clinvar-database

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

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:

• By gene: BRCA1[gene]
• By clinical significance: pathogenic[CLNSIG]
• By condition: breast cancer[disorder]
• By variant: NM_000059.3:c.1310_1313del[variant name]
• By chromosome: 13[chr]
• Combined: BRCA1[gene] AND pathogenic[CLNSIG]

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+pathogenic[CLNSIG]&retmode=json"

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
• ★ Single submitter with criteria - Standard weight
• ☆ No assertion criteria - 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 ftp://ftp.ncbi.nlm.nih.gov/pub/clinvar/

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):

• VCV (Variation) files: xml/clinvar_variation/ - Variant-centric aggregation
• RCV (Record) files: xml/RCV/ - Variant-condition pairs
• 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 XML release
wget ftp://ftp.ncbi.nlm.nih.gov/pub/clinvar/xml/clinvar_variation/ClinVarVariationRelease_00-latest.xml.gz

# Download VCF for GRCh38
wget ftp://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('ClinVarVariationRelease.xml.gz', 'rt') as f:
    for event, elem in ET.iterparse(f, events=('end',)):
        if elem.tag == 'VariationArchive':
            variation_id = elem.attrib.get('VariationID')
            # Extract clinical significance, review status, etc.
            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 PyVCF in Python:

import vcf

vcf_reader = vcf.Reader(filename='clinvar.vcf.gz')
for record in vcf_reader:
    clnsig = record.INFO.get('CLNSIG', [])
    if 'Pathogenic' in clnsig:
        gene = record.INFO.get('GENEINFO', [''])[0]
        print(f"{record.CHROM}:{record.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
df = pd.read_csv('variant_summary.txt.gz', sep='\t', compression='gzip')

# 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 variants for specific gene
zcat variant_summary.txt.gz | \
  awk -F'\t' '$7=="TP53" && $13~"Pathogenic"' | \
  cut -f1,5,7,13,14

5. Handle Conflicting Interpretations

When multiple submitters provide different classifications for the same variant, ClinVar reports "Conflicting interpretations of pathogenicity."

Resolution strategy:

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

Search query to exclude conflicts:

TP53[gene] AND pathogenic[CLNSIG] NOT conflicting[RVSTAT]

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