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Start with a genebank discovery service, but treat its result as an accession lead—not as proof that genomic data or seed is available. Identify the holding institution and accession number, preserve any DOI or other persistent identifier, then read passport, trait, evaluation and molecular records as separate evidence types. A valid comparison states which accessions and individuals were sampled, where they came from, which marker or sequencing method was used, and how the statistic was calculated.
What “genetic diversity data” can mean
Genetic diversity is not a single field in a germplasm record. It is evidence about variation within and among plant materials, collected at different levels.
- Passport data identifies an accession and records its taxonomy, collecting or breeding origin, dates, identifiers and biological status. It provides context and hypotheses, not a measured genetic difference.
- Characterization data describes observable, generally heritable features recorded with standardized descriptors, such as seed color, plant height or days to flowering.
- Evaluation data records performance in a stated environment or stress treatment. A disease score or yield value is conditional on location, season, management and the applied stress.
- Genotypic data includes molecular-marker profiles (for example, SSR, SNP or DArT) and sequence data. These can estimate similarity or variation, but the inference depends on sampling, marker coverage and analysis.
These categories answer different questions. Passport origin can suggest that two landraces experienced different environments, but it does not prove that a particular gene causes drought tolerance. A high trait value does not establish a genetic cause unless the study design and data support that conclusion.
Where to find crop germplasm and diversity records
Genesys PGR for cross-genebank discovery
Genesys PGR is a global portal that aggregates crop-diversity records from genebanks. Its page dated January 2026 reported the following holdings:
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| Genesys PGR measure | Reported figure | How to use it |
|---|---|---|
| Accession records | 4.5 million | Broad starting point for finding candidate accessions across collections |
| Genebanks represented | More than 580 | Shows the portal’s institutional reach |
| Trait datasets and subsets | More than 900 | Useful for locating phenotype observations |
| Characterization and evaluation datasets | More than 440 | Useful for standardized descriptors and environment-specific tests |
Those counts describe portal holdings as of that January 2026 snapshot. They do not mean that every accession has SNPs, sequence data, or seed ready to ship. Use the accession/passport search first, select the crop or taxon, and narrow by origin, holding institute, collecting details, biological status, date, climate or other relevant filters. Taxonomic synonyms may be included, so search both the accepted name and important synonyms.
USDA GRIN-Global and the National Plant Germplasm System
For material held in the United States, search the USDA Agricultural Research Service GRIN-Global accession system directly. The U.S. National Plant Germplasm System (NPGS) acquires, conserves, characterizes, evaluates, documents and distributes germplasm. The live interface displayed version 2.3.12 and a notice that U.S. shipping procedures changed effective June 1, 2026. Availability, request instructions and shipping rules can change, so verify the current accession record before planning an experiment.
A GRIN-Global search result is not a guarantee that stock is immediately distributable. The record and the responsible NPGS unit are the authority for current quantity, regeneration status, request conditions and any restrictions.
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When a portal points to a particular genebank, open that institution’s own record. Local systems may contain fuller characterization, evaluation or genotyping details than the discovery portal. FAO’s Genebank Standards for Plant Genetic Resources for Food and Agriculture (2014) names systems such as GRIN-Global and Genesys as genebank information-management resources.
CGIAR Genebanks’ network overview, accessed in 2026, describes more than 700,000 accessions covering more than 3,000 plant species across 11 genebanks. This is a network portfolio figure, not a count of sequenced accessions. Crop-specific CGIAR datasets can be more useful than a larger general catalog when you need a particular trait, marker panel or environmental sampling design.
Search from a question, not from a database count
Before entering a search term, state the decision the data must support. “Find diversity in wheat” is too broad to determine which records or methods matter.
- Define the comparison or use. Decide whether you need variation within one collection, differences between named groups, a possible source of a trait, duplicate detection, or candidates from an environment similar to your target site.
- Search broadly, then filter. Use Genesys for multi-genebank discovery. Filter by crop or taxon and provenance, then inspect each holding institute. For U.S. accessions, repeat the search in GRIN-Global.
- Open the accession-level record. Record the holding institution, accession identifier, names and synonyms, biological status, collecting or breeding information and any persistent identifier before downloading a dataset.
- Follow linked datasets. Check whether a characterization, evaluation or genotype dataset explicitly lists the same accession identifier. A crop name alone is not a safe join key.
- Read the method and sampling notes. Capture descriptor definitions and units for traits; for molecular data capture the platform, marker panel or sequence region, quality filters, missing-data rules and analysis method.
- Check material status separately. If you need seed, cuttings or other planting material, follow the holder’s request process and confirm stock, season, legal terms and phytosanitary requirements.
