Figure - available from: Frontiers in Plant Science
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Minor allele frequencies according to physical location of markers along barley chromosomes. (A–G) 50K SNP-array data for chromosomes 1H–7H top to bottom respectively (left panel) and (H–N) GBS data chromosomes 1H–7H top to bottom respectively (right panel). SNPs are color coded according to MAF.
Source publication
We compared the performance of two commonly used genotyping platforms, genotyping-by-sequencing (GBS) and single nucleotide polymorphism-arrays (SNP), to investigate the extent and pattern of genetic variation within a collection of 1,000 diverse barley genotypes selected from the German Federal ex situ GenBank hosted at IPK Gatersleben. Each platf...
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Citations
... Classical methods for this modelling, such as ADMIXTURE, were designed for genotyping microarray data. Such microarray data differ significantly from modern genotyping-by-sequencing (GBS) in both file format and interpretation, particularly with respect to the handling of ungenotyped loci and homozygous reference loci [11]. As a consequence, although it is possible to apply ADMIXTURE and related methods to modern GBS data in VCF format, this is complex, errorprone, and comes with significant manual and computational overhead ( Figure 2). ...
Genetic ancestry is a significant confounding factor in genetic association studies. It is therefore essential to estimate and account for ancestry if we hope to translate genomic research into equitable clinical utility for ethnically diverse populations. Despite this importance, accurate ancestry information is difficult to obtain, and existing methods to estimate ancestry are not designed for modern sequencing data. This methodological gap hinders the integration of genetic ancestry information in modern research, and limits progress in finding the genetic determinants of disease in diverse populations. To address this gap we present AEon, a probabilistic model-based global ancestry estimation tool, ready for use on modern genomic data. AEon predicts fractional population membership, accounting for possible admixture. Unlike previous global ancestry estimation tools such as EIGENSTRAT and ADMIXTURE, AEon takes input directly from a VCF/BCF, includes default training data based on the 26 reference populations of the 1000 Genomes Project, and produces visualisation aids and diagnostics to complement data output. AEon’s turnkey design significantly reduces the time taken to estimate ancestry from VCFs, without compromising on accuracy compared to ADMIXTURE analysis. AEon is available at github.com/GenomicRisk/aeon .
... Wheat harvests affect the global economy. Breeders are keen to develop improved cultivars based on phenotypic records of grain yield and quality data, expecting that stable harvests will be maintained under various environmental conditions [3]. The efforts to develop cultivars combining resistance to adverse biotic and abiotic factors with high yield have remained relevant for many years. ...
... GWAS is a modern approach to molecular and genetic studies required for such a complex plant as wheat due to the size and structure of its genome [7]. GWAS is a field of biological research that studies associations between genome variants and phenotypic traits [3,8]. Through GWAS, new genetic markers associated with grain yield and quality can be identified, and the association of previously detected loci can be confirmed. ...
Genome-wide association studies (GWAS) are among the genetic tools for the mining of genomic loci associated with useful agronomic traits. The study enabled us to find new genetic markers associated with grain yield as well as quality. The sample under study consisted of spring wheat cultivars developed in different decades of the last century. A panel of 186 accessions was evaluated at VIR’s experiment station in Pushkin across a 3-year period of field trials. In total, 24 SNPs associated with six productivity characteristics were revealed. Along with detecting significant markers for each year of the field study, meta-analyses were conducted. Loci associated with useful yield-related agronomic characteristics were detected on chromosomes 4A, 5A, 6A, 6B, and 7B. In addition to previously described regions, novel loci associated with grain yield and quality were identified during the study. We presume that the utilization of contrast cultivars which originated in different breeding periods allowed us to identify new markers associated with useful agronomic characteristics.
... For blueberry, we hypothesized that a targeted-amplicon sequenced-based approach would be the most beneficial for breeders. Unlike Genotyping-by-Sequencing (GBS), targeted amplicon-based genotyping technologies such as DArTag (Diversity Array Technology -DArT), Flex-Seq (RAPiD Genomics), and Capture-Seq (LGC Genomics) have low missing data rates and query the same loci in all samples across genotyping projects, allowing new data to be easily appended to existing data (Darrier et al, 2019;Telfer et al, 2019;Wang et al, 2020). The amount of data returned is in the tens of thousands or less, rather than the millions of reads from GBS, simplifying downstream bioinformatics processing (Darrier et al, 2019;Milner et al, 2019). ...
