The population frequency of Marfan syndrome and the associated cardiac risks in a normal population

The population frequency of Marfan syndrome and the associated cardiac risks in a normal population | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article The population frequency of Marfan syndrome and the associated cardiac risks in a normal population K Choi, M Huang, J Savige This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4975062/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 18 Mar, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Marfan syndrome is an autosomal dominantly (AD)-inherited disease that results from pathogenic variants in the Fibrillin 1 ( FBN1 ) gene, and is characterised by tall stature, elongated limbs and digits, lens abnormalities and aortic root dilatation, aneurysms and dissection but milder forms also occur. Radiological imaging suggests that Marfan syndrome affects between one in 3000 and 5000 of the population. The aim of this study was to determine the population frequency of Marfan syndrome from the number of pathogenic FBN1 variants found in a normal variant database. FBN1 variants were downloaded from gnomAD v2.1.1 and annotated with ANNOVAR. The population frequency was determined from the number of pathogenic null and structural variants, and the number of predicted pathogenic missense changes classified by rarity and computational scores. This population frequency was then compared with the frequencies in the control subset, and from gnomAD variants assessed as Pathogenic or Likely pathogenic in the ClinVar or LOVD databases. Our strategy identified predicted pathogenic FBN1 variants in one in 416 individuals, which was confirmed in the control subset (one in 356, p NS). Predicted pathogenic variants were most common in East Asian people (one in 243, p < 0.0001) and least common in Ashkenazim (one in 5,185, p = 0.0082). The population frequencies based on pathogenic variants in the ClinVar or LOVD databases were one in 718 and one in 1014 respectively. Null variants which are associated with aortic aneurysms affected only one in 8624. Thus, Marfan syndrome is more common than previously recognised. Emergency departments and cardiac clinics in particular should be aware of undiagnosed Marfan syndrome and its cardiac risks, but many individuals may have a milder phenotype. Biological sciences/Genetics Health sciences/Cardiology Health sciences/Molecular medicine Health sciences/Risk factors Introduction Marfan syndrome (MIM154700) is a genetic disease that primarily affects connective tissue in the skeleton, cardiovascular system and eye 1,2 . It results from pathogenic variants in the gene for Fibrillin-1 ( FBN1 ) 3-6 which codes for the main component of the extracellular microfibrils that form elastic fibres 7,8 . Marfan syndrome demonstrates autosomal dominant inheritance that is highly penetrant but with variable expression even within individual families (Table 1) 9 . The disease occurs worldwide 10 . Marfan syndrome is characterised by tall stature, elongated limbs and digits, chest wall and spine abnormalities, cardiovascular anomalies with aortic root dilatation, aneurysms, dissection and rupture, as well as myopia and ectopic lens 11 . Aneurysms also affect the thoracic and abdominal aorta. The diagnosis is usually made using the revised Ghent Nosology based on family history, clinical examination, aorta imaging and, in some cases, genetic testing 12 . Affected individuals are particularly susceptible to lethal complications from aortic dissection and ruptured aneurysms 13,14 . There is no specific treatment but control of hypertension and lifestyle factors are important 1,15,16 . Surgical interventions include prophylactic aortic and valvular repair. Some individuals with Marfan syndrome have isolated features such as the ectopic lens, ascending aortic aneurysm or skeletal features. Milder forms are recognised increasingly. Affected individuals are not necessarily taller than average. Mitral valve prolapse affects half but mitral regurgitation is often mild 17,18 . A mild cardiomyopathy unrelated to valve disease occurs too 19 . Some have pectus excavatum, scoliosis, mild arachnodactyly, 19 or emphysema with upper lobe bullae that predispose to spontaneous pneumothorax. Joint hypermobility and high arched palate are considered non-specific 12 . Genetic testing represents the gold standard for the diagnosis of Marfan syndrome. All types of genetic variants are found and they are typically different in each family 20 . Null variants appear to be associated more often with aortic aneurysms and rupture 21 . Many missense variants occur within the 47 repeated epidermal growth factor (EGF)-like domains or from cysteine substitutions that interfere with disulfide bond formation and Fibrillin1 expression 22,23 . Marfan syndrome is reported to affect between one in 3,000 and 5,000 of the population 24-26 , based on radiological phenotyping 4,27 or clinical examination 24,26 . However these are probably underestimates because of the variable phenotype. Nevertheless it remains important to diagnose Marfan syndrome because of its associated cardiac risk. Genetic testing makes the diagnosis with certainty, and allows early treatment and monitoring, but the interpretation of FBN1 variants may still be problematic. This study determined the population frequency of Marfan syndrome from assessing genetic variants in the gnomAD database for pathogenicity using the principles underlying the ACMG/AMP guidelines 28 but without having access to clinical data. Similar but slightly different and less rigorous strategies have been used previously to estimate the population frequencies of many other rare genetic diseases, including AD Polycystic Kidney Disease, XL and AD Alport syndrome, Fabry disease, Gitelman disease, mucopolysaccharidoses and Menke syndrome 29-34 . In at least some cases, this approach has been confirmed with an independent non-genetic method such as histology or biochemical testing 29,34 . In addition this study determined the population frequency using the control gnomAD subset and variants independently identified as Pathogenic or Likely Pathogenic in the ClinVar or LOVD FBN1 databases where clinical data were typically used in the assessment. Methods Population database The population frequency of Marfan syndrome was estimated using genetic variants (GRCh37/hg19) from the Genome Aggregation Database (gnomAD v2.1.1, www.gnomAD.broadinstitute.org, n=141,456) and its control subset (gnomAD v2.1.1. n=52,806) (canonical transcript: ENST00000316623.5). gnomAD is an aggregated database with information from 125,748 Whole Exome Sequencing (WES) and 15,708 Whole Genome Sequencing (WGS) samples. Participants were unrelated individuals with adult-onset illnesses such as diabetes, or cardiac or neuropsychiatric diseases, but not with severe paediatric conditions or their family members. Sex, age and ancestry but not clinical data were available. Databases were examined between July 2023 and April 2024, and checked between April and June 2024. Written informed consent had been provided by all participants in gnomAD for the use of their anonymised data at recruitment into the original studies so that this project did not require IRB approval. Annotation Variants in the FBN1 gene from gnomAD were downloaded and annotated in ANNOVAR (https://annovar.openbioinformatics.org/), and assessed for pathogenicity based on whether they were structural or null in nature, and in the case of missense changes, whether they were rare, pathogenic in three computational tools and affected an amino acid that was conserved in vertebrates. This population frequency was then compared with that found in the Control subset, or from the number of variants found in gnomAD that were assessed as Pathogenic or Likely Pathogenic by the ClinVar or LOVD databases. Filtering processes Variants that were located in the 5’ or 3’ UTR, in the intronic or non-canonical splice regions, or were synonymous were excluded. Structural variants. Structural variants were only available for a subset of 10,847 individuals, and any considered pathogenic in gnomAD were classified here as Predicted pathogenic. Null variants. Null variants with any allele count including nonsense (stop gained), frameshift and canonical splice site variants were classified as Predicted pathogenic. Null variants in the last exon or last 50 nucleotides of the penultimate exon were excluded since they were assumed to not result in nonsense-mediate decay 35 . Missense variants. Missense variants were Predicted Pathogenic if they occurred in fewer than 6 individuals in gnomAD; and were pathogenic in all bioinformatic prediction tools: PolyPhen-2 (PP2, score ≥ 0.95, http://genetics.bwh.harvard.edu/pph2/), Sorting Intolerant From Tolerant (SIFT4G, score ≤ 0.05 from Varsome, https://varsome.com/), MutationTaster (MT, prediction: ‘Disease-causing, D or A https://www.mutationtaster.org/) and if they affected an amino acid that was conserved in vertebrates (humans, mice and chickens). Conservation was assessed with Clustal Omega (https://www.ebi.ac.uk/Tools/ msa/clustalo/) based on reference sequences downloaded from Ensembl (http://asia.ensembl.org/index.html), where residues had the same physicochemical properties (indicated with an asterisk (*) or colon (:). Our assessment strategy for ‘Predicted Pathogenic missense variants’ was validated as follows. Twenty Pathogenic or Likely Pathogenic and 20 Benign or Likely Benign missense variants were selected randomly from ClinVar and LOVD and assessed according to our strategy for Predicted pathogenicity. The sensitivities, specificities, and positive and negative predictive values were then calculated for both ClinVar and LOVD (Table 2) . All values were at least 80%. Missense variants were also assessed by REVEL (score >0.932) which has the best performance for distinguishing pathogenic from rare neutral variants with allele