clinvar-annotation

When to Use

• User wants to check if variants in ENCODE regulatory peaks have clinical significance in ClinVar
• User asks about "ClinVar", "pathogenic variants", "clinical significance", or "variant classification"
• User needs to annotate ENCODE-derived regulatory variants with disease associations
• User wants to find clinically relevant variants within enhancers, promoters, or open chromatin regions
• Example queries: "check ClinVar for variants in my ATAC-seq peaks", "find pathogenic variants in pancreas enhancers", "annotate regulatory variants with clinical significance"

Annotating ENCODE Regulatory Variants with ClinVar Clinical Significance

Cross-reference ENCODE functional genomic elements with ClinVar clinical variant classifications to identify pathogenic variants in regulatory regions and understand non-coding disease mechanisms.

Scientific Rationale

The question: "Do any clinically significant variants fall within my ENCODE regulatory elements, and can ENCODE data explain their pathogenic mechanism?"

ClinVar is NCBI's public archive of variant-disease associations, aggregating submissions from clinical laboratories, research groups, and expert panels. Most ClinVar annotations focus on coding variants, but a growing number of non-coding variants are being classified. ENCODE provides the functional context to explain WHY a non-coding variant is pathogenic — by showing that it disrupts an active enhancer, promoter, or insulator in disease-relevant tissue.

This bidirectional integration serves two use cases:

1. Forward: Start from ENCODE peaks, find clinically significant variants within them
2. Reverse: Start from ClinVar pathogenic variants, use ENCODE to explain their mechanism

The Non-Coding Variant Challenge

• ~90% of GWAS-associated variants are in non-coding regions (Maurano et al. 2012)
• ClinVar increasingly includes non-coding variants, but most lack mechanistic annotation
• ENCODE regulatory annotations provide the "why" behind non-coding pathogenicity
• A variant classified as VUS (variant of uncertain significance) may be reclassified with ENCODE functional evidence

Key Literature

• Landrum et al. 2018 "ClinVar: improving access to variant interpretations and supporting evidence" (Nucleic Acids Research, ~2,000 citations). Describes the ClinVar database architecture, submission standards, and the star-rating review system for variant classifications. DOI: 10.1093/nar/gkx1153
• Riggs et al. 2020 "Technical standards for the interpretation and reporting of constitutional copy-number variants: a joint consensus recommendation of the ACMG and ClinGen" (Genetics in Medicine, ~500 citations). Framework for interpreting structural variants, relevant when ENCODE elements overlap CNVs. DOI: 10.1038/s41436-019-0686-8
• Richards et al. 2015 "Standards and guidelines for the interpretation of sequence variants: ACMG/AMP joint consensus recommendation" (Genetics in Medicine, ~12,000 citations). The ACMG variant classification framework (pathogenic through benign). ENCODE functional data can provide evidence for PS3/BS3 (functional studies) criteria. DOI: 10.1038/gim.2015.30
• ENCODE Project Consortium 2020 (Nature, ~1,656 citations). Registry of 926,535 human cCREs — the functional annotation layer for interpreting non-coding ClinVar variants. DOI: 10.1038/s41586-020-2493-4

ClinVar Clinical Significance Categories

Classification	Meaning	ENCODE Relevance
Pathogenic	Causes disease	If in regulatory region, ENCODE explains mechanism
Likely pathogenic	Strong evidence for disease causation	ENCODE data may upgrade to pathogenic
Uncertain significance (VUS)	Not enough evidence to classify	ENCODE functional data may help resolve
Likely benign	Strong evidence against pathogenicity	—
Benign	Does not cause disease	—
Conflicting interpretations	Labs disagree on classification	ENCODE data may resolve conflict
Risk factor	Increases disease risk	May overlap ENCODE regulatory elements

ClinVar Star Ratings

Stars	Review Status	Confidence
0	No assertion criteria	Very low — treat with caution
1	Single submitter with criteria	Low-moderate
2	Multiple submitters, no conflict	Moderate
3	Expert panel reviewed	High
4	Practice guideline	Highest

Always check star ratings. A 0-star "pathogenic" classification has very different reliability than a 3-star classification.

NCBI E-utilities API Reference

Base URL: https://eutils.ncbi.nlm.nih.gov/entrez/eutils/

No authentication required for low-volume use. Rate limit: 3 requests/second without API key, 10/second with NCBI API key.

Key Endpoints

Endpoint	Purpose	Example
esearch.fcgi?db=clinvar&term=...	Search ClinVar	Search by gene, variant, condition
efetch.fcgi?db=clinvar&id=...	Fetch full record	Get complete variant details
esummary.fcgi?db=clinvar&id=...	Summary record	Get classification, review status
elink.fcgi?db=clinvar&dbfrom=...	Cross-database links	Link to PubMed, Gene, etc.

