Rare Disease Genome Interpretation

Folklore classifies candidate variants, compares their genes with the patient's HPO terms, retrieves linked literature, and ranks the resulting evidence for geneticist review.

5-7 years

Average time to rare disease diagnosis

3-5

Specialists consulted before diagnosis

41%

Patients receiving at least one VUS

7.3%

VUS that are ever reclassified

The Interpretation Bottleneck

Rare-disease sequencing produces more candidate variants than a reviewer can assess through ad hoc database and literature searches.

Variant Volume

Whole exome sequencing produces 20,000-30,000 variants per patient. Whole genome sequencing produces 4-5 million. Each pathogenic candidate requires cross-referencing multiple databases, literature sources, and phenotype associations.

Manual Evidence Gathering

For each candidate variant, a geneticist must search ClinVar, gnomAD, PubMed, OMIM, and functional prediction tools individually. A single rare disease case with dozens of candidate variants can consume 5-10 days of expert time.

VUS Accumulation

Rare-disease cases often contain Variants of Uncertain Significance that require additional population, functional, phenotype, and literature evidence before reclassification.

Phenotype Complexity

Rare diseases often present with overlapping phenotypes, incomplete penetrance, and variable expressivity. Connecting a patient's specific clinical presentation to the correct gene-disease association requires structured phenotype matching, not keyword searches.

The Rare-Disease Review Path

Six stages connect variant annotation, ACMG classification, phenotype matching, literature retrieval, evidence synthesis, and report preparation.

Variant Annotation

Variants are annotated against eight reference sources: gnomAD population frequencies, ClinVar clinical significance, dbNSFP functional predictions, SpliceAI splice impact, gnomAD gene constraint, HPO phenotype associations, ClinGen dosage sensitivity, and Ensembl VEP consequence prediction.

Phenotype-First Prioritization

Patient HPO terms are matched against gene-disease phenotype profiles using semantic similarity analysis that accounts for ontology hierarchy and information content. A variant in a gene associated with the patient's specific phenotype is prioritized over an equally classified variant in an unrelated gene. This is not keyword matching - it is structured ontological reasoning.

Bayesian ACMG Classification

The pipeline evaluates 19 automatable ACMG criteria using the Tavtigian Bayesian point framework and BayesDel ClinGen SVI-calibrated thresholds. VCEP gene-specific specifications apply for approximately 50-60 genes, and the output includes a classification confidence score.

Automated Literature Evidence

For every candidate gene and variant, Folklore searches a local database of millions of genetics-relevant PubMed publications with pre-extracted gene mentions, variant mentions, and phenotype associations. Evidence is ranked by clinical relevance and returned with full PMID attribution.

Clinical Evidence Synthesis

An on-premise AI assistant summarizes classification results, phenotype correlations, and linked literature in a structured narrative for geneticist review.

Structured Clinical Report

A tiered report presents Tier 1 and Tier 2 variants with the ACMG classification, phenotype match score, supporting literature, population frequency, and computational predictions for clinical review.

Rare Disease Analysis Controls

Rare-disease review combines variant classification with phenotype matching, literature search, and evidence traceability.

Phenotype Matching That Understands Ontology

HPO semantic similarity uses information content and ontological hierarchy to identify gene-phenotype relationships that exact keyword matching would miss. "Seizures" and "Epilepsy" are recognized as related, not treated as different terms.

How semantic similarity works

VUS Evidence Aggregation

Each VUS is presented with population frequencies, functional predictions, literature citations, and phenotype correlations to support later review or reclassification.

Understanding confidence scores

Whole Genome Processing

Folklore processes whole-genome VCF files containing 4-5 million variants in under an hour. The pipeline does not pre-filter variants by frequency or predicted impact before classification.

See the full pipeline

Transparent Evidence Chains

Each classification records the applied ACMG criteria, database versions, computational thresholds, and evidence strength for reviewer inspection.

ACMG classification methodology

The Geneticist Remains Central

Rare-disease diagnosis requires a geneticist to interpret incomplete phenotypes, recognize atypical presentations, integrate family history, and communicate uncertainty to patients.

Before that review, Folklore queries the configured databases, searches literature, matches the phenotype, and applies the ACMG criteria. The geneticist receives the resulting evidence and retains the clinical decision.

Folklore prepares the evidence. The geneticist makes the clinical decision.

Review a Rare-Disease Case Workflow

See a rare-disease genome move from VCF upload through phenotype matching to a report prepared for review.