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

2021 ◽  
Vol 12 ◽  
Author(s):  
Felipe Bitencourt Martins ◽  
Aline Costa Lima Moraes ◽  
Alexandre Hild Aono ◽  
Rebecca Caroline Ulbricht Ferreira ◽  
Lucimara Chiari ◽  
...  
Keyword(s):  
Principal Component ◽  
Genotyping By Sequencing ◽  
Snp Markers ◽  
Correct Identification ◽  
Forage Grasses ◽  
Mendelian Segregation ◽  
Breeding Programs ◽  
Tropical Forage ◽  
Genotypic Analysis ◽  
Shiny App

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.

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

2021 ◽  
Author(s):  
Felipe Bitencourt Martins ◽  
Aline da Costa Lima Moraes ◽  
Alexandre Hild Aono ◽  
Rebecca Caroline Ulbricht Ferreira ◽  
Lucimara Chiari ◽  
...  
Keyword(s):  
Principal Component ◽  
Genotyping By Sequencing ◽  
Snp Markers ◽  
Correct Identification ◽  
Forage Grasses ◽  
Mendelian Segregation ◽  
Breeding Programs ◽  
Tropical Forage ◽  
Genotypic Analysis ◽  
Shiny App

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.

scholarly journals Identification of SNP Markers Associated with Iron and Zinc Concentrations in Cicer Seeds

2020 ◽  
Vol 21 (3) ◽  
pp. 212-223
Author(s):  
Nur Karaca ◽  
Duygu Ates ◽  
Seda Nemli ◽  
Esin Ozkuru ◽  
Hasan Yilmaz ◽  
...  
Keyword(s):  
Principal Component ◽  
Genotyping By Sequencing ◽  
Mixed Linear Model ◽  
Snp Markers ◽  
Sequencing Analysis ◽  
Single Nucleotide ◽  
Breeding Programs ◽  
Zn Concentration ◽  
Iron And Zinc ◽  
Zinc Concentrations

Background: Cicer reticulatum L. is the wild progenitor of chickpea Cicer arietinum L., the fourth most important pulse crop in the world. Iron (Fe) and zinc (Zn) are vital micronutrients that play crucial roles in sustaining life by acting as co-factors for various proteins. Aims and Objectives: In order to improve micronutrient-dense chickpea lines, this study aimed to investigate variability and detect DNA markers associated with Fe and Zn concentrations in the seeds of 73 cultivated (C. arietinum L.) and 107 C. reticulatum genotypes. Methods: A set of 180 accessions was genotyped using 20,868 single nucleotide polymorphism (SNP) markers obtained from genotyping by sequencing analysis. Results: The results revealed substantial variation in the seed Fe and Zn concentration of the surveyed population. Using STRUCTURE software, the population structure was divided into two groups according to the principal component analysis and neighbor-joining tree analysis. A total of 23 and 16 associated SNP markers related to Fe and Zn concentrations, respectively were identified in TASSEL software by the mixed linear model method. Significant SNP markers found in more than two environments were accepted as more reliable than those that only existed in a single environment. Conclusion: The identified markers can be used in marker-assisted selection in chickpea breeding programs for the improvement of seed Fe and Zn concentrations in the chickpea.

scholarly journals Whole Genome Diversity, Population Structure, and Linkage Disequilibrium Analysis of Chickpea (Cicer arietinum L.) Genotypes Using Genome-Wide DArTseq-Based SNP Markers

Genes ◽  
2019 ◽  
Vol 10 (9) ◽  
pp. 676 ◽  
Author(s):  
Farahani ◽  
Maleki ◽  
Mehrabi ◽  
Kanouni ◽  
Scheben ◽  
...  
Keyword(s):  
Genetic Diversity ◽  
Population Structure ◽  
Linkage Disequilibrium ◽  
Large Scale ◽  
Principal Component ◽  
Snp Markers ◽  
Genome Diversity ◽  
High Genetic Diversity ◽  
Breeding Programs ◽  
Population Structure Analysis

