2015;Zwart et al, 2016). Another common approach in polyploids is to estimate pairwise relatedness (r) (Huang et al, 2015;Amadeu et al, 2020), for example, to assess the relationships between parents and offspring and full-sibs and half-sibs in progenies. In addition, identity-bydescent (IBD) has been used to assess the probabilities of inheritance of particular combinations of parental haplotypes (Zheng et al, 2016), which are also quite difficult to evaluate in polyploid progenies.…”

Section: Discussionmentioning

confidence: 99%

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

Martins

Moraes

Aono

et al. 2021

Preprint

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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, self-fertilized individuals, half-siblings and full contaminants, 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 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.

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Estimation of Molecular Pairwise Relatedness in Autopolyploid Crops

Cited by 12 publications

References 61 publications

Genomic Selection in an Outcrossing Autotetraploid Fruit Crop: Lessons From Blueberry Breeding

Genomic Selection in an Outcrossing Autotetraploid Fruit Crop: Lessons From Blueberry Breeding

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

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

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