alterlab-clinvar

ClinVar Database

Overview

ClinVar is NCBI's freely accessible archive of reports on relationships between human genetic variants and phenotypes, with supporting evidence. The database aggregates information about genomic variation and its relationship to human health, providing standardized variant classifications used in clinical genetics and research.

When to Use This Skill

This skill should be used when:

Searching for variants by gene, condition, or clinical significance
Interpreting clinical significance classifications (pathogenic, benign, VUS)
Accessing ClinVar data programmatically via E-utilities API
Downloading and processing bulk data from FTP
Understanding review status and star ratings
Resolving conflicting variant interpretations
Annotating variant call sets with clinical significance

Does NOT Trigger

Scenario	Use Instead
Population allele frequency / ancestry-specific AF, gnomAD constraint	`alterlab-gnomad`
Somatic cancer mutation catalog, Cancer Gene Census, mutational signatures	`alterlab-cosmic`
How often a gene is mutated in tumor cohorts (TCGA, MSK-IMPACT)	`alterlab-cbioportal`
Pharmacogenomic gene–drug dosing (CPIC/DPWG)	`alterlab-clinpgx`

Three classification types (since 2024). ClinVar now keeps separate aggregate classifications for germline pathogenicity (ACMG/AMP), somatic clinical impact (AMP/ASCO/CAP tiers), and oncogenicity (ClinGen/CGC/VICC). APIs and files expose them separately: esummary JSON has germline_classification, clinical_impact_classification, and oncogenicity_classification objects (each with description and review_status; there is no longer a clinical_significance field); the VCF has CLNSIG/CLNREVSTAT (germline), ONC/ONCREVSTAT, and SCI/SCIREVSTAT; variant_summary.txt has ClinicalSignificance (germline) plus SomaticClinicalImpact and Oncogenicity columns.

Core Capabilities

1. Search and Query ClinVar

Web Interface Queries

Search ClinVar using the web interface at https://www.ncbi.nlm.nih.gov/clinvar/

Common search patterns (field tags verified against the live einfo field list — there is no [CLNSIG] or [RVSTAT] field; using them silently falls back to [All Fields] and does NOT filter):

By gene: BRCA1[gene]
By clinical significance: clinsig_pathogenic[Properties] (also clinsig_likely_pathogenic, clinsig_benign, clinsig_likely_benign, clinsig_vus, clinsig_has_conflicts — note VUS is clinsig_vus; clinsig_uncertain matches nothing)
By review status: "reviewed by expert panel"[Review status], "practice guideline"[Review status], "criteria provided, single submitter"[Review status]
By condition: "breast cancer"[Disease/Phenotype]
By variant: "c.1310_1313del"[Variant name]
By chromosome: 13[chr]
Combined: BRCA1[gene] AND clinsig_pathogenic[Properties]

Programmatic Access via E-utilities

Access ClinVar programmatically using NCBI's E-utilities API. Refer to references/api_reference.md for comprehensive API documentation including:

esearch - Search for variants matching criteria
esummary - Retrieve variant summaries
efetch - Download full XML records
elink - Find related records in other NCBI databases

Quick example using curl:

# Search for pathogenic BRCA1 variants
curl "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi?db=clinvar&term=BRCA1[gene]+AND+clinsig_pathogenic[Properties]&retmode=json"

Always inspect the querytranslation field in the response: if a [...] tag is unknown, NCBI rewrites it to [All Fields] and your filter is silently dropped.

Best practices:

Test queries on the web interface before automating
Use API keys to increase rate limits from 3 to 10 requests/second
Implement exponential backoff for rate limit errors
Set Entrez.email when using Biopython

2. Interpret Clinical Significance

Understanding Classifications

ClinVar uses standardized terminology for variant classifications. Refer to references/clinical_significance.md for detailed interpretation guidelines.

Key germline classification terms (ACMG/AMP):

Pathogenic (P) - Variant causes disease (~99% probability)
Likely Pathogenic (LP) - Variant likely causes disease (~90% probability)
Uncertain Significance (VUS) - Insufficient evidence to classify
Likely Benign (LB) - Variant likely does not cause disease
Benign (B) - Variant does not cause disease

Review status (star ratings):

★★★★ Practice guideline - Highest confidence
★★★ Expert panel review (e.g., ClinGen) - High confidence
★★ Multiple submitters, no conflicts - Moderate confidence
★ Criteria provided, single submitter — or criteria provided, conflicting classifications
☆ No assertion criteria / no classification provided - Low confidence

Critical considerations:

Always check review status - prefer ★★★ or ★★★★ ratings
Conflicting interpretations require manual evaluation
Classifications may change as new evidence emerges
VUS (uncertain significance) variants lack sufficient evidence for clinical use

3. Download Bulk Data from FTP

Access ClinVar FTP Site

Download complete datasets from https://ftp.ncbi.nlm.nih.gov/pub/clinvar/ (same tree over HTTPS or FTP)

Refer to references/data_formats.md for comprehensive documentation on file formats and processing.

Update schedule:

Monthly releases: First Thursday of each month (complete dataset, archived)
Weekly updates: Every Monday (incremental updates)

Available Formats

XML files (most comprehensive; current format since 2025-08-07, with separate germline / somatic elements):

VCV (Variation) files: xml/ClinVarVCVRelease_YYYY-MM.xml.gz (+ _00-latest symlink) - Variant-centric aggregation
RCV (Record) files: xml/RCV_release/ClinVarRCVRelease_YYYY-MM.xml.gz - Variant-condition pairs
The old ClinVarVariationRelease / ClinVarFullRelease formats (single classification element) are frozen in xml/VCV_xml_old_format/ and xml/RCV_xml_old_format/ and no longer supported
Include full submission details, evidence, and metadata

VCF files (for genomic pipelines):

GRCh37: vcf_GRCh37/clinvar.vcf.gz
GRCh38: vcf_GRCh38/clinvar.vcf.gz
Limitations: Excludes variants >10kb and complex structural variants

Tab-delimited files (for quick analysis):

tab_delimited/variant_summary.txt.gz - Summary of all variants
tab_delimited/var_citations.txt.gz - PubMed citations
tab_delimited/cross_references.txt.gz - Database cross-references

Example download:

# Download latest monthly VCV XML release
wget https://ftp.ncbi.nlm.nih.gov/pub/clinvar/xml/ClinVarVCVRelease_00-latest.xml.gz

# Download VCF for GRCh38
wget https://ftp.ncbi.nlm.nih.gov/pub/clinvar/vcf_GRCh38/clinvar.vcf.gz

4. Process and Analyze ClinVar Data

Working with XML Files

Process XML files to extract variant details, classifications, and evidence.

Python example with xml.etree:

import gzip
import xml.etree.ElementTree as ET

with gzip.open('ClinVarVCVRelease_00-latest.xml.gz', 'rt') as f:
    for event, elem in ET.iterparse(f, events=('end',)):
        if elem.tag == 'VariationArchive':
            variation_id = elem.attrib.get('VariationID')
            # Aggregate germline call lives under ClassifiedRecord/Classifications
            germline = elem.find('ClassifiedRecord/Classifications/GermlineClassification')
            if germline is not None:
                classification = germline.findtext('Description')      # e.g. "Pathogenic/Likely pathogenic"
                review_status = germline.findtext('ReviewStatus')
            elem.clear()  # Free memory

Working with VCF Files

Annotate variant calls or filter by clinical significance using bcftools or Python.

Using bcftools:

# Filter pathogenic variants
bcftools view -i 'INFO/CLNSIG~"Pathogenic"' clinvar.vcf.gz

# Extract specific genes
bcftools view -i 'INFO/GENEINFO~"BRCA"' clinvar.vcf.gz

# Annotate your VCF with ClinVar
bcftools annotate -a clinvar.vcf.gz -c INFO your_variants.vcf

Using pysam in Python (the old PyVCF/import vcf package is unmaintained and breaks on Python 3.10+; use pysam or cyvcf2 instead):

import pysam

vcf = pysam.VariantFile("clinvar.vcf.gz")
for rec in vcf:
    # CLNSIG is a comma/pipe-delimited string (e.g. "Pathogenic/Likely_pathogenic");
    # match as substring, not equality.
    clnsig = str(rec.info.get("CLNSIG", ""))
    if "Pathogenic" in clnsig:
        gene = rec.info.get("GENEINFO", "")
        print(f"{rec.chrom}:{rec.pos} {gene} - {clnsig}")

Working with Tab-Delimited Files

Use pandas or command-line tools for rapid filtering and analysis.