Characterization of genetic diversity, population structure, and linkage disequilibrium is a prerequisite for proper management of breeding programs and conservation of genetic resources. In this study, 186 chickpea genotypes, including advanced “Kabuli” breeding lines and Iranian landrace “Desi” chickpea genotypes, were genotyped using DArTseq-Based single nucleotide polymorphism (SNP) markers. Out of 3339 SNPs, 1152 markers with known chromosomal position were selected for genome diversity analysis. The number of mapped SNP markers varied from 52 (LG8) to 378 (LG4), with an average of 144 SNPs per linkage group. The chromosome size that was covered by SNPs varied from 16,236.36 kbp (LG8) to 67,923.99 kbp (LG5), while LG4 showed a higher number of SNPs, with an average of 6.56 SNPs per Mbp. Polymorphism information content (PIC) value of SNP markers ranged from 0.05 to 0.50, with an average of 0.32, while the markers on LG4, LG6, and LG8 showed higher mean PIC value than average. Unweighted neighbor joining cluster analysis and Bayesian-based model population structure grouped chickpea genotypes into four distinct clusters. Principal component analysis (PCoA) and discriminant analysis of principal component (DAPC) results were consistent with that of the cluster and population structure analysis. Linkage disequilibrium (LD) was extensive and LD decay in chickpea germplasm was relatively low. A few markers showed r2 ≥ 0.8, while 2961 pairs of markers showed complete LD (r2 = 1), and a huge LD block was observed on LG4. High genetic diversity and low kinship value between pairs of genotypes suggest the presence of a high genetic diversity among the studied chickpea genotypes. This study also demonstrates the efficiency of DArTseq-based SNP genotyping for large-scale genome analysis in chickpea. The genotypic markers provided in this study are useful for various association mapping studies when combined with phenotypic data of different traits, such as seed yield, abiotic, and biotic stresses, and therefore can be efficiently used in breeding programs to improve chickpea.

scholarly journals Genotyping by Sequencing Reveals Genetic Relatedness of Southwestern U.S. Blue Maize Landraces

2021 ◽  
Vol 22 (7) ◽  
pp. 3436
Author(s):  
Amol N. Nankar ◽  
Richard C. Pratt
Keyword(s):  
Genetic Relatedness ◽  
Plant Traits ◽  
Phenotypic Diversity ◽  
Phenotypic Variability ◽  
Principal Component ◽  
Genotyping By Sequencing ◽  
Genotypic Analysis ◽  
Maize Landraces ◽  
Blue Maize ◽  
Kernel Traits

Maize has played a key role in the sustenance and cultural traditions of the inhabitants of the southwestern USA for many centuries. Blue maize is an important component of the diverse landraces still cultivated in the region but the degree to which they are related is unknown. This research was designed to ascertain the genotypic, morphological, and phenotypic diversity of six representative southwestern blue maize landraces. Their genotypic diversity was examined using tunable genotyping-by-sequencing (tGBS™). A total of 81,038 high quality SNPs were identified and obtained through tGBS. A total of 45 morphological and biochemical traits were evaluated at two locations in New Mexico. The varieties Los Lunas High and Flor del Rio were genetically less related with other southwestern landraces whereas diffusion between Navajo Blue, Hopi Blue, Yoeme Blue, and Taos Blue demonstrated that these landraces were genetically related. Phenotypic variability was highest for kernel traits and least for plant traits. Plant, ear, and kernel traits were fairly consistent within and across locations. Principal component analysis and tGBS showed that Corn Belt variety ‘Ohio Blue’ was distinctly different from southwestern landraces. Genotypic analysis displayed that southwestern landraces are genetically closely related, but selection has resulted in differing phenotypes. This study has provided additional insight into the genetic relatedness of southwestern blue maize landraces.

scholarly journals Whole Genome Diversity, Population Structure and Linkage Disequilibrium Analysis of Chickpea (Cicer arietinum L.) Genotypes Using Genome-Wide DArTseq-Based SNP Markers

2019 ◽  
Author(s):  
Somayeh Farahani ◽  
Mojdeh Maleki ◽  
Rahim Mehrabi ◽  
Homayoun Kanouni ◽  
Reza Talebi
Keyword(s):  
Genetic Diversity ◽  
Population Structure ◽  
Linkage Disequilibrium ◽  
Large Scale ◽  
Principal Component ◽  
Snp Markers ◽  
Genome Diversity ◽  
High Genetic Diversity ◽  
Breeding Programs ◽  
Population Structure Analysis

