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Expand Up @@ -21,7 +21,7 @@ The goals of this document are:
### Some examples of phenotype data

| Category | Example datasets | Example phenotype |
|-------------------------------------------|-----------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------|
|---|---|---|
| Gene to phenotype associations | [Online Mendelian Inheritance in Man (OMIM)](https://www.omim.org/), [Human Phenotype Ontology (HPO)](https://hpo.jax.org/app/), [Gene Ontology (GO)](http://geneontology.org/) | Achondroplasia (associated with FGFR3 gene mutations) |
| Gene to disease associations | [The Cancer Genome Atlas (TCGA)](https://www.cancer.gov/about-nci/organization/ccg/research/structural-genomics/tcga), [Online Mendelian Inheritance in Man (OMIM)](https://www.omim.org/), [GWAS Catalog](https://www.ebi.ac.uk/gwas/) | Breast invasive carcinoma (associated with BRCA1/BRCA2 mutations) |
| Phenotype-phenotype semantic similarity | [Human Phenotype Ontology (HPO)](https://hpo.jax.org/app/), [Unified Medical Language System (UMLS)](https://www.nlm.nih.gov/research/umls/index.html), [Disease Ontology (DO)](http://disease-ontology.org/) | Cardiac abnormalities (semantic similarity with congenital heart defects) |
Expand Down Expand Up @@ -308,7 +308,7 @@ Pre-coordinated phenotype data is popular in the clinical domain, where a lot of
}
```

Apart from clinical diagnostics, pre-coordinated phenotype terms are used in many other contexts such as model organism research (e.g. [IMPC](https://www.mousephenotype.org/)) or the curation of [Genome Wide Association Studies](https://www.ebi.ac.uk/gwas/)).
Apart from clinical diagnostics, pre-coordinated phenotype terms are used in many other contexts such as model organism research (e.g. [IMPC](https://www.mousephenotype.org/)) or the curation of [Genome Wide Association Studies](https://www.ebi.ac.uk/gwas/).

<a id="postcoordinated"></a>

Expand All @@ -320,16 +320,116 @@ For example, the phenotype space is _enormous_, as you can measure variations in

There are at least three flavours of post-coordinated phenotype curation prevalent in the biomedical domain, four if you count quantified phenotypes:

- [Trait + modifier](#traitmodifier)
- [Trait + modifier](#traitmodifier)
- [Bearer only](#beareronly)
- [Characteristics + modifier + bearer](#charmodbear)

Note that bearers can be arbitrarily complex.
<a id="traitmodifier"></a>

_Trait + modifier_ pattern is used for example by databases such as the [Saccharomyces Genome Database (SGD)](https://www.yeastgenome.org/observable/APO:0000106). Here are some examples:

| dateAssigned | evidence/publicationId | objectId | phenotypeStatement | phenotypeTermIdentifiers/0/termId | phenotypeTermIdentifiers/1/termId | conditionRelations/0/conditions/0/chemicalOntologyId | conditionRelations/0/conditions/0/conditionClassId |
|---|---|---|---|---|---|---|---|
| 2010-07-08T00:07:00-00:00 | PMID:1406694 | SGD:S000003901 | abnormal RNA accumulation | APO:0000002 | APO:0000224 | | |
| 2006-05-05T00:05:00-00:00 | PMID:785224 | SGD:S000000854 | decreased resistance to chemicals | APO:0000003 | APO:0000087 | CHEBI:78661 | ZECO:0000111 |
| 2010-07-07T00:07:00-00:00 | PMID:10545447 | SGD:S000000969 | decreased cell size | APO:0000003 | APO:0000052 | | |

- `APO:0000002` (abnormal) and `APO:0000003` (decreased) are modifiers.
- `APO:0000087` (resistance to chemicals), `APO:0000224` (RNA accumulation), `APO:0000052` (cell size) are biological attributes/traits.
- `CHEBI:78661` is recorded as an experimental condition, but should probably be interpreted as part of the bearer expression.
- Note: SGD has different kinds of phenotype data, and it should be carefully evaluated which one it is.

Data was obtained [from the Alliance of Genome Resources](https://fms.alliancegenome.org/download/PHENOTYPE_SGD.json.gz) on the 30.03.2023 and simplified for illustration.

<a id="beareronly"></a>

The _bearer-only_ pattern is used by many databases, such as [Flybase](https://flybase.org/reports/FBal0016988).
In the data, we only find references of bearers, such as anatomical entities or biological processes.
Instead of explicitly stating phenotypic modifiers (abnormal, morphology, changed), it is implicit in the definition of the dataset.