... Unlike Genotyping-by-Sequencing (GBS), targeted amplicon-based genotyping technologies such as DArTag (Diversity Array Technology -DArT), Flex-Seq (RAPiD Genomics), and Capture-Seq (LGC Genomics) have low missing data rates and query the same loci in all samples across genotyping projects, allowing new data to be easily appended to existing data (Darrier et al, 2019;Telfer et al, 2019;Wang et al, 2020). The amount of data returned is in the tens of thousands or less, rather than the millions of reads from GBS, simplifying downstream bioinformatics processing (Darrier et al, 2019;Milner et al, 2019). This in turn speeds up the analysis time for marker-assisted selection (MAS), introgression tracking, linkage mapping, GWAS and genomic prediction (Darrier et al, 2019). ...
... The amount of data returned is in the tens of thousands or less, rather than the millions of reads from GBS, simplifying downstream bioinformatics processing (Darrier et al, 2019;Milner et al, 2019). This in turn speeds up the analysis time for marker-assisted selection (MAS), introgression tracking, linkage mapping, GWAS and genomic prediction (Darrier et al, 2019). ...
Small public breeding programmes have many barriers to adopting technology, particularly creating and using genetic marker panels for genomic-based decisions in selection. Here we report the creation of a DArTag panel of 3,000 loci distributed across the tetraploid genome of blueberry (Vaccinium corymbosum) for use in molecular breeding and genomic prediction. The creation of this marker panel brings cost-effective and rapid genotyping capabilities to public and private breeding programmes. The open access provided by this platform will allow genetic data sets generated on the marker panel to be compared and joined across projects, institutions and countries. This genotyping resource has the power to make routine genotyping a reality for any breeder of blueberry.
... Wheat yields affect the global economy. Breeders are keen to develop improved cultivars based on phenotypic records of grain yield and quality data expecting that stable harvest will be maintained under various environmental conditions [3]. The efforts to develop cultivars combining resistance to adverse biotic and abiotic factors with high yield have remained relevant for many years. ...
... GWAS is new modern approaches to molecular and genetic studies that are required for such a complex plant as wheat due to the size and structure of its genome [7]. Genome wide association studies (GWAS) is a field of biological 2 research studying associations between genome variants and phenotypic traits [3,8]. Through GWAS, new genetic markers associated with grain yield and quality can be identified, and the association of previously detected loci can be confirmed. ...
Climate change, growing world population and increasing food demand require higher yields and adaptability of crops, including spring bread wheat. Modern plant breeding based on achievements of genetics has a significant impact on plant resistance to diseases, climate variability and other factors. Genome wide association studies (GWAS) is one of genetics tools for mining of genome loci associated with economically important traits. In the current study, we applied GWAS to reveal QTL for spike productivity and grain quality using a panel of spring varieties of Triticum aestivum L. bred in different decades of the last century in Russia and Germany. The study has enabled to find new genetic markers associated with grain yield and quality. Studying genetic basis of grain yield and quality by comparison of wheat varieties from different breeding periods, including landraces preserved as the source of valuable genetic diversity for breeding and genetic studies, makes it possible not only to understand selection processes in the past, but also to develop strategies for future breeding work.
... For the barley analysis, genotypic information of 1,000 accessions was downloaded from the Germinate Barley SNP Platforms [35]. The collection of 1,000 genotypes is a representative subset of the global 22,626 barley accessions from the German Federal ex situ GenBank. ...
Background
DNA marker profiles play a crucial role in the identification and registration of germplasm, as well as in the distinctness, uniformity, and stability (DUS) testing of new plant variety protection. However, selecting minimal marker sets from large-scale SNP dataset can be challenging to distinguish a maximum number of samples. Results: Here, we developed the CoreSNP pipeline using a “divide and conquer” strategy and a “greedy” algorithm. The pipeline offers adjustable parameters to guarantee the distinction of each sample pair with at least two markers. Additionally, it allows datasets with missing loci as input. The pipeline was tested in barley, soybean, wheat, rice and maize. A few dozen of core SNPs were efficiently selected in different crops with SNP array, GBS, and WGS dataset, which can differentiate thousands of individual samples. The core SNPs were distributed across all chromosomes, exhibiting lower pairwise linkage disequilibrium (LD) and higher polymorphism information content (PIC) and minor allele frequencies (MAF). It was shown that both the genetic diversity of the population and the characteristics of the original dataset can significantly influence the number of core markers. In addition, the core SNPs capture a certain level of the original population structure.
Conclusions
CoreSNP is an efficiency way of core marker sets selection based on Genome-wide SNP datasets of crops. Combined with low-density SNP chip or genotyping technologies, it can be a cost-effective way to simplify and expedite the evaluation of genetic resources and differentiate different crop varieties. This tool is expected to have great application prospects in the rapid comparison of germplasm and intellectual property protection of new varieties.
