Genotyping By Sequencing (GBS) Service
Overview
CD Genomics GBS platform provides an end-to-end solution from sample to interpretable data. We integrate optimized restriction enzyme strategies, robust library preparation, high-throughput sequencing, and population genetics analysis in a single service.
What we offer
- Study design support: marker density, sample size, and sequencing strategy
- Library preparation and sequencing on established next-generation platforms
- Reference-based or de novo pipelines tailored to your species
- Standard and advanced population genetics analyses
Key advantages
- Broad species coverage – plants, fish, invertebrates, livestock, wildlife, and more
- Flexible throughput – suitable for pilot studies and large panels
- Compatible with non-model species – including those lacking high-quality reference genomes
- Publication-ready outputs – VCFs, matrices, plots, and methods summaries that fit common journal expectations
- Robust QC – quality checks at DNA, library, and sequencing levels
- Secure data handling – controlled access and reliable data transfer
Is GBS Right for Your Project?
Genotyping-by-Sequencing is well suited for researchers who need dense genome-wide markers while balancing cost, throughput, and flexibility.
GBS is particularly useful when you:
- Run breeding and selection programs – marker-assisted selection and genomic selection in crops and aquaculture, building training populations for genomic prediction.
- Characterize population structure and diversity – estimating diversity and inbreeding, inferring relatedness, distinguishing subpopulations or lineages.
- Perform GWAS and QTL mapping – detecting SNP–trait associations across many individuals with dense but cost-effective markers.
- Study domestication and local adaptation – comparing wild vs cultivated populations and identifying genomic regions under selection or environmental change.
- Support conservation and management – assessing genetic health of endangered or fragmented populations and informing translocation, breeding, or reintroduction plans.
If you are unsure whether GBS, RAD-seq, whole-genome resequencing, or arrays are better for your project, our scientists can help you evaluate options based on species, budget, and study objectives.
What is Genotyping By Sequencing
Genotyping By Sequencing is a reduced-representation sequencing technique that enables rapid and cost-effective discovery of genome-wide genetic variation, especially single nucleotide polymorphisms (SNPs), across a large number of individuals. Unlike Whole Genome Re-sequencing, which sequences the entire genome, GBS selectively captures informative genomic regions using restriction enzymes, focusing on a representative subset of the genome. This targeted yet high-throughput approach makes GBS particularly suitable for population-scale studies, enabling efficient analysis of genetic diversity, population structure, and evolutionary dynamics. By capturing both neutral and potentially adaptive variants, GBS supports research in natural selection, gene flow, demographic history, and local adaptation—providing a scalable solution for modern population genomics and breeding applications.
Genotyping By Sequencing Service pipeline. (Scheben, et al. 2019)
Sequencing Platforms
NextSeq 500
Illumina NovaSeq
PacBio Sequel II
Workflow of GBS in Population Genetics
Our Genotyping By Sequencing service follows an optimised workflow encompassing sample collection, restriction enzyme digestion, library construction, high-throughput sequencing, stringent quality control, and SNP genotyping through advanced bioinformatics analysis. This efficient pipeline enables the accurate detection of genome-wide genetic variants, particularly SNPs, which are essential for studies in population genetics, association mapping, and genomic selection. To ensure high-quality results, we advise clients to adhere to recommended sample preservation protocols and clearly communicate their research goals prior to project initiation. Our experienced team is available to assist with selecting suitable enzyme combinations, customizing sequencing strategies, and interpreting results, providing full support at every stage of your project.
Study Design and Technology Options
Choosing the right configuration is critical for a successful GBS project. We can help you optimize enzyme choice, marker density, and sequencing depth.
- Restriction enzyme strategy – Single- or dual-enzyme designs tuned for marker density, genome size, and genome complexity.
- Reference-based vs de novo pipelines – Reference-based GBS when a genome is available; de novo tag-based pipelines for non-model species without a high-quality reference.
- Coverage and multiplexing – Recommendations on sequencing depth and multiplexing levels for diversity panels, mapping populations, and breeding germplasm, balancing cost per sample and number of samples.
- Complex or polyploid genomes – Adapted workflows for polyploid or highly repetitive genomes where standard settings are not sufficient.
Data Analysis
We apply established population genomics workflows tailored to GBS data.
Typical analysis steps
- Raw data QC (adapter trimming, read quality control)
- Read alignment to reference or de novo tag assembly for non-model species
- SNP (and optional indel) calling and filtering (call rate, depth, MAF, basic quality checks)
- Sample- and marker-level QC, outlier detection, optional LD-based pruning
- Population genetics analyses: summary statistics, PCA, structure/cluster analysis, diversity and differentiation metrics
- Optional association / mapping analyses (e.g., GWAS)
Data analysis of Genotyping By Sequencing Service. (Peterson, et al. 2014)
What You'll Receive
We provide structured deliverables so you can move quickly from raw reads to biological interpretation.
