Background
Hereditary hemorrhagic telangiectasia (HHT) is considered a fully
penetrant autosomal dominant disorder characterized by the development of
arteriovenous malformations. Up to 96% of HHT cases are caused by
heterozygous loss-of-function mutations
inACVRL1orENG,
which encode proteins that function in bone morphogenetic protein signaling.
HHT prevalence is estimated at 1 in 5000 and is accordingly classified as
rare. However, HHT is suspected to be underdiagnosed due to variable age of
onset and expressivity and lack of awareness of HHT among the medical
community.
Methods
To estimate the true prevalence of HHT, we summed allele frequencies of
predicted pathogenic variants
inACVRL1andENGusing
three methods. For method one, we included Genome Aggregation Database
(gnomAD v4.1) variants with ClinVar annotations of pathogenic or likely
pathogenic, plus unannotated variants with a high probability of causing
disease. For method two, we evaluated
allACVRL1andENGgnomAD
variants using threshold filters based on accessiblein
silicopathogenicity prediction algorithms. For method
three, we developed a machine learning-based classification system to
improve the classification of missense variants.
Results
Based on gnomAD variants, we calculated an HHT prevalence of between 2.1
in 5000 (method 1, most conservative) and 11.9 in 5000 (method 3, least
conservative), or roughly 2 to 12-times higher than current estimates.
Application of our machine learning-based classification method, which
performed with over 97% accuracy, revealed missense variants as the greatest
contributor to pathogenic allele frequency and similar HHT prevalence across
genetic ancestries.
Conclusions
Our results support the notion that HHT is underdiagnosed and that HHT
may not actually be a ”rare” disease.