Pan-genome Sampling Strategy: Humans, Animals, and Plants

Strong pan-genome results begin with smart sampling. In practice, pangenome sampling is about balancing diversity, cohort design, and realistic sample size. Put simply: define the question, map the population, and collect representative material with consistent metadata and QC. This article turns that into an actionable plan for study designers and core facilities. We cover how to set objectives, build strata, and size your cohort for discovery or association. We also offer domain-specific checklists for humans, animals, and plants. Use this guide to reduce uncertainty before you commit budget or sequencing lanes and to keep downstream assembly efficient.

1) Objective & Population Scope

Start by writing the scientific question in one sentence. Avoid jargon. State the trait, phenotype, or process you want to study. Then define the population that can answer it. Is the pangenome intended for broad reference improvement, for structural variant discovery, or for trait-linked association? Clarity here drives every later decision in cohort design and sample size.

Decide whether your species and aims imply an open or bounded pan-genome. Bacteria with high gene flow often look open. In contrast, inbred lines or closed colonies feel bounded. This matters because open systems reward breadth—more diverse accessions—while bounded systems can tolerate deeper sampling within strata. Note any ethical, permitting, or biosecurity constraints early. These limit what can be collected and how fast.

Document concrete deliverables next. Examples include a graph-aware reference, a catalog of presence–absence variation, or a shortlist of candidates for follow-up. Tie each deliverable to minimal metadata. For instance, a variant catalog without geography or ancestry loses context. A trait study without consistent phenotype measurement will stall. By fixing outputs and metadata upfront, you prevent mid-project drift.

Finally, sketch the sources you can access. Humans may require multi-site recruitment. Animals might rely on breeding programs or wildlife permits. Plants often draw from germplasm banks, landraces, or breeding panels. These sources shape your sampling frame and the practical pace of collection.

2) Cohort Design for Diversity

Think "structure before size." Good pangenome sampling captures population structure without bias. Define a small set of strata that reflect meaningful axes of variation:

Ancestry or breed for humans and animals; subspecies, landrace, or ecotype for plants.
Geography to reflect migration, domestication, or adaptation.
Environment or management (wild vs. domestic; irrigated vs. rain-fed).
Phenotype if you have a measured trait of interest.

Overview of 47 diverse diploid human assemblies, each near-complete and high-accuracy.（Liao W.W. et al. (2023) Nature） Presenting 47 accurate and near-complete diverse diploid human genome assemblies. (Liao W.W. et al. (2023) Nature)

Build a sampling frame from registries, colonies, herds, panels, or seed banks. Within each stratum, use balanced or random draws. Avoid convenience sampling that drifts toward easy sites or well-funded lines. Predefine quotas for underrepresented groups so they do not vanish as the project scales.

Plan early to limit close relatives unless pedigree is the goal. Relatedness reduces effective diversity and can distort allele frequency estimates. Run quick relatedness screens or low-pass genotyping on pilot samples to catch duplicates and close kin. In plants, replicate accessions can slip through as multiple seed packets; in animals, siblings appear in seasonal cohorts; in humans, community recruitment can create hidden clusters. Detect and correct this up front.

Add a diversity check before full-scale sequencing. Lightweight methods help: k-mer sketches, principal components from pilot genotypes, or distance checks on barcoded reads. If pilot results show narrow coverage of the population, revisit quotas and add under-sampled strata. This small step prevents expensive rebalancing later.

3) Sample Size & Power: Practical Heuristics

Link sample size to purpose. There is no single magic number. The right cohort design depends on whether you optimise for variant discovery or trait association.

For discovery and graph construction

Prioritise breadth over depth until novelty plateaus. Early samples should maximise lineage diversity.
Track variant saturation: plot unique gene families or structural variants versus sampled genomes. When the curve flattens, add depth within key strata rather than chasing new accessions.
A practical rhythm is to batch in waves. After each wave, re-estimate novelty per stratum and redirect the next batch accordingly.

For association to traits (e.g., SV–trait links)

Ensure minimum counts per stratum so models can include ancestry or environment covariates. Underpowered strata inflate false positives or mask real effects.
Power depends on effect size, allele frequency, and phenotype noise. Rules of thumb: raise counts when effects are small, frequencies are low, or phenotypes are noisy.
Reserve 10–15% of the budget for validation or targeted resequencing, ideally in independent strata.

Depth vs. count trade-offs

For large structural variant discovery, some long molecules or phased reads are invaluable. If budget is fixed, keep modest depth across many diverse samples, plus higher depth for carefully chosen representatives per stratum.
For gene presence–absence mapping in microbes or plants, balanced coverage with uniform QC often beats extremes of depth.

Track decisions in a simple table: stratum, planned n, achieved n, exclusion reasons, and replacement rules. This keeps pangenome sampling auditable and makes later analysis easier to reproduce.

4) Domain Checklists

Humans

Strata: ancestry, geography, age/sex where relevant, and recruitment channel (clinical networks vs. community registries).
Ethics: define consent scope clearly (data sharing, recontact, future use). De-identify and standardise workflows across sites.
Logistics: balance recruitment across sites and time. Harmonise collection kits and storage. Pilot relatedness checks to avoid household clusters.
Notes: document exclusion criteria (e.g., sample integrity or missing metadata). Record language or region to interpret cultural or environmental context.

