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. license: MIT metadata: author: AI Group version: "1.0.0" created: "2026-01-20" compatibility:

system: Python 3.10+ allowed-tools:
run_shell_command
read_file
write_file

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

Non-Linear PRS: Capture gene-gene interactions via deep learning.
Network Integration: Incorporate protein-protein interaction networks.
Interpretability: Identify important pathways and gene modules.
Cross-Ancestry Transfer: Improved portability via learned biology.
Multi-Task Learning: Joint modeling of related traits.
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

Input: Individual genotypes, PPI network, training phenotypes.
Gene Summarization: Aggregate SNPs to gene-level scores.
Network Encoding: Learn representations on PPI graph.
Prediction: Non-linear disease risk prediction.
Interpretation: Extract important genes and pathways.
Cross-Ancestry: Apply to diverse populations.
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

Interpretability Trade-offs: More complex = less interpretable
Computational Requirements: GPU accelerates training
Network Quality: PPI accuracy affects results
Gene Mapping: SNP-to-gene assignment matters
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

PRS-Net Deep Learning Agent for Polygenic Risk Score Prediction