Genotyping-by-sequencing Protocol Research Articles

Efficiency of genotyping by sequencing in inferring genomic relatedness and molecular insights into fat tail selection in Tunisian sheep.

In developing countries, the use of simple and cost-efficient molecular technology is crucial for genetic characterization of local animal resources and better development of conservation strategies. The genotyping by sequencing (GBS) technique, also called restriction enzyme- reduced representational sequencing, is an efficient, cost-effective method for simultaneous discovery and genotyping of many markers. In the present study, we applied a two-enzyme GBS protocol (PstI/MspI) to discover and genotype SNP markers among 197 Tunisian sheep samples. A total of 100 333 bi-allelic SNPs were discovered and genotyped with an SNP call rate of 0.69 and mean sample depth 3.33. The genomic relatedness between 183 samples grouped the samples perfectly to their populations and pointed out a high genetic relatedness of inbred subpopulation reflecting the current adopted reproductive strategies. The genome-wide association study contrasting fat vs. thin-tailed breeds detected 41 significant variants including a peak positioned on OAR20. We identified FOXC1, GMDS, VEGFA, OXCT1, VRTN and BMP2 as the most promising for sheep tail-type trait. The GBS data have been useful to assess the population structure and improve our understanding of the genomic architecture of distinctive characteristics shaped by selection pressure in local sheep breeds. This study successfully investigates a cost-efficient method to discover genotypes, assign populations and understand insights into sheep adaptation to arid area. GBS could be of potential utility in livestock species in developing/emerging countries.

UGbS-Flex, a novel bioinformatics pipeline for imputation-free SNP discovery in polyploids without a reference genome: finger millet as a case study

BackgroundResearch on orphan crops is often hindered by a lack of genomic resources. With the advent of affordable sequencing technologies, genotyping an entire genome or, for large-genome species, a representative fraction of the genome has become feasible for any crop. Nevertheless, most genotyping-by-sequencing (GBS) methods are geared towards obtaining large numbers of markers at low sequence depth, which excludes their application in heterozygous individuals. Furthermore, bioinformatics pipelines often lack the flexibility to deal with paired-end reads or to be applied in polyploid species.ResultsUGbS-Flex combines publicly available software with in-house python and perl scripts to efficiently call SNPs from genotyping-by-sequencing reads irrespective of the species’ ploidy level, breeding system and availability of a reference genome. Noteworthy features of the UGbS-Flex pipeline are an ability to use paired-end reads as input, an effective approach to cluster reads across samples with enhanced outputs, and maximization of SNP calling. We demonstrate use of the pipeline for the identification of several thousand high-confidence SNPs with high representation across samples in an F3-derived F2 population in the allotetraploid finger millet. Robust high-density genetic maps were constructed using the time-tested mapping program MAPMAKER which we upgraded to run efficiently and in a semi-automated manner in a Windows Command Prompt Environment. We exploited comparative GBS with one of the diploid ancestors of finger millet to assign linkage groups to subgenomes and demonstrate the presence of chromosomal rearrangements.ConclusionsThe paper combines GBS protocol modifications, a novel flexible GBS analysis pipeline, UGbS-Flex, recommendations to maximize SNP identification, updated genetic mapping software, and the first high-density maps of finger millet. The modules used in the UGbS-Flex pipeline and for genetic mapping were applied to finger millet, an allotetraploid selfing species without a reference genome, as a case study. The UGbS-Flex modules, which can be run independently, are easily transferable to species with other breeding systems or ploidy levels.

Genotyping-by-sequencing performance in selected livestock species

Application of next generation sequencing for large scale genotyping in livestock is limited by high costs and challenging data analysis process. However, available restriction enzyme-based enrichment techniques like e.g. genotyping-by-sequencing (GBS) are promising tools allowing reduction of financial outlies by a high sample multiplexing and narrowing down the sequenced genome areas to the randomly distributed read tags. In this study, we tested the performance of standard, PstI endonuclease-adapted GBS protocol for population genetics in cattle, horse and sheep with application of different, including low-depth sequencing setups. It was found that the detected SNPs display desirable polymorphism parameters and are evenly scattered across the whole genome including gene coding regions. It was also shown that the SNPs can be successfully applied in population genetics, revealing the genetic differentiation of the studied breeds. The GBS approach represents a cost-effective alternative to existing genotyping methods which may find adoption in various research applications.