Characterization of genetic diversity, population structure and linkage disequilibrium is prerequisite for proper management of breeding programs and conservation of genetic resources. In this study, 186 chickpea genotypes including advanced “Kabuli” breeding lines and Iranian landrace “Desi” chickpea genotypes were genotyped using DArTseq-Based SNP markers. Out of 3339 SNPs, 1152 markers with known chromosomal position were selected for genome diversity analysis. The number of mapped SNP markers varied from 52 (LG8) to 378 (LG4), with an average of 144 SNPs per linkage group. The chromosome size that covered by SNPs varied from 16236.36 kbp (LG8) to 67923.99 kbp (LG5), while LG4 showed higher number of SNPs, with an average of 6.56 SNPs per Mbp. Polymorphism information content (PIC) value of SNP markers ranged from 0.05 to 0.50, with an average of 0.32, while the markers on LG4, LG6 and LG8 showed higher mean PIC value than average. Un-weighted Neighbor Joining cluster analysis and Bayesian-based model population structure grouped chickpea genotypes into four distinct clusters. Principal component analysis (PCoA) and Discriminant Analysis of Principal Component (DAPC) results were consistent with that of the cluster and population structure analysis. Linkage disequilibrium (LD) was extensive and LD decay in chickpea germplasm was relatively low. A few markers showed r2≥0.8, while 2961 pairs of markers showed complete LD (r2=1) and a huge LD block was observed on LG4. High genetic diversity and low kinship value between pairs of genotypes suggesting the presence of a high genetic diversity among studied chickpea genotypes. This study also demonstrated the efficiency of DArTseq-based SNP genotyping for large scale genome analysis in chickpea. The genotypic markers provided in this study are useful for various association mapping studies when combined with phenotypic data of different traits such as seed yield, abiotic and biotic stresses and therefore can be efficiently used in breeding programs to improve chickpea.

scholarly journals Marginal Grapevine Germplasm from Apulia (Southern Italy) Represents an Unexplored Source of Genetic Diversity

Agronomy ◽  
2020 ◽  
Vol 10 (4) ◽  
pp. 563 ◽  
Author(s):  
Monica Marilena Miazzi ◽  
Nunzio D’Agostino ◽  
Valentina di Rienzo ◽  
Pasquale Venerito ◽  
Vito Nicola Savino ◽  
...  
Keyword(s):  
Genetic Diversity ◽  
Genetic Resources ◽  
Genotyping By Sequencing ◽  
Snp Markers ◽  
Gene Pools ◽  
Single Nucleotide ◽  
Breeding Programs ◽  
Snp Data ◽  
Data Points ◽  
Genetic Clusters

The investigation on the genetic diversity of grapevine germplasm is crucial for a more efficient use of grapevine genetic resources in light of changing environmental conditions. Here, we used simple sequence repeats (SSRs) coupled with single nucleotide polymorphism (SNP) markers to disclose grapevine genetic diversity of a collection of Apulian minor/neglected genotypes. Their relationships with national or international cultivars were also examined. Genetic diversity was investigated using 10 SSR markers and 1,178 SNPs generated by genotyping by sequencing (GBS). Based on the SSR data, the 128 genotypes were classified into six main genetic clusters. Twenty-four putative cases of synonymy and 2 of misnamings were detected. Ten “unknown” autochthonous genotypes did not show high similarity to Apulian, national, or international varieties. We took advantage of available GBS-derived SNP data points for only forty genotypes to better investigate the genetic distance among them, identify private SNP alleles, and divergent loci putatively under selection. Based on SNP alleles, two interesting gene pools of minor/neglected Apulian samples were identified. Genetic divergence was investigated by FST and allowed the detection of loci capable of differentiating the gene pools. Overall, this work emphasizes the need for recovering the untapped genetic variability that characterizes minor/neglected grapevine Apulian genotypes and the requirement to preserve and use more efficiently grapevine genetic resources in breeding programs.

scholarly journals Genomic Selection in Tropical Forage Grasses: Current Status and Future Applications