Using pandas:

import pandas as pd

# Load variant summary (one row per variant *per assembly* — keep one build,
# or every count is doubled)
df = pd.read_csv('variant_summary.txt.gz', sep='\t', compression='gzip', low_memory=False)
df = df[df['Assembly'] == 'GRCh38']

# Filter pathogenic variants in specific gene
pathogenic_brca = df[
    (df['GeneSymbol'] == 'BRCA1') &
    (df['ClinicalSignificance'].str.contains('Pathogenic', na=False))
]

# Count variants by clinical significance
sig_counts = df['ClinicalSignificance'].value_counts()

Using command-line tools:

# Extract pathogenic TP53 variants (GRCh38 rows). Columns: 3 Name, 5 GeneSymbol,
# 7 ClinicalSignificance (germline), 17 Assembly, 25 ReviewStatus, 31 VariationID
zcat variant_summary.txt.gz | \
  awk -F'\t' '$5=="TP53" && $7~/Pathogenic/ && $17=="GRCh38"' | \
  cut -f3,5,7,25,31

5. Handle Conflicting Interpretations

When multiple submitters provide different classifications for the same variant, ClinVar reports "Conflicting classifications of pathogenicity" (renamed from "Conflicting interpretations" in 2024; VCF value Conflicting_classifications_of_pathogenicity, review status "criteria provided, conflicting classifications").

Resolution strategy:

Check review status (star rating) - higher ratings carry more weight
Examine evidence and assertion criteria from each submitter
Consider submission dates - newer submissions may reflect updated evidence
Review population frequency data (e.g., gnomAD) for context
Consult expert panel classifications (★★★) when available
For clinical use, always defer to a genetics professional

Search query to exclude conflicts:

TP53[gene] AND clinsig_pathogenic[Properties] NOT clinsig_has_conflicts[Properties]

6. Track Classification Updates

Variant classifications may change over time as new evidence emerges.

Why classifications change:

New functional studies or clinical data
Updated population frequency information
Revised ACMG/AMP guidelines
Segregation data from additional families

Best practices:

Document ClinVar version and access date for reproducibility
Re-check classifications periodically for critical variants
Subscribe to ClinVar mailing list for major updates
Use monthly archived releases for stable datasets

7. Submit Data to ClinVar

Organizations can submit variant interpretations to ClinVar.

Submission methods:

Web submission portal: https://submit.ncbi.nlm.nih.gov/clinvar/
API submission (requires service account): See references/api_reference.md
Batch submission via Excel templates

Requirements:

Organizational account with NCBI
Assertion criteria (preferably ACMG/AMP guidelines)
Supporting evidence for classification

Contact: clinvar@ncbi.nlm.nih.gov for submission account setup.

Workflow Examples

Example 1: Identify High-Confidence Pathogenic Variants in a Gene

Objective: Find pathogenic variants in CFTR gene with expert panel review.

Steps:

Search using web interface or E-utilities:

CFTR[gene] AND clinsig_pathogenic[Properties] AND ("reviewed by expert panel"[Review status] OR "practice guideline"[Review status])

Review results, noting review status (should be ★★★ or ★★★★)
Export variant list or retrieve full records via efetch
Cross-reference with clinical presentation if applicable

Example 2: Annotate VCF with ClinVar Classifications

Objective: Add clinical significance annotations to variant calls.

Steps:

Download appropriate ClinVar VCF (match genome build: GRCh37 or GRCh38):

wget https://ftp.ncbi.nlm.nih.gov/pub/clinvar/vcf_GRCh38/clinvar.vcf.gz
wget https://ftp.ncbi.nlm.nih.gov/pub/clinvar/vcf_GRCh38/clinvar.vcf.gz.tbi

Annotate using bcftools:

bcftools annotate -a clinvar.vcf.gz \
  -c INFO/CLNSIG,INFO/CLNDN,INFO/CLNREVSTAT \
  -o annotated_variants.vcf \
  your_variants.vcf

Filter annotated VCF for pathogenic variants:

bcftools view -i 'INFO/CLNSIG~"Pathogenic"' annotated_variants.vcf

Example 3: Analyze Variants for a Specific Disease

Objective: Study all variants associated with hereditary breast cancer.