Low-depth genotyping-by-sequencing (GBS) in a bovine population: strategies to maximize the selection of high quality genotypes and the accuracy of imputation

BackgroundGenotyping-by-sequencing (GBS) has emerged as a powerful and cost-effective approach for discovering and genotyping single-nucleotide polymorphisms. The GBS technique was largely used in crop species where its low sequence coverage is not a drawback for calling genotypes because inbred lines are almost homozygous. In contrast, only a few studies used the GBS technique in animal populations (with sizeable heterozygosity rates) and many of those that have been published did not consider the quality of the genotypes produced by the bioinformatic pipelines. To improve the sequence coverage of the fragments, an alternative GBS preparation protocol that includes selective primers during the PCR amplification step has been recently proposed. In this study, we compared this modified protocol with the conventional two-enzyme GBS protocol. We also described various procedures to maximize the selection of high quality genotypes and to increase the accuracy of imputation.ResultsThe in silico digestions of the bovine genome showed that the combination of PstI and MspI is more suitable for sequencing bovine GBS libraries than the use of single digestions with PstI or ApeKI. The sequencing output of the GBS libraries generated a total of 123,666 variants with the selective-primer approach and 272,103 variants with the conventional approach. Validating our data with genotypes obtained from mass spectrometry and Illumina’s bovine SNP50 array, we found that the genotypes produced by the conventional GBS method were concordant with those produced by these alternative genotyping methods, whereas the selective-primer method failed to call heterozygotes with confidence. Our results indicate that high accuracy in genotype calling (>97%) can be obtained using low read-depth thresholds (3 to 5 reads) provided that markers are simultaneously filtered for genotype quality scores. We also show that factors such as the minimum call rate and the minor allele frequency positively influence the accuracy of imputation of missing GBS data. The highest accuracies (around 85%) of imputed GBS markers were obtained with the FIMPUTE program when GBS and SNP50 array genotypes were combined (80,190 to 100,297 markers) before imputation.ConclusionsWe discovered that the conventional two-enzyme GBS protocol could produce a large number of high-quality genotypes provided that appropriate filtration criteria were used. In contrast, the selective-primer approach resulted in a substantial proportion of miscalled genotypes and should be avoided for livestock genotyping studies. Overall, our study demonstrates that carefully adjusting the different filtering parameters applied to the GBS data is critical to maximize the selection of high quality genotypes and to increase the accuracy of imputation of missing data. The strategies and results presented here provide a framework to maximize the output of the GBS technique in animal populations and qualified the PstI/MspI GBS assay as a low-cost high-density genotyping platform. The conclusions reported here regarding read-depth and genotype quality filtering could benefit many GBS applications, notably genome-wide association studies, where there is a need to increase the density of markers genotyped across the target population while preserving the quality of genotypes.

Whole-genome single-nucleotide polymorphism (SNP) marker discovery and association analysis with the eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) content in Larimichthys crocea.

Whole-genome single-nucleotide polymorphism (SNP) markers are valuable genetic resources for the association and conservation studies. Genome-wide SNP development in many teleost species are still challenging because of the genome complexity and the cost of re-sequencing. Genotyping-By-Sequencing (GBS) provided an efficient reduced representative method to squeeze cost for SNP detection; however, most of recent GBS applications were reported on plant organisms. In this work, we used an EcoRI-NlaIII based GBS protocol to teleost large yellow croaker, an important commercial fish in China and East-Asia, and reported the first whole-genome SNP development for the species. 69,845 high quality SNP markers that evenly distributed along genome were detected in at least 80% of 500 individuals. Nearly 95% randomly selected genotypes were successfully validated by Sequenom MassARRAY assay. The association studies with the muscle eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) content discovered 39 significant SNP markers, contributing as high up to ∼63% genetic variance that explained by all markers. Functional genes that involved in fat digestion and absorption pathway were identified, such as APOB, CRAT and OSBPL10. Notably, PPT2 Gene, previously identified in the association study of the plasma n-3 and n-6 polyunsaturated fatty acid level in human, was re-discovered in large yellow croaker. Our study verified that EcoRI-NlaIII based GBS could produce quality SNP markers in a cost-efficient manner in teleost genome. The developed SNP markers and the EPA and DHA associated SNP loci provided invaluable resources for the population structure, conservation genetics and genomic selection of large yellow croaker and other fish organisms.

SNP discovery and genotyping using Genotyping-by-Sequencing in Pekin ducks.

