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◆ QJM : monthly journal of the Association of Physicians2026-08-26

Translating evolutionary history and protein-focused machine learning supports increased prevalence of hereditary haemorrhagic telangiectasia, one of the most common inherited disorders.

Adriana Macko, Jill Pecon-Slattery, Claire L Shovlin

一句话结论 · In one sentence

Our findings suggest tools to simplify variant pathogenicity predictions; molecular diagnoses to revisit for HHT families, but do not materially influence prevalence estimates for 'genetic' HHT, challenging current clinical policies, training and standards.

原始摘要(英文原文)· Original abstract
BACKGROUND: Recent genetic data suggest hereditary haemorrhagic telangiectasia (HHT) is 2-12 times more common than the clinically-ascertained prevalence, potentially above the 'rare disease' designation threshold, and undermining clinical predictions for asymptomatic individuals diagnosed by genetic testing. AIM: To test, we examined if missense variants in HHT disease-causing genes may have been misclassified as pathogenic (LP/P) or benign (B/LB). DESIGN: Evaluation of ClinVar-annotated missense variants in ENG, ACVRL1 and SMAD4. METHODS: Human-independent methods using CodeXome for pan-primate evolutionary history, and AlphaMissense which incorporates AlphaFold predictions for protein misfolding were used to validate/reclassify pathogenic and benign missense variants. RESULTS: ClinVar annotations were commonly conservative with 35-90% of rare missense substitutions in ENG, ACVRL1 and SMAD4 classified as variants of uncertain significance (VUS). CodeXome identified 92% of ClinVar-annotated B/LB variants were shared with other primate species, supporting their benign classification. AlphaMissense metrics strongly correlated with CodeXome, and 380/403 (94.3%) variants matched ClinVar benign-pathogenic annotations. However, a small number of variants showed conflicting classifications with ClinVar: 19/293 (6.5%) appeared to be over-called as LP/P by ClinVar representing 15/408 (3.7%) of genotyped families at Imperial, while 4/110 (3.6%) were apparently under-called as B/LB, and not accessible through clinical gene test reports. Newer pathobiological understanding of variants, and recognition of shared familial tendencies reflecting non-HHT heritable burdens were identified as possible explanations of over-calls. CONCLUSIONS: Our findings suggest tools to simplify variant pathogenicity predictions; molecular diagnoses to revisit for HHT families, but do not materially influence prevalence estimates for 'genetic' HHT, challenging current clinical policies, training and standards.
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Translating evolutionary history and protein-focused machine learning supports increased prevalence of hereditary haemorrhagic telangiectasia, one of the most common inherited disorders. — 科研速览 Science Skim