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Phenotype / Trait

vr.wm-liv-015 · wm-liv-015-phenotype-trait

Describe a traceable assertion about a biological trait for a specified subject and context, preserving the distinction between a trait concept, evidence and an asserted result.

World Models Physical world and living systems PHY.LIV.PHN
Bundle → Layer → Finding → QuestionsClassifiersWhat it isWhy it existsDistinguishing featuresWhat robots and AI may and may not doMoral aspectsOwnersLinks to other meta-modelsIdentity and identifiersDirect propertiesRecognitionCapabilities and actionsHazards and failure modesStandards and interfacesContext of useSources

Bundle → Layer → Finding → Questions Filled

3 bundles · 3 layers · 5 findings · 10 questions

Trait definition What is being observed.

Trait and method

The trait concept, its measurement method and its scale.

Trait concept

The trait named with an ontology term.

  1. Which trait is observed, and which ontology term identifies it?
  2. Is the trait quantitative, ordinal or categorical?

Method and scale

How the trait is measured and on which scale or unit.

  1. Which measurement method and instrument were used?
  2. In which unit or scale is the value expressed?
Observation What was observed on which organism.

Observed value

A value for one organism at one time.

Observation record

The organism, value, date and observer.

  1. Which organism or sample was observed, and on which date?
  2. What value was recorded, and who recorded it?

Conditions

Environment and developmental stage at observation.

  1. At which developmental stage or age was the organism observed?
  2. Under which environment, treatment or management was it kept?
Use and linkage How the trait is used.

Genetic and clinical linkage

Links to genotypes, breeding values or clinical findings.

Linkage

Associations with genotype, pedigree or health records.

  1. Is the observation linked to a genotype or pedigree record?
  2. Is the observation used for selection decisions or clinical care, and with which consent?

Classifiers Filled

Family
World Models
Category
Physical world and living systems
Entry kind
entity
Navigation path
NAV.PHY.LIV.PHN
Domain
PHY.LIV.PHN
Industry
Cross-industry
Tags
phenotypetraitphy.liv.phn

What it is Filled

A phenotype trait is an observable characteristic of an organism, such as height, yield, colour, behaviour or a clinical sign, recorded as a trait definition and as observed values for individuals under stated conditions. It links organisms to health, breeding and research; the underlying genes and variants, and diagnoses of disease, are separate subjects.

In scope

  • Assertion identity and subject resolution; trait and variable references with versioned meaning.
  • Result form, assessment status, method, time, anatomy, developmental stage, environment and uncertainty.
  • Evidence links, correction lineage, local record governance and qualified exchange projections.

Out of scope

  • Master organism, taxon, specimen, population, genome, disease and study lifecycles.
  • Clinical diagnosis, treatment, breeding or selection decisions, genetic causation and biological interventions.
  • Authoring a universal ontology, executing instruments, association analysis or inferring sensitive traits from unrelated personal data.

Why it exists Filled

Describe a traceable assertion about a biological trait for a specified subject and context, preserving the distinction between a trait concept, evidence and an asserted result.

Distinguishing features Filled

  • A trait is observed, not inferred from genes, and its value depends on environment and stage.
  • The trait definition and the observed value are separate records, so values from different studies can be compared.
  • It attaches to individual organisms, while a population or breed record summarizes many.
  • Distinct from a disease or diagnosis, which is a clinical judgement that may draw on traits.

What robots and AI may and may not do Filled

Must not

  • Infer a diagnosis from human phenotype data in place of a clinician.
  • Combine human phenotype and genetic data without consent and a lawful basis.
  • Drop the method, unit or environment from an observed value.
  • Present predicted trait values as observations.

Only with a human decision

  • Selection or culling decisions in breeding based on traits.
  • Clinical interpretation of human phenotypes.
  • Sharing identifiable human phenotype data with third parties.

May

  • Record trait observations with method, unit, stage and environment.
  • Map local trait names to ontology terms.
  • Summarize trait distributions across a population or trial.
  • Flag values outside plausible ranges for review.

Moral aspects Filled

  • Human phenotype data is health data and can identify people and families.
  • Selection on traits affects animal welfare and genetic diversity.
  • Traits must not be used to support discrimination or eugenic claims.

Who is affected

  • Patients and research participants
  • Animals and plants under selection
  • Breeders, farmers and researchers

Owners Filled

Steward

The research group, breeding programme or clinical service that made the observation, under the data policy of its institution.

Roles

Data steward
Sets local scope, mastership and disposition policy.
Observer or importer
Records method, source, uncertainty and acquisition mode without inventing facts.
Domain reviewer
Checks trait meaning, subject scope, evidence and corrections; clinical judgments stay in the responsible clinical system.
Access custodian
Approves recipient-purpose scope and exceptions under the applicable policy.
Projection maintainer
Pins mapping versions and reports losses and unsupported fields.

