List of participants and affiliations:
- Melissa Landrum, NIH/NCBI (Co-Team Leader)
- Guangfeng Song, NIH/NCBI (Co-Team Leader)
- Lauren Edgar, NIH/NHGRI
- Benjamin Kesler, Vanderbilt University
- Nicholas Minor, University of Wisconsin
- Michael Muchow, Unaffiliated
- Rebecca Orris, NIH/NCBI
- Wengang Zhang, NIH/NCI
Naming for human genetic diseases is complex. Diseases may be named for the phenotype; other information may be alluded to including the relevant gene, mode of inheritance, or the mechanism of disease. Diseases may be described at a high level with a generic name, or at a lower level with a more specific name; however, in the context of a variant in a specific gene, these differences may not be considered important. ClinVar data would be easier to ingest in bulk and to read in web displays if there were a meaningful way to aggregate diseases that effectively mean the same thing in the context of a gene.
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Problem Statement: Diseases in ClinVar are very granular and result in many variant-disease records.
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Can we use an ML/AI approach to aggregate disease terms in ClinVar to reduce the number of variant-disease records?
Example of granularity for Familial Hypercholesterolemia
Develop a ML/AI approach to aggregating diseases for 5 genes with variants in ClinVar: LDLR, KCNQ1, USH2A, SCN5A, TSC1.
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Use the gene symbol to guide whether different terms are meaningfully different for that gene, e.g. Familial hypercholesterolemia vs Hypercholesterolemia, familial, 1
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Use other information such as mode of inheritance, clinical features, and mechanism of disease to decide if terms should be aggregated or not
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Demonstrate how RCV records for variant-condition pairs in ClinVar for one or more genes would be different using aggregated diseases
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Extract disease names from all RCV records associated with a single gene
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Extract all unique MedGen ID and their disease names
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Cluster the name into their umbrella disease category
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Program LLM to cluster the disease terms
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Fork https://github.com/simonw/llm-cluster/blob/main/llm_cluster.py
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Identify variant records with similar disease names that belong to the same umbrella diseases
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Assign those RCV records with the corrected (umbrella) name
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Evaluate and test other LLMs with more training in biomedical knowledge to reduce the amount of curation needed
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Generate a test set of ClinVar data using aggregated disease terms
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Review test data set with ClinVar users to determine the value added and areas to improve
| Abbreviation | Acronym |
|---|---|
| ACMG | American College of Medical Genetics and Genomics |
| CGV | Comparative Genome Viewer |
| DBSCAN | Density-Based Spatial Clustering of Applications with Noise |
| GPT | Generative Pre-trained Transformer |
| HGNC | HUGO Gene Nomenclature Committee |
| HGVS | Human Genome Variation Society |
| HI | Haploinsufficiency |
| HUGO | Human Genome Organization |
| LLM | Large Language Model |
| MeSH | Medical Subject Headings |
| NGS | Next-Generation Sequencing |
| NLP | Natural Language Processing |
| OMIM | Online Mendelian Inheritance in Man |
| PMC | PubMed Central |
| RCV | Reference (variant-condition) ClinVar Variant Accession Identifier |
| SCV | Submitted ClinVar Variant Accession Identifier |
| SNP | Single-Nucleotide Polymorphism |
| SRA | Sequence Read Archive |
| TS | Triplosensitivity |
| VUS | Variant of Uncertain Significance |
| VCV | Variant ClinVar Variant Accession Identifier |
This software was created as part of an NCBI codeathon, a hackathon-style event focused on rapid innovation. While we encourage you to explore and adapt this code, please be aware that NCBI does not provide ongoing support for it.
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