PRISMA: A tensor-based framework for deconstructing the genetic architecture of complex diseases, with application to diabetic retinopathy

Abstract

Complex-disease GWAS compress heterogeneous, tissue-dependent genetic effects into global locus-level statistics, masking local regulatory routes through which polygenic risk contributes to disease architecture. Here we introduce PRISMA (Polygenic Risk Integration via Summary-statistics Multi-tissue Array-decomposition), a summary-statistics framework for quantifying tissue-resolved genetic heterogeneity hidden within aggregate GWAS signals. PRISMA integrates GWAS and multi-tissue cis-eQTL data using graph Laplacian-regularized block-wise factorization, preserving local linkage disequilibrium topology during decomposition. Applied to diabetic retinopathy, PRISMA deconvolved aggregated polygenic risk into three tissue-biased genetic axes corresponding to vascular-metabolic, systemic immune-inflammatory, and retina-specific neurodegenerative trajectories. The framework prioritized 549 axis-associated targets, including 403 below the conventional genome-wide significance threshold, and showed higher tissue-regulatory resolution than PCA, NMF, and K-means. A height GWAS negative-control analysis supported trait-dependent regulatory reprioritization within a shared eQTL-mappable framework. Independent single-cell transcriptomic analyses supported axis-specific enrichment across fibrovascular, immune, and retinal compartments. Exploratory vitreous humor proteomic and metabolomic profiling further nominated candidate molecular correlates of downstream convergence. PRISMA reframes complex-disease GWAS from aggregate locus discovery toward quantitative, LD-aware mapping of tissue-resolved genetic trajectories.