aws-samples/amazon-bedrock-agents-healthcare-lifesciences/genomics-variant-interpretation

Genomics Variant Interpretation

When to use this skill

• Interpret variants in specific genes (BRCA1/2, TP53, CYP2D6)
• Classify variant pathogenicity using ClinVar + VEP annotations
• Compare allele frequencies against population databases (1000 Genomes)
• Analyze a patient cohort for pharmacogenomic or cancer-risk variants
• Generate clinical-grade variant reports

Workflow: Clinical Variant Interpretation

Step 1: Select the appropriate analysis tool

Step 2: Apply quality filtering

All queries automatically enforce:

• qual > 30 (quality score threshold)
• PASS filter status
• Cardinality checks on VEP annotation arrays

These filters ensure only high-confidence variants enter clinical interpretation.

Step 3: Interpret clinical significance

Classification hierarchy (act on highest applicable):

Priority scoring (used in query results):

Pathogenic + HIGH impact = 10
Pathogenic + MODERATE = 9
Likely_pathogenic + HIGH = 8
Likely_pathogenic + MODERATE = 7
VUS + HIGH = 6
HIGH impact (no ClinVar) = 5
VUS + MODERATE = 4
All others = 1

Step 4: Assess population frequency context

Use analyze_allele_frequencies with 1000 Genomes data:

Rule: Pathogenic variants for Mendelian diseases are almost always < 1% frequency.

Step 5: Generate clinical report

Structure: Patient ID, Gene, Variant (chr:pos:ref>alt), Consequence, Impact, ClinVar significance, Population frequency (1000G AF + rarity category), Associated disease (CLNDN), Clinical interpretation, Recommended follow-up.

Tool Reference

query_variants_by_gene

Input: gene_symbols ("BRCA1,BRCA2,TP53"), sample_ids (optional), include_frequency (bool)
Output: Variants with VEP annotation, ClinVar significance, priority score
Use for: Targeted gene panels, cancer predisposition, pharmacogenomics

query_variants_by_chromosome

Input: chromosome ("17"), sample_ids (optional), position_range ("32000000-33000000")
Output: All PASS variants in region with annotations
Use for: CNV analysis, specific loci investigation, regional patterns

analyze_allele_frequencies

Input: sample_ids (optional), frequency_threshold (default 0.01)
Output: Variants with rarity classification, quality tiers, 1000G comparison
Use for: Rare disease analysis, novel variant discovery, population genetics

compare_sample_variants

Input: sample_ids ("NA21135,NA21137" -- minimum 2)
Output: Per-sample summary: total variants, pathogenic count, impact distribution, quality metrics
Use for: Family studies, cohort stratification, trio analysis

execute_dynamic_genomics_query

Input: user_question (natural language), sample_ids (optional)
Output: Custom SQL generated and executed against HealthOmics stores
Use for: Complex questions not covered by specialized tools

Data Architecture

Variant data lives in AWS HealthOmics stores queried via Athena:

• Variant Store (genomicsvariantstore): sample, position, alleles, quality, VEP annotations
• Annotation Store (genomicsannotationstore): ClinVar attributes (CLNSIG, CLNDN, GENEINFO)

Stores are joined on: contigname + start + referenceallele + alternatealleles[1]

VEP annotation fields: symbol, impact, consequence, biotype, sift_prediction, polyphen_prediction ClinVar fields: CLNSIG, CLNDN, GENEINFO, CLNREVSTAT, RS, ALLELEID

Common Analysis Patterns

Conventions

• Lead reports with the most actionable finding first
• Include population frequency context for every pathogenic call
• Distinguish germline (inherited) from somatic (tumor) context
• For VUS: note classification may change with new evidence
• Never state a variant is definitively causal without functional evidence
• Include quality metrics (qual, depth) to assess confidence