Genomic selection and genome-wide association studies need thousands to millions of SNPs. However, many non-model species do not have reference chips for detecting variation. Our goal was to develop and validate an inexpensive but effective method for detecting SNP variation. Genotyping by sequencing (GBS) can be a highly efficient strategy for genome-wide SNP detection, as an alternative to microarray chips. Here, we developed a GBS protocol for ducks and tested it to genotype 49 Pekin ducks. A total of 169,209 SNPs were identified from all animals, with a mean of 55,920 SNPs per individual. The average SNP density reached 1156 SNPs/MB. In this study, the first application of GBS to ducks, we demonstrate the power and simplicity of this method. GBS can be used for genetic studies in to provide an effective method for genome-wide SNP discovery.

Development of SNP markers for population structure and phylogeography characterization in little owl (Athene noctua) using a genotyping- by-sequencing approach

Single nucleotide polymorphisms (SNPs) are becoming the most utilized markers in population genetics studies. Reduced representation genome techniques, as genotyping-by-sequencing (GBS), allow identifying great numbers of polymorphisms that are useful to analyze genetic diversity in non-model organisms. In this investigation, we implemented a GBS protocol and different bioinformatics tools to identify a novel SNP panel and investigate the genetic structure and variability in populations of little owl (Athene noctua) sampled from seven geographical regions in Europe (Balkans, north-west Europe, north, central and south Italy, Spain and Portugal). We obtained a total of 22,185 putative SNPs and 1306 indels. After the filtering procedure, 7175 SNP loci and 53 individuals met the stringent quality control measures. Neutrality test identified 281 candidate loci under positive selection, which were used for population genetic analyses. The selected SNP panel allowed an accurate description of the genetic structure of the little owl.

Genotyping-By-Sequencing (GBS) Detects Genetic Structure and Confirms Behavioral QTL in Tame and Aggressive Foxes (Vulpes vulpes).

The silver fox (Vulpes vulpes) offers a novel model for studying the genetics of social behavior and animal domestication. Selection of foxes, separately, for tame and for aggressive behavior has yielded two strains with markedly different, genetically determined, behavioral phenotypes. Tame strain foxes are eager to establish human contact while foxes from the aggressive strain are aggressive and difficult to handle. These strains have been maintained as separate outbred lines for over 40 generations but their genetic structure has not been previously investigated. We applied a genotyping-by-sequencing (GBS) approach to provide insights into the genetic composition of these fox populations. Sequence analysis of EcoT22I genomic libraries of tame and aggressive foxes identified 48,294 high quality SNPs. Population structure analysis revealed genetic divergence between the two strains and more diversity in the aggressive strain than in the tame one. Significant differences in allele frequency between the strains were identified for 68 SNPs. Three of these SNPs were located on fox chromosome 14 within an interval of a previously identified behavioral QTL, further supporting the importance of this region for behavior. The GBS SNP data confirmed that significant genetic diversity has been preserved in both fox populations despite many years of selective breeding. Analysis of SNP allele frequencies in the two populations identified several regions of genetic divergence between the tame and aggressive foxes, some of which may represent targets of selection for behavior. The GBS protocol used in this study significantly expanded genomic resources for the fox, and can be adapted for SNP discovery and genotyping in other canid species.

Flexible and scalable genotyping-by-sequencing strategies for population studies.

BackgroundMany areas critical to agricultural production and research, such as the breeding and trait mapping in plants and livestock, require robust and scalable genotyping platforms. Genotyping-by-sequencing (GBS) is a one such method highly suited to non-human organisms. In the GBS protocol, genomic DNA is fractionated via restriction digest, then reduced representation is achieved through size selection. Since many restriction sites are conserved across a species, the sequenced portion of the genome is highly consistent within a population. This makes the GBS protocol highly suited for experiments that require surveying large numbers of markers within a population, such as those involving genetic mapping, breeding, and population genomics. We have modified the GBS technology in a number of ways. Custom, enzyme specific adaptors have been replaced with standard Illumina adaptors compatible with blunt-end restriction enzymes. Multiplexing is achieved through a dual barcoding system, and bead-based library preparation protocols allows for in-solution size selection and eliminates the need for columns and gels.ResultsA panel of eight restriction enzymes was selected for testing on B73 maize and Nipponbare rice genomic DNA. Quality of the data was demonstrated by identifying that the vast majority of reads from each enzyme aligned to restriction sites predicted in silico. The link between enzyme parameters and experimental outcome was demonstrated by showing that the sequenced portion of the genome was adaptable by selecting enzymes based on motif length, complexity, and methylation sensitivity. The utility of the new GBS protocol was demonstrated by correctly mapping several in a maize F2 population resulting from a B73 × Country Gentleman test cross.ConclusionsThis technology is readily adaptable to different genomes, highly amenable to multiplexing and compatible with over forty commercially available restriction enzymes. These advancements represent a major improvement in genotyping technology by providing a highly flexible and scalable GBS that is readily implemented for studies on genome-wide variation.Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2164-15-979) contains supplementary material, which is available to authorized users.

