Anthony R Anzell, Carter White, Brenda Diergaarde, Jenna C Carlson, Beth L Roman
{"title":"从GnomAD等位基因频率计算出遗传性出血性毛细血管扩张症在ENG和ACVRL1中预测的致病变异。","authors":"Anthony R Anzell, Carter White, Brenda Diergaarde, Jenna C Carlson, Beth L Roman","doi":"10.1161/CIRCGEN.124.005061","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Hereditary hemorrhagic telangiectasia (HHT) is a near-fully penetrant autosomal dominant disorder characterized by nosebleeds, anemia, and arteriovenous malformations. The great majority of HHT cases are caused by heterozygous loss-of-function mutations in <i>ACVRL1</i> or <i>ENG</i>, 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.</p><p><strong>Methods: </strong>To estimate the true prevalence of HHT, we summed allele frequencies of predicted pathogenic variants in <i>ACVRL1</i> and <i>ENG</i> using 3 methods. For method 1, 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 2, we evaluated all <i>ACVRL1</i> and <i>ENG</i> gnomAD variants using threshold filters based on accessible in silico pathogenicity prediction algorithms. For method 3, we developed a machine learning-based classification system to improve the classification of missense variants.</p><p><strong>Results: </strong>We calculated an HHT prevalence of between 2.1 in 5000 and 11.9 in 5000, or 2 to 12× higher than current estimates. Application of our machine learning-based classification method revealed missense variants as the greatest contributor to pathogenic allele frequency and similar HHT prevalence across genetic ancestries.</p><p><strong>Conclusions: </strong>Our results support the notion that HHT is underdiagnosed and that HHT prevalence may be above the threshold of a rare disease.</p>","PeriodicalId":10326,"journal":{"name":"Circulation: Genomic and Precision Medicine","volume":" ","pages":"e005061"},"PeriodicalIF":5.5000,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Hereditary Hemorrhagic Telangiectasia Prevalence Estimates Calculated From GnomAD Allele Frequencies of Predicted Pathogenic Variants in <i>ENG</i> and <i>ACVRL1</i>.\",\"authors\":\"Anthony R Anzell, Carter White, Brenda Diergaarde, Jenna C Carlson, Beth L Roman\",\"doi\":\"10.1161/CIRCGEN.124.005061\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Hereditary hemorrhagic telangiectasia (HHT) is a near-fully penetrant autosomal dominant disorder characterized by nosebleeds, anemia, and arteriovenous malformations. The great majority of HHT cases are caused by heterozygous loss-of-function mutations in <i>ACVRL1</i> or <i>ENG</i>, 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.</p><p><strong>Methods: </strong>To estimate the true prevalence of HHT, we summed allele frequencies of predicted pathogenic variants in <i>ACVRL1</i> and <i>ENG</i> using 3 methods. For method 1, 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 2, we evaluated all <i>ACVRL1</i> and <i>ENG</i> gnomAD variants using threshold filters based on accessible in silico pathogenicity prediction algorithms. For method 3, we developed a machine learning-based classification system to improve the classification of missense variants.</p><p><strong>Results: </strong>We calculated an HHT prevalence of between 2.1 in 5000 and 11.9 in 5000, or 2 to 12× higher than current estimates. Application of our machine learning-based classification method revealed missense variants as the greatest contributor to pathogenic allele frequency and similar HHT prevalence across genetic ancestries.</p><p><strong>Conclusions: </strong>Our results support the notion that HHT is underdiagnosed and that HHT prevalence may be above the threshold of a rare disease.</p>\",\"PeriodicalId\":10326,\"journal\":{\"name\":\"Circulation: Genomic and Precision Medicine\",\"volume\":\" \",\"pages\":\"e005061\"},\"PeriodicalIF\":5.5000,\"publicationDate\":\"2025-09-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Circulation: Genomic and Precision Medicine\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1161/CIRCGEN.124.005061\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"CARDIAC & CARDIOVASCULAR SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Circulation: Genomic and Precision Medicine","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1161/CIRCGEN.124.005061","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CARDIAC & CARDIOVASCULAR SYSTEMS","Score":null,"Total":0}
Hereditary Hemorrhagic Telangiectasia Prevalence Estimates Calculated From GnomAD Allele Frequencies of Predicted Pathogenic Variants in ENG and ACVRL1.
Background: Hereditary hemorrhagic telangiectasia (HHT) is a near-fully penetrant autosomal dominant disorder characterized by nosebleeds, anemia, and arteriovenous malformations. The great majority 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.
Methods: To estimate the true prevalence of HHT, we summed allele frequencies of predicted pathogenic variants in ACVRL1 and ENG using 3 methods. For method 1, 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 2, we evaluated all ACVRL1 and ENG gnomAD variants using threshold filters based on accessible in silico pathogenicity prediction algorithms. For method 3, we developed a machine learning-based classification system to improve the classification of missense variants.
Results: We calculated an HHT prevalence of between 2.1 in 5000 and 11.9 in 5000, or 2 to 12× higher than current estimates. Application of our machine learning-based classification method 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 prevalence may be above the threshold of a rare disease.
期刊介绍:
Circulation: Genomic and Precision Medicine is a distinguished journal dedicated to advancing the frontiers of cardiovascular genomics and precision medicine. It publishes a diverse array of original research articles that delve into the genetic and molecular underpinnings of cardiovascular diseases. The journal's scope is broad, encompassing studies from human subjects to laboratory models, and from in vitro experiments to computational simulations.
Circulation: Genomic and Precision Medicine is committed to publishing studies that have direct relevance to human cardiovascular biology and disease, with the ultimate goal of improving patient care and outcomes. The journal serves as a platform for researchers to share their groundbreaking work, fostering collaboration and innovation in the field of cardiovascular genomics and precision medicine.