prs-net-deep-learning-agent

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---

name: 'prs-net-deep-learning-agent'

description: 'Geometric deep learning-based polygenic risk score prediction using PRS-Net for modeling gene interactions, enhanced disease prediction, and cross-ancestry portability.'

measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes.

allowed-tools:

• read_file
• run_shell_command

---

PRS-Net Deep Learning Agent

The PRS-Net Deep Learning Agent implements interpretable geometric deep learning for polygenic risk score prediction. PRS-Net models non-linear gene-gene interactions and biological network relationships to enhance disease prediction accuracy and improve cross-ancestry portability compared to traditional linear PRS methods.

When to Use This Skill

• When linear PRS methods show limited performance.
• For modeling complex gene-gene interactions.
• To improve PRS portability across ancestries.
• When biological interpretability of PRS is needed.
• For integrating pathway and network information.

Core Capabilities

1. Non-Linear PRS: Capture gene-gene interactions via deep learning.

1. Network Integration: Incorporate protein-protein interaction networks.

1. Interpretability: Identify important pathways and gene modules.

1. Cross-Ancestry Transfer: Improved portability via learned biology.

1. Multi-Task Learning: Joint modeling of related traits.

1. Uncertainty Quantification: Provide prediction confidence.

PRS-Net Architecture

| Component | Function | Innovation |

|-----------|----------|------------|

| Input Layer | Gene-level summaries | Aggregated variant effects |

| Network Encoder | PPI graph convolution | Biological structure |

| Attention Layer | Gene importance | Interpretability |

| Predictor | Disease/trait prediction | Non-linear mapping |

| Explanation | Pathway enrichment | Biological insights |

Comparison to Traditional PRS

| Aspect | Linear PRS | PRS-Net |

|--------|------------|---------|

| Gene Interactions | Not modeled | GNN captures |

| Network Biology | Ignored | Integrated |

| Interpretability | Limited (SNP weights) | Pathway-level |

| Cross-Ancestry | Often poor | Improved |

| Computational Cost | Low | Moderate |

| Training Data Needed | Low | Moderate |

Workflow

1. Input: Individual genotypes, PPI network, training phenotypes.

1. Gene Summarization: Aggregate SNPs to gene-level scores.

1. Network Encoding: Learn representations on PPI graph.

1. Prediction: Non-linear disease risk prediction.

1. Interpretation: Extract important genes and pathways.

1. Cross-Ancestry: Apply to diverse populations.

1. Output: Risk scores, uncertainty, biological explanations.

Example Usage

User: "Calculate PRS-Net scores for Type 2 Diabetes with pathway-level interpretation."

Agent Action:

python3 Skills/Precision_Medicine/PRS_Net_Deep_Learning_Agent/prs_net_predict.py \
    --genotypes cohort_genotypes.vcf.gz \
    --ppi_network string_ppi.graphml \
    --trait type2_diabetes \
    --model_weights prs_net_t2d_v1.pt \
    --interpret_pathways true \
    --ancestry_calibration multi \
    --output prs_net_results/

Input Requirements

| Input | Format | Purpose |

|-------|--------|---------|

| Genotypes | VCF/PLINK | SNP data |

| PPI Network | GraphML, edge list | Gene relationships |

| Gene Mapping | BED | SNP-to-gene |

| Training Labels | Phenotype file | Model training |

| GWAS Summary | Optional | Initialization |

Output Components

| Output | Description | Format |

|--------|-------------|--------|

| PRS-Net Score | Non-linear polygenic score | .csv |

| Risk Percentile | Population ranking | .csv |

| Gene Importance | Attention weights | .csv |

| Pathway Enrichment | Top pathways | .csv |

| Module Visualization | Network subgraphs | .png |

| Uncertainty | Prediction confidence | .json |

Network Biology Integration

| Network | Source | Genes | Edges |

|---------|--------|-------|-------|

| STRING PPI | String-db | 19,000 | 5.5M |

| BioGRID | BioGRID | 18,000 | 1.2M |

| Reactome | Reactome | 10,000 | 250K |

| GO Biological Process | Gene Ontology | 18,000 | Hierarchical |

Performance Benchmarks

| Disease | Linear PRS AUC | PRS-Net AUC | Improvement |

|---------|----------------|-------------|-------------|

| Type 2 Diabetes | 0.65 | 0.72 | +7% |

| Coronary Artery Disease | 0.70 | 0.76 | +6% |

| Schizophrenia | 0.62 | 0.68 | +6% |

| Alzheimer's Disease | 0.68 | 0.74 | +6% |

Cross-Ancestry Portability

| Ancestry | Linear PRS Drop | PRS-Net Drop |

|----------|-----------------|--------------|

| EUR → EAS | -15% | -8% |

| EUR → AFR | -30% | -18% |

| EUR → SAS | -20% | -12% |

| EUR → AMR | -18% | -10% |

AI/ML Components

Graph Neural Networks:

• Graph convolutional networks (GCN)
• Graph attention networks (GAT)
• Message passing neural networks

Interpretability:

• Attention visualization
• Integrated gradients
• Pathway enrichment analysis

Transfer Learning:

• Pre-training on EUR
• Fine-tuning on diverse
• Domain adaptation

Prerequisites

• Python 3.10+
• PyTorch, PyTorch Geometric
• NetworkX, igraph
• Scanpy (optional for visualization)
• GPU recommended

Related Skills

• Multi_Ancestry_PRS_Agent - Traditional multi-ancestry PRS
• PopEVE_Variant_Predictor_Agent - Variant interpretation
• Pharmacogenomics_Agent - Drug-gene interactions
• Pathway_Analysis - Pathway enrichment

Biological Interpretation

| Interpretation Level | Output | Clinical Use |

|---------------------|--------|--------------|

| Gene | Top contributing genes | Target identification |

| Pathway | Enriched pathways | Mechanism understanding |

| Module | Network subgraphs | Biological insight |

| Hub Genes | Central genes | Druggable targets |

Training Considerations

| Factor | Recommendation | Rationale |

|--------|----------------|-----------|

| Sample Size | >10,000 | Deep learning needs data |

| Class Balance | Oversample or weight | Avoid bias |

| Validation | Cross-validation | Avoid overfitting |

| Regularization | Dropout, L2 | Generalization |

Special Considerations

1. Interpretability Trade-offs: More complex = less interpretable
2. Computational Requirements: GPU accelerates training
3. Network Quality: PPI accuracy affects results
4. Gene Mapping: SNP-to-gene assignment matters
5. Overfitting: Regularization essential

Clinical Applications

| Application | PRS-Net Advantage | Benefit |

|-------------|-------------------|---------|

| Risk Stratification | Higher accuracy | Better prediction |

| Biological Insight | Pathway interpretation | Mechanism |

| Drug Targets | Hub gene identification | Therapeutic targets |

| Ancestry Equity | Better portability | Fairer prediction |

Limitations

| Limitation | Impact | Future Direction |

|------------|--------|------------------|

| Training Data | EUR-dominated | Diverse cohorts |

| Network Completeness | Missing edges | Multi-network integration |

| Rare Variants | Not well captured | WGS + rare variant methods |

| Clinical Validation | Limited trials | Prospective studies |

Author

AI Group - Biomedical AI Platform

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