How to read an accession record
Passport fields: identity and provenance
Passport data follows the Multi-Crop Passport Descriptors (MCPD) standard used for passport exchange in Genesys. Typical fields include accession identifier, institution code, taxon, donor or collecting information, collection date and place, biological status and related identifiers.
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Origin is valuable context. A landrace collected in one climate and a breeding line developed in another may represent different histories, but provenance alone cannot establish adaptation, uniqueness or a causal allele. Treat it as a hypothesis that must be tested with trait or genotype evidence.
Characterization: standardized description
Characterization observations are intended to describe and distinguish material under defined conditions. Check the descriptor definition, scoring scale, units, observer rules and growth conditions. “Seed color” may be a categorical descriptor; “days to flowering” requires a stated starting point and environment. Records using different scales or definitions should not be merged without a documented conversion.
Evaluation: performance in context
Evaluation data comes from trials or stress tests. Interpret each value with its location, season, management, replication and stress treatment. A line that performs well under one drought protocol is not automatically drought tolerant in every production region. If the record omits the environmental details needed to interpret a value, limit your conclusion to the observed trial.
Genotype and sequence records
Molecular records may report SSR alleles, SNP genotypes, DArT profiles or sequence variation. Ask which individuals were sampled, whether DNA came from one plant or a bulk, how missing calls and quality were handled, and whether the analysis estimates relationships among accessions or variation within an accession. Digital sequence information can help assess collection diversity and identify material adapted to different conditions, but conclusions remain tied to the sampled material and analytical design.
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Images and linked files
Images can help check identity or phenotype, especially when a record includes a date and accession label. They do not replace a descriptor definition, experimental conditions or genotype metadata.
Protect accession identity when combining datasets
Identity is often lost when records move between institutional systems. The same material can have a local number, a donor number, a collector number and a genebank accession identifier. Genesys notes that an accession may carry multiple identifiers; CGIAR has identified incompatible data structures and loss of accession identity as barriers to reuse.
Maintain an identity table with at least these fields:
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- Holding institution or genebank code
- Accession number exactly as displayed
- Taxon and any synonym used in the source file
- Donor, collector or breeder identifier when supplied
- Persistent identifier such as a DOI, when available
- Source dataset identifier and download date
- Sample or individual labels used for the molecular or trait record
Do not join records on a common name such as “local maize 12.” Confirm the institution code and accession number, then verify that the phenotype or genotype sample lineage refers to that accession. A DOI is especially useful because, as the Genesys passport-data manual states, “A DOI is a permanent link that allows anyone to find the exact same record, regardless of the database they use.”
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Match the statistic to the biological question
Allelic richness, heterozygosity, sequence diversity and clustering summarize different properties. Allelic richness counts distinct alleles, often with sample-size sensitivity. Heterozygosity describes the probability that two sampled gene copies differ under a defined calculation. Sequence-diversity measures depend on the sequence region, alignment and filtering. A clustering plot summarizes relationships under a chosen distance or model.
There is no universal “diversity score” that ranks all crops, populations and datasets. A larger number can reflect more markers, more sampled individuals, a different rare-allele correction or a different definition rather than genuinely greater biological diversity.
Check population and sample scope
Record the number of accessions and individuals, their geographic origins, biological status and missingness. A study of one plant per accession answers a different question from a study of many plants per accession. The former is mainly suited to relationships among accessions; the latter can reveal variation within an accession or population.
Check marker and sequence coverage
Two SNP panels can produce different diversity estimates because they target different genomic regions, ascertain variants differently or apply different quality thresholds. SSR, SNP, DArT and sequence data should not be treated as interchangeable evidence. Compare datasets directly only when marker or sequence coverage and processing are sufficiently comparable, or explain why a cross-method comparison is still informative.
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Separate observed association from cause
If a genotype group is common among accessions with a trait, that is an association until an appropriate design supports causation. Population structure, relatedness, environment and management can create apparent links. Use evaluation data and molecular data together, while preserving the limits of each.
A framework for comparing accessions or datasets
| Question | What to verify | Why it changes the interpretation |
|---|---|---|
| Are the materials comparable? | Same taxon; wild relative, landrace, breeding line or cultivar status; same accession and sample lineage | Different biological categories and lineages can have different histories and levels of within-group variation |
| What evidence is being compared? | Passport, standardized characterization, environment-dependent evaluation, marker profile or sequence data | Each evidence type supports a different claim |
| How complete is coverage? | Number and representativeness of accessions and individuals; geographic scope; missing records | A small or biased sample can misrepresent collection-wide diversity |
| Are methods aligned? | Trait descriptor and conditions, or marker/sequence platform, genomic coverage, filtering and statistic | Method differences can change the measured value |
| Can the result be used? | Accession-level traceability and current requestability under legal and phytosanitary rules | An interesting candidate is not useful for a trial if its identity or access cannot be confirmed |
Do not rank portals by total accession count alone. A broad catalog improves discovery, while a smaller crop-specific dataset may offer better trait or genotype coverage for your question.