frequencies less than 0.5% 36,37 . The population frequencies were then calculated based on the assumption that each variant was found in only one individual. Population frequencies of Predicted pathogenic variants in different ancestries Population frequencies of Predicted Pathogenic variants were calculated in people from all 8 ancestries available in gnomAD (African/African American, Latino/Admixed American, Ashkenazi Jewish, East Asian, Finnish, European (Non-Finnish), South Asian, and Others). Population frequencies of Marfan syndrome using different databases The population frequencies of Predicted Pathogenic variants were also calculated based on how often variants assessed as Pathogenic or Likely Pathogenic in ClinVar and LOVD databases were also found in gnomAD (ClinVar https://www.ncbi.nlm.nih.gov/clinvar/; LOVD, https://www.lovd.nl/). Variants in ClinVar classified as Conflicting with both a VUS and Pathogenic or Likely pathogenic assessment were considered Predicted pathogenic for the calculation. Variants were included even if the allele were found more than 5 times because the initial assessments had often included clinical data that increased the likelihood of being correct. Statistical analysis Results were compared with Chi-square with Yates correction (Graph pad). Results Predicted pathogenic FBN1 variants in overall gnomAD cohort The FBN1 gnomAD dataset included 1157 variants including 21 structural, 17 null and 1119 missense changes from a mean of 241,491 alleles or 120,745 individuals (Supplementary material includesFiltering FBN1 for Predicted pathogenic variants;andAssessment of our Filtering strategy). Predicted pathogenic variants comprised no structural variants, but 14 null variants and 173 Predicted pathogenic missense variants. The 14 null variants corresponded to a population frequency of one in 8624. Altogether there were 185 Predicted pathogenic variants in 293 people in gnomAD which corresponded to a population frequency of 293/120,745 or one in 412 (0.24%) (Table 3). When the missense variants identified by REVEL were included instead (42 in 76 individuals) this corresponded to a population frequency of 90/120,745 or one in 1588 (Table 4) . Predicted pathogenic FBN1 variants in gnomAD control subset The Control subset included 112 Predicted pathogenic variants in 148 people in a cohort of 52,806 individuals. This was equivalent to a population frequency of 148/52,806 (0.28%) or one in 356 which was not different from the results for the overall gnomAD cohort (Chi square = 1.399, p value = 0.24) (Table 4). Predicted pathogenic FBN1 variants in people of different ancestries in gnomAD The population frequencies of Predicted pathogenic variants varied in people of different ancestries. Predicted pathogenic variants were more common in people of East Asian (one in 243, p<0.0001) and South Asian (one in 300, p=0.0009) backgrounds than in Europeans (one in 525). Predicted pathogenic variants were least common in Ashkenazim (one in 5185, p=0.0082) (Table 5) . Predicted pathogenic FBN1 variants according to different Pathogenic variant databases ClinVar. Fifty-two Pathogenic or Likely Pathogenic variants in ClinVar were found in 168 individuals in gnomAD. This corresponded to a population frequency of 168/120,745 or one in 718. LOVD. Twenty-one Pathogenic or Likely Pathogenic variants in LOVD were found in 119 individuals in gnomAD. This corresponded to a population frequency of 119/120,745 or one in 1014 (Table 4). Discussion This study used diverse approaches to demonstrate that genetically-diagnosed Marfan syndrome is more common than the previous estimates of one in three to five thousand. Our strategy based on identifying Predictive pathogenic or rare damaging variants in FBN1 found that Marfan syndrome affected one in 412 of the gnomAD population and one in 356 of its control subset. An alternative approach using highly stringent REVEL scores to assess missense variants found a population frequency of one in 1588. Pathogenic or Likely Pathogenic assessments in ClinVar or LOVD corresponded to population frequencies of one in 718 or one in 1014 respectively. These results indicate that Marfan syndrome affects a range of individuals from one in 356 to one in 1588 of the population. Predicted pathogenic variants were most common in people of East or South Asian ancestries. Each of the approaches used here had strengths and weaknesses, with some likely to overestimate and others to underestimate population frequencies. Missense variants are always the most difficult to assess accurately for pathogenicity. Our strategy for missense variants used a more rigorous threshold than previous assessments in other rare diseases (rare variant, positive in three computational tools, and affecting a conserved residue) 29-34 . These criteria performed well in the validation studies. When a high REVEL score (>0.932) 36 was used for missense variants instead the population frequency was still one in 1588. Interestingly REVEL scores include all three computational tools (PP2, SIFT and MT) used in our approach. Both ClinVar and LOVD datasets are valuable because their assessments use clinical data and rely on multiple submissions from diagnostic laboratories to confirm pathogenicity in individuals where Marfan syndrome is suspected clinically. ClinVar was established after the publication of the ACMG guidelines 35 and depends on variants submitted by diagnostic testing laboratories that use these guidelines. The LOVD database includes earlier submissions made before the ACMG guidelines and large datasets for calculating allele frequencies were available. Both databases depend on the ad hoc submission of further variants rather than the systematic examination of all possible variants in a gene, which means that they underestimate population frequencies. This study did not formally evaluate the number of pathogenic variants from the HGMD and UMD-FBN1 databases because they both included many ‘pathogenic’ variants that were present in large numbers of individuals. Although gnomAD included samples from participants with known cardiac disease there was no difference in the number with a Predicted pathogenic FBN1 variant and Marfan syndrome (one in 412) and in the control cohort (one in 356, pNS) with no known cardiac disease. Indeed our population frequencies may even underestimate the population frequency of Marfan syndrome because up to 10% of cohorts with clinical features of Marfan syndrome have no pathogenic variant identified in FBN1 38 . Instead theyhave possible complex structural variants, deletions, or intronic regulatory sequences that are not detected by the Whole Exome Sequencing, or disease caused by variants in other genes 39 . Our results that Marfan syndrome is more common than previously estimated do not mean that aortic dilatation and rupture are being overlooked. This study found that null variants affected one in 8624 individuals and it is these variants that are usually associated with a more severe phenotype and a greater likelihood of aortic dissection and rupture 21 . No structural variants were found probably in part because so few samples were examined with Whole Genomic Sequencing. However missense variants in our study occurred nearly 20 times as often as null changes, which may explain the difference from the population frequencies observed previously since missense variants are typically associated with less severe and incompletely penetrant features 40 . Skeletal anomalies, aortic dilatation and lens abnormalities may be absent but mitral valve prolapse or mitral regurgitation may still occur. Overall, predicted pathogenic FBN1 variants were found most often in people of East and South Asian ancestries, and least often in Ashkenazim which is probably explained by their cultural and geographic isolation. Null variants which are associated with the more severe phenotype and aortic aneurysms were absent from both the Ashkenazim and Latino/Admixed American groups. The strengths of this study were the use of the large gnomAD database and the confirmation with different criteria for pathogenicity and the use of different datasets. The study’s limitations were the use of Whole Exome Sequencing in this version of gnomAD; the lack of clinical data; the difficulty with assessing missense variants; and the incompleteness of the ClinVar and LOVD databases. A more accurate population frequency of Marfan syndrome is important in order to improve clinician awareness of the disease, and the cardiac risks; for health systems to assist with health service planning; and for pharmaceutical companies to develop targeted treatments. Confirmation in further variant databases will help further improve the accuracy of population frequencies. Declarations Acknowledgements We would like to thank gnomAD, REVEL, ClinVar and LOVD for the use of their databases; the many patients who contributed to these databases; and the data contributors and developers of the in-silico tools used in this analysis (PP2, SIFT, Mutation Taster, Clustal Omega). KC undertook this project as part of her research towards an M Sc. Author contributions KC undertook the analysis, produced the tables and the first draft of the manuscript. MH helped KC with downloading the variants in ANNOVAR and with the analysis. JS devised the project, and produced the final manuscript draft. Competing interests None of the authors of this manuscript has any financial or non-financial competing interests relevant to this study or the manuscript itself. Data availability statement Data is provided within the manuscript or supplementary files. References Dietz H. FBN1-Related Marfan Syndrome. In: Adam MP, Mirzaa GM, Pagon RA, et al., eds. GeneReviews((R)). Seattle (WA)1993. Mannucci L, Luciano S, Salehi LB, et al. Mutation analysis of the FBN1 gene in a cohort of patients with Marfan Syndrome: A 10-year single center experience. Clin Chim Acta. 2020;501:154-164. Boucek RJ, Noble NL, Gunja-Smith Z, Butler WT. The Marfan syndrome: a deficiency in chemically stable collagen cross-links. N Engl J Med. 1981;305(17):988-991. Byers PH, Siegel RC, Peterson KE, et al. Marfan syndrome: abnormal alpha 2 chain in type I collagen. Proc Natl Acad Sci U S A. 1981;78(12):7745-7749. von Kodolitsch Y, Robinson PN. Marfan syndrome: an update of genetics, medical and surgical management. Heart. 