Gain-of-Function Variants in SMAD4 Compromise Respiratory Epithelial Function.

BACKGROUND: Myhre syndrome (MIM #139210) is an exceedingly rare yet increasingly diagnosed genetic disorder arising from germline variants in the SMAD4 gene. Its core manifestation is the progression of stiffness and fibrosis across multiple organs. Individuals with Myhre syndrome exhibit a propensity for upper respiratory remodeling and infections. The molecular and cellular mechanisms underlying this phenotype remain unclear. OBJECTIVE: We aim to investigate how SMAD4 pathogenic variants associated with Myhre syndrome impact SMAD4 protein levels, activation, and physiological functions in patient-derived nasal epithelial cells. METHODS: Clinical observations were conducted on a cohort of 47 patients recruited at MGH from 2016 to 2023. Nasal epithelial basal cells were isolated and cultured from inferior turbinate brushings of healthy subjects (n=8) and Myhre syndrome patients (n=3, SMAD4-Ile500Val, Arg496Cys, and Ile500Thr). Transcriptomic analysis and functional assays were employed to assess SMAD4 levels, transcriptional activity, and epithelial cell host defense functions, including cell proliferation, mucociliary differentiation, and bacterial elimination. RESULTS: Clinical observations revealed a prevalent history of otitis media and sinusitis among most individuals with Myhre syndrome. Analyses of nasal epithelial cells indicated that SMAD4 mutations do not alter SMAD4 protein stability or upstream regulatory SMAD phosphorylation but enhance signaling transcriptional activity, supporting a gain-of-function mechanism, likely attributable to increased protein-protein interaction of the SMAD complex. Consequently, Myhre syndrome nasal basal cells exhibit reduced potential in cell proliferation and mucociliary differentiation. Furthermore, Myhre syndrome nasal epithelia are impaired in bacterial killing. CONCLUSIONS: Compromised innate immunity originating from epithelial cells in Myhre syndrome may contribute to increased susceptibility to upper respiratory infections.

The AORTA Gene score for detection and risk stratification of ascending aortic dilation.

BACKGROUND AND AIMS: This study assessed whether a model incorporating clinical features and a polygenic score for ascending aortic diameter would improve diameter estimation and prediction of adverse thoracic aortic events over clinical features alone. METHODS: Aortic diameter estimation models were built with a 1.1 million-variant polygenic score (AORTA Gene) and without it. Models were validated internally in 4394 UK Biobank participants and externally in 5469 individuals from Mass General Brigham (MGB) Biobank, 1298 from the Framingham Heart Study (FHS), and 610 from All of Us. Model fit for adverse thoracic aortic events was compared in 401 453 UK Biobank and 164 789 All of Us participants. RESULTS: AORTA Gene explained more of the variance in thoracic aortic diameter compared to clinical factors alone: 39.5% (95% confidence interval 37.3%-41.8%) vs. 29.3% (27.0%-31.5%) in UK Biobank, 36.5% (34.4%-38.5%) vs. 32.5% (30.4%-34.5%) in MGB, 41.8% (37.7%-45.9%) vs. 33.0% (28.9%-37.2%) in FHS, and 34.9% (28.8%-41.0%) vs. 28.9% (22.9%-35.0%) in All of Us. AORTA Gene had a greater area under the receiver operating characteristic curve for identifying diameter ≥ 4 cm: 0.836 vs. 0.776 (P < .0001) in UK Biobank, 0.808 vs. 0.767 in MGB (P < .0001), 0.856 vs. 0.818 in FHS (P < .0001), and 0.827 vs. 0.791 (P = .0078) in All of Us. AORTA Gene was more informative for adverse thoracic aortic events in UK Biobank (P = .0042) and All of Us (P = .049). CONCLUSIONS: A comprehensive model incorporating polygenic information and clinical risk factors explained 34.9%-41.8% of the variation in ascending aortic diameter, improving the identification of ascending aortic dilation and adverse thoracic aortic events compared to clinical risk factors.

Contributions of Germline and Somatic Mosaic Genetics to Thoracic Aortic Aneurysms in Nonsyndromic Individuals.

BACKGROUND: Thoracic aortic aneurysm (TAA) is associated with significant morbidity and mortality. Although individuals with family histories of TAA often undergo clinical molecular genetic testing, adults with nonsyndromic TAA are not typically evaluated for genetic causes. We sought to understand the genetic contribution of both germline and somatic mosaic variants in a cohort of adult individuals with nonsyndromic TAA at a single center. METHODS AND RESULTS: One hundred eighty-one consecutive patients <60 years who presented with nonsyndromic TAA at the Massachusetts General Hospital underwent deep (>500×) targeted sequencing across 114 candidate genes associated with TAA and its related functional pathways. Samples from 354 age- and sex-matched individuals without TAA were also sequenced, with a 2:1 matching. We found significant enrichments for germline (odds ratio [OR], 2.44, P=4.6×10-6 [95% CI, 1.67-3.58]) and also somatic mosaic variants (OR, 4.71, P=0.026 [95% CI, 1.20-18.43]) between individuals with and without TAA. Likely genetic causes were present in 24% with nonsyndromic TAA, of which 21% arose from germline variants and 3% from somatic mosaic alleles. The 3 most frequently mutated genes in our cohort were FLNA (encoding Filamin A), NOTCH3 (encoding Notch receptor 3), and FBN1 (encoding Fibrillin-1). There was increased frequency of both missense and loss of function variants in TAA individuals. CONCLUSIONS: Likely contributory dominant acting genetic variants were found in almost one quarter of nonsyndromic adults with TAA. Our findings suggest a more extensive genetic architecture to TAA than expected and that genetic testing may improve the care and clinical management of adults with nonsyndromic TAA.