1. Raw sequencing data
- Demultiplexed FASTQ files per sample
- Basic sequencing report (reads, read length, quality, GC content)
2. Variant-level outputs
- High-confidence SNP (and optional indel) calls
- Standard VCF files for common downstream tools
- Brief summary of main filtering criteria
3. Sample- and marker-level QC
- Per-sample metrics (reads, missing data, heterozygosity)
- Per-marker metrics (call rate, MAF, basic HWE checks where applicable)
- Optional relatedness / kinship and distance matrices
4. Population genetics analyses (optional)
- Population structure and diversity (PCA, structure/cluster plots, diversity and differentiation metrics)
- Association or mapping outputs (GWAS statistics, Manhattan/QQ plots, top markers with basic annotations where feasible)
5. Reporting and documentation
- Concise analysis report with methods, QC summaries, key figures and tables
- Short guide to help you plug the data into your existing pipelines or software
Demo Results
Genotyping-By-Sequencing for Plant Genetic Diversity Analysis (Peterson, et al. 2014)
Sample Requirements and QC Criteria
To ensure robust GBS data, please prepare samples according to the guidelines below. Exact acceptance criteria may be adjusted based on species and project design.
Sample Requirements (Recommended)
| Item | Recommended specification | Notes |
| Accepted sample type | Purified genomic DNA (preferred) | For tissue / blood / leaf / fin clips, please contact us for SOPs. |
| DNA amount per sample | Typically ≥ 200 ng | Additional material is helpful for repeats or troubleshooting. |
| DNA concentration | Generally ≥ 20 ng/µL | In low-EDTA buffer or nuclease-free water. |
| DNA purity | A260/280 ~ 1.8–2.0; minimal contaminants | Avoid carryover of salts, polysaccharides, phenol, or detergents. |
| DNA integrity | High molecular weight DNA with limited degradation | Assessed by gel or fragment analyzer where applicable. |
| Tubes / plate format | Clearly labeled tubes or 96-well plates | Match physical labels to the sample manifest. |
| Shipping conditions | Chilled with ice packs; dry ice for long-distance if needed | Pack securely to prevent leakage or freeze–thaw cycles. |
QC and Handling
- All submissions undergo DNA, library, and sequencing QC before downstream analysis.
- If some samples fall below recommended thresholds, we will inform you and discuss whether to proceed, exclude them, or replace them.
- Please include a sample manifest (IDs, species, grouping information) with your shipment so that we can verify and track all samples accurately.
Case Study
Applications of genotyping-by-sequencing in maize genetics and breeding
Journal: Scientific Reports
Published: 2020
https://doi.org/10.1038/s41598-020-73321-8
Genotyping-by-Sequencing is a low-cost, high-throughput genotyping method that relies on restriction enzymes to reduce genome complexity. GBS is being widely used for various genetic and breeding applications. In the present study, 2240 individuals from eight maize populations, including two association populations (AM), backcross first generation (BC1), BC1F2, F2, double haploid (DH), intermated B73 × Mo17 (IBM), and a recombinant inbred line (RIL) population, were genotyped using GBS. A total of 955,120 of raw data for SNPs was obtained for each individual, with an average genotyping error of 0.70%. The rate of missing genotypic data for these SNPs was related to the level of multiplex sequencing: ~ 25% missing data for 96-plex and ~ 55% for 384-plex. Imputation can greatly reduce the rate of missing genotypes to 12.65% and 3.72% for AM populations and bi-parental populations, respectively, although it increases total genotyping error. For analysis of genetic diversity and linkage mapping, unimputed data with a low rate of genotyping error is beneficial, whereas, for association mapping, imputed data would result in higher marker density and would improve map resolution. Because imputation does not influence the prediction accuracy, both unimputed and imputed data can be used for genomic prediction. In summary, GBS is a versatile and efficient SNP discovery approach for homozygous materials and can be effectively applied for various purposes in maize genetics and breeding.
- Genotyping-by-Sequencing
- Bioinfomatics
Both unimputed and imputed data from eight populations were used to observe the impact of imputation on population structure analysis using PCA and multidimensional scaling (MDS). When using unimputed data, different subgroups could be separated by PCA in both association panels (Fig.1A, C). For Pop1, clusters of lines from CIMMYT-Columbia, CIMMYT-Zimbabwe, and some CIMMYT-Physiology lines extended in three directions, while others were concentrated in the middle (Fig.1A), which was consistent with the observations in a previous study34. For Pop2, different subgroups clustered along the PC1 axis, with popcorn and sweet corn on one side, and the non-stiff stalk lines on the other side. The stiff stalk and tropical lines could not be separated by the first two PCs (Fig.1C), which was in congruence with Romay's study35. When using imputed data, the two PCs explained more information, but the distribution of the lines was the sameforPop1 and Pop2 (Fig.1B, D).
