Molecular Diagnosis as a Probability of Necessity

Genetic diagnosis requires attributing a patient's disease to a variant. We develop the Probability of Necessity (PN), a counterfactual estimand giving the probability that disease would not have occurred absent a germline variant, given both are observed; and we use this formalism to explore the logic and limitations of attributing a disease to genetic variants. We show PN dissociates from penetrance: identical penetrance yields different PN depending on baseline disease prevalence. Case selection biases PN estimation, favoring population-scale cohorts over ascertained case series. PN falls as competing-cause prevalence rises, formalizing why necessity differs from penetrance under causal heterogeneity. We introduce the Probability of Mediated Necessity (PMN), updating PN using mediator biomarkers proxying a variant's operative pathway. Using UK Biobank data, we estimate PN and PMN for rare LDLR variants in ischemic heart disease (IHD, ICD-10 I25), using hyperlipidemia (E78) as mediator, stratifying by ClinVar class and computational pathogenicity score. PN resolves heterogeneity within the variant of unknown significance (VUS) class beyond classification alone. Replicating in GBA1 carriers with Parkinson's disease, a mechanistically distinct, low-penetrance system, reproduces this ordering and shows high PN despite modest penetrance. These results support further development of causal necessity as a framework in genetic diagnostics with implications for policy and clinical decisions in both monogenic and common complex diseases.