Structural Variant Analysis Workflow

1. Complex molecular consequences - SVs can cause gene dosage changes, gene disruption, gene fusions, position effects
2. Size matters - Pathogenicity depends on size, gene content, and breakpoint precision
3. Limited databases - Fewer curated SVs in ClinVar compared to SNVs
4. Dosage sensitivity - Haploinsufficiency and triplosensitivity are critical but gene-specific
5. Population frequency - Large benign CNVs are common; distinguishing pathogenic from benign is challenging

This skill provides: A systematic workflow integrating SV classification, gene content analysis, dosage sensitivity assessment, population frequencies, and ACMG-adapted criteria into clinically actionable interpretations.

Triggers

Use this skill when users:

• Ask about structural variant interpretation
• Have CNV data from array or sequencing
• Ask "is this deletion/duplication pathogenic?"
• Need ACMG classification for SVs
• Want to assess gene dosage effects
• Ask about chromosomal rearrangements
• Have large-scale genomic alterations requiring interpretation

Workflow Overview

┌─────────────────────────────────────────────────────────────────┐
│              STRUCTURAL VARIANT INTERPRETATION                   │
├─────────────────────────────────────────────────────────────────┤
│                                                                  │
│  Phase 1: SV IDENTITY & CLASSIFICATION                          │
│  ├── Normalize SV coordinates (hg19/hg38)                       │
│  ├── Determine SV type (DEL/DUP/INV/TRA/CPX)                   │
│  ├── Calculate SV size                                          │
│  └── Assess breakpoint precision                                │
│                                                                  │
│  Phase 2: GENE CONTENT ANALYSIS                                  │
│  ├── Identify genes fully contained in SV                       │
│  ├── Identify genes with breakpoints (disrupted)                │
│  ├── Annotate gene function and disease associations            │
│  ├── Identify regulatory elements affected                      │
│  └── Assess gene orientation (for inversions/translocations)    │
│                                                                  │
│  Phase 3: DOSAGE SENSITIVITY ASSESSMENT                          │
│  ├── ClinGen dosage sensitivity scores                          │
│  │   └─ Haploinsufficiency / Triplosensitivity ratings          │
│  ├── DECIPHER haploinsufficiency predictions                    │
│  ├── pLI scores (gnomAD) for loss-of-function intolerance       │
│  ├── OMIM gene-disease associations (dominant/recessive)        │
│  └── Known dosage-sensitive genes from literature               │
│                                                                  │
│  Phase 4: POPULATION FREQUENCY CONTEXT                           │
│  ├── gnomAD SV database (overlapping SVs)                       │
│  ├── DGV (Database of Genomic Variants)                         │
│  ├── ClinVar (known pathogenic/benign SVs)                      │
│  └── Calculate reciprocal overlap with population SVs           │
│                                                                  │
│  Phase 5: PATHOGENICITY SCORING                                  │
│  ├── Pathogenicity score (0-10 scale)                           │
│  │   ├─ Gene content weight (40%)                               │
│  │   ├─ Dosage sensitivity weight (30%)                         │
│  │   ├─ Population frequency weight (20%)                       │
│  │   └─ Inheritance/phenotype match weight (10%)                │
│  ├── Apply ACMG SV criteria                                     │
│  └── Generate classification recommendation                      │
│                                                                  │
│  Phase 6: LITERATURE & CLINICAL EVIDENCE                         │
│  ├── PubMed: Similar SVs, gene disruption studies               │
│  ├── DECIPHER: Developmental disorder cases                     │
│  ├── Clinical case reports                                      │
│  └── Functional evidence for gene dosage effects                │
│                                                                  │
│  Phase 7: ACMG-ADAPTED CLASSIFICATION                            │
│  ├── Apply SV-specific evidence codes                           │
│  ├── Calculate final classification                             │
│  ├── Identify limiting factors                                  │
│  └── Generate clinical recommendations                          │
│                                                                  │
└─────────────────────────────────────────────────────────────────┘

Phase Details

Phase 1: SV Identity & Classification

Goal: Standardize SV notation and classify type

SV Types:

Key Information to Capture:

• Chromosome(s) involved
• Coordinates (start, end) in hg19/hg38
• SV size (bp or Mb)
• SV type (DEL/DUP/INV/TRA/CPX)
• Breakpoint precision (±50bp, ±1kb, etc.)
• Inheritance pattern (de novo, inherited, unknown)

Example:

SV: arr[GRCh38] 17q21.31(44039927-44352659)x1
- Type: Deletion (heterozygous)
- Size: 313 kb
- Genes: MAPT, KANSL1 (fully contained)
- Breakpoints: Well-defined (array resolution ±5kb)

Phase 2: Gene Content Analysis

Goal: Comprehensive annotation of genes affected by SV

Tools:

Gene Categories:

1. Fully contained genes - Entire gene within SV boundaries

   • Deletion: Complete loss of one copy (haploinsufficiency)
   • Duplication: Extra copy (triplosensitivity)

2. Partially disrupted genes - Breakpoint within gene

   • Likely loss-of-function for affected allele
   • Check if critical domains disrupted

3. Flanking genes - Within 1 Mb of breakpoints

   • May be affected by position effects
   • Regulatory disruption possible

Example Gene Content Analysis:

def analyze_gene_content(tu, chrom, sv_start, sv_end, sv_type):
    """
    Identify and annotate all genes within SV region.
    """
    genes = {
        'fully_contained': [],
        'partially_disrupted': [],
        'flanking': []
    }

    # Use Ensembl to find overlapping genes
    # This is pseudocode - actual implementation depends on available tools

    for gene in genes_in_region:
        gene_start = gene['start']
        gene_end = gene['end']

        # Classify gene relationship to SV
        if gene_start >= sv_start and gene_end <= sv_end:
            # Fully contained
            gene_info = annotate_gene(tu, gene['symbol'])
            genes['fully_contained'].append(gene_info)

        elif (gene_start < sv_start < gene_end) or (gene_start < sv_end < gene_end):
            # Partially disrupted
            gene_info = annotate_gene(tu, gene['symbol'])
            genes['partially_disrupted'].append(gene_info)

        elif abs(gene_start - sv_end) < 1000000 or abs(gene_end - sv_start) < 1000000:
            # Flanking (within 1 Mb)
            gene_info = annotate_gene(tu, gene['symbol'])
            genes['flanking'].append(gene_info)

    return genes

def annotate_gene(tu, gene_symbol):
    """
    Comprehensive gene annotation.
    """
    # OMIM associations
    omim = tu.tools.OMIM_search(
        operation="search",
        query=gene_symbol,
        limit=5
    )

    # DisGeNET associations
    disgenet = tu.tools.DisGeNET_search_gene(
        operation="search_gene",
        gene=gene_symbol,
        limit=10
    )

    # Gene Ontology
    # Note: Need gene ID first
    ncbi = tu.tools.NCBI_gene_search(
        term=gene_symbol,
        organism="human"
    )

    return {
        'symbol': gene_symbol,
        'omim': omim,
        'disgenet': disgenet,
        'ncbi': ncbi
    }

Report Section:

### 2.1 Fully Contained Genes (Complete Dosage Effect)

| Gene | Function | Disease Association | Inheritance | Evidence |
|------|----------|---------------------|-------------|----------|
| **MAPT** | Microtubule-associated protein tau | Frontotemporal dementia (AD) | Autosomal Dominant | ★★★ |
| **KANSL1** | Histone acetyltransferase complex | Koolen-De Vries syndrome (AD) | Autosomal Dominant | ★★★ |

**Interpretation**: Deletion results in haploinsufficiency of two dosage-sensitive genes. KANSL1 haploinsufficiency is the primary cause of pathogenicity.

*Sources: OMIM, DisGeNET, Ensembl*

### 2.2 Partially Disrupted Genes (Breakpoint Within Gene)

| Gene | Breakpoint Location | Effect | Critical Domains Lost |
|------|-------------------|--------|----------------------|
| **NF1** | Intron 28 of 58 | 5' portion deleted | Yes - GTPase-activating domain |

**Interpretation**: Breakpoint disrupts NF1 coding sequence, likely resulting in loss-of-function. NF1 is haploinsufficient (causes neurofibromatosis type 1).