Genotyping-By-Sequencing for Plant Genetic Diversity Analysis: A Lab Guide for SNP Genotyping

Genotyping-by-sequencing (GBS) has recently emerged as a promising genomic approach for exploring plant genetic diversity on a genome-wide scale. However, many uncertainties and challenges remain in the application of GBS, particularly in non-model species. Here, we present a GBS protocol we developed and use for plant genetic diversity analysis. It uses two restriction enzymes to reduce genome complexity, applies Illumina multiplexing indexes for barcoding and has a custom bioinformatics pipeline for genotyping. This genetic diversity-focused GBS (gd-GBS) protocol can serve as an easy-to-follow lab guide to assist a researcher through every step of a GBS application with five main components: sample preparation, library assembly, sequencing, SNP calling and diversity analysis. Specifically, in this presentation, we provide a brief overview of the GBS approach, describe the gd-GBS procedures, illustrate it with an application to analyze genetic diversity in 20 flax (Linum usitatissimum L.) accessions and discuss related issues in GBS application. Following these lab bench procedures and using the custom bioinformatics pipeline, one could generate genome-wide SNP genotype data for a conventional genetic diversity analysis of a non-model plant species.

Rapid Identification of Alleles at the Soybean Maturity Gene E3 using genotyping by Sequencing and a Haplotype‐Based Approach

In eastern Canada, earliness is an important trait for soybean given the short growing season. The aim of this work was to develop tools for breeders to rapidly identify alleles present in their germplasm at the recently cloned maturity locus E3 (GmPhyA3). The tremendous throughput of modern DNA sequencing technology has allowed the use of genotyping by sequencing (GBS) approaches to identify and genotype thousands of single nucleotide polymorphisms (SNPs) across the entire genome. We have used a GBS protocol and SNP‐calling pipeline optimized for soybean to characterize 53 near‐isogenic lines (NILs) contrasting for maturity loci. Results obtained clearly showed the suitability of GBS to provide a dense SNP coverage and very accurate information on the location and size of introgressed regions. We then developed a GBS haplotype method to characterize 91 plant introductions (PIs) as well as a set of 305 lines representative of the Eastern Canadian germplasm for their allelic status at the GmPhyA3 gene. Six distinct haplotypes in and around the E3 locus were observed. Subsequent tests on two genotypes per haplotype (PCR test for a previously reported allele, sequencing entire gene), and validation on a subset of lines, allowed to determine that each of these corresponded to a different allele of this gene. We found that the functional allele E3Ha and the loss of function allele e3‐tr were the two most prevalent in the Eastern Canadian germplasm, while the e3‐fs allele was found at low frequency and e3‐ns was absent. These results show that this approach is a powerful method for rapid allelic characterization, and its application to other maturity genes will be useful for breeding purposes.

TASSEL-GBS: A High Capacity Genotyping by Sequencing Analysis Pipeline

Genotyping by sequencing (GBS) is a next generation sequencing based method that takes advantage of reduced representation to enable high throughput genotyping of large numbers of individuals at a large number of SNP markers. The relatively straightforward, robust, and cost-effective GBS protocol is currently being applied in numerous species by a large number of researchers. Herein we describe a bioinformatics pipeline, tassel-gbs, designed for the efficient processing of raw GBS sequence data into SNP genotypes. The tassel-gbs pipeline successfully fulfills the following key design criteria: (1) Ability to run on the modest computing resources that are typically available to small breeding or ecological research programs, including desktop or laptop machines with only 8–16 GB of RAM, (2) Scalability from small to extremely large studies, where hundreds of thousands or even millions of SNPs can be scored in up to 100,000 individuals (e.g., for large breeding programs or genetic surveys), and (3) Applicability in an accelerated breeding context, requiring rapid turnover from tissue collection to genotypes. Although a reference genome is required, the pipeline can also be run with an unfinished “pseudo-reference” consisting of numerous contigs. We describe the tassel-gbs pipeline in detail and benchmark it based upon a large scale, species wide analysis in maize (Zea mays), where the average error rate was reduced to 0.0042 through application of population genetic-based SNP filters. Overall, the GBS assay and the tassel-gbs pipeline provide robust tools for studying genomic diversity.