2007;93(6):755-760. Rybczynski M, Bernhardt AM, Rehder U, et al. The spectrum of syndromes and manifestations in individuals screened for suspected Marfan syndrome. Am J Med Genet A. 2008;146A(24):3157-3166. Reinhardt DP, Keene DR, Corson GM, et al. Fibrillin-1: organization in microfibrils and structural properties. J Mol Biol. 1996;258(1):104-116. Zhang H, Hu W, Ramirez F. Developmental expression of fibrillin genes suggests heterogeneity of extracellular microfibrils. J Cell Biol. 1995;129(4):1165-1176. Pereira L, Levran O, Ramirez F, et al. A molecular approach to the stratification of cardiovascular risk in families with Marfan's syndrome. N Engl J Med. 1994;331(3):148-153. Judge DP, Dietz HC. Marfan's syndrome. Lancet. 2005;366(9501):1965-1976. Milewicz DM, Braverman AC, De Backer J, et al. Marfan syndrome. Nat Rev Dis Primers. 2021;7(1):64. Loeys BL, Dietz HC, Braverman AC, et al. The revised Ghent nosology for the Marfan syndrome. J Med Genet. 2010;47(7):476-485. Aranda-Michel E, Bianco V, Yousef S, et al. National trends in thoracic aortic aneurysms and dissections in patients with Marfans and Ehlers Danlos syndrome. J Card Surg. 2022;37(10):3313-3321. Bitterman AD, Sponseller PD. Marfan Syndrome: A Clinical Update. J Am Acad Orthop Surg. 2017;25(9):603-609. Brooke BS, Habashi JP, Judge DP, Patel N, Loeys B, Dietz HC, 3rd. Angiotensin II blockade and aortic-root dilation in Marfan's syndrome. N Engl J Med. 2008;358(26):2787-2795. Milewicz DM, Dietz HC, Miller DC. Treatment of aortic disease in patients with Marfan syndrome. Circulation. 2005;111(11):e150-157. Rybczynski M, Mir TS, Sheikhzadeh S, et al. Frequency and age-related course of mitral valve dysfunction in the Marfan syndrome. Am J Cardiol. 2010;106(7):1048-1053. Faivre L, Collod-Beroud G, Loeys BL, et al. Effect of mutation type and location on clinical outcome in 1,013 probands with Marfan syndrome or related phenotypes and FBN1 mutations: an international study. Am J Hum Genet. 2007;81(3):454-466. Alpendurada F, Wong J, Kiotsekoglou A, et al. Evidence for Marfan cardiomyopathy. Eur J Heart Fail. 2010;12(10):1085-1091. Du Q, Zhang D, Zhuang Y, Xia Q, Wen T, Jia H. The Molecular Genetics of Marfan Syndrome. Int J Med Sci. 2021;18(13):2752-2766. Baudhuin LM, Kotzer KE, Lagerstedt SA. Increased frequency of FBN1 truncating and splicing variants in Marfan syndrome patients with aortic events. Genet Med. 2015;17(3):177-187. Schrijver I, Liu W, Brenn T, Furthmayr H, Francke U. Cysteine substitutions in epidermal growth factor-like domains of fibrillin-1: distinct effects on biochemical and clinical phenotypes. Am J Hum Genet. 1999;65(4):1007-1020. Liu X, Liu K, Nie D, et al. Case report: Biochemical and clinical phenotypes caused by cysteine substitutions in the epidermal growth factor-like domains of fibrillin-1. Front Genet. 2022;13:928683. Groth KA, Hove H, Kyhl K, et al. Prevalence, incidence, and age at diagnosis in Marfan Syndrome. Orphanet J Rare Dis. 2015;10:153. Salik I, Rawla P. Marfan Syndrome. In: StatPearls. Treasure Island (FL)2023. Ammash NM, Sundt TM, Connolly HM. Marfan syndrome-diagnosis and management. Curr Probl Cardiol. 2008;33(1):7-39. Ha HI, Seo JB, Lee SH, et al. Imaging of Marfan syndrome: multisystemic manifestations. Radiographics. 2007;27(4):989-1004. Gudmundsson S, Singer-Berk M, Watts NA, et al. Variant interpretation using population databases: Lessons from gnomAD. Hum Mutat. 2022;43(8):1012-1030. Kondo A, Nagano C, Ishiko S, et al. Examination of the predicted prevalence of Gitelman syndrome by ethnicity based on genome databases. Sci Rep. 2021;11(1):16099. Kermond-Marino A, Weng A, Xi Zhang SK, Tran Z, Huang M, Savige J. Population Frequency of Undiagnosed Fabry Disease in the General Population. Kidney Int Rep. 2023;8(7):1373-1379. Borges P, Pasqualim G, Giugliani R, Vairo F, Matte U. Estimated prevalence of mucopolysaccharidoses from population-based exomes and genomes. Orphanet J Rare Dis. 2020;15(1):324. Kaler SG, Ferreira CR, Yam LS. Estimated birth prevalence of Menkes disease and ATP7A-related disorders based on the Genome Aggregation Database (gnomAD). Mol Genet Metab Rep. 2020;24:100602. Lanktree MB, Haghighi A, Guiard E, et al. Prevalence Estimates of Polycystic Kidney and Liver Disease by Population Sequencing. J Am Soc Nephrol. 2018;29(10):2593-2600. Gibson J, Fieldhouse R, Chan MMY, et al. Prevalence Estimates of Predicted Pathogenic COL4A3-COL4A5 Variants in a Population Sequencing Database and Their Implications for Alport Syndrome. J Am Soc Nephrol. 2021;32(9):2273-2290. Richards S, Aziz N, Bale S, et al. Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Genet Med. 2015;17(5):405-424. Pejaver V, Byrne AB, Feng BJ, et al. Calibration of computational tools for missense variant pathogenicity classification and ClinGen recommendations for PP3/BP4 criteria. Am J Hum Genet. 2022;109(12):2163-2177. Ioannidis NM, Rothstein JH, Pejaver V, et al. REVEL: An Ensemble Method for Predicting the Pathogenicity of Rare Missense Variants. Am J Hum Genet. 2016;99(4):877-885. Loeys B, De Backer J, Van Acker P, et al. Comprehensive molecular screening of the FBN1 gene favors locus homogeneity of classical Marfan syndrome. Hum Mutat. 2004;24(2):140-146. Dietz HC. Marfan syndrome GeneReviews, University of Washington, Seattle 2011. Baudhuin LM, Kotzer KE, Lagerstedt SA. Decreased frequency of FBN1 missense variants in Ghent criteria-positive Marfan syndrome and characterization of novel FBN1 variants. J Hum Genet. 2015;60(5):241-252. Tables Table 1. Marfan syndrome and FBN1 variants Disease Gene and protein features Gene function Disease mechanism Clinical features and risks Treatment Marfan syndrome (MIM154700) AD FBN1 ( Fibrillin 1) 65 exons, encodes 350kDa fibrillin; caused by all types of variants distributed throughout the gene, by a loss of function mechanism Production of glycoproteins in the extracellular matrix - structural components of microfibrils that bind calcium ions One mechanism is the disruption in cysteine binding leading to the reduced expression of Fibrillin 1 Cardiac, skeletal, ocular manifestations: aortic aneurysms, arachnodactyly, and ectopic lens Beta-blockers, beta-adrenergic receptor antagonist therapy, and surgical interventions to reduce the size of the aneurysms AD = autosomal dominant Table 2. Validation of filtering strategy for pathogenic variants in FBN1 Database Sensitivity (%) Specificity (%) Positive predictive value (PPV) (%) Negative predictive value (NPV) (%) FBN1 ClinVar (B/LB n=19/20, P/LP n=16/20) 80% 95% 94% 82% LOVD (B/LB n=20/20, P/LP n=20/20) 100% 100% 100% 100% B – Benign; LB - Likely Benign; P – Pathogenic; LP - Likely Pathogenic Table 3. Predicted population frequency of pathogenic FBN1 variants in gnomAD Pathogenic structural variants Predicted pathogenic null variants Predicted pathogenic missense variants Population frequency of predicted pathogenic variants None No. of variants No. of people No. of variants No. of people No. of variants No. of people Frequency 14 14 171 279 185 293 (177 male and 116 female) 293/120,745 (0.24%) or one in 412 Table 4. FBN1 population frequencies using different strategies REVEL Predicted pathogenic variants 42 variants in 76 people (plus 14 null variants) Frequency 90/120,745 (0.07%) = one in 1341 people Control subset Predicted pathogenic variants 112 variants in 148 people Frequency 148/52,806 (0.28%) = one in 356 people ClinVar Predicted pathogenic variants 52 variants in 168 people Frequency 168/120,745 (0.14%) = one in 718 people LOVD Predicted pathogenic variants 21 variants in 119 people Frequency 119/120,745 (0.10%) = one in 1,014 people Table 5. Predicted pathogenic null and missense variants in people of different ancestries FBN1 Predicted pathogenic null variants Predicted pathogenic missense variants Total Predicted pathogenic variants Ancestry gnomAD v.2.1.1. (n=141,456) African/ African American (n=12,487) One variant in 1 person 1/12,487 = one in 12,487 19 variants in 24 people 24/12,487 = one in 520 20 variants in 25 people 25/12,487 = one in 499 (p=0.014) Latino/ Admixed American (n=17,720) N/A 21 variants in 36 people 36/17,720 = one in 492 21 variants in 36 people 36/17,720 = one in 492 (p=0.061) Ashkenazi Jewish (n=5,185) N/A One variant in 1 person 1/5,185 = one in 5,185 One variant in 1 person 1/5,185 = one in 5,185 (p=0.0082) East Asian (n=9,977) N/A 21 variants in 41 people 41/9,977 = one in 243 21 variants in 41 people 41/9,977 = one in 243 (p<0.0001) Finnish (n=12,562) One variant in 1 person 1/12,562 = one in 12,562 8 variants in 12 people 12/12,562 = one in 1,046 9 variants in 13 people 13/12,562 = one in 966 (p=0.0446) European (n=64,603) 6 variants in 6 people 6/64,603 = one in 10,767 89 variants in 117 people 117/64,603 = one in 552 95 variants in 123 people 123/64,603 = one in 525 South Asian (n=15,308) 5 variants in 5 people 5/15,308 = one in 3,061 37 variants in 46 people 46/15,308 = one in 332 42 variants in 51 people 51/15,308 = one in 300 (p=0.0009) Other (n=3,614) One variant in 1 person 1/3,614 = one in 3,614 2 variants in 2 people 2/3,614 = one in 1,807 3 variants in 3 people 3/3,614 = one in 1,204 (p=0.2062) Total 14 variants in 14 people 14/141,456 = one in 10,104 198 variants in 279 people 279/141,456 = one in 507 212 variants in 293 people 293/141,456 = one in 482 Comparisons performed with Chi squared test with Yate’s correction Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterial.pdf Cite Share Download PDF Status: Published Journal Publication published 18 Mar, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 29 Oct, 2024 Reviews received