### 2.3 Flanking Genes (Potential Position Effects)

| Gene | Distance from SV | Regulatory Risk | Evidence |
|------|------------------|-----------------|----------|
| **KCNJ2** | 450 kb upstream | Low | ★☆☆ |

**Note**: Position effects are possible but less common. Consider if phenotype unexplained by contained genes.

Phase 3: Dosage Sensitivity Assessment

Goal: Determine if affected genes are dosage-sensitive

Tools:

ClinGen Dosage Sensitivity Scores:

pLI Score Interpretation (gnomAD):

Implementation:

def assess_dosage_sensitivity(tu, gene_list):
    """
    Assess dosage sensitivity for all genes in SV.
    Returns dosage scores and interpretation.
    """
    dosage_data = []

    for gene_symbol in gene_list:
        # 1. ClinGen dosage sensitivity (gold standard)
        clingen = tu.tools.ClinGen_search_dosage_sensitivity(
            gene=gene_symbol
        )

        hi_score = None
        ts_score = None
        if clingen.get('data'):
            for entry in clingen['data']:
                hi_score = entry.get('Haploinsufficiency Score')
                ts_score = entry.get('Triplosensitivity Score')
                break

        # 2. ClinGen gene validity (supports dosage sensitivity)
        validity = tu.tools.ClinGen_search_gene_validity(
            gene=gene_symbol
        )

        validity_level = None
        if validity.get('data'):
            for entry in validity['data']:
                validity_level = entry.get('Classification')
                break

        # 3. pLI score from gnomAD (if available via gene search)
        # Note: May need to use myvariant or other tools
        # pli_score = get_pli_score(tu, gene_symbol)

        # 4. OMIM inheritance pattern
        omim = tu.tools.OMIM_search(
            operation="search",
            query=gene_symbol,
            limit=3
        )

        inheritance_pattern = None
        if omim.get('data', {}).get('entries'):
            for entry in omim['data']['entries']:
                mim = entry.get('mimNumber')
                details = tu.tools.OMIM_get_entry(
                    operation="get_entry",
                    mim_number=str(mim)
                )
                # Extract inheritance from details
                # inheritance_pattern = parse_inheritance(details)

        # Integrate evidence
        dosage_assessment = {
            'gene': gene_symbol,
            'hi_score': hi_score,
            'ts_score': ts_score,
            'validity_level': validity_level,
            'inheritance': inheritance_pattern,
            'is_dosage_sensitive': (hi_score == '3' or ts_score == '3'),
            'evidence_grade': calculate_evidence_grade(hi_score, ts_score, validity_level)
        }

        dosage_data.append(dosage_assessment)

    return dosage_data

def calculate_evidence_grade(hi_score, ts_score, validity):
    """
    Calculate evidence grade for dosage sensitivity.
    """
    if (hi_score == '3' or ts_score == '3') and validity == 'Definitive':
        return '★★★'  # High confidence
    elif (hi_score in ['2', '3'] or ts_score in ['2', '3']):
        return '★★☆'  # Moderate confidence
    else:
        return '★☆☆'  # Low confidence

Report Section:

### 3. Dosage Sensitivity Assessment

#### Haploinsufficient Genes (Deletions/Disruptions)

| Gene | ClinGen HI Score | pLI | Validity | Disease | Evidence |
|------|-----------------|-----|----------|---------|----------|
| **KANSL1** | 3 (Sufficient) | 0.99 | Definitive | Koolen-De Vries syndrome | ★★★ |
| **MAPT** | 2 (Emerging) | 0.85 | Strong | FTD (rare) | ★★☆ |

**Interpretation**: KANSL1 has definitive evidence for haploinsufficiency. Deletion of one copy is expected to cause Koolen-De Vries syndrome (intellectual disability, hypotonia, distinctive facial features).

*Sources: ClinGen Dosage Sensitivity Map, gnomAD pLI*

#### Triplosensitive Genes (Duplications)

| Gene | ClinGen TS Score | Disease Mechanism | Evidence |
|------|-----------------|-------------------|----------|
| **MECP2** | 3 (Sufficient) | MECP2 duplication syndrome | ★★★ |
| **PMP22** | 3 (Sufficient) | Charcot-Marie-Tooth 1A | ★★★ |

**Note**: For this deletion, triplosensitivity is not applicable. Listed for reference.

#### Non-Dosage-Sensitive Genes

| Gene | HI Score | TS Score | Interpretation |
|------|----------|----------|----------------|
| **GENE_X** | 0 | 0 | No established dosage sensitivity |
| **GENE_Y** | 1 | 1 | Insufficient evidence |

**Interpretation**: These genes lack evidence for dosage sensitivity. Deletion/duplication less likely to be pathogenic solely due to these genes.

Phase 4: Population Frequency Context

Goal: Determine if SV is common in general population (likely benign) or rare (supports pathogenicity)

Tools:

Frequency Interpretation (adapted from ACMG):

Reciprocal Overlap Calculation:

For proper comparison, calculate reciprocal overlap between query SV and population SV:

Reciprocal Overlap = min(overlap_with_A, overlap_with_B)