| dateAssigned | evidence/crossReference/id | evidence/publicationId | objectId | phenotypeStatement | phenotypeTermIdentifiers/0/termId |
|---|---|---|---|---|---|
| 2024-01-05T11:54:24-05:00 | FB:FBrf0052655 | PMID:2385293 | FB:FBal0016988 | embryonic telson | FBbt:00000184 |
| 2024-01-05T11:54:24-05:00 | FB:FBrf0058077 | PMID:8223248 | FB:FBal0001571 | larva | FBbt:00001727 |

- `FBbt:00000184` (embryonic telson) and `FBbt:00001727` (larva) are bearer terms.
- The modifier is implicit in the data rather than explicitly stated. For example, [Flybase states on their website about the Dmel\torrv66 Allele (FBal0016988)](https://flybase.org/reports/FBal0016988) that the "phenotype manifests in the embryonic telson".
- Note: FlyBase has different kinds of phenotype data (including pre-coordinated), and it should be carefully evaluated which one is which prior to integration.

Data was obtained [from the Alliance of Genome Resources](https://fms.alliancegenome.org/download/PHENOTYPE_FB.json.gz) on the 30.03.2023 and simplified for illustration.

<a id="charmodbear"></a>

The most complex pattern for phenotype descriptions which essentially decomposes the entire phenotype expression into atomic consituents can be found, for example, in the [The Zebrafish Information Network (ZFIN)](https://zfin.org/).

Examples:

| Fish ID | Affected Structure or Process 1 subterm ID | Affected Structure or Process 1 subterm Name | Post-composed Relationship ID | Post-composed Relationship Name | Affected Structure or Process 1 superterm ID | Affected Structure or Process 1 superterm Name | Phenotype Keyword ID | Phenotype Keyword Name | Phenotype Tag | Affected Structure or Process 2 subterm ID | Affected Structure or Process 2 subterm name | Post-composed Relationship (rel) ID | Post-composed Relationship (rel) Name | Affected Structure or Process 2 superterm ID | Affected Structure or Process 2 superterm name | Publication ID |
|-----------------------|--------------------------------------------|----------------------------------------------|-------------------------------|---------------------------------|----------------------------------------------|--------------------------------------------------|----------------------|-------------------------------------|---------------|--------------------------------------------|----------------------------------------------|-------------------------------------|---------------------------------------|----------------------------------------------|--------------------------------------------------|-------------------|
| ZDB-FISH-150901-29105 | ZFA:0009366 | hair cell | BFO:0000050 | part_of | ZFA:0000051 | otic vesicle | PATO:0000374 | increased distance | abnormal | ZFA:0009366 | hair cell | BFO:0000050 | part_of | ZFA:0000051 | otic vesicle | ZDB-PUB-171025-12 |
| ZDB-FISH-150901-29105 | ZFA:0009366 | hair cell | BFO:0000050 | part_of | ZFA:0000051 | otic vesicle | PATO:0000374 | increased distance | abnormal | ZFA:0009366 | hair cell | BFO:0000050 | part_of | ZFA:0000051 | otic vesicle | ZDB-PUB-171025-12 |
| ZDB-FISH-150901-11537 | | | | | ZFA:0000051 | otic vesicle | PATO:0001905 | has normal numbers of parts of type | normal | ZFA:0009366 | hair cell | BFO:0000050 | part_of | ZFA:0000051 | otic vesicle | ZDB-PUB-150318-1 |
| ZDB-FISH-150901-18770 | | | | | ZFA:0000119 | retinal inner nuclear layer | PATO:0002001 | has fewer parts of type | abnormal | ZFA:0009315 | horizontal cell | BFO:0000050 | part_of | ZFA:0000119 | retinal inner nuclear layer | ZDB-PUB-130222-28 |
| ZDB-FISH-190806-7 | BSPO:0000084 | ventral region | BFO:0000050 | part_of | ZFA:0000101 | diencephalon | PATO:0002001 | has fewer parts of type | abnormal | ZFA:0009301 | dopaminergic neuron | BFO:0000050 | part_of | ZFA:0000101 | diencephalon | ZDB-PUB-190216-5 |
| ZDB-FISH-190807-7 | BSPO:0000084 | ventral region | BFO:0000050 | part_of | ZFA:0000101 | diencephalon | PATO:0001905 | has normal numbers of parts of type | normal | ZFA:0009301 | dopaminergic neuron | BFO:0000050 | part_of | ZFA:0000101 | diencephalon | ZDB-PUB-190216-5 |
| ZDB-FISH-190807-8 | BSPO:0000084 | ventral region | BFO:0000050 | part_of | ZFA:0000101 | diencephalon | PATO:0002001 | has fewer parts of type | abnormal | ZFA:0009301 | dopaminergic neuron | BFO:0000050 | part_of | ZFA:0000101 | diencephalon | ZDB-PUB-190216-5 |
| ZDB-FISH-150901-29105 | | | | | ZFA:0000101 | diencephalon | PATO:0001555 | has number of | normal | ZFA:0009301 | dopaminergic neuron | BFO:0000050 | part_of | ZFA:0000101 | diencephalon | ZDB-PUB-161120-7 |
| ZDB-FISH-210421-9 | ZFA:0009290 | glutamatergic neuron | BFO:0000050 | part_of | ZFA:0000008 | brain | PATO:0040043 | increased proportionality to | abnormal | ZFA:0009276 | GABAergic neuron | BFO:0000050 | part_of | ZFA:0000008 | brain | ZDB-PUB-191011-2 |
| ZDB-FISH-210421-9 | ZFA:0009290 | glutamatergic neuron | BFO:0000050 | part_of | ZFA:0000008 | brain | PATO:0040043 | increased proportionality to | abnormal | ZFA:0009276 | GABAergic neuron | BFO:0000050 | part_of | ZFA:0000008 | brain | ZDB-PUB-191011-2 |

Lets break down the second to last row:

- ZFA:0009290 (glutamatergic neuron)
- BFO:0000050 (part of): a relation used to connect the hair cell to the structure its part of)
- ZFA:0000008 (brain)
- PATO:0040043 (increased proportionality to): the modified characteristic being observed.