Myhre syndrome in adulthood: clinical variability and emerging genotype-phenotype correlations.

Myhre syndrome (MS, MIM 139210) is a rare multisystemic disorder caused by recurrent pathogenic missense variants in SMAD4. The clinical features have been mainly documented in childhood and comprise variable neurocognitive development, recognizable craniofacial features, a short stature with a pseudo-muscular build, hearing loss, thickened skin, joint limitations, diverse cardiovascular and airway manifestations, and increased fibrosis often following trauma or surgery. In contrast, adults with MS are underreported obscuring potential clinical variability. Here, we describe 24 adults with MS, including 17 diagnosed after the age of 18 years old, and we review the literature on adults with MS. Overall, our cohort shows a milder phenotype as well as lower mortality rates compared to what has been published in literature. Individuals with a codon 500 variant in SMAD4 present with a more pronounced neurodevelopmental and systemic phenotype. However, in contrast to the literature, we observe cardiovascular abnormalities in individuals with the p.(Arg496Cys) variant. In addition, we describe scoliosis as a new manifestation and we report fertility in two additional males with the p.(Arg496Cys). In conclusion, our study contributes novel insights into the clinical variability of MS and underscores the importance of variant-specific considerations, and we provide recommendations for the management of MS in adulthood.

Emergence of the natural history of Myhre syndrome: 47 patients evaluated in the Massachusetts General Hospital Myhre Syndrome Clinic (2016-2023).

Myhre syndrome is an increasingly diagnosed ultrarare condition caused by recurrent germline autosomal dominant de novo variants in SMAD4. Detailed multispecialty evaluations performed at the Massachusetts General Hospital (MGH) Myhre Syndrome Clinic (2016-2023) and by collaborating specialists have facilitated deep phenotyping, genotyping and natural history analysis. Of 47 patients (four previously reported), most (81%) patients returned to MGH at least once. For patients followed for at least 5 years, symptom progression was observed in all. 55% were female and 9% were older than 18 years at diagnosis. Pathogenic variants in SMAD4 involved protein residues p.Ile500Val (49%), p.Ile500Thr (11%), p.Ile500Leu (2%), and p.Arg496Cys (38%). Individuals with the SMAD4 variant p.Arg496Cys were less likely to have hearing loss, growth restriction, and aortic hypoplasia than the other variant groups. Those with the p.Ile500Thr variant had moderate/severe aortic hypoplasia in three patients (60%), however, the small number (n = 5) prevented statistical comparison with the other variants. Two deaths reported in this cohort involved complex cardiovascular disease and airway stenosis, respectively. We provide a foundation for ongoing natural history studies and emphasize the need for evidence-based guidelines in anticipation of disease-specific therapies.

Using Genomics to Identify Novel Therapeutic Targets for Aortic Disease.

Aortic disease, including dissection, aneurysm, and rupture, carries significant morbidity and mortality and is a notable cause of sudden cardiac death. Much of our knowledge regarding the genetic basis of aortic disease has relied on the study of individuals with Mendelian aortopathies and, until recently, the genetic determinants of population-level variance in aortic phenotypes remained unclear. However, the application of machine learning methodologies to large imaging datasets has enabled researchers to rapidly define aortic traits and mine dozens of novel genetic associations for phenotypes such as aortic diameter and distensibility. In this review, we highlight the emerging potential of genomics for identifying causal genes and candidate drug targets for aortic disease. We describe how deep learning technologies have accelerated the pace of genetic discovery in this field. We then provide a blueprint for translating genetic associations to biological insights, reviewing techniques for locus and cell type prioritization, high-throughput functional screening, and disease modeling using cellular and animal models of aortic disease.

Treatment of calcific arterial disease via enhancement of autophagy using GSK343.

Vascular calcification is a hallmark of atherosclerotic disease and serves as a strong predictor and risk factor for cardiovascular events. Growing evidence suggests that autophagy may play a protective role in early atherosclerosis. The precise effects of autophagy on VSMC-mediated calcification remain unknown. In this study, we utilized multi-omic profiling to investigate impaired autophagy at the transcriptional level as a key driver of VSMC calcification. Our findings revealed that impaired autophagy is an essential determinant of VSMC calcification. We observed that an osteogenic environment affects the open chromatin status of VSMCs, compromising the transcriptional activation of autophagy initiation genes. In vivo experiments involve pharmacological and genetic activation of autophagy using mouse models of spontaneous large (Mgp-/-) and small (Abcc6-/-) artery calcification. Taken together, these data advance our mechanistic understanding of vascular calcification and provide important insights for a broad range of cardiovascular diseases involving VSMC phenotype switch.

Transcriptional profile of the rat cardiovascular system at single cell resolution.

Background: Despite the critical role of the cardiovascular system, our understanding of its cellular and transcriptional diversity remains limited. We therefore sought to characterize the cellular composition, phenotypes, molecular pathways, and communication networks between cell types at the tissue and sub-tissue level across the cardiovascular system of the healthy Wistar rat, an important model in preclinical cardiovascular research. We obtained high quality tissue samples under controlled conditions that reveal a level of cellular detail so far inