COMPUTE, DON'T DESCRIBE

NOT for (use other skills instead):

• Coding variant pathogenicity -> Use tooluniverse-variant-interpretation
• Full clinical variant classification (ACMG) -> Use tooluniverse-variant-interpretation
• Gene-disease associations (not variant-specific) -> Use tooluniverse-gene-disease-association
• Pharmacogenomic variant annotation -> Use tooluniverse-pharmacogenomics
• Epigenomics data processing (BED/narrowPeak files) -> Use tooluniverse-epigenomics

Non-Coding Variant Impact Reasoning

When evaluating a non-coding variant, build evidence across four questions:

1. Is the variant in a regulatory element? Use RegulomeDB to assess whether the variant overlaps TF binding sites, chromatin accessibility peaks, or known regulatory annotations. A low RegulomeDB score (categories 1a-2a) indicates strong evidence that the position is functionally active. Confirm with ENCODE histone marks: H3K27ac signals active enhancers and active promoters; H3K4me1 alone marks poised enhancers; H3K4me3 marks active promoters; H3K27me3 marks silenced regions.

2. Does it alter a transcription factor binding site? Check RegulomeDB's TF binding evidence and ENCODE TF ChIP-seq experiments. A variant that falls within a TF footprint and disrupts the consensus motif is mechanistically actionable, especially if the TF is known to be relevant in the disease tissue.

3. Is there eQTL evidence linking it to a gene? Query GTEx to determine whether the variant (or variants in tight LD) modulates expression of a nearby gene in a tissue-specific or ubiquitous manner. A tissue-specific eQTL suggests cell-type-specific regulation; a ubiquitous eQTL suggests a core regulatory element. The direction of the NES (positive = alternative allele increases expression, negative = decreases) and effect size matter for interpretation.

4. Is there GWAS evidence for trait association? Search the GWAS Catalog for the rsID or the surrounding locus. Genome-wide significant associations (p < 5×10⁻⁸) in relevant traits anchor the variant's biological importance. Cross-reference with OpenTargets for locus-to-gene mapping from multiple GWAS studies.

Synthesizing the evidence: Build a multi-layer case. A variant with GWAS significance + eQTL evidence + RegulomeDB score 1a-2a + active chromatin (H3K27ac) in the relevant tissue represents high-confidence regulatory impact. Two or three converging lines of evidence (e.g., eQTL plus active enhancer) constitute moderate confidence. A single line, or a variant only in a poised but not active regulatory context, represents lower confidence.

Workflow Overview

Input (rsID, genomic coordinates, trait/disease, gene)
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Phase 0: Variant/Trait Resolution
  Resolve rsIDs, map trait names to EFO/MONDO IDs via OLS
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Phase 1: GWAS Association Lookup
  GWAS Catalog associations, p-values, effect sizes, study metadata
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Phase 2: eQTL Analysis
  GTEx tissue-specific eQTLs, target gene identification
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Phase 3: Regulatory Element Annotation
  ENCODE histone marks, RegulomeDB scores, chromatin state
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Phase 4: OpenTargets GWAS Integration
  OpenTargets GWAS study aggregation, locus-to-gene mapping
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Phase 5: Functional Impact Synthesis
  Integrate all evidence, assign regulatory impact level
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Phase 6: Report
  Evidence-graded regulatory variant report

Phase 0: Variant/Trait Resolution

Use ols_search_terms to resolve trait names to ontology IDs before GWAS queries. Restrict to ontology="efo" for GWAS traits; OpenTargets prefers MONDO IDs (e.g., MONDO_0005148 for type 2 diabetes rather than EFO_0001360). Use EnsemblVEP_annotate_rsid (param is variant_id, not rsid) for initial consequence annotation and nearest gene identification.

Phase 1: GWAS Association Lookup

gwas_search_associations is the primary tool: accepts disease_trait (free text), efo_id (preferred for precision), rs_id, and p_value threshold. Use p_value=5e-8 for genome-wide significance. For locus-level discovery, gwas_get_variants_for_trait retrieves all SNPs for a trait. gwas_get_snps_for_gene finds GWAS-cataloged SNPs mapped to a specific gene.

Reasoning tip: When GWAS Catalog returns empty for a free-text trait, switch to the efo_id parameter — the catalog uses controlled vocabulary and free-text matching is imprecise.

Phase 2: eQTL Analysis

GTEx_query_eqtl accepts a gene symbol (auto-resolved to GENCODE ID) or Ensembl gene ID. It returns tissue-specific SNP-gene associations with NES (normalized effect size) and p-value per tissue.

When interpreting results, ask: does the eQTL effect occur in the tissue most relevant to the disease? A brain-specific eQTL for a neurodegenerative disease variant is more compelling than a ubiquitous one. Use GTEx_get_median_gene_expression to confirm that the target gene is actually expressed in the relevant tissue before placing weight on eQTL evidence.