at journal 27 Oct, 2024 Reviews received at journal 15 Oct, 2024 Reviewers agreed at journal 14 Oct, 2024 Reviewers agreed at journal 30 Sep, 2024 Reviewers invited by journal 15 Sep, 2024 Editor assigned by journal 15 Sep, 2024 Editor invited by journal 10 Sep, 2024 Submission checks completed at journal 10 Sep, 2024 First submitted to journal 25 Aug, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4975062","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":371628628,"identity":"fbc43ff5-460f-4106-9fd7-cddbdbb260d7","order_by":0,"name":"K Choi","email":"","orcid":"","institution":"The University of Melbourne, Royal Melbourne Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"K","middleName":"","lastName":"Choi","suffix":""},{"id":371628629,"identity":"6e165174-3e0b-4945-8e32-02aee3890c9e","order_by":1,"name":"M Huang","email":"","orcid":"","institution":"The University of Melbourne, Royal Melbourne Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"M","middleName":"","lastName":"Huang","suffix":""},{"id":371628630,"identity":"8d1069ec-7674-4613-82f6-ca56b91d326e","order_by":2,"name":"J Savige","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBUlEQVRIiWNgGAWjYBACxgYQacDAwA+kDjAwWCRAuMRokWwAa5EgrAUODA6AKSK0MLcff/iZp6BWzvhG7sFDNyok8hjYm7dJMNQcxu2wnhxjaR6D48ZmN/ISDueckShm4DlWJsFwDI+WhhwGyRkGxxK33cgxOJzbJpHYIJFjJsHAhkdL//PHP4Fa6jfPAGn5B9Qi/wao5R8eLTMSzCQ+GNQkGEiAtDSAbOExk2Bsw6fljZnFB4MDhjPOvDE4nHNMopiNJ63YIrEvHacWw/70xzcS/tTJ87fnGH/OqbHJ42c/vPHGh2/WuLU0gCkkZ7CBiAScGhgY5CFUHR4lo2AUjIJRMOIBAP/iVog2eXSyAAAAAElFTkSuQmCC","orcid":"","institution":"The University of Melbourne, Royal Melbourne Hospital","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"J","middleName":"","lastName":"Savige","suffix":""}],"badges":[],"createdAt":"2024-08-26 03:59:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4975062/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4975062/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-93832-6","type":"published","date":"2025-03-18T15:57:17+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":79120417,"identity":"e1e25d3c-65f7-4332-a557-2c66b0d1a9c1","added_by":"auto","created_at":"2025-03-24 16:07:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1059926,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4975062/v1/2820583f-9592-45c0-91ec-6d83a3126693.pdf"},{"id":72487582,"identity":"bf91f0b2-2019-4ef0-bae1-97d4a95200e6","added_by":"auto","created_at":"2024-12-27 19:48:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":3610458,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4975062/v1/d913101fd0347b8e05e1f45b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The population frequency of Marfan syndrome and the associated cardiac risks in a normal population","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMarfan syndrome (MIM154700) is a genetic disease that primarily affects connective tissue in the skeleton, cardiovascular system and eye \u003csup\u003e1,2\u003c/sup\u003e. It results from pathogenic variants in the gene for Fibrillin-1 (\u003cem\u003eFBN1\u003c/em\u003e) \u003csup\u003e3-6\u003c/sup\u003e which codes for the main component of the extracellular microfibrils that form elastic fibres \u003csup\u003e7,8\u003c/sup\u003e. Marfan syndrome demonstrates autosomal dominant inheritance that is highly penetrant but with variable expression even within individual families \u003cstrong\u003e(Table 1)\u003c/strong\u003e\u003csup\u003e9\u003c/sup\u003e\u003cstrong\u003e.\u003c/strong\u003e The disease occurs worldwide \u003csup\u003e10\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eMarfan syndrome is characterised by tall stature, elongated limbs and digits, chest wall and spine abnormalities, cardiovascular anomalies with aortic root dilatation, aneurysms, dissection and rupture, as well as myopia and ectopic lens \u003csup\u003e11\u003c/sup\u003e. Aneurysms also affect the thoracic and abdominal aorta. The diagnosis is usually made using the revised Ghent Nosology\u0026nbsp;based on family history, clinical examination, aorta imaging and, in some cases, genetic testing\u0026nbsp;\u003csup\u003e12\u003c/sup\u003e. Affected individuals are particularly susceptible to lethal complications from aortic dissection and ruptured aneurysms\u0026nbsp;\u003csup\u003e13,14\u003c/sup\u003e. There is no specific treatment but control of hypertension and lifestyle factors are important\u0026nbsp;\u003csup\u003e1,15,16\u003c/sup\u003e. Surgical interventions include prophylactic aortic and valvular repair.\u003c/p\u003e\n\u003cp\u003eSome individuals with Marfan syndrome \u0026nbsp;have isolated features such as the ectopic lens, ascending aortic aneurysm or skeletal features. Milder forms are recognised increasingly. Affected individuals are not necessarily taller than average. Mitral valve prolapse affects half but mitral regurgitation is often mild \u003csup\u003e17,18\u003c/sup\u003e. \u0026nbsp;A mild cardiomyopathy unrelated to valve disease occurs too\u003csup\u003e19\u003c/sup\u003e. Some have pectus excavatum, scoliosis, mild arachnodactyly, \u003csup\u003e19\u003c/sup\u003e or emphysema with upper lobe bullae that predispose to spontaneous pneumothorax. Joint hypermobility and high arched palate are considered non-specific\u003csup\u003e12\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGenetic testing represents the gold standard for the diagnosis of Marfan syndrome. All types of genetic variants are found and they are typically different in each family\u003csup\u003e20\u003c/sup\u003e. Null variants appear to be associated more often with aortic aneurysms and rupture\u003csup\u003e21\u003c/sup\u003e. Many missense variants occur within the 47 repeated epidermal growth factor (EGF)-like domains \u0026nbsp;or from cysteine substitutions that interfere with disulfide bond formation \u0026nbsp;and Fibrillin1 expression\u003csup\u003e22,23\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMarfan syndrome is reported to affect between one in 3,000 and 5,000 of the population \u003csup\u003e24-26\u003c/sup\u003e, based on radiological phenotyping\u003csup\u003e4,27\u003c/sup\u003e or clinical examination \u003csup\u003e24,26\u003c/sup\u003e. However these are probably underestimates because of the variable phenotype. Nevertheless it remains important to diagnose Marfan syndrome because of its associated cardiac risk. \u0026nbsp;Genetic testing makes the diagnosis with certainty, and allows early treatment and monitoring, but the interpretation of \u003cem\u003eFBN1\u003c/em\u003e variants may still be problematic.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study determined the population frequency of Marfan syndrome from assessing genetic variants in the gnomAD database for pathogenicity using the principles underlying the ACMG/AMP guidelines\u003csup\u003e28\u003c/sup\u003e but without having access to clinical data. Similar but slightly different and less rigorous strategies have been used previously to estimate the population frequencies of many other rare genetic diseases, including AD Polycystic Kidney Disease, XL and AD Alport syndrome, Fabry disease, Gitelman disease, mucopolysaccharidoses and Menke syndrome\u003csup\u003e29-34\u003c/sup\u003e. \u0026nbsp;In at least some cases, this approach has been confirmed with an independent non-genetic method such as histology or biochemical testing\u003csup\u003e29,34\u003c/sup\u003e. In addition this study determined the population frequency using the control gnomAD subset and variants independently \u0026nbsp;identified as Pathogenic or Likely Pathogenic in the ClinVar or LOVD \u003cem\u003eFBN1\u003c/em\u003e databases where clinical data were typically used in the assessment.\u0026nbsp;\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003ePopulation database\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe population frequency of Marfan syndrome was estimated using genetic variants (GRCh37/hg19) from the Genome Aggregation Database (gnomAD v2.1.1, www.gnomAD.broadinstitute.org, n=141,456) and its control subset (gnomAD v2.1.1. n=52,806) (canonical transcript: ENST00000316623.5). gnomAD is an aggregated database with information from 125,748 Whole Exome Sequencing (WES) and 15,708 Whole Genome Sequencing (WGS) samples. Participants were unrelated individuals with adult-onset illnesses such as diabetes, or cardiac or neuropsychiatric diseases, but not with severe paediatric conditions or their family members. Sex, age and ancestry but not clinical data were available. Databases were examined between July 2023 and April 2024, and checked between April and June 2024.\u003c/p\u003e\n\u003cp\u003eWritten informed consent had been provided by all participants in gnomAD for the use of their anonymised data at recruitment into the original studies so that this project did not require IRB approval.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnnotation\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eVariants in the \u003cem\u003eFBN1\u003c/em\u003e gene from gnomAD were downloaded and annotated in ANNOVAR (https://annovar.openbioinformatics.org/), and assessed for pathogenicity based on whether they were structural or null in nature, and in the case of missense changes, whether they were rare, pathogenic in three computational tools and affected an amino acid that was conserved in vertebrates. This population frequency was then compared with that found in the Control subset, or from the number of variants found in gnomAD that were assessed as Pathogenic or Likely Pathogenic by the ClinVar or LOVD databases.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFiltering processes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eVariants that were located in the 5\u0026rsquo; or 3\u0026rsquo; UTR, in the intronic or non-canonical splice regions, or were synonymous were excluded.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStructural variants.\u0026nbsp;\u003c/strong\u003eStructural variants were only available for a subset of 10,847 individuals, and any considered pathogenic in gnomAD were classified here as Predicted pathogenic. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNull variants.\u003c/strong\u003e Null variants with any allele count including nonsense (stop gained), frameshift and canonical splice site variants were classified as Predicted pathogenic. Null variants in the last exon or last 50 nucleotides of the penultimate exon were excluded since they were assumed to not result in nonsense-mediate decay \u003csup\u003e35\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMissense variants.\u0026nbsp;\u003c/strong\u003eMissense variants were Predicted Pathogenic if they occurred in fewer than 6 individuals in gnomAD; and were pathogenic in all bioinformatic prediction tools: PolyPhen-2 (PP2, score \u0026ge; 0.95, http://genetics.bwh.harvard.edu/pph2/), Sorting Intolerant From Tolerant (SIFT4G, score \u0026le; 0.05 from Varsome, https://varsome.com/), MutationTaster (MT, prediction: \u0026lsquo;Disease-causing, D or A\u0026nbsp;https://www.mutationtaster.org/) and if they affected an amino acid that was conserved in vertebrates (humans, mice and chickens). Conservation was assessed with Clustal Omega (https://www.ebi.ac.uk/Tools/\u003c/p\u003e\n\u003cp\u003emsa/clustalo/) based on reference sequences downloaded from Ensembl (http://asia.ensembl.org/index.html), where residues had the same physicochemical properties (indicated with an asterisk (*) or colon (:).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOur assessment strategy for \u0026lsquo;Predicted Pathogenic missense variants\u0026rsquo; was validated as follows. Twenty Pathogenic or Likely Pathogenic and 20 Benign or Likely Benign missense variants were selected randomly from ClinVar and LOVD and assessed according to our strategy for Predicted pathogenicity. The sensitivities, specificities, and positive and negative predictive values were then calculated for both ClinVar and LOVD \u003cstrong\u003e(Table 2)\u003c/strong\u003e. All values were at least 80%.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMissense variants were also assessed by REVEL (score \u0026gt;0.932) which has the best performance for distinguishing pathogenic from rare neutral variants with allele frequencies less than 0.5% \u003csup\u003e36,37\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe population frequencies were then calculated based on the assumption that each variant was found in only one individual.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePopulation frequencies of Predicted pathogenic variants in different ancestries\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePopulation frequencies of Predicted Pathogenic variants were calculated in people from all 8 ancestries available in gnomAD (African/African American, Latino/Admixed American, Ashkenazi Jewish, East Asian, Finnish, European (Non-Finnish), South Asian, and Others). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePopulation frequencies of Marfan syndrome using different databases\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe population frequencies of Predicted Pathogenic variants were also calculated based on how often variants assessed as Pathogenic or Likely Pathogenic in ClinVar and LOVD databases were also found in gnomAD (ClinVar https://www.ncbi.nlm.nih.gov/clinvar/; LOVD, https://www.lovd.nl/). \u0026nbsp;Variants in ClinVar classified as Conflicting with both a VUS and Pathogenic or Likely pathogenic assessment were considered Predicted pathogenic for the calculation. Variants were included even if the allele were found more than 5 times because the initial assessments had often included clinical data that increased the likelihood of being correct.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eResults were compared with Chi-square with Yates correction (Graph pad).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003ePredicted pathogenic \u003cem\u003eFBN1\u003c/em\u003e variants in overall gnomAD cohort\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe \u003cem\u003eFBN1\u003c/em\u003e gnomAD dataset included 1157 variants including 21 structural, \u0026nbsp;17 null and 1119 missense changes from a mean of 241,491 alleles or 120,745 individuals \u003cstrong\u003e(Supplementary material\u0026nbsp;\u003c/strong\u003eincludesFiltering FBN1 for Predicted pathogenic variants;andAssessment of our Filtering strategy).\u003c/p\u003e\n\u003cp\u003ePredicted pathogenic variants comprised no structural variants, but 14 null variants and 173 Predicted pathogenic missense variants. \u0026nbsp;The 14 null variants corresponded to a population frequency of one in 8624. Altogether there were 185 Predicted pathogenic variants in 293 people in gnomAD which corresponded to a population frequency of 293/120,745 or one in 412 (0.24%) \u003cstrong\u003e(Table 3).\u0026nbsp;\u003c/strong\u003eWhen the missense variants identified by REVEL were included instead (42 in 76 individuals) this corresponded to a population frequency of 90/120,745 or one in 1588 \u003cstrong\u003e(Table 4)\u003c/strong\u003e. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePredicted pathogenic \u003cem\u003eFBN1\u003c/em\u003e variants in gnomAD control subset\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Control subset included 112 Predicted pathogenic variants in 148 people in a cohort of 52,806 individuals. This was equivalent to a population frequency of 148/52,806 (0.28%) or one in 356 which was not different from the results for the overall gnomAD cohort (Chi square = 1.399, p value = 0.24) \u003cstrong\u003e(Table 4).\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePredicted pathogenic \u003cem\u003eFBN1\u003c/em\u003e variants in people of different ancestries in gnomAD\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe population frequencies of Predicted pathogenic variants varied in people of different ancestries. Predicted pathogenic variants were more common in people of East Asian (one in 243, p\u0026lt;0.0001) and South Asian (one in 300, p=0.0009) backgrounds than in Europeans (one in 525). Predicted pathogenic variants were least common in Ashkenazim (one in 5185, p=0.0082) \u003cstrong\u003e(Table 5)\u003c/strong\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePredicted pathogenic \u003cem\u003eFBN1\u0026nbsp;\u003c/em\u003evariants according to different Pathogenic variant databases\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinVar.\u0026nbsp;\u003c/strong\u003eFifty-two Pathogenic or Likely Pathogenic variants in ClinVar were found in 168 individuals in gnomAD. This corresponded to a population frequency of 168/120,745 or one in 718.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLOVD.\u0026nbsp;\u003c/strong\u003eTwenty-one Pathogenic or Likely Pathogenic variants in LOVD were found in 119 individuals in gnomAD. This corresponded to a population frequency of 119/120,745 or one in 1014 \u003cstrong\u003e(Table 4).\u003c/strong\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study used diverse approaches to demonstrate that genetically-diagnosed Marfan syndrome is more common than the previous estimates of one in three to five thousand. Our strategy based on identifying Predictive pathogenic or rare damaging variants in \u003cem\u003eFBN1\u0026nbsp;\u003c/em\u003efound that Marfan syndrome affected one in 412 of the gnomAD population and one in 356 of its control subset. An alternative approach using highly stringent REVEL scores to assess missense variants found a population frequency of one in 1588. Pathogenic or Likely Pathogenic assessments in ClinVar or LOVD corresponded to population frequencies of one in 718 or one in 1014 respectively. These results indicate that Marfan syndrome affects a range of individuals from one in 356 to one in 1588 of the population. Predicted pathogenic variants were most common in people of East or South Asian ancestries. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEach of the approaches used here had strengths and weaknesses, with some likely to overestimate and others to underestimate population frequencies. \u0026nbsp;Missense variants are always the most difficult to assess accurately for pathogenicity. Our strategy for missense variants used a more rigorous threshold than previous assessments in other rare diseases (rare variant, positive in three computational tools, and affecting a conserved residue)\u003csup\u003e29-34\u003c/sup\u003e. These criteria performed well in the validation studies. When a high REVEL score (\u0026gt;0.932)\u003csup\u003e36\u003c/sup\u003e was used for missense variants instead the population frequency was still one in 1588. Interestingly REVEL scores include all three computational tools (PP2, SIFT and MT) used in our approach.\u003c/p\u003e\n\u003cp\u003eBoth ClinVar and LOVD datasets are valuable because their assessments use clinical data and rely on multiple submissions from diagnostic laboratories to confirm pathogenicity in individuals where Marfan syndrome is suspected clinically. ClinVar was established after the publication of the ACMG guidelines \u003csup\u003e35\u003c/sup\u003e and depends on variants submitted by diagnostic testing laboratories that use these guidelines. The LOVD database includes earlier submissions made before the ACMG guidelines and large datasets for calculating allele frequencies were available. Both databases depend on the ad hoc submission of further variants rather than the systematic examination of all possible variants in a gene, which means that they underestimate population frequencies. \u0026nbsp;This study did not formally evaluate the number of pathogenic variants from the HGMD and UMD-FBN1 databases because they both included many \u0026lsquo;pathogenic\u0026rsquo; variants that were present in large numbers of individuals. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAlthough gnomAD included samples from participants with known cardiac disease there was no difference in the number with a Predicted pathogenic \u003cem\u003eFBN1\u003c/em\u003e variant and Marfan syndrome (one in 412) and in the control cohort (one in 356, pNS) with no known cardiac disease.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIndeed our population frequencies may even underestimate the population frequency of Marfan syndrome because up to 10% of cohorts with clinical features of Marfan syndrome have no pathogenic variant identified in\u003cem\u003e\u0026nbsp;FBN1\u0026nbsp;\u003c/em\u003e\u003cem\u003e\u003csup\u003e38\u003c/sup\u003e\u003c/em\u003e. Instead theyhave possible complex structural variants, deletions, or intronic regulatory sequences that are not detected by the Whole Exome Sequencing, or disease caused by variants in other genes\u003csup\u003e39\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOur results that Marfan syndrome is more common than previously estimated do not mean that aortic dilatation and rupture are being overlooked. This study found that null variants affected one in 8624 individuals and it is these variants that are usually associated with a more severe phenotype and a greater likelihood of aortic dissection and rupture \u003csup\u003e21\u003c/sup\u003e. No structural variants were found probably in part because so few samples were examined with Whole Genomic Sequencing. \u0026nbsp; However missense variants in our study occurred nearly 20 times as often as null changes, which may explain the difference from the population frequencies observed previously since missense variants are typically associated with less severe and incompletely penetrant features\u003csup\u003e40\u003c/sup\u003e. Skeletal anomalies, aortic dilatation and lens abnormalities may be absent but mitral valve prolapse or mitral regurgitation may still occur.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOverall, predicted pathogenic \u003cem\u003eFBN1\u003c/em\u003e variants were found most often in people of East and South Asian ancestries, and least often in Ashkenazim which is probably explained by their cultural and geographic isolation. Null variants which are associated with the more severe phenotype and aortic aneurysms were absent from both the Ashkenazim and Latino/Admixed American groups.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe strengths of this study were the use of the large gnomAD database and the confirmation with different \u0026nbsp;criteria for pathogenicity and the use of different datasets. The study\u0026rsquo;s limitations were the use of Whole Exome Sequencing in this version of gnomAD; the lack of clinical data; the difficulty with assessing missense variants; and the incompleteness of the ClinVar and LOVD databases.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA more accurate population frequency of Marfan syndrome is important in order to improve \u0026nbsp;clinician awareness of the disease, and the cardiac risks; for health systems to assist with \u0026nbsp;health service planning; and for pharmaceutical companies to develop targeted treatments. Confirmation in further variant databases will help further improve the accuracy of population frequencies. \u0026nbsp; \u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank gnomAD, REVEL, ClinVar and LOVD for the use of their databases; the many patients who contributed to these databases; and the data contributors and developers of the in-silico tools used in this analysis (PP2, SIFT, Mutation Taster, Clustal Omega). KC undertook this project as part of her research towards an M Sc.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKC undertook the analysis, produced the tables and the first draft of the manuscript. MH helped KC with downloading the variants in ANNOVAR and with the analysis. JS devised the project, and produced the final manuscript draft.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone of the authors of this manuscript has any financial or non-financial competing interests relevant to this study or the manuscript itself.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData is provided within the manuscript or supplementary files.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eDietz H. FBN1-Related Marfan Syndrome. In: Adam MP, Mirzaa GM, Pagon RA, et al., eds. \u003cem\u003eGeneReviews((R)).\u003c/em\u003e Seattle (WA)1993.\u003c/li\u003e\n\u003cli\u003eMannucci L, Luciano S, Salehi LB, et al. Mutation analysis of the FBN1 gene in a cohort of patients with Marfan Syndrome: A 10-year single center experience. \u003cem\u003eClin Chim Acta. \u003c/em\u003e2020;501:154-164.\u003c/li\u003e\n\u003cli\u003eBoucek RJ, Noble NL, Gunja-Smith Z, Butler WT. The Marfan syndrome: a deficiency in chemically stable collagen cross-links. \u003cem\u003eN Engl J Med. \u003c/em\u003e1981;305(17):988-991.\u003c/li\u003e\n\u003cli\u003eByers PH, Siegel RC, Peterson KE, et al. Marfan syndrome: abnormal alpha 2 chain in type I collagen. \u003cem\u003eProc Natl Acad Sci U S A. \u003c/em\u003e1981;78(12):7745-7749.\u003c/li\u003e\n\u003cli\u003evon Kodolitsch Y, Robinson PN. Marfan syndrome: an update of genetics, medical and surgical management. \u003cem\u003eHeart. \u003c/em\u003e2007;93(6):755-760.\u003c/li\u003e\n\u003cli\u003eRybczynski M, Bernhardt AM, Rehder U, et al. The spectrum of syndromes and manifestations in individuals screened for suspected Marfan syndrome. \u003cem\u003eAm J Med Genet A. \u003c/em\u003e2008;146A(24):3157-3166.\u003c/li\u003e\n\u003cli\u003eReinhardt DP, Keene DR, Corson GM, et al. Fibrillin-1: organization in microfibrils and structural properties. \u003cem\u003eJ Mol Biol. \u003c/em\u003e1996;258(1):104-116.\u003c/li\u003e\n\u003cli\u003eZhang H, Hu W, Ramirez F. Developmental expression of fibrillin genes suggests heterogeneity of extracellular microfibrils. \u003cem\u003eJ Cell Biol. \u003c/em\u003e1995;129(4):1165-1176.\u003c/li\u003e\n\u003cli\u003ePereira L, Levran O, Ramirez F, et al. A molecular approach to the stratification of cardiovascular risk in families with Marfan's syndrome. \u003cem\u003eN Engl J Med. \u003c/em\u003e1994;331(3):148-153.\u003c/li\u003e\n\u003cli\u003eJudge DP, Dietz HC. Marfan's syndrome. \u003cem\u003eLancet. \u003c/em\u003e2005;366(9501):1965-1976.\u003c/li\u003e\n\u003cli\u003eMilewicz DM, Braverman AC, De Backer J, et al. Marfan syndrome. \u003cem\u003eNat Rev Dis Primers. \u003c/em\u003e2021;7(1):64.\u003c/li\u003e\n\u003cli\u003eLoeys BL, Dietz HC, Braverman AC, et al. The revised Ghent nosology for the Marfan syndrome. \u003cem\u003eJ Med Genet. \u003c/em\u003e2010;47(7):476-485.\u003c/li\u003e\n\u003cli\u003eAranda-Michel E, Bianco V, Yousef S, et al. National trends in thoracic aortic aneurysms and dissections in patients with Marfans and Ehlers Danlos syndrome. \u003cem\u003eJ Card Surg. \u003c/em\u003e2022;37(10):3313-3321.\u003c/li\u003e\n\u003cli\u003eBitterman AD, Sponseller PD. Marfan Syndrome: A Clinical Update. \u003cem\u003eJ Am Acad Orthop Surg. \u003c/em\u003e2017;25(9):603-609.\u003c/li\u003e\n\u003cli\u003eBrooke BS, Habashi JP, Judge DP, Patel N, Loeys B, Dietz HC, 3rd. Angiotensin II blockade and aortic-root dilation in Marfan's syndrome. \u003cem\u003eN Engl J Med. \u003c/em\u003e2008;358(26):2787-2795.\u003c/li\u003e\n\u003cli\u003eMilewicz DM, Dietz HC, Miller DC. Treatment of aortic disease in patients with Marfan syndrome. \u003cem\u003eCirculation. \u003c/em\u003e2005;111(11):e150-157.\u003c/li\u003e\n\u003cli\u003eRybczynski M, Mir TS, Sheikhzadeh S, et al. Frequency and age-related course of mitral valve dysfunction in the Marfan syndrome. \u003cem\u003eAm J Cardiol. \u003c/em\u003e2010;106(7):1048-1053.\u003c/li\u003e\n\u003cli\u003eFaivre L, Collod-Beroud G, Loeys BL, et al. Effect of mutation type and location on clinical outcome in 1,013 probands with Marfan syndrome or related phenotypes and FBN1 mutations: an international study. \u003cem\u003eAm J Hum Genet. \u003c/em\u003e2007;81(3):454-466.\u003c/li\u003e\n\u003cli\u003eAlpendurada F, Wong J, Kiotsekoglou A, et al. Evidence for Marfan cardiomyopathy. \u003cem\u003eEur J Heart Fail. \u003c/em\u003e2010;12(10):1085-1091.\u003c/li\u003e\n\u003cli\u003eDu Q, Zhang D, Zhuang Y, Xia Q, Wen T, Jia H. The Molecular Genetics of Marfan Syndrome. \u003cem\u003eInt J Med Sci. \u003c/em\u003e2021;18(13):2752-2766.\u003c/li\u003e\n\u003cli\u003eBaudhuin LM, Kotzer KE, Lagerstedt SA. Increased frequency of FBN1 truncating and splicing variants in Marfan syndrome patients with aortic events. \u003cem\u003eGenet Med. \u003c/em\u003e2015;17(3):177-187.\u003c/li\u003e\n\u003cli\u003eSchrijver I, Liu W, Brenn T, Furthmayr H, Francke U. Cysteine substitutions in epidermal growth factor-like domains of fibrillin-1: distinct effects on biochemical and clinical phenotypes. \u003cem\u003eAm J Hum Genet. \u003c/em\u003e1999;65(4):1007-1020.\u003c/li\u003e\n\u003cli\u003eLiu X, Liu K, Nie D, et al. Case report: Biochemical and clinical phenotypes caused by cysteine substitutions in the epidermal growth factor-like domains of fibrillin-1. \u003cem\u003eFront Genet. \u003c/em\u003e2022;13:928683.\u003c/li\u003e\n\u003cli\u003eGroth KA, Hove H, Kyhl K, et al. Prevalence, incidence, and age at diagnosis in Marfan Syndrome. \u003cem\u003eOrphanet J Rare Dis. \u003c/em\u003e2015;10:153.\u003c/li\u003e\n\u003cli\u003eSalik I, Rawla P. Marfan Syndrome. In: \u003cem\u003eStatPearls.\u003c/em\u003e Treasure Island (FL)2023.\u003c/li\u003e\n\u003cli\u003eAmmash NM, Sundt TM, Connolly HM. Marfan syndrome-diagnosis and management. \u003cem\u003eCurr Probl Cardiol. \u003c/em\u003e2008;33(1):7-39.\u003c/li\u003e\n\u003cli\u003eHa HI, Seo JB, Lee SH, et al. Imaging of Marfan syndrome: multisystemic manifestations. \u003cem\u003eRadiographics. \u003c/em\u003e2007;27(4):989-1004.\u003c/li\u003e\n\u003cli\u003eGudmundsson S, Singer-Berk M, Watts NA, et al. Variant interpretation using population databases: Lessons from gnomAD. \u003cem\u003eHum Mutat. \u003c/em\u003e2022;43(8):1012-1030.