Genetic diversity analysis of yellow mustard (Sinapis alba L.) germplasm based on genotyping by sequencing

Recent advances in next generation sequencing technologies make genotyping by sequencing (GBS) more feasible for molecular characterization of plant germplasm with complex and unsequenced genomes. We used a GBS protocol consisting of Roche 454 pyrosequencing, genomic reduction and advanced bioinformatics tools to analyze genetic diversity of 24 diverse yellow mustard accessions. One and one half 454 pyrosequencing runs generated roughly 1.2 million sequence reads totaling about 392 million nucleotides. Application of the computational pipeline DIAL identified 512 contigs and 828 SNPs. The BLAST algorithm revealed alignments of 214 contigs with the sequences reported in NCBI nr/nt database. Sanger sequencing confirmed 95 % of 41 selected contigs and 94 % of 240 putative SNPs. The 454 scored SNPs were highly imbalanced among assayed samples. Diversity analysis of these SNPs revealed that 26.1 % of the total variation resided among landrace, cultivar and breeding lines and 24.7 % between yellow- and black-seeded germplasm. Cluster analysis showed that the black-seeded accessions were largely clustered together and the breeding lines were grouped with known origin. Computer simulation was performed to assess the impact of 454 SNPs missing and revealed considerable changes in allelic count, bias in detection of genetic structure, and large deviations from the expected genetic-distance matrix. These findings are useful for parental selection consideration in yellow mustard breeding, and our detailed analyses help illustrate the utility of GBS in genetic-diversity analysis of plant germplasm, particularly for genetic-relationship assessment.

Bridging the genotyping gap: using genotyping by sequencing (GBS) to add high-density SNP markers and new value to traditional bi-parental mapping and breeding populations

Genotyping by sequencing (GBS) is the latest application of next-generation sequencing protocols for the purposes of discovering and genotyping SNPs in a variety of crop species and populations. Unlike other high-density genotyping technologies which have mainly been applied to general interest "reference" genomes, the low cost of GBS makes it an attractive means of saturating mapping and breeding populations with a high density of SNP markers. One barrier to the widespread use of GBS has been the difficulty of the bioinformatics analysis as the approach is accompanied by a high number of erroneous SNP calls which are not easily diagnosed or corrected. In this study, we use a 384-plex GBS protocol to add 30,984 markers to an indica (IR64)×japonica (Azucena) mapping population consisting of 176 recombinant inbred lines of rice (Oryza sativa) and we release our imputation and error correction pipeline to address initial GBS data sparsity and error, and streamline the process of adding SNPs to RIL populations. Using the final imputed and corrected dataset of 30,984 markers, we were able to map recombination hot and cold spots and regions of segregation distortion across the genome with a high degree of accuracy, thus identifying regions of the genome containing putative sterility loci. We mapped QTL for leaf width and aluminum tolerance, and were able to identify additional QTL for both phenotypes when using the full set of 30,984 SNPs that were not identified using a subset of only 1,464 SNPs, including a previously unreported QTL for aluminum tolerance located directly within a recombination hotspot on chromosome 1. These results suggest that adding a high density of SNP markers to a mapping or breeding population through GBS has a great value for numerous applications in rice breeding and genetics research.

Development of High-Density Genetic Maps for Barley and Wheat Using a Novel Two-Enzyme Genotyping-by-Sequencing Approach

Advancements in next-generation sequencing technology have enabled whole genome re-sequencing in many species providing unprecedented discovery and characterization of molecular polymorphisms. There are limitations, however, to next-generation sequencing approaches for species with large complex genomes such as barley and wheat. Genotyping-by-sequencing (GBS) has been developed as a tool for association studies and genomics-assisted breeding in a range of species including those with complex genomes. GBS uses restriction enzymes for targeted complexity reduction followed by multiplex sequencing to produce high-quality polymorphism data at a relatively low per sample cost. Here we present a GBS approach for species that currently lack a reference genome sequence. We developed a novel two-enzyme GBS protocol and genotyped bi-parental barley and wheat populations to develop a genetically anchored reference map of identified SNPs and tags. We were able to map over 34,000 SNPs and 240,000 tags onto the Oregon Wolfe Barley reference map, and 20,000 SNPs and 367,000 tags on the Synthetic W9784×Opata85 (SynOpDH) wheat reference map. To further evaluate GBS in wheat, we also constructed a de novo genetic map using only SNP markers from the GBS data. The GBS approach presented here provides a powerful method of developing high-density markers in species without a sequenced genome while providing valuable tools for anchoring and ordering physical maps and whole-genome shotgun sequence. Development of the sequenced reference genome(s) will in turn increase the utility of GBS data enabling physical mapping of genes and haplotype imputation of missing data. Finally, as a result of low per-sample costs, GBS will have broad application in genomics-assisted plant breeding programs.