Note: GTEx API uses v8 data; gtex_v10 endpoints may return empty for some queries.

Phase 3: Regulatory Element Annotation

RegulomeDB_query_variant (param: rsid) returns a regulatory score and feature annotations. Scores in categories 1a–2a indicate strong regulatory evidence (eQTL overlap + TF binding + chromatin accessibility). Scores 3a–6 represent progressively weaker evidence.

ENCODE_search_histone_experiments accepts histone_mark (e.g., "H3K27ac") and biosample_term_name (tissue or cell line name — NOT a disease name; ENCODE uses biological sample names like "liver" or "breast epithelium"). Use assay_title="TF ChIP-seq" (not just "ChIP-seq") when querying TF binding data.

Reasoning tip: RegulomeDB aggregates ENCODE, Roadmap, and other data. If ENCODE doesn't have the specific biosample, RegulomeDB may still have aggregate evidence from related cell types.

Phase 4: OpenTargets GWAS Integration

OpenTargets_search_gwas_studies_by_disease takes diseaseIds as an array of MONDO IDs. It provides locus-to-gene (L2G) scores from multiple GWAS studies, which go beyond simple proximity to incorporate colocalisation, eQTL, and chromatin data. Use OpenTargets_multi_entity_search or OpenTargets_get_disease_id_description_by_name to resolve disease names to MONDO/EFO IDs first.

Phase 5: Functional Impact Synthesis

After collecting evidence, reason through the layers:

• High impact: GWAS genome-wide significant + eQTL with meaningful NES + RegulomeDB score ≤ 2 + active chromatin (H3K27ac) in relevant tissue. Multiple independent lines converge on the same locus and gene.
• Moderate impact: Two to three lines of evidence (e.g., eQTL + active enhancer overlap, or GWAS significant + RegulomeDB ≤ 3) without full convergence.
• Low impact: Single line of evidence, or only computational annotation (VEP consequence category) without functional data.
• No evidence: No regulatory annotations in any source; the variant may be in a non-functional region or the relevant cell type is not represented in available datasets.

Fallback Strategies

• GWAS Catalog returns empty: Switch from free-text disease_trait to efo_id; broaden the trait term.
• GTEx eQTL empty for gene: Verify gene symbol spelling; try Ensembl ID; increase size parameter.
• RegulomeDB returns no data: Query ENCODE directly; the variant may lack regulatory annotations in available data.
• OpenTargets GWAS returns None: Verify MONDO/EFO ID format; try OpenTargets_multi_entity_search first to confirm the correct ID.
• ENCODE tissue not found: ENCODE uses specific biosample names; RegulomeDB aggregates data from many cell types and may cover the gap.

Example Workflows

GWAS Variant Functional Annotation (rs429358 / APOE)

Step 1: gwas_search_associations(rs_id="rs429358")
  -> All trait associations (Alzheimer's disease, LDL cholesterol, etc.)

Step 2: GTEx_query_eqtl(gene_symbol="APOE")
  -> Tissue-specific eQTL evidence; note effect in brain vs liver

Step 3: RegulomeDB_query_variant(rsid="rs429358")
  -> Regulatory score and TF binding annotations

Step 4: ENCODE_search_histone_experiments(histone_mark="H3K27ac", biosample_term_name="brain")
  -> Active enhancer context near the variant

Step 5: Synthesize: does GWAS significance + eQTL + active chromatin converge on one gene?

Non-Coding Variant Assessment (Intronic/UTR Variant)

Step 1: EnsemblVEP_annotate_rsid(variant_id="rs12345678")
  -> Confirm non-coding consequence, identify nearest gene

Step 2: RegulomeDB_query_variant(rsid="rs12345678")
  -> Is this position in a regulatory context?

Step 3: gwas_search_associations(rs_id="rs12345678")
  -> Any GWAS associations in relevant traits?

Step 4: GTEx_query_eqtl(gene_symbol=nearest_gene)
  -> Does this variant or nearby variants modulate expression?

Step 5: ENCODE_search_histone_experiments(histone_mark="H3K27ac", biosample_term_name=relevant_tissue)
  -> Active chromatin confirmation

Step 6: Classify impact based on convergence of evidence lines

Limitations

• GWAS Catalog covers published GWAS only; unpublished studies are not included.
• GTEx eQTL data is from v8; v10 endpoints may return empty.
• RegulomeDB annotations depend on available ENCODE/Roadmap data for the specific cell type.
• eQTL analysis identifies correlation, not causation; fine-mapping is needed to identify causal variants.
• RegulomeDB scores are heuristic; a score of 1a does not guarantee functional impact.
• GWAS associations are population-level; individual variant effects depend on genetic background.