ClinVar VCF Downloads

For bulk intersection with ENCODE peaks, download the ClinVar VCF:

• GRCh38: https://ftp.ncbi.nlm.nih.gov/pub/clinvar/vcf_GRCh38/clinvar.vcf.gz
• GRCh37: https://ftp.ncbi.nlm.nih.gov/pub/clinvar/vcf_GRCh37/clinvar.vcf.gz

Updated monthly on the first Thursday.

Step 1: Define the Scope

Determine which direction the analysis runs:

Forward: ENCODE Peaks to ClinVar Variants

Starting from ENCODE regulatory elements, find clinically significant variants within them.

# Get ENCODE peaks for target tissue
encode_search_experiments(
    assay_title="Histone ChIP-seq",
    target="H3K27ac",
    organ="pancreas",
    biosample_type="tissue"
)

encode_list_files(
    experiment_accession="ENCSR...",
    file_format="bed",
    output_type="IDR thresholded peaks",
    assembly="GRCh38"
)

Reverse: ClinVar Variants to ENCODE Context

Starting from ClinVar pathogenic variants, determine if they overlap ENCODE regulatory elements.

import requests

# Search ClinVar for pathogenic variants in a gene
url = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi"
params = {
    "db": "clinvar",
    "term": "INS[gene] AND pathogenic[clinical significance]",
    "retmax": 50,
    "retmode": "json"
}
response = requests.get(url, params=params)
result = response.json()
variant_ids = result["esearchresult"]["idlist"]

Step 2: Query ClinVar via E-utilities

Search for Variants by Gene

import requests
import time

def search_clinvar(gene_symbol, significance="pathogenic"):
    """Search ClinVar for variants in a gene with given clinical significance."""
    url = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi"
    term = f"{gene_symbol}[gene] AND {significance}[clinical significance]"
    params = {
        "db": "clinvar",
        "term": term,
        "retmax": 100,
        "retmode": "json"
    }
    response = requests.get(url, params=params)
    time.sleep(0.34)  # Rate limit: 3/sec
    return response.json()["esearchresult"]["idlist"]

Get Variant Details

def get_clinvar_summary(variant_ids):
    """Get summary for ClinVar variant IDs."""
    url = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esummary.fcgi"
    params = {
        "db": "clinvar",
        "id": ",".join(variant_ids[:20]),  # Max 20 per request
        "retmode": "json"
    }
    response = requests.get(url, params=params)
    time.sleep(0.34)
    return response.json()["result"]

Search by Genomic Region

# Search for ClinVar variants in a specific genomic region (GRCh38)
term = "11[chromosome] AND 2159000:2162000[chrpos38] AND pathogenic[clinical significance]"

Step 3: Intersect ClinVar with ENCODE Peaks

Using bedtools (Command Line)

# Download ClinVar VCF (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

# Filter to pathogenic/likely pathogenic only
bcftools view -i 'INFO/CLNSIG~"Pathogenic" || INFO/CLNSIG~"Likely_pathogenic"' \
    clinvar.vcf.gz | \
    bcftools query -f '%CHROM\t%POS0\t%END\t%ID\t%INFO/CLNSIG\t%INFO/CLNDN\n' \
    > clinvar_pathogenic.bed

# Intersect with ENCODE peaks
# NOTE: ClinVar VCF is 1-based, BED is 0-based — bcftools query with %POS0 handles this
bedtools intersect \
    -a clinvar_pathogenic.bed \
    -b encode_h3k27ac_peaks.bed \
    -wa -wb \
    > clinvar_in_encode_enhancers.bed

Using Python

import pysam

# Open ClinVar VCF
vcf = pysam.VariantFile("clinvar.vcf.gz")

# Define ENCODE peak region (0-based)
chrom, start, end = "chr11", 2159000, 2162000

# Find ClinVar variants in region
for record in vcf.fetch(chrom, start, end):
    clnsig = record.info.get("CLNSIG", [])
    clndn = record.info.get("CLNDN", [])
    print(f"{record.chrom}:{record.pos} {record.ref}>{record.alts} "
          f"Significance: {clnsig} Condition: {clndn}")

Step 4: Classify the Regulatory Impact

For each ClinVar variant overlapping an ENCODE element, assess the regulatory impact:

Impact Classification Framework

ClinVar Variant in...	ENCODE Context	Interpretation
Active enhancer (H3K27ac+)	Tissue-specific, near disease gene	High impact — variant may disrupt enhancer
Active promoter (H3K4me3+)	At TSS of disease gene	High impact — variant may affect transcription initiation
CTCF binding site	TAD boundary	High impact — may disrupt chromatin insulation
Open chromatin only (ATAC+)	No histone marks	Moderate — accessible but function unclear
TF binding site	Specific TF known for disease gene	High impact — may disrupt TF binding
No ENCODE overlap	Not in regulatory element	Mechanism may be coding, splicing, or untested tissue

ACMG Evidence Integration

ENCODE functional data can support ACMG criteria for variant classification:

ACMG Criterion	How ENCODE Data Contributes
PS3 (Functional studies)	ENCODE shows variant disrupts active regulatory element
PM1 (Critical domain)	Variant in a regulatory element active in disease tissue
PP3 (Computational evidence)	Multiple ENCODE annotations converge on regulatory disruption
BS3 (No functional impact)	ENCODE shows region is inactive in all relevant tissues

Step 5: Report Findings

Per-Variant Summary Table

Variant	ClinVar ID	Classification	Stars	Condition	ENCODE Overlap	Tissue Active	Impact
chr11:2160994 A>G	VCV000012345	Pathogenic	3	Neonatal diabetes	H3K27ac enhancer	Pancreas	High
chr7:87654321 C>T	VCV000067890	VUS	1	Cystic fibrosis	ATAC-seq peak	Lung	Moderate

Summary Statistics

Report:

• Total ClinVar variants in region/gene
• Number overlapping ENCODE regulatory elements (by element type)
• Breakdown by clinical significance
• Star rating distribution
• Tissues with ENCODE data used

Step 6: Log Provenance

encode_log_derived_file(
    file_path="/path/to/clinvar_encode_intersection.tsv",
    source_accessions=["ENCSR...", "ENCSR..."],
    description="Intersection of ClinVar pathogenic variants with ENCODE H3K27ac and ATAC-seq peaks in pancreas",
    file_type="variant_annotation",
    tool_used="bedtools intersect + ClinVar VCF (2024-01 release)",
    parameters="GRCh38, pathogenic+likely_pathogenic, IDR thresholded peaks"
)

encode_link_reference(
    experiment_accession="ENCSR...",
    reference_type="other",
    reference_id="ClinVar:VCV000012345",
    description="Pathogenic variant for neonatal diabetes overlapping pancreas enhancer"
)

Pitfalls & Edge Cases

• ClinVar classifications change over time: A variant classified as VUS today may be reclassified as pathogenic tomorrow. Always record the ClinVar version/date when annotating variants. Re-check classifications before publication.
• Star rating indicates review quality: ClinVar uses a 0-4 star system for assertion confidence. Single-submitter entries (1 star) may conflict with expert panel reviews (3-4 stars). Always prefer higher star ratings.
• Coordinate system mismatch: ClinVar uses 1-based coordinates while BED files are 0-based. Off-by-one errors when intersecting ClinVar with ENCODE peaks are extremely common. Always convert before comparison.
• Regulatory variants are underrepresented: ClinVar is heavily biased toward coding and splice-site variants. Absence of a regulatory variant in ClinVar does NOT mean it is benign — it likely has not been assessed.
• Multiple classifications for the same variant: Different submitters may classify the same variant differently (one says pathogenic, another says benign). Check the "conflicting interpretations" flag and review individual submissions.
• GRCh37 vs GRCh38 in ClinVar: ClinVar provides coordinates in both assemblies but some older submissions only have GRCh37. Always specify the assembly when downloading and verify coordinate consistency.

Walkthrough: Annotating ENCODE Regulatory Variants with Clinical Significance

Goal: Cross-reference variants in ENCODE-defined regulatory elements with ClinVar clinical significance to identify non-coding variants with known disease associations. Context: Most GWAS hits fall in non-coding regions. ENCODE maps the regulatory landscape; ClinVar provides clinical interpretation.

