GeneRisk: An Explainable, Privacy-Preserving Framework for Multi-Disease Polygenic Risk Assessment and Evidence-Grounded Clinical Guidance

While high-throughput genomic profiling provides unprecedented resolution into inherited polygenic vulnerability, clinical translation remains constrained by three pervasive barriers: high computational barriers to client-side Variant Call Format (VCF) analysis, the black-box nature of complex machine learning classifiers, and severe privacy risks tied to unencrypted cloud transmission of raw genomic sequence data. In this paper, we present GeneRisk, an end-to-end, privacypreserving computational intelligence platform for explainable multi-disease risk assessment. GeneRisk implements a zero-disk client-side streaming parser capable of extracting clinically validated single-nucleotide polymorphisms (SNPs) across a 250 kb linkage disequilibrium (LD) clumping window at genome-wide significance (p < 5 × 10⁻⁸). These genomic factors are integrated with patient lifestyle, demographic, and biometric parameters via a calibrated XGBoost late-fusion ensemble. Global and instance-level interpretability is established using Game-Theoretic TreeSHAP, decomposing individual multi-morbidity projections into exact additive log-odds attributions and proportional heritability splits. To bridge quantitative risk scores with clinical decision-making, an evidence-grounded Retrieval-Augmented Generation (RAG) pipeline links dense vector embeddings of clinical guidelines (AHA, ADA, NCCN, CPIC) via Qdrant to an optimized multi-model inference engine. Evaluated across N = 5,000 patient profiles across five major clinical panels, the framework demonstrated robust discriminatory power, achieving 5- fold cross-validation ROC-AUCs of 0.7113 ± 0.0131 for Type 2 Diabetes, 0.6918 ± 0.0361 for Alzheimer's Disease, and 0.6916 ± 0.0258 for Coronary Artery Disease. In live clinical benchmark trials, the RAG architecture achieved an empirically measured Overall Groundedness of 0.9053 ± 0.0306 ('Very Good' grade), 100% citation accuracy, and 91.25% numerical consistency. By eliminating raw genomic data uploads and providing actionable, auditable explanations, GeneRisk establishes a robust, privacycompliant foundation for genomic intelligence in primary healthcare.