Psychiatric and neurodegenerative diseases are not isolated pathologies — they are distinct temporal projections of a single shared latent manifold. This framework unifies their genetics, proteomics, and pathway biology.
The holomorphic modeling approach is grounded in landmark multi-omics findings from Wingo et al. (2022), which systematically mapped shared genetic and molecular architecture across the major brain disease categories.
Each disease is modeled as a 6-dimensional state projected from a shared latent substrate Ω. Different diseases occupy different regions and temporal windows of the same underlying manifold — not separate systems.
The holomorphic pipeline carries structure forward through each representational layer — from genotype to protein to transcript to network to biology — without collapsing relational information.
25 genome-wide association studies across 3 disease families. Up to 807,553 participants.
LD score regression quantifies pairwise genetic correlations across all 24 brain traits (300+ pairs).
Proteome-wide association: 722 brain proteomes. 25 independent PWAS identify 839 cis-regulated proteins.
Mendelian randomization disambiguates pleiotropy from LD. Bayesian colocalization confirms causal variants.
888 human brain transcriptomes. 615 psychiatric + 64 neurodegen. causal mRNAs identified.
BioGRID physical PPIs map network topology. Gene set enrichment extracts dominant pathway basins.
The unified manifold maps across 24 brain traits in three groups — each a distinct temporal and mechanistic projection of the shared substrate Ω.
The disease is not changing type — it is changing projection regime. Treating shared mechanisms early may alter late-life neurodegenerative risk.
These 13 proteins are singular intersection points where the psychiatric and neurodegenerative manifold sheets meet. They represent 30% of all neurodegenerative causal proteins, and are the highest-priority targets for cross-disease therapeutic development. The two "dual-layer" proteins (ADAM10, CCDC6) are stable across both protein and mRNA representational levels.
Beyond the 13 directly shared proteins, physical PPI data (BioGRID) reveals 120 physical interactions between 99 psychiatric and 30 neurodegenerative causal proteins — yielding 118 total shared/interacting proteins. This is 2.6-fold more than expected by chance (bootstrap p = 0.003). The manifold is not just connected at isolated points; it has network curvature. Notable interaction hubs: PDHA1 · MAPT · LACTB · SCFD1 · CCDC6 · STX6
Gene set enrichment analysis (GSEA) condenses the shared causal protein network into three dominant pathway basins — the dual-core engine of the shared disease field.
The cross-disease field is not random pleiotropy — it is a conserved bioenergetic-synaptic control architecture, mirrored at both protein and transcript levels. The transcript layer adds chromatin/RNA regulation as an upstream control shell: 𝓣shared → 𝓟shared → 𝓜shared. Notably, only 2 of the 13 bridge proteins (ADAM10 & CCDC6) are validated at both molecular layers — making them the most robust mechanistic anchors in the model.
The multi-dimensional holomorphic geometric modeling framework enables a new generation of cross-disease research — from early intervention design to precision therapeutic targets.
Extend the holomorphic state-space representation to cover additional disease domains, incorporate longitudinal biobank data, and formally characterize the projection operators Πᵢ connecting Ω to each observable phenotype.
Perform deep functional characterization of 𝓑PN — particularly ADAM10, CCDC6, MAPT, and AKT3 — across cell types, brain regions, and disease stages to identify tractable intervention points.
Use longitudinal cohort data to empirically validate the temporal holomorphic model — testing whether psychiatric phenotype severity at t₁ predicts structural changes at t₂ and neurodegeneration risk at t₃ through shared protein expression.
Map the full 118-protein interacting causal network using high-resolution proximity proteomics and spatial transcriptomics — particularly resolving the 66-protein mitochondrial interactome and its synaptic coupling.
Leverage the shared mechanism kernel to design therapeutic strategies targeting the bioenergetic-synaptic dual core (𝓢synapse ⊗ 𝓜mito) — potentially modifying both psychiatric symptoms and long-term neurodegeneration risk simultaneously.
Develop computational tools that simulate disease field propagation — allowing researchers to perturb individual nodes in the shared manifold and predict downstream phenotypic consequences across psychiatric and neurodegenerative outcome measures.