Step 1: Find regulatory element experiments

encode_search_experiments(assay_title="ATAC-seq", organ="heart", organism="Homo sapiens")

Expected output:

{
  "total": 18,
  "results": [
    {"accession": "ENCSR789HRT", "assay_title": "ATAC-seq", "biosample_summary": "heart left ventricle", "status": "released"}
  ]
}

Step 2: Download peak files for regulatory regions

encode_list_files(accession="ENCSR789HRT", file_format="bed", output_type="IDR thresholded peaks", assembly="GRCh38")

Expected output:

{
  "files": [
    {"accession": "ENCFF101ATK", "output_type": "IDR thresholded peaks", "file_format": "bed narrowPeak", "file_size_mb": 0.8}
  ]
}

Step 3: Query ClinVar for variants in peaks

Using ClinVar E-utilities (via skill guidance):

GET https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi?db=clinvar&term=chr1[chr]+AND+10000:20000[chrpos]+AND+pathogenic[clnsig]

Expected response:

{
  "esearchresult": {
    "count": "3",
    "idlist": ["12345", "67890", "11111"]
  }
}

Step 4: Interpret clinical significance in regulatory context

For each ClinVar variant in an ENCODE peak:

• Pathogenic/Likely pathogenic in a heart ATAC-seq peak = high-confidence disease-regulatory variant
• VUS (Variant of Uncertain Significance) in an active enhancer = candidate for functional validation
• Benign in an open chromatin region = regulatory region tolerates this variant

Interpretation: Non-coding pathogenic variants in heart-specific open chromatin suggest regulatory disruption of cardiac gene expression. These are candidates for CRISPR validation.

Integration with downstream skills

• ENCODE peaks from regulatory-elements define the regions to query in ClinVar
• Clinical variants feed into variant-annotation for comprehensive annotation
• Pathogenic regulatory variants inform disease-research for mechanism studies
• Population frequencies from gnomad-variants contextualize ClinVar findings

Code Examples

1. Find ENCODE regulatory data matching ClinVar tissue

encode_get_facets(facet_field="organ", assay_title="ATAC-seq", organism="Homo sapiens")

Expected output:

{
  "facets": {
    "organ": {"brain": 32, "heart": 18, "liver": 14, "lung": 10, "kidney": 8}
  }
}

2. Get experiment details for quality check

encode_get_experiment(accession="ENCSR789HRT")

Expected output:

{
  "accession": "ENCSR789HRT",
  "assay_title": "ATAC-seq",
  "biosample_summary": "heart left ventricle",
  "replicates": 2,
  "status": "released",
  "audit": {"WARNING": 0, "ERROR": 0}
}

3. Track experiments used for clinical annotation

encode_track_experiment(accession="ENCSR789HRT", notes="Heart ATAC-seq for ClinVar regulatory variant annotation")

Expected output:

{
  "status": "tracked",
  "accession": "ENCSR789HRT",
  "notes": "Heart ATAC-seq for ClinVar regulatory variant annotation"
}

Integration

This skill produces...	Feed into...	Purpose
Clinical variant annotations	variant-annotation	Comprehensive variant annotation with clinical significance
Pathogenic regulatory variants	disease-research	Connect non-coding variants to disease mechanisms
ClinVar gene-disease associations	peak-annotation	Prioritize peaks near clinically relevant genes
Variant pathogenicity scores	gwas-catalog	Overlay GWAS hits with ClinVar clinical evidence
Regulatory variant coordinates	gnomad-variants	Add population frequency context to clinical variants
Tissue-specific clinical variants	gtex-expression	Check expression of genes near pathogenic regulatory variants
Clinical regulatory elements	regulatory-elements	Classify ClinVar-annotated elements by regulatory function

Presenting Results

When reporting ClinVar annotation results:

• Variant table: Present a table with columns: variant_id (rsID or HGVS), clinical_significance, review_status, star_rating (0-4), condition(s), and whether the variant overlaps an ENCODE peak
• Always report: ClinVar release date used, genome assembly (must be GRCh38 for ENCODE compatibility), total variants queried, and number with ClinVar entries vs no entry
• Key fields to include: Number of pathogenic/likely pathogenic variants in regulatory regions, number of VUS that overlap active enhancers or promoters, and the breakdown by clinical significance category
• Context to provide: Note that absence from ClinVar does not imply benign status (especially for non-coding variants), and that ClinVar classifications change monthly as new evidence accumulates
• Star rating guidance: Emphasize that 0-1 star variants have limited review and should be interpreted cautiously; 2+ stars indicate multiple submitters with concordant interpretation
• Next steps: Suggest gnomad-variants for population frequency context, or variant-annotation for a full ENCODE-based regulatory variant prioritization workflow

Related Skills

• variant-annotation — Full ENCODE variant annotation workflow with prioritization scoring
• gwas-catalog — GWAS variants in ENCODE peaks (population-level associations)
• gnomad-variants — Population frequency context for ClinVar variants
• disease-research — Disease-focused ENCODE analysis workflows
• cross-reference — Linking ENCODE experiments to ClinVar and other databases
• regulatory-elements — Characterizing the regulatory elements disrupted by variants
• publication-trust — Verify literature claims backing analytical decisions

For the request: "$ARGUMENTS"