Steps:

Search by condition:

"hereditary breast cancer"[Disease/Phenotype] OR "Breast-ovarian cancer, familial"[Disease/Phenotype]

Download results as CSV or retrieve via E-utilities
Filter by review status to prioritize high-confidence variants
Analyze distribution across genes (BRCA1, BRCA2, PALB2, etc.)
Examine variants with conflicting interpretations separately

Example 4: Bulk Download and Database Construction

Objective: Build a local ClinVar database for analysis pipeline.

Steps:

Download monthly release for reproducibility:

wget https://ftp.ncbi.nlm.nih.gov/pub/clinvar/xml/ClinVarVCVRelease_YYYY-MM.xml.gz
# previous years' monthly files move to xml/archive/<year>/

Parse XML and load into database (PostgreSQL, MySQL, MongoDB)
Index by gene, position, clinical significance, review status
Implement version tracking for updates
Schedule monthly updates from FTP site

Important Limitations and Considerations

Data Quality

Not all submissions have equal weight - Check review status (star ratings)
Conflicting interpretations exist - Require manual evaluation
Historical submissions may be outdated - Newer data may be more accurate
VUS classification is not a clinical diagnosis - Means insufficient evidence

Scope Limitations

Not for direct clinical diagnosis - Always involve genetics professional
Population-specific - Variant frequencies vary by ancestry
Incomplete coverage - Not all genes or variants are well-studied
Version dependencies - Coordinate genome build (GRCh37/GRCh38) across analyses

Technical Limitations

VCF files exclude large variants - Variants >10kb not in VCF format
Rate limits on API - 3 req/sec without key, 10 req/sec with API key
File sizes - Full XML releases are multi-GB compressed files
No real-time updates - Website updated weekly, FTP monthly/weekly

Resources

Reference Documentation

This skill includes comprehensive reference documentation:

references/api_reference.md - Complete E-utilities API documentation with examples for esearch, esummary, efetch, and elink; includes rate limits, authentication, and Python/Biopython code samples
references/clinical_significance.md - Detailed guide to interpreting clinical significance classifications, review status star ratings, conflict resolution, and best practices for variant interpretation
references/data_formats.md - Documentation for XML, VCF, and tab-delimited file formats; FTP directory structure, processing examples, and format selection guidance

External Resources

ClinVar home: https://www.ncbi.nlm.nih.gov/clinvar/
ClinVar documentation: https://www.ncbi.nlm.nih.gov/clinvar/docs/help/
E-utilities documentation: https://www.ncbi.nlm.nih.gov/books/NBK25501/
ACMG variant interpretation guidelines: Richards et al., 2015 (PMID: 25741868)
ClinGen expert panels: https://clinicalgenome.org/

Contact

For questions about ClinVar or data submission: clinvar@ncbi.nlm.nih.gov

Scripts

scripts/query_clinvar.py — runnable helper for ClinVar via NCBI E-utilities (no key; NCBI_API_KEY lifts the rate limit):

python scripts/query_clinvar.py search "BRCA1[gene] AND clinsig_pathogenic[Properties]" --retmax 5
python scripts/query_clinvar.py summary 12345,12346

About this skill

Overview

When to Use This Skill

Does NOT Trigger

Core Capabilities

1. Search and Query ClinVar

Web Interface Queries

Programmatic Access via E-utilities

2. Interpret Clinical Significance

Understanding Classifications

3. Download Bulk Data from FTP

Access ClinVar FTP Site

Available Formats

4. Process and Analyze ClinVar Data

Working with XML Files

Working with VCF Files

Working with Tab-Delimited Files

5. Handle Conflicting Interpretations

6. Track Classification Updates

7. Submit Data to ClinVar

Workflow Examples

Example 1: Identify High-Confidence Pathogenic Variants in a Gene

Example 2: Annotate VCF with ClinVar Classifications

Example 3: Analyze Variants for a Specific Disease

Example 4: Bulk Download and Database Construction

Important Limitations and Considerations

Data Quality

Scope Limitations

Technical Limitations

Resources

Reference Documentation

External Resources

Contact

Scripts