Animals

Strata: breed or line, wild vs. domestic, geography, and management system.
Kinship: cap close relatives per stratum unless pedigree mapping is the goal.
Biosecurity: coordinate permits and quarantine when sampling across facilities.
Seasonality: plan for breeding seasons or migration windows.
Notes: include founder or heritage lines to capture rare alleles. Cross-reference phenotypes such as growth, behaviour, or production traits if collected under consistent protocols.

Pangenome graph at the IGLL1 locus illustrating allelic paths and structural variation.（Rice E.S. et al. (2023) BMC Biology） A visual representation of the pangenome graph for the gene IGLL1. (Rice E.S. et al. (2023) BMC Biology)

Plants

Strata: species or subspecies, landraces vs. elite lines, ecotypes, and geography.
Material type: seeds vs. clonal tissue; record passport data and deposit voucher specimens.
Repositories: leverage germplasm banks for breadth; layer in breeding panels for current relevance.
Wild relatives: include where feasible to expand the accessory genome and capture adaptive alleles.
Notes: in mixed-ploidy groups, set clear rules for sample inclusion and analysis labels to avoid downstream confusion.

Phylogenetic relationships and population structure among Capsicum accessions.（Liu F. et al. (2023) Nature Communications） Phylogeny and population structure of Capsicum accessions. (Liu F. et al. (2023) Nature Communications)

5) Execution: Metadata, Ethics, QC, Timeline & Risks

Metadata

Define a minimal core set that every sample must carry. For example: unique ID, stratum labels, collection date and location, source repository, and phenotype fields if applicable. Add an extended layer for optional richness (environmental variables, management practices, or experimental batch). Keep names controlled with dictionaries and codebooks. This avoids downstream relabelling.

Ethics and permits

Register permits, consent, and data governance before the first shipment. Create a permission matrix that links each sample to allowed uses and sharing levels. For animals and plants, attach collection or transfer permits to the manifest.

Specimen integrity and QC

Standardise collection kits, storage temperatures, and transport. At intake, perform identity checks, contamination screens, and integrity assays. Flag problematic lots early. For multi-site efforts, run periodic cross-site comparisons to detect drift in quality. Agree on exclusion criteria in writing: low integrity, mismatched metadata, contamination beyond set thresholds.

Handoff to sequencing and assembly

Prepare a clean manifest with sample IDs, strata, and priority tiers. Include notes on replacements and any deviations from the plan. This enables the sequencing team to schedule lanes efficiently and helps analysts to build the graph or call presence–absence variation with confidence.

Pan-GWAS identifies significant loci associated with leaf chloride concentration.（Cochetel N. et al. (2023) Genome Biology） Pan-GWAS revealed significant associations with chloride concentration in the leaves. (Cochetel N. et al. (2023) Genome Biology)

Timeline

Use a phased plan:

Pilot diversity check (small n across all strata).
Scale-up in balanced waves with periodic novelty and relatedness reviews.
Validation targeting uncertain regions, key accessions, or underpowered strata.

Include a contingency buffer for replacements. Samples fail. Weather, holidays, and harvest schedules cause delays. A small reserve keeps momentum.

Common pitfalls and defenses

Over-sampling convenience groups: enforce quotas and monitor accrual by stratum in real time.
Late discovery of relatedness: run early kinship screens and set per-family caps.
Rich samples, poor metadata: require minimal fields at collection, not after.
Locking size too early: revisit counts after each wave using saturation and power checks.
Fragmented governance: keep one master manifest and one decision log.

Conclusion and next steps

A well-designed pangenome sampling plan does three things. It captures genuine diversity, it keeps cohorts balanced and auditable, and it delivers clean inputs for assembly and variant analysis. By focusing on objectives first, structuring your cohort, and using practical sample size heuristics, you avoid costly rework and shorten the path to insight. Whether you are assembling a graph reference, cataloguing presence–absence variation, or linking structural variants to traits, the same principles apply: represent the population, measure consistently, and document every decision.

Research-use only note and service support

CD Genomics provides research-only population and pan-genomics solutions for institutional teams—project scoping, sampling plan consultation, sequencing coordination, and downstream analysis support. If you need a planning review or a second opinion on cohort design, our team can help align scope, diversity targets, and budget before you start generating data.

Related reading:

References

Liao, W.-W., Asri, M., Ebler, J. et al. A draft human pangenome reference. Nature 617, 312–324 (2023).
Rice, E.S., Alberdi, A., Alfieri, J. et al. A pangenome graph reference of 30 chicken genomes allows genotyping of large and complex structural variants. BMC Biology 21, 267 (2023).
Liu, F., Zhao, J., Wu, Z. et al. Genomes of cultivated and wild Capsicum species provide insights into pepper domestication and population differentiation. Nature Communications 14, 5487 (2023).
Cochetel, N., Minio, A., Massonnet, M. et al. A super-pangenome of the North American wild grape species. Genome Biology 24, 290 (2023).

* Designed for biological research and industrial applications, not intended for individual clinical or medical purposes.