\u003c/li\u003e\n\u003cli\u003eKondo A, Nagano C, Ishiko S, et al. Examination of the predicted prevalence of Gitelman syndrome by ethnicity based on genome databases. \u003cem\u003eSci Rep. \u003c/em\u003e2021;11(1):16099.\u003c/li\u003e\n\u003cli\u003eKermond-Marino A, Weng A, Xi Zhang SK, Tran Z, Huang M, Savige J. Population Frequency of Undiagnosed Fabry Disease in the General Population. \u003cem\u003eKidney Int Rep. \u003c/em\u003e2023;8(7):1373-1379.\u003c/li\u003e\n\u003cli\u003eBorges P, Pasqualim G, Giugliani R, Vairo F, Matte U. Estimated prevalence of mucopolysaccharidoses from population-based exomes and genomes. \u003cem\u003eOrphanet J Rare Dis. \u003c/em\u003e2020;15(1):324.\u003c/li\u003e\n\u003cli\u003eKaler SG, Ferreira CR, Yam LS. Estimated birth prevalence of Menkes disease and ATP7A-related disorders based on the Genome Aggregation Database (gnomAD). \u003cem\u003eMol Genet Metab Rep. \u003c/em\u003e2020;24:100602.\u003c/li\u003e\n\u003cli\u003eLanktree MB, Haghighi A, Guiard E, et al. Prevalence Estimates of Polycystic Kidney and Liver Disease by Population Sequencing. \u003cem\u003eJ Am Soc Nephrol. \u003c/em\u003e2018;29(10):2593-2600.\u003c/li\u003e\n\u003cli\u003eGibson J, Fieldhouse R, Chan MMY, et al. Prevalence Estimates of Predicted Pathogenic COL4A3-COL4A5 Variants in a Population Sequencing Database and Their Implications for Alport Syndrome. \u003cem\u003eJ Am Soc Nephrol. \u003c/em\u003e2021;32(9):2273-2290.\u003c/li\u003e\n\u003cli\u003eRichards S, Aziz N, Bale S, et al. Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. \u003cem\u003eGenet Med. \u003c/em\u003e2015;17(5):405-424.\u003c/li\u003e\n\u003cli\u003ePejaver V, Byrne AB, Feng BJ, et al. Calibration of computational tools for missense variant pathogenicity classification and ClinGen recommendations for PP3/BP4 criteria. \u003cem\u003eAm J Hum Genet. \u003c/em\u003e2022;109(12):2163-2177.\u003c/li\u003e\n\u003cli\u003eIoannidis NM, Rothstein JH, Pejaver V, et al. REVEL: An Ensemble Method for Predicting the Pathogenicity of Rare Missense Variants. \u003cem\u003eAm J Hum Genet. \u003c/em\u003e2016;99(4):877-885.\u003c/li\u003e\n\u003cli\u003eLoeys B, De Backer J, Van Acker P, et al. Comprehensive molecular screening of the FBN1 gene favors locus homogeneity of classical Marfan syndrome. \u003cem\u003eHum Mutat. \u003c/em\u003e2004;24(2):140-146.\u003c/li\u003e\n\u003cli\u003eDietz HC. Marfan syndrome \u003cem\u003eGeneReviews, University of Washington, Seattle \u003c/em\u003e2011.\u003c/li\u003e\n\u003cli\u003eBaudhuin LM, Kotzer KE, Lagerstedt SA. Decreased frequency of FBN1 missense variants in Ghent criteria-positive Marfan syndrome and characterization of novel FBN1 variants. \u003cem\u003eJ Hum Genet. \u003c/em\u003e2015;60(5):241-252.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1. Marfan syndrome and \u003cem\u003eFBN1\u0026nbsp;\u003c/em\u003evariants\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDisease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGene and protein features\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGene function\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDisease mechanism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eClinical features and risks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTreatment\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMarfan syndrome (MIM154700)\u003c/p\u003e\n \u003cp\u003eAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eFBN1 (\u003c/em\u003eFibrillin 1)\u003c/p\u003e\n \u003cp\u003e65 exons, encodes 350kDa fibrillin; caused by all types of variants distributed throughout the gene, by a loss of function mechanism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eProduction of glycoproteins in the extracellular matrix - structural components of microfibrils that bind calcium ions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOne mechanism is the disruption in cysteine binding leading to the reduced expression of Fibrillin 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCardiac, skeletal, ocular manifestations: aortic aneurysms, arachnodactyly, and ectopic lens\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBeta-blockers, beta-adrenergic receptor antagonist therapy, and surgical interventions to reduce the size of the aneurysms\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAD = autosomal dominant\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Validation of filtering strategy for pathogenic variants in \u003cem\u003eFBN1\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDatabase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSensitivity (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSpecificity (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePositive predictive value (PPV) (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNegative predictive value (NPV) (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eFBN1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eClinVar (B/LB n=19/20, P/LP n=16/20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e80%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e95%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e94%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e82%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLOVD (B/LB n=20/20, P/LP n=20/20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eB \u0026ndash; Benign; LB - Likely Benign; P \u0026ndash; Pathogenic; LP - Likely Pathogenic\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. Predicted population frequency of pathogenic FBN1 variants in gnomAD\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePathogenic structural variants\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003ePredicted pathogenic null variants\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003ePredicted pathogenic missense variants\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003ePopulation frequency of predicted pathogenic variants\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eNone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNo. of variants\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNo. of people\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNo. of variants\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNo. of people\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNo. of variants\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNo. of people\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFrequency\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e171\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e279\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e185\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e293 (177 male and 116 female)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e293/120,745\u003c/p\u003e\n \u003cp\u003e(0.24%) or one in 412\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4. \u003cem\u003eFBN1\u0026nbsp;\u003c/em\u003epopulation frequencies using different strategies\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eREVEL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003ePredicted pathogenic\u003c/p\u003e\n \u003cp\u003evariants\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 424px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e42 variants in 76 people (plus 14 null variants)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eFrequency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 424px;\"\u003e\n \u003cp\u003e90/120,745\u003c/p\u003e\n \u003cp\u003e(0.07%)\u003c/p\u003e\n \u003cp\u003e= \u003cstrong\u003eone in 1341 people\u003c/strong\u003e \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eControl subset\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003ePredicted pathogenic\u003c/p\u003e\n \u003cp\u003evariants\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 424px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e112 variants in 148 people\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eFrequency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 424px;\"\u003e\n \u003cp\u003e148/52,806\u003c/p\u003e\n \u003cp\u003e(0.28%)\u003c/p\u003e\n \u003cp\u003e= \u003cstrong\u003eone in 356 people\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eClinVar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003ePredicted pathogenic\u003c/p\u003e\n \u003cp\u003evariants\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 424px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e52 variants in 168 people\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eFrequency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 424px;\"\u003e\n \u003cp\u003e168/120,745\u003c/p\u003e\n \u003cp\u003e(0.14%)\u003c/p\u003e\n \u003cp\u003e= \u003cstrong\u003eone in 718 people\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eLOVD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003ePredicted pathogenic\u003c/p\u003e\n \u003cp\u003evariants\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 424px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e21 variants in 119 people\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eFrequency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 424px;\"\u003e\n \u003cp\u003e119/120,745\u003c/p\u003e\n \u003cp\u003e(0.10%)\u003c/p\u003e\n \u003cp\u003e= \u003cstrong\u003eone in 1,014 people\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5. Predicted pathogenic null and missense variants in people of different ancestries\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eFBN1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePredicted pathogenic null variants\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePredicted pathogenic missense variants\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTotal Predicted pathogenic variants\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"9\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eAncestry