- abnormal (the change modifier)
- ZFA:0009276 (GABAergic neuron)
- ZFA:0000008 (brain)
- The interested reader may look at an integrated version of that huge post-coordinated expression [here (brain increased proportionality to glutamatergic neuron GABAergic neuron brain, abnormal - ZP:0141834)](https://www.ebi.ac.uk/ols4/ontologies/zp/classes/http%253A%252F%252Fpurl.obolibrary.org%252Fobo%252FZP_0141834).

Data was obtained [from ZFIN](https://zfin.org/downloads) (Phenotype of Zebrafish Genes) on the 30.03.2023 and simplified for illustration.

As one can see in the last example, bearers can be anything from simple atomic entities to arbitrarily complex compositions:

- "lysine" (`lysine`)
- "lysine in the blood" (`lysine` part_of `blood`)
- "lysine in heart muscle cells" (`lysine` part_of `cell` part_of (`muscle` part of `heart`))
- "lysine in the cytoplasm of heart muscle cells" (`lysine` part_of (`cytoplasm` part_of (`cell` part_of (`muscle` part of `heart`))))
- etc, etc

<a id="standardized"></a>

#### Standardised/non-standardized

Phenotype data can be standardised to varying degrees. It is not uncommon for data to be completely unstandardised.
Unfortunately, only a fraction of the available data is actually annotated using terms from controlled phenotype ontologies.
Here are some of the more "typical" kinds of data on the standardised/non-standardised spectrum:

1. Free text in clinical notes and scientific publications
1. Free text in specific database fields (for example a "height" column in a table about measurements of Giraffes)
1. Controlled but non-standardised vocabulary like enums in a datamodel (for example the keyword "abnormal" in the [ZFIN example above](#charmodbear))
1. Controlled standardised vocabulary (like all the examples on this page)
1. Ontology terms (controlled vocabulary terms with well defined semantics - all the examples on this page)

<a id="qual"></a>

#### Quantitative/qualitative

Qualitative and quantitative phenotype data represent two fundamental ways of describing characteristics or traits in biology, each providing different types of information:

Qualitative Phenotype Data:

- Nature: This type of data describes qualities or characteristics that are observed but not measured with numbers. It often involves categorical or descriptive information.
- Examples: The presence or absence of a specific physical trait (like eye color or wing shape in animals) or types of behavior (aggressive vs. passive).
- Analysis: Qualitative data is analyzed by categorization and identification of patterns or variations. It is more about the 'type' or 'kind' of trait rather than its 'amount'.
- Interpretation: Since it's descriptive, this data relies on subjective interpretation and classification.

Quantitative Phenotype Data:

- Nature: This data is numerical and quantifies traits. It involves measurements of characteristics, often allowing for more precise and objective analysis.
- Examples: Height, weight, blood pressure, cholesterol levels, or the number of fruit produced by a plant. Quantitative traits can often be measured on a continuous scale, for example height of 35 cm, weight of 67 KG or blood pressure of 120/80.
- Analysis: It involves statistical analysis, such as calculating mean, median, standard deviation, and applying various statistical tests. It allows for a more objective and replicable assessment.
- Interpretation: Quantitative data provides a more concrete and measurable understanding of traits, making comparisons and statistical testing more straightforward.

Qualitative data is descriptive and categorical, while quantitative data is numerical and measurable. Both types are essential for a comprehensive understanding of phenotypic traits, each offering unique insights into biological variation and complexity.

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