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Finding a candidate is not the same as obtaining planting material
After identifying an accession, contact or follow the holding genebank’s request process. Stock can be limited, under regeneration, unavailable in a particular season or subject to a material-transfer agreement. International movement may require treaty compliance, import permits, quarantine inspection and phytosanitary certificates. The holder’s current instructions control; an older general explanation of availability does not override them.
Keep the accession identifier on every request, label, shipment and experiment record. If a replacement sample is supplied, document the new lot or regeneration information so that the material used in the experiment remains traceable to the database record.
Common interpretation failures and fixes
Using a portal total as a sequenced-accession count
Failure: Treating 4.5 million Genesys records as 4.5 million accessions with genomic data.
Fix: Use the portal total to describe discovery scope, then count only the accessions and samples listed in the specific genotype dataset.
Assuming origin proves adaptation
Failure: Calling an accession drought adapted because it was collected in a dry region.
Fix: Treat origin as a selection hypothesis and seek evaluation data from a stated drought environment or a suitable genomic analysis.
Merging unlike phenotype records
Failure: Combining flowering-time values recorded with different definitions or seasons.
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Fix: Align descriptor, units, measurement date, location and protocol; analyze incompatible observations separately.
Joining by name only
Failure: Matching “Red rice 4” across files without an institution code.
Fix: Require holding institute plus accession identifier, and retain DOI or another persistent identifier where supplied.
Comparing diversity values without method details
Failure: Declaring one crop group more diverse because its reported number is larger.
Fix: Check sample size, marker or sequence method, coverage, filters and statistic before comparing; otherwise report the values as method-specific summaries.
Assuming a record guarantees seed
Failure: Designing a trial around a portal result without checking stock or shipping eligibility.
Fix: Confirm requestability and current legal and phytosanitary requirements with the holding institution before finalizing the design.
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Suppose a breeder wants candidates for a trait in a regional landrace collection. The following sequence keeps discovery, interpretation and acquisition distinct:
- Write the target as a measurable question, such as whether accessions from a defined region differ in a specified trait under a stated test environment.
- Search Genesys by crop or taxon and region, recording every candidate’s institution code and accession number. Search synonyms if the crop has changed taxonomic names.
- Open each holding institution’s record and remove entries whose biological status or taxon does not fit the study.
- Locate linked characterization or evaluation datasets. Save descriptor definitions, units, trial locations, seasons and stress protocols before comparing values.
- For molecular evidence, record the sampled individuals, marker or sequence platform, coverage, quality filtering and statistic. Confirm that sample labels map to the same accessions in the passport table.
- Compare only groups with compatible sampling and methods. Report the exact statistic and scope instead of creating a single overall ranking.
- Check the holder’s current stock and request terms, then document any lot, regeneration or replacement information when material arrives.
This workflow can identify a promising accession without overstating what the evidence proves: the trait observation remains environment-specific, and the molecular result remains sample- and method-specific.
Minimum metadata to save with an analysis
- Accession and institution identifiers, taxon and synonyms
- Persistent identifier or DOI, if supplied
- Source dataset name or identifier and retrieval date
- Trait descriptor, units, scoring rules and experimental conditions
- Genotyping or sequencing platform, genomic region or marker panel
- Individuals or accessions sampled, geographic origin and biological status
- Quality-control, missing-data and filtering decisions
- Statistic definition, software or model description and comparison groups
- Material-request status, lot information and applicable access conditions
FAO’s Genebank Standards (2014) captures the underlying principle: “All data and information generated throughout the process of acquisition, registration, storage, monitoring, regeneration, characterization, evaluation, and distribution should be recorded in a suitably designed database and employed to improve conservation and use of the germplasm.”
Quick Recap
Key takeaways for defensible comparisons
- Use Genesys for broad discovery, GRIN-Global for U.S. NPGS records, and the holding genebank for authoritative status and detailed data.
- Keep passport, characterization, evaluation and genotype evidence separate in your interpretation.
- Trace every result to an institution, accession and sample; preserve a DOI or other persistent identifier.
- Compare diversity statistics only after checking population scope, method, coverage and sampling context.
- Verify that material can actually be requested under current stock, legal and phytosanitary conditions.
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