gnomAD v.2.1.1. (n=141,456)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAfrican/\u003c/p\u003e\n \u003cp\u003eAfrican American\u003c/p\u003e\n \u003cp\u003e(n=12,487)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOne variant in 1 person\u003c/p\u003e\n \u003cp\u003e1/12,487\u003c/p\u003e\n \u003cp\u003e= \u003cstrong\u003eone in 12,487\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e19 variants in 24 people\u003c/p\u003e\n \u003cp\u003e24/12,487\u003c/p\u003e\n \u003cp\u003e= \u003cstrong\u003eone in 520\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e20 variants in 25 people\u003c/p\u003e\n \u003cp\u003e25/12,487\u003c/p\u003e\n \u003cp\u003e= \u003cstrong\u003eone in 499\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(p=0.014)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLatino/\u003c/p\u003e\n \u003cp\u003eAdmixed American\u003c/p\u003e\n \u003cp\u003e(n=17,720)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e21 variants in 36 people\u003c/p\u003e\n \u003cp\u003e36/17,720\u003c/p\u003e\n \u003cp\u003e= \u003cstrong\u003eone in 492\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e21 variants in 36 people\u003c/p\u003e\n \u003cp\u003e36/17,720\u003c/p\u003e\n \u003cp\u003e= \u003cstrong\u003eone in 492\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(p=0.061)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAshkenazi Jewish\u003c/p\u003e\n \u003cp\u003e(n=5,185)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOne variant in 1 person\u003c/p\u003e\n \u003cp\u003e1/5,185\u003c/p\u003e\n \u003cp\u003e= \u003cstrong\u003eone in 5,185\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOne variant in 1 person\u003c/p\u003e\n \u003cp\u003e1/5,185\u003c/p\u003e\n \u003cp\u003e= \u003cstrong\u003eone in 5,185\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(p=0.0082)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEast Asian\u003c/p\u003e\n \u003cp\u003e(n=9,977)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e21 variants in 41 people\u003c/p\u003e\n \u003cp\u003e41/9,977\u003c/p\u003e\n \u003cp\u003e= \u003cstrong\u003eone in 243\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e21 variants in 41 people\u003c/p\u003e\n \u003cp\u003e41/9,977\u003c/p\u003e\n \u003cp\u003e= \u003cstrong\u003eone in 243 (p\u0026lt;0.0001)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFinnish\u003c/p\u003e\n \u003cp\u003e(n=12,562)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOne variant in 1 person\u003c/p\u003e\n \u003cp\u003e1/12,562\u003c/p\u003e\n \u003cp\u003e= \u003cstrong\u003eone in 12,562\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8 variants in 12 people\u003c/p\u003e\n \u003cp\u003e12/12,562\u003c/p\u003e\n \u003cp\u003e= \u003cstrong\u003eone in 1,046\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9 variants in 13 people\u003c/p\u003e\n \u003cp\u003e13/12,562\u003c/p\u003e\n \u003cp\u003e= \u003cstrong\u003eone in 966\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(p=0.0446)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEuropean\u003c/p\u003e\n \u003cp\u003e(n=64,603)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6 variants in 6 people\u003c/p\u003e\n \u003cp\u003e6/64,603\u003c/p\u003e\n \u003cp\u003e= \u003cstrong\u003eone in 10,767\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e89 variants in 117 people\u003c/p\u003e\n \u003cp\u003e117/64,603\u003c/p\u003e\n \u003cp\u003e= \u003cstrong\u003eone in 552\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e95 variants in 123 people\u003c/p\u003e\n \u003cp\u003e123/64,603\u003c/p\u003e\n \u003cp\u003e= \u003cstrong\u003eone in 525\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSouth Asian\u003c/p\u003e\n \u003cp\u003e(n=15,308)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5 variants in 5 people\u003c/p\u003e\n \u003cp\u003e5/15,308\u003c/p\u003e\n \u003cp\u003e= \u003cstrong\u003eone in 3,061\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e37 variants in 46 people\u003c/p\u003e\n \u003cp\u003e46/15,308\u003c/p\u003e\n \u003cp\u003e= \u003cstrong\u003eone in 332\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e42 variants in 51 people\u003c/p\u003e\n \u003cp\u003e51/15,308\u003c/p\u003e\n \u003cp\u003e= \u003cstrong\u003eone in 300\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e(p=0.0009)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003cp\u003e(n=3,614)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOne variant in 1 person\u003c/p\u003e\n \u003cp\u003e1/3,614\u003c/p\u003e\n \u003cp\u003e= \u003cstrong\u003eone in 3,614\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2 variants in 2 people\u003c/p\u003e\n \u003cp\u003e2/3,614\u003c/p\u003e\n \u003cp\u003e= \u003cstrong\u003eone in 1,807\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3 variants in 3 people\u003c/p\u003e\n \u003cp\u003e3/3,614\u003c/p\u003e\n \u003cp\u003e= \u003cstrong\u003eone in 1,204\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(p=0.2062)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e14 variants in 14 people\u003c/p\u003e\n \u003cp\u003e14/141,456\u003c/p\u003e\n \u003cp\u003e= \u003cstrong\u003eone in 10,104\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e198 variants in 279 people\u003c/p\u003e\n \u003cp\u003e279/141,456\u003c/p\u003e\n \u003cp\u003e= \u003cstrong\u003eone in 507\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e212 variants in 293 people\u003c/p\u003e\n \u003cp\u003e293/141,456\u003c/p\u003e\n \u003cp\u003e= \u003cstrong\u003eone in 482\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eComparisons performed with Chi squared test with Yate\u0026rsquo;s correction\u0026nbsp;\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-4975062/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4975062/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMarfan syndrome is an autosomal dominantly (AD)-inherited disease that results from pathogenic variants in the Fibrillin 1 (\u003cem\u003eFBN1\u003c/em\u003e) gene, and is characterised by tall stature, elongated limbs and digits, lens abnormalities and aortic root dilatation, aneurysms and dissection but milder forms also occur. Radiological imaging suggests that Marfan syndrome affects between one in 3000 and 5000 of the population.\u003c/p\u003e \u003cp\u003eThe aim of this study was to determine the population frequency of Marfan syndrome from the number of pathogenic \u003cem\u003eFBN1\u003c/em\u003e variants found in a normal variant database.\u003c/p\u003e \u003cp\u003e \u003cem\u003eFBN1\u003c/em\u003e variants were downloaded from gnomAD v2.1.1 and annotated with ANNOVAR. The population frequency was determined from the number of pathogenic null and structural variants, and the number of predicted pathogenic missense changes classified by rarity and computational scores. This population frequency was then compared with the frequencies in the control subset, and from gnomAD variants assessed as Pathogenic or Likely pathogenic in the ClinVar or LOVD databases.\u003c/p\u003e \u003cp\u003eOur strategy identified predicted pathogenic \u003cem\u003eFBN1\u003c/em\u003e variants in one in 416 individuals, which was confirmed in the control subset (one in 356, p NS). Predicted pathogenic variants were most common in East Asian people (one in 243, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and least common in Ashkenazim (one in 5,185, p\u0026thinsp;=\u0026thinsp;0.0082). The population frequencies based on pathogenic variants in the ClinVar or LOVD databases were one in 718 and one in 1014 respectively. Null variants which are associated with aortic aneurysms affected only one in 8624.\u003c/p\u003e \u003cp\u003eThus, Marfan syndrome is more common than previously recognised. Emergency departments and cardiac clinics in particular should be aware of undiagnosed Marfan syndrome and its cardiac risks, but many individuals may have a milder phenotype.\u003c/p\u003e","manuscriptTitle":"The population frequency of Marfan syndrome and the associated cardiac risks in a normal population","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-27 19:48:01","doi":"10.21203/rs.3.rs-4975062/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-10-29T08:07:33+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-27T20:03:35+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-15T16:33:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"161356828826151722706289925145098374304","date":"2024-10-14T17:52:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"314187103494222606782809071973163360337","date":"2024-09-30T06:52:39+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-09-15T10:13:49+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-09-15T10:11:29+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-09-10T10:33:50+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-09-10T05:51:00+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-08-26T03:57:00+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d5579c38-44e4-4a83-afb1-a7640d311b4e","owner":[],"postedDate":"December 27th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":39547215,"name":"Biological sciences/Genetics"},{"id":39547216,"name":"Health sciences/Cardiology"},{"id":39547217,"name":"Health sciences/Molecular medicine"},{"id":39547218,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2025-03-24T16:00:26+00:00","versionOfRecord":{"articleIdentity":"rs-4975062","link":"https://doi.org/10.1038/s41598-025-93832-6","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-03-18 15:57:17","publishedOnDateReadable":"March 18th, 2025"},"versionCreatedAt":"2024-12-27 19:48:01","video":"","vorDoi":"10.1038/s41598-025-93832-6","vorDoiUrl":"https://doi.org/10.1038/s41598-025-93832-6","workflowStages":[]},"version":"v1","identity":"rs-4975062","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4975062","identity":"rs-4975062","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}