... For alfalfa, we determined that a targeted-amplicon sequenced-based approach would be the most beneficial for breeders. Unlike Genotyping-by-Sequencing (GBS), targeted, amplicon-based genotyping technologies such as DArTag (Diversity Array Technology -DArT), and Capture-Seq (LGC Genomics) have low missing data rates and query the same exact loci in all samples across genotyping projects, allowing new data to be easily appended to existing data (Telfer et al, 2019;Darrier et al, 2019;Wang et al, 2020). The amount of data returned is in the tens of thousands or less, rather than the millions of reads from GBS, simplifying downstream bioinformatics processing (Darrier et al, 2019;Milner et al, 2019). ...
... Unlike Genotyping-by-Sequencing (GBS), targeted, amplicon-based genotyping technologies such as DArTag (Diversity Array Technology -DArT), and Capture-Seq (LGC Genomics) have low missing data rates and query the same exact loci in all samples across genotyping projects, allowing new data to be easily appended to existing data (Telfer et al, 2019;Darrier et al, 2019;Wang et al, 2020). The amount of data returned is in the tens of thousands or less, rather than the millions of reads from GBS, simplifying downstream bioinformatics processing (Darrier et al, 2019;Milner et al, 2019). This in turn speeds up the analysis time for marker-assisted selection (MAS), introgression tracking, linkage mapping and genomic prediction (Darrier et al, 2019). ...
... The amount of data returned is in the tens of thousands or less, rather than the millions of reads from GBS, simplifying downstream bioinformatics processing (Darrier et al, 2019;Milner et al, 2019). This in turn speeds up the analysis time for marker-assisted selection (MAS), introgression tracking, linkage mapping and genomic prediction (Darrier et al, 2019). ...
Small public breeding programmes have many barriers to adopting technology, particularly creating and using genetic marker panels for genomic-based decisions in selection. Here we report the creation of a DArTag panel of 3,000 loci distributed across the alfalfa (Medicago sativa L.) genome for use in molecular breeding and genomic insight. The creation of this marker panel brings cost-effective and rapid genotyping capabilities to alfalfa breeding programmes. The open access provided by this platform will allow genetic data sets generated on the marker panel to be compared and joined across projects, institutions and countries. This genotyping resource has the power to make routine genotyping a reality for any breeder of alfalfa.
... In GBS technology, the allele-calling does not require a reference genome, offering an unbiased method to assess genetic diversity in a large collection of accessions, especially in orphan crops because SNP discovery and genotyping can be done simultaneously with less bias toward genetic backgrounds (Rasheed et al., 2017;Darrier et al., 2019). When the germplasm includes commercial materials combined with exotic materials, GBS would be the most appropriate genotyping. ...
... SNP loci displaying very low frequency alleles may derive into genotyping errors and provide poor statistical power to reveal association with phenotypic traits or establishing relative kinship. Thus, the recommended conditions that SNPs should meet are: (i) up to 10% of missing genotype calls; (ii) up to 10% of heterozygous calls (assuming inbred lines are being genotyped); (iii) the number of heterozygous calls does not exceed the number homozygous minor allele counts; and (iv) minor allele frequency (MAF) > 0.05 (Wu et al., 2014;Darrier et al., 2019;Milner et al., 2019;Pavan et al., 2020). ...
One of the greatest challenges facing humanity is the development of sustainable strategies to ensure food availability in response to population growth and climate change. One approach that can contribute to increase food security is to close yield gaps and enhancing genetic gain; to such end, what is known as “molecular breeding” plays a fundamental role. Since a crop breeding program is mainly based on the quality of the germplasm, its detailed genetic characterization is mandatory to ensure the efficient use of genetic resources and accelerating development of superior varieties. Deep genotyping is an essential tool for a comprehensive characterization of the germplasm of interest and, fortunately, the technology is now accessible at a reasonable cost. What must be ensured is the correct interpretation of the genotypic information and on that basis develop efficient practical molecular crop breeding strategies that respond to the real needs of the breeding program. Key words: breeding population, genetic resources, marker assisted selection, Single Nucleotide Polymorphism (SNP)
... Diversity arrays technology sequencing (DArTseq) and genotyping by sequencing (GBS) are both widely adopted SNP-based methods for genetic studies that involve a genome complexity reduction step, which selectively avoids highly repetitive genomic regions (Elshire et al. 2011;Kilian et al. 2012). Performance differences were reported in the use of DArT and GBS data but strong significant [P ≤ 0.01] correlations of genetic diversity matrices [r = 0.6 to 0.72], and similar structures across the main principal components between the two approaches showed that both are efficient (Elbasyoni et al. 2018;Darrier et al. 2019). ...
Common bean is a grain legume of global importance especially for proteins and micronutrients. The crop is a staple food in sub-Saharan Africa, where it has gained importance in iron biofortification for people prone to anemia. However, biotic and abiotic constraints, long cooking time, and high phytic acid and polyphenols both of which affect bioavailable iron, hinder the production and health benefits. To inform breeding decisions, the study determined genetic diversity and population structure within 725 breeding lines, varieties, or landraces mostly from Uganda and South America. Genotyping by sequencing and diversity array technology (DarTseq) were used to generate single nucleotide polymorphic markers on Set1 (427) and Set2 (298) germplasm, respectively. The germplasm were grouped into Andean and Mesoamerican gene pools, with the latter as the larger subpopulation. Analysis of molecular variance revealed 46% (Set1) and 50% (Set2) of genetic variation among the subpopulations, with fixation indices (FST) of 0.54 (Set1) and 0.71 (Set2) among Andean and Mesoamerican beans, respectively. The overall germplasm’s gene diversities were 0.206 (Set1) and 0.332 (Set2). Admixtures were the most diverse (0.193) in both sets of germplasm. The germplasm exhibited high genetic diversity and as a result they have a high potential for use in plant breeding. Inter-gene pool crosses within and across market classes are possible and considering both approaches is expected to increase diversity to realize genetic gain. The structure and polymorphic information generated provided useful perspectives for genomic breeding and genome-wide association study using the population.
... RRS is a cost-effective molecular characterization method that captures rare variations in "exotic" (including Crop Wild Relatives) material. Most forms of sequencing are less affected by ascertainment bias than SNP arrays (Darrier et al., 2019;Chu et al., 2020). Examples of the application of RRS technology are BRIDGE König et al., 2020) for barley or Seeds of Discovery (Sansaloni et al., 2020; https://seedsofdiscovery.org) for wheat and maize. ...
Passport data (basic information about an accession, e.g., information about the crop classification, collecting site, existence of specimens and images, availability), phenotypic data, seed stock regeneration and requests for germplasm are among many data that require management within and among genebanks. Platforms such as Germinate, Legume Information system and BRIDGE address this requirement by providing access to 286detailed experimental and trial data on subsets of germplasm, which may or may not be held within the genebank system. The open-access platforms GRIN-Global, EURISCO and Genesys form the key components that provide central on-line searchable databases on material held in genebanks. Portals and databases that are providing an interface to PGR collections have issues and complexities that arise from the introduction of current omics data and bring up challenges and opportunities that will present themselves in the next generation of tools. New solutions should facilitate germplasm tracking, germplasm tracing and enable aggregators of genebank data, such as Genesys and EURISCO, to provide access to additional data and information about the material across a large and diverse set of collections.
... BARCSoySNP6K in soybean [55] and C7AIR in rice [42]) providing a robust genotype calling of multiple known polymorphic sites at the same time and across different populations allowing for a direct comparison of data between experiments, germplasm and studies [6,26]. However, SNP arrays present ascertainment issues [41], an inability to target loci that were not included during the array development and need to be developed independently for each species and population [11]. In addition to these, the cost of genotyping using SNP arrays, even after development, is considerably higher than sequencingbased approaches [15]. ...
Despite the increased efficiency of sequencing technologies and the development of reduced-representation sequencing (RRS) approaches allowing high-throughput sequencing (HTS) of multiplexed samples, the per-sample genotyping cost remains the most limiting factor in the context of large-scale studies. For example, in the context of genomic selection (GS), breeders need genome-wide markers to predict the breeding value of large cohorts of progenies, requiring the genotyping of thousands candidates. Here, we introduce 3D-GBS, an optimized GBS procedure, to provide an ultra-high-throughput and ultra-low-cost genotyping solution for species with small to medium-sized genome and illustrate its use in soybean. Using a combination of three restriction enzymes (PstI/NsiI/MspI), the portion of the genome that is captured was reduced fourfold (compared to a “standard” ApeKI-based protocol) while reducing the number of markers by only 40%. By better focusing the sequencing effort on limited set of restriction fragments, fourfold more samples can be genotyped at the same minimal depth of coverage. This GBS protocol also resulted in a lower proportion of missing data and provided a more uniform distribution of SNPs across the genome. Moreover, we investigated the optimal number of reads per sample needed to obtain an adequate number of markers for GS and QTL mapping (500–1000 markers per biparental cross). This optimization allows sequencing costs to be decreased by ~ 92% and ~ 86% for GS and QTL mapping studies, respectively, compared to previously published work. Overall, 3D-GBS represents a unique and affordable solution for applications requiring extremely high-throughput genotyping where cost remains the most limiting factor.