2021 ◽  
Vol 12 ◽  
Author(s):  
Rosangela M. Simeão ◽  
Marcos D. V. Resende ◽  
Rodrigo S. Alves ◽  
Marco Pessoa-Filho ◽  
Ana Luisa S. Azevedo ◽  
...  
Keyword(s):  
Genomic Selection ◽  
Current Status ◽  
World Population ◽  
Genome Wide Association Studies ◽  
Primary Role ◽  
Forage Grasses ◽  
Breeding Programs ◽  
Genetic Gains ◽  
Tropical Forage ◽  
Forage Species

The world population is expected to be larger and wealthier over the next few decades and will require more animal products, such as milk and beef. Tropical regions have great potential to meet this growing global demand, where pasturelands play a major role in supporting increased animal production. Better forage is required in consonance with improved sustainability as the planted area should not increase and larger areas cultivated with one or a few forage species should be avoided. Although, conventional tropical forage breeding has successfully released well-adapted and high-yielding cultivars over the last few decades, genetic gains from these programs have been low in view of the growing food demand worldwide. To guarantee their future impact on livestock production, breeding programs should leverage genotyping, phenotyping, and envirotyping strategies to increase genetic gains. Genomic selection (GS) and genome-wide association studies play a primary role in this process, with the advantage of increasing genetic gain due to greater selection accuracy, reduced cycle time, and increased number of individuals that can be evaluated. This strategy provides solutions to bottlenecks faced by conventional breeding methods, including long breeding cycles and difficulties to evaluate complex traits. Initial results from implementing GS in tropical forage grasses (TFGs) are promising with notable improvements over phenotypic selection alone. However, the practical impact of GS in TFG breeding programs remains unclear. The development of appropriately sized training populations is essential for the evaluation and validation of selection markers based on estimated breeding values. Large panels of single-nucleotide polymorphism markers in different tropical forage species are required for multiple application targets at a reduced cost. In this context, this review highlights the current challenges, achievements, availability, and development of genomic resources and statistical methods for the implementation of GS in TFGs. Additionally, the prediction accuracies from recent experiments and the potential to harness diversity from genebanks are discussed. Although, GS in TFGs is still incipient, the advances in genomic tools and statistical models will speed up its implementation in the foreseeable future. All TFG breeding programs should be prepared for these changes.

scholarly journals Admixture Analysis Using Genotyping-by-Sequencing Reveals Genetic Relatedness and Parental Lineage Distribution in Highbush Blueberry Genotypes and Cross Derivatives

2020 ◽  
Author(s):  
Krishnanand P. Kulkarni ◽  
Nicholi Vorsa ◽  
Purushothaman Natarajan ◽  
Sathya Elavarthi ◽  
Massimo Iorizzo ◽  
...  
Keyword(s):  
Genetic Relatedness ◽  
Principal Component ◽  
Genotyping By Sequencing ◽  
Vaccinium Corymbosum ◽  
Snp Markers ◽  
Fixation Index ◽  
Nucleotide Polymorphisms ◽  
Admixture Analysis ◽  
Cross Derivatives ◽  
Southern Highbush

Blueberries (Vaccinium section Cyanococcus) are perennial shrubs widely cultivated for their edible fruits. In this study, we used admixture and genetic relatedness analysis of northern highbush (NHB, V. corymbosum) and southern highbush (SHB, V. darrowii) blueberry genotypes and F2 progenies of the V. corymbosum × V. darrowii cross. Using genotyping-by-sequencing (GBS), we generated ~3.34 billion reads (75 bp). The GBS reads were aligned to the Vaccinium corymbosum cv. Draper v1.0 reference genome sequence, and ~2.8 million reads were successfully mapped. From the alignments, we identified 2,244,039 single nucleotide polymorphisms (SNPs), which were used for principal component, haplotype, and admixture analysis. PCA formed three main groups: 1) NHB cultivars, 2) SHB cultivars, and 3) BNJ16-5 progenies. The overall fixation index (FST) and nucleotide diversity for NHB and SHB, indicated wide genetic differentiation, and haplotype analysis revealed that SHB cultivars are more genetically diverse than NHB cultivars. The admixture analysis identified a mix of various lineages of parental genomic introgression. This study demonstrated the effectiveness of GBS-derived SNP markers in genetic and admixture analyses to reveal genetic relatedness and to examine parental lineages in blueberry, which may be useful for future breeding plans.

