Porcine Genotyping Array Services (Pig)
Porcine SNP array genotyping for research—delivering standardized Sus scrofa genotypes with clear QC gates and analysis-ready outputs. Choose SwineArray Core, Pro, or Custom to match your study goal, receive organized genotype files plus QC and methods notes, and add optional bioinformatics checks (PCA/relatedness) when you need fast data-readiness confirmation.
Service Highlights
Why Pig SNP Arrays Matter for Research
Porcine SNP genotyping arrays are a practical route when you need consistent, comparable genotypes across a cohort—especially for population genetics, association workflows (GWAS/QTL), and method development where documentation and reproducibility influence downstream analysis and reporting.
Compared with approaches that prioritize novel variant discovery, SNP arrays emphasize standardization: a consistent marker set and predictable output structures. This makes them a useful foundation for studies that depend on cross-sample comparability, cohort integrity review, and repeatable QC decisions across batches.
How CD Genomics delivers this service
SwineArray Core vs Pro vs Custom: Choose the Right Tier
Use your research goal and your downstream analysis plan to select a tier. If you are not sure, request a fit-check and we will recommend a tier and an intake checklist based on your project description.
| Tier | Best for | Typical cohort reality | What's emphasized in delivery |
|---|---|---|---|
| SwineArray Core | Pilot studies, screening, structure readiness | Smaller cohorts or first-pass sampling | QC documentation + analysis-ready genotypes for exploratory workflows |
| SwineArray Pro | GWAS/QTL support and association-ready datasets | Standard cohorts with phenotype/meta | Structured QC reporting + analysis-ready outputs for association pipelines |
| SwineArray Custom | Study-specific constraints and harmonization | Multi-batch/multi-center, legacy compatibility | Project-fit planning + documentation for reproducible integration |
Common question: “Which tier fits my cohort and analysis goals?”
How we answer: a fit-check that maps your goal → tier → deliverables package (not performance promises).
Request a tier recommendation by sending:
For more background, see LC-WGS vs GBS vs SNP arrays.
Workflow (From Sample Intake to Deliverables)
⚡ Your project follows a clear end-to-end workflow from sample intake to an analysis-ready genotype package.
What you can expect at each step:
• Sample intake: confirm sample IDs and intake type (DNA or biological samples).
• DNA QC: document readiness checks to reduce avoidable technical failures.
• Genotyping + calling: generate a standardized genotype matrix (samples × SNPs).
• QC review: summarize sample- and marker-level QC in a readable report.
• Deliverables: deliver organized outputs plus methods/parameter notes.
QC Gates (What We Check and What You Receive)
QC Gate 1: Sample-level QC
Purpose: identify samples that may compromise downstream analysis.
You receive: a QC summary table with pass/flag labeling.
QC Gate 2: Marker-level QC
Purpose: identify markers with inconsistent behavior.
You receive: marker QC summary and flag indication.
QC Gate 3: Batch awareness
Purpose: keep cohort/batch labels explicit.
You receive: batch-aware QC notes when applicable.
How issues are handled (transparent options)
When samples do not meet project-defined QC expectations, the outcome is documented and—where feasible—one of the following actions is used:
Bioinformatics Analysis
Add optional bioinformatics support to turn genotypes into analysis-ready context for your study—especially helpful for cohort screening, multi-batch projects, and GWAS/QTL preparation.
Bioinformatics outputs (if selected): short analysis summary + key tables/plots (when applicable) + methods/parameter notes.
Deliverables (Files You Receive)
You receive a structured package that your team can load directly, with documentation that explains what each file represents.
Genotype results package
Standardized genotype dataset (sample × SNP) in the agreed format.
Sample manifest / ID mapping
Final sample list and any crosswalk used.
Marker/SNP annotation
As applicable: marker identifiers and basic annotations used in outputs.
QC summary report
Sample-level + marker-level QC tables with pass/flag labels.
QC notes
Brief explanations of key QC findings and how flags are represented.
Methods/parameter notes
Genotype calling and QC logic, plus project conventions.
If bioinformatics is selected: PCA/relatedness result files and report figures (when applicable) are included in the same package.
For downstream support, explore agricultural genomic data analysis.
Demo: Example Outputs
Applications in Porcine Genetics Research
Population genetics / structure / diversity
GWAS / QTL support
Pedigree verification, sample tracking, and cohort QC
Genomic selection method development
Learn more in our genomic selection guide.
Case Study
Citation
Ponsuksili, S., Reyer, H., Trakooljul, N., Murani, E., & Wimmers, K. (2016). Single- and Bayesian Multi-Marker Genome-Wide Association for Haematological Parameters in Pigs. PLOS ONE. DOI: 10.1371/journal.pone.0159212.
Background: GWAS projects in pig cohorts often require standardized genotype inputs plus clear QC documentation so downstream association testing and reporting remain reproducible across collaborators.
Methods: The study analyzed an array-based SNP genotype dataset and applied both single-marker and Bayesian multi-marker genome-wide association approaches to evaluate links between genetic variation and hematological traits.
Results: The analysis identified genomic regions associated with hematological parameters, illustrating how array genotypes can be used in trait-association workflows when cohort design and QC decisions are clearly documented.
Manhattan plots of single-marker GWAS results for porcine haematological traits (German Landrace), showing genome-wide SNP association signals and the study's significance threshold.
Conclusion: A documented, analysis-ready genotype package—paired with transparent QC summaries—helps research teams execute GWAS/QTL workflows more efficiently and report methods consistently.
Sample & Metadata Requirements
If you only have the minimum items, you can still submit an RFQ—missing details can be confirmed during the fit-check.
Minimum to get an RFQ (send these 3 items)
Nice-to-have (optional, speeds up accuracy)
| Item | Minimum | Optional (recommended) |
|---|---|---|
| Sample type | DNA or biological sample | Standardized DNA input where possible |
| Sample IDs | Unique, stable | Crosswalk table to internal IDs |
| Metadata | Study goal | Cohort grouping + phenotype/covariates (as applicable) |
FAQ
For fully customized solutions, explore our custom SNP microarrays service.
References
Ponsuksili, S., Reyer, H., Trakooljul, N., Murani, E., & Wimmers, K. (2016). Single- and Bayesian Multi-Marker Genome-Wide Association for Haematological Parameters in Pigs. PLOS ONE. DOI: 10.1371/journal.pone.0159212.
For research purposes only, not intended for clinical diagnosis, treatment, or individual health assessments.
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