Principal component analysis of Pop1 and Pop2 using unimputed and imputed data. (A) Pop1 using unimputed data; (B) Pop1 using imputed data; (C) Pop2 using unimputed data; (D) Pop2 using imputed data.
GBS vs Other Genotyping Options
When planning a genotyping project, GBS is one of several possible platforms. The best choice depends on your species, existing resources, and study goals.
A simplified comparison:
| Feature / Aspect | GBS | RAD-seq / 2b-RAD | Whole-Genome Resequencing | SNP Arrays / Panels |
| Marker density & coverage | Medium–high, genome-wide | Medium–high, genome-wide | Very high, near-complete | Fixed markers, moderate–high |
| Upfront design effort | Moderate (enzyme & protocol) | Moderate | Lower (if reference exists) | Higher (array/panel design) |
| Cost per sample (relative) | Low–moderate | Low–moderate | Higher | Low–moderate after design |
| Reference genome required? | Helpful but not mandatory | Not strictly required | Yes (for standard pipelines) | Usually used in well-studied species |
| Best for sample numbers | Hundreds to thousands | Hundreds to thousands | Tens to hundreds (budget-dependent) | Hundreds to thousands |
| Typical applications | Breeding, diversity, GWAS | Diversity, structure, mapping | Fine mapping, selection scans | Routine genotyping, validation |
If you are unsure which platform is most suitable, we can assess options based on your species, sample size, and budget.
FAQs
Genotyping By Sequencing (GBS) coverage efficiency refers to the proportion of the genome's targeted regions that are effectively captured and sequenced at sufficient depth to enable accurate SNP genotyping. Unlike Whole Genome Re-sequencing, which provides uniform coverage across the entire genome, GBS focuses on a reduced representation of the genome using restriction enzymes to selectively sequence consistent, reproducible loci. This approach greatly enhances efficiency and cost-effectiveness in population-scale studies. High GBS coverage efficiency ensures reliable detection of genetic variation in shared genomic regions, enabling robust analysis of population structure, genetic diversity, and marker-trait associations.
GBS is highly efficient for detecting genome-wide single nucleotide polymorphisms (SNPs) across large numbers of individuals, particularly in species with limited genomic resources. Although it does not capture the entire genome, GBS consistently targets specific subsets of the genome, generating high-density SNP datasets suitable for a wide range of population genetics applications. GBS excels at identifying common and moderately frequent variants, supporting studies such as genetic mapping, population differentiation, and genomic selection. Its low cost, scalability, and streamlined analysis pipeline make GBS an ideal choice for variant detection in large populations, especially in breeding programs and evolutionary studies.
The appropriate sequencing depth in GBS depends on study goals, population size, and desired marker density:
- 3X–5X per sample: Adequate for broad-scale genotyping, enabling detection of common SNPs across large populations with minimal cost—ideal for genetic diversity surveys and population structure analysis.
- 6X–10X per sample: Recommended for higher accuracy in genotype calling, especially in diploid and polyploid species or when performing genome-wide association studies (GWAS).
- 10X or higher: Suitable for applications requiring precise allele frequency estimation, parentage analysis, or detecting rare variants within the captured genomic regions.
GBS is particularly attractive for projects ranging from dozens to thousands of samples. During consultation, we will recommend study designs that match your goals and resources.
Yes. We can provide guidance on restriction enzyme strategies, expected marker density, and recommended sequencing depth ranges for your species and application.
Depending on data format and quality, we may support analysis-only projects. Please share a summary of your existing data and requirements for evaluation.
If a subset of samples does not meet our recommended QC criteria, we will inform you and discuss options such as proceeding with reduced expectations, excluding affected samples, or re-extraction/replacement coordinated by your team.
Yes. For non-model species, we can use de novo GBS pipelines or work with draft assemblies and pseudo-references where appropriate.
We provide analysis reports, figures, and methods summaries that are suitable as a basis for manuscripts. While we do not provide medical or diagnostic interpretation, we can help you understand the population genetics outputs in a research context.
References
- Peterson, G. W., Dong, Y., Horbach, C., & Fu, Y.-B. (2014). Genotyping-By-Sequencing for Plant Genetic Diversity Analysis: A Lab Guide for SNP Genotyping. Diversity, 6(4), 665-680. https://doi.org/10.3390/d6040665
- Scheben, A., Batley, J., & Edwards, D. (2017). Genotyping-by-sequencing approaches to characterise crop genomes: choosing the right tool for the right application. Plant biotechnology journal, 15(2), 149–161. https://doi.org/10.1111/pbi.12645
- Wang, N., Yuan, Y., Wang, H., Yu, D., Liu, Y., Zhang, A., Gowda, M., Nair, S. K., Hao, Z., Lu, Y., San Vicente, F., Prasanna, B. M., Li, X., & Zhang, X. (2020). Applications of genotyping-by-sequencing (GBS) in maize genetics and breeding. Scientific reports, 10(1), 16308. https://doi.org/10.1038/s41598-020-73321-8
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