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Chapter 4: Databases for Clinical Variant Interpretation: ClinVar, ClinGen & HPO

Tested Stacks: GATK 4.6.1 Samtools 1.21 Nextflow 24.10 Seurat v5.1 Python 3.12
Prerequisites: Basic Linux CLI & Sequencing Formats
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Chapter 4: Databases for Clinical Variant Interpretation: ClinVar, ClinGen & HPO

No clinical geneticist works in isolation. Interpreting genetic variants relies on centralized knowledgebases that aggregate worldwide clinical observations, functional laboratory assays, and population frequencies.

Interpreting ClinVar Review Stars
  • 0 Stars: Submitted without assertion criteria or single submitter without supporting evidence.
  • 1 Star: Criteria provided, single submitter, or conflicting interpretations between labs.
  • 2 Stars: Multiple submitters with assertion criteria provided, concordant interpretation.
  • 3 Stars: Reviewed by an FDA-recognized ClinGen Variant Curation Expert Panel (VCEP). Gold standard for diagnostic reporting.
  • 4 Stars: Established professional practice guideline (e.g. ACMG practice guideline).

1. Population Frequency Filtering with gnomAD

If a variant occurs in the general population at a frequency higher than the disease prevalence in the clinic, it cannot be the cause of a fully penetrant Mendelian disease. ClinGen defines gene-specific maximum credible population allele frequencies (e.g. \(f_{\text{max}} < 0.0001\) for severe dominant pediatric disorders).

2. Phenotype-Driven Prioritization with HPO

The Human Phenotype Ontology (HPO) provides standardized, machine-readable vocabularies for human clinical symptoms (e.g. HP:0001250: Seizure, HP:0001263: Global developmental delay). Tools like Exomiser combine semantic HPO phenotype similarities with variant pathogenicity scores to rank candidate diagnoses.

# Running Exomiser phenotype matching from CLI
java -jar exomiser-cli-13.3.0.jar \\
    --analysis-mode PASSENGER \\
    --vcf patient_exome.vcf.gz \\
    --hpo-ids HP:0001250,HP:0001263,HP:0000252 \\
    --output-prefix patient_diagnostic_rankings
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Common Pitfalls & Troubleshooting Checklist

  • Java Heap Exhaustion (GATK / Picard): If processes terminate with java.lang.OutOfMemoryError, set your environment variable explicitly: export _JAVA_OPTIONS="-Xmx16g -Djava.io.tmpdir=/tmp".
  • Index Timestamp Mismatch: If tools complain that a BAM index is missing or out of date, regenerate with samtools index -@ 4 sample.bam.
  • Conda Channel Conflicts: Avoid ABI incompatibilities between bioconda and conda-forge by setting strict priority: conda config --set channel_priority strict.
  • Chromosome Naming Contamination: Always verify whether reference FASTA headers use chr1 or 1; divergent naming causes silent alignment drops.
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