Master systems

  • Breeding programme database
  • Clinical phenotype record
  • Research phenotype repository

Links to other meta-models Filled

references

  • WM-LIV-002 - Conditional organism subject or source binding; registry parent is not inheritance.
  • WM-LIV-023 - Immediate specimen and provenance reference; specimen custody remains external.
  • WM-LIV-003 - Population or pooled-subject context; never assign its summary to every individual.
  • WM-LIV-001 - Taxonomic interpretation context with externally governed identity.
  • WM-LIV-012 - Optional genomic interpretation link, without variant assessment or causation inference.
  • WM-LIV-021 - Optional disease interpretation link, without making a diagnosis.

aligned

  • GA4GH Phenopackets 2.0 - Profile-dependent exchange of feature assertions and measurements; test missing-state projection.
  • MIAPPE and Crop Ontology - Plant method, scale, unit and context alignment; pin releases before implementation.
  • SOSA/SSN and PROV-O - Conceptual observation and lineage references; no RDF conformance claimed.

neighbor

  • WM-LIV-002 Organism Individual - Registry parent is contextual association, not is-a inheritance: an assertion is about an organism and is not an organism. The relation ledger contains no rows for this model. Require a subject binding; individual binding is conditional because a specimen or biological plot can be the immediate subject.
  • Trait vocabularies and observation variables - Reuse versioned definitions. The root stores a reference and a meaning snapshot, not authority to redefine the source ontology. A label match does not prove equivalence.
  • WM-LIV-023 Clinical / Biological Specimen and WM-LIV-003 Population - Keep immediate subject, source organism and aggregation level distinct. Sample findings and plot summaries cannot automatically be attributed to each contributing organism. Pure environmental observations are outside this biological assertion model.
  • WM-LIV-012 Genomic Sequence / Variant and WM-LIV-021 Disease / Biological Condition - Carry optional externally governed links to interpretations. A feature, measurement or association is not a diagnosis or proof that a variant causes a trait.
  • Study, instrument, policy and provenance services - Keep external identifiers and subject-specific context only. Local functions manipulate records; they do not operate devices, perform experiments, authorize disclosure or implement external audit infrastructure.

parent

  • WM-LIV-002

What else AI and robots need to interact with it Filled

Identity and identifiers required Filled

  • Traits are identified by ontology terms such as Human Phenotype Ontology, Crop Ontology or Trait Ontology identifiers.
  • Observations are identified by study, observation unit and date identifiers, as in MIAPPE.

Direct properties required Filled

  • Quantitative trait values with SI units, such as height in centimetres or body mass in kilograms.
  • Ordinal scores on a named scale, with the scale definition.
  • Developmental stage or age at observation, in days or a named stage scale.
  • Environmental conditions at observation, such as temperature in degrees Celsius and location.

Recognition required Filled

  • A trait is recognised by measurement, imaging or expert scoring of the organism.
  • Environmental effects, measurement error and observer bias can mimic genetic differences.

Capabilities and actions required Filled

  • Trait values can be measured repeatedly over the life of an organism.
  • Observations can be aggregated across trials and linked to genotypes for association studies.

Hazards and failure modes required Filled

  • Re-identification of people from rare phenotypes.
  • Wrong selection or care decisions from mis-scored traits.
  • Handling risks when measuring large animals.

Standards and interfaces required Filled

  • MIAPPE for plant phenotyping experiments.
  • Human Phenotype Ontology and Phenopackets for human phenotype exchange.
  • Crop Ontology trait dictionaries and the BrAPI breeding API.

Context of use required Filled

  • Used in plant and animal breeding, clinical genetics, ecology and biomedical research.
  • Human phenotype data falls under health data protection law.

Sources Filled

  1. PhenotypicFeature - Phenopacket schema - Global Alliance for Genomics and Health
  2. Measurement - Phenopacket schema - Global Alliance for Genomics and Health
  3. How does the HPO define phenotype? - Human Phenotype Ontology consortium
  4. MIAPPE checklist and data model - MIAPPE community
  5. Crop Ontology Pages - Trait Dictionary V5 - Planteome project
  6. The Unified Code for Units of Measure - UCUM Organization
  7. Semantic Sensor Network Ontology - World Wide Web Consortium
  8. PROV-O: The PROV Ontology - World Wide Web Consortium
  9. Framework for responsible sharing of genomic and health-related data - Global Alliance for Genomics and Health
  10. Human Phenotype Ontology
  11. MIAPPE Minimum Information About a Plant Phenotyping Experiment
  12. GA4GH Phenopackets (Global Alliance for Genomics and Health)

Open questions

  • Pin and independently verify source editions, ontology and scale releases, source licenses and adopting model bindings.
  • Build executable profiles and adversarial fixtures for negative versus missing results, repeated versus corrected observations, unit conversion, pooled subjects, uncertain time and restricted evidence.
  • Validate species-specific and jurisdiction-specific uses with domain and data-governance reviewers before operational adoption.
  • Restore independent external review before any canonical or publishable-draft promotion.
  • Executable nested instance schemas, controlled-state validation, source profile mappings and adversarial round-trip fixtures.
  • Pinned neighbor model versions, vocabulary releases, licenses and independently checked source availability.
  • Animal, microbial, ecological and species-specific reference ranges and measurement uncertainty profiles.
  • Jurisdiction-specific privacy, consent, retention, data sovereignty and clinical or breeding governance review.

Machine files

Card as JSON Specification AGENTS.md Source Card backlog vercy.card/1

Provenance

world-models research · reviewable-draft

Built from: models/wm-liv-015-phenotype-trait/spec.yaml, ver-cy/world-models/card-supplements/wm-liv-015-phenotype-trait.json