Case framing at a high level
Multisystem disease creates a representational problem. If neurologic, cardiac, muscle, vision, hearing, and metabolic features are analyzed separately, the result fragments into competing partial explanations. If they are pooled without structure, the branch with the most terms can dominate even when many terms are redundant.
We defined the desired conclusion as a cross-system explanation that remains plausible when any one organ branch is reduced or removed. Organ-specific alternatives were retained rather than penalized, because real cases can involve blended phenotypes, secondary effects, incomplete ascertainment, or more than one process.
Organ-aware phenotype graph
Observed terms were organized into neurologic, cardiac, skeletal-muscle, ophthalmologic, auditory, endocrine, hepatic, renal, and biochemical branches. Onset, progression, episodic features, triggers, frequency, and documented absence were retained. Exact terms and informative ancestors remained separately addressable.
Within each branch, redundant terms were down-weighted so multiple descriptions of one feature did not count as independent evidence. Across branches, scores were balanced to prevent the most densely phenotyped system from overwhelming the analysis. Weighting schemes were predefined and included equal-branch, observation-quality, and information-content views.
Candidate and mechanism layers
Candidate relationships included nuclear and mitochondrial genomic mechanisms, inheritance, subcellular process, tissue expression, and disease annotations. Oxidative phosphorylation, mitochondrial translation, dynamics, quality control, substrate metabolism, and cofactor pathways were kept distinct. “Mitochondrial” was not treated as one pathway.
Variant consequence and inheritance compatibility were evaluated independently of phenotype similarity. Tissue-specific heteroplasmy, threshold effects, and maternal inheritance cannot be inferred from an ontology score, so mitochondrial-DNA candidates retained explicit missing-data flags.
Sensitivity and reconciliation
We removed each organ branch, each high-influence term, and each biochemical observation in turn. We compared exact-term, ancestor-expanded, branch-balanced, and conventionally pooled rankings. Candidate stability was summarized as a branch-support profile rather than one aggregate score.
The pooled model over-weighted the most extensively documented branch. Branch balancing recovered candidates supported by multiple systems even when no single branch produced the top score. It also exposed alternatives supported almost entirely by one organ system, which are important but represent a different explanatory claim.
Technical findings
A stable core remained when neurologic, cardiac, and muscle branches were perturbed individually. These candidates were supported by shared energetic or organelle-maintenance biology and by nonredundant phenotype evidence across systems. The core was not identical to the top of any one organ-specific list.
Several alternatives were strongly dependent on a single branch or one highly specific observation. Instead of discarding them, we linked each to the confirmatory evidence most likely to change the decision: focused biochemical testing, imaging review, tissue-specific molecular analysis, inheritance clarification, or more precise onset and progression data.
Decision value
The reconciled output separates three categories: cross-system core candidates, organ-dependent alternatives, and representation-sensitive candidates. Each category implies a different next step. The first supports integrated mechanism review, the second supports targeted specialty or biochemical evaluation, and the third supports phenotype clarification before more computation.
This structure is more honest than a single list and more useful than separate organ lists. It shows which candidate explains the pattern, which explains only part of it, and which appears high because of how the phenotype was encoded.
What the analysis establishes
A cross-system core survived branch ablation
Several candidates retained support across neurologic, cardiac, and muscle representations rather than depending on one organ list.
Pooled rankings were branch-size sensitive
The system with the most recorded terms exerted disproportionate influence without redundancy and branch balancing.
Organ-dependent alternatives remained valuable
Strong single-branch candidates were retained with targeted confirmation needs instead of forced into a unified mechanism.
The model prioritized new observations
Term influence identified the phenotyping and biochemical questions most likely to resolve candidate order.
What we conclude
We conclude that multisystem phenotype reconciliation requires a branch-aware model. The strongest cross-system candidates are those supported by nonredundant observations across organs and stable to removal of any one phenotype branch. A pooled term list cannot provide that assurance.
The next step is evidence acquisition, not automatic selection of the highest rank. Biochemical assays, inheritance review, tissue-aware molecular analysis, and clarification of high-influence phenotypes should be chosen according to the candidate’s branch-support profile. This makes the computational result a plan for resolving uncertainty.
Limitations
- Ontology annotations incompletely represent severity, longitudinal course, and quantitative biochemical measurements.
- Tissue-specific heteroplasmy and threshold effects are not inferable from aggregate phenotype similarity.
- Branch balancing involves value judgments that must be reported and stress-tested.
- A stable cross-system fit can still reflect correlated secondary effects rather than one primary mechanism.