Hereditary hemorrhagic telangiectasia prevalence estimates calculated from gnomAD allele frequencies of predicted pathogenic variants in ENG and ACVRL1

Abstract

BackgroundHereditary 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 in ACVRL1 or ENG, 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. MethodsTo estimate the true prevalence of HHT, we summed allele frequencies of predicted pathogenic variants in ACVRL1 and ENG using 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 all ACVRL1 and ENG gnomAD variants using threshold filters based on accessible in silico pathogenicity prediction algorithms. For method three, we developed a machine learning-based classification system to improve the classification of missense variants. ResultsBased 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. ConclusionsOur results support the notion that HHT is underdiagnosed and that HHT may not actually be a "rare" disease.