In vitro regeneration from leaf-base segments in three genotypes of Urochloa spp.

2018 ◽  
Vol 69 (5) ◽  
pp. 527 ◽  
Author(s):  
Diliane Harumi Yaguinuma ◽  
Luciana Midori Takamori ◽  
Adriana Mendonça de Oliveira ◽  
Luiz Gonzaga Esteves Vieira ◽  
Alessandra Ferreira Ribas
Keyword(s):  
In Vitro Regeneration ◽  
Forage Grasses ◽  
Leaf Base ◽  
Breeding Programs ◽  
Seed Formation ◽  
Regenerated Plants ◽  
Tropical Forage ◽  
Half Strength ◽  
The Media

The key agricultural species of Urochloa P.Beauv. (signal grass), important as tropical forage grasses, are characterised by asexual seed formation (apomixis), and this presents a challenge for breeding programs. Biotechnological approaches could be an option to develop improved cultivars. We evaluated the regenerative potential from three commercial genotypes, U. brizantha cv. Marandu, U. decumbens cv. Basilisk and U. ruziziensis cv. Ruziziensis, by using leaf-base segments as explants. We tested two auxins (2,4-D and picloram) and one cytokinin (TDZ) at four concentrations (1, 2, 3 and 4 mg L–1). Seeds were scarified, peeled and disinfected before inoculation on half-strength MS media in the dark for 14 days. Leaf-base explants were sectioned in thin slices and inoculated into the media. We analysed the number of primary calluses, number of calluses with shoots clusters and the average of regenerated plants. The lowest concentration of auxins tested (1 mg L–1) yielded the highest number of regenerated plants for Marandú and Basilisk, whereas the optimum for Ruziziensis was 2 mg L–1. Medium with higher concentrations of TDZ (4 mg L–1) was required to produce high frequency of plants for all genotypes. Explants cultured on media with TDZ produced very few calluses. These results indicate that the auxins and cytokinin tested can induce plant regeneration from Urochloa leaf-base segments, and may be used to produce transgenic plants in genetic transformation studies.

scholarly journals Genetic Diversity and Population Structure Analysis of the USDA Olive Germplasm Using Genotyping-By-Sequencing (GBS)

Genes ◽  
2021 ◽  
Vol 12 (12) ◽  
pp. 2007
Author(s):  
A. S. M. Faridul Islam ◽  
Dean Sanders ◽  
Amit Kumar Mishra ◽  
Vijay Joshi
Keyword(s):  
Genetic Diversity ◽  
Population Structure ◽  
Structure Analysis ◽  
Genotyping By Sequencing ◽  
The United States ◽  
Snp Markers ◽  
Breeding Programs ◽  
The Us ◽  
The Mediterranean ◽  
Olive Breeding

Olives are one of the most important fruit and woody oil trees cultivated in many parts of the world. Olive oil is a critical component of the Mediterranean diet due to its importance in heart health. Olives are believed to have been brought to the United States from the Mediterranean countries in the 18th century. Despite the increase in demand and production areas, only a few selected olive varieties are grown in most traditional or new growing regions in the US. By understanding the genetic background, new sources of genetic diversity can be incorporated into the olive breeding programs to develop regionally adapted varieties for the US market. This study aimed to explore the genetic diversity and population structure of 90 olive accessions from the USDA repository along with six popular varieties using genotyping-by-sequencing (GBS)-generated SNP markers. After quality filtering, 54,075 SNP markers were retained for the genetic diversity analysis. The average gene diversity (GD) and polymorphic information content (PIC) values of the SNPs were 0.244 and 0.206, respectively, indicating a moderate genetic diversity for the US olive germplasm evaluated in this study. The structure analysis showed that the USDA collection was distributed across seven subpopulations; 63% of the accessions were grouped into an identifiable subpopulation. The phylogenetic and principal coordinate analysis (PCoA) showed that the subpopulations did not align with the geographical origins or climatic zones. An analysis of the molecular variance revealed that the major genetic variation sources were within populations. These findings provide critical information for future olive breeding programs to select genetically distant parents and facilitate future gene identification using genome-wide association studies (GWAS) or a marker-assisted selection (MAS) to develop varieties suited to production in the US.

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