BibTex format
@article{Macko:2026:qjmed/hcag209,
author = {Macko, A and Pecon-Slattery, J and Shovlin, CL},
doi = {qjmed/hcag209},
journal = {QJM: An International Journal of Medicine},
title = {Translating evolutionary history and protein-focussed machine learning supports increased prevalence of hereditary haemorrhagic telangiectasia, one of the most common inherited disorders},
url = {http://dx.doi.org/10.1093/qjmed/hcag209},
year = {2026}
}
RIS format (EndNote, RefMan)
TY - JOUR
AB - <jats:title>Abstract</jats:title> <jats:sec> <jats:title>Background</jats:title> <jats:p>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.</jats:p> </jats:sec> <jats:sec> <jats:title>Aim</jats:title> <jats:p>To test, we examined if missense variants in HHT disease-causing genes may have been misclassified as pathogenic (LP/P) or benign (B/LB).</jats:p> </jats:sec> <jats:sec> <jats:title>Design</jats:title> <jats:p>Evaluation of ClinVar-annotated missense variants in ENG, ACVRL1 and SMAD4.</jats:p> </jats:sec> <jats:sec> <jats:title>Methods</jats:title> <jats:p>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</jats:p> </jats:sec> <jats:sec> <jats:title>Results</jats:title> <jats:p>ClinVar annotations were commonly conservative with 35–90% of rare missense substitutions in ENG, ACVRL1 and SMAD4 classified as variants of uncertain significance. CodeXome identified 92% of ClinVar-annotated B/LB variants were shared with other primate species, supporting their benign classification. AlphaMissense metrics stro
AU - Macko,A
AU - Pecon-Slattery,J
AU - Shovlin,CL
DO - qjmed/hcag209
PY - 2026///
SN - 1460-2725
TI - Translating evolutionary history and protein-focussed machine learning supports increased prevalence of hereditary haemorrhagic telangiectasia, one of the most common inherited disorders
T2 - QJM: An International Journal of Medicine
UR - http://dx.doi.org/10.1093/qjmed/hcag209
UR - https://doi.org/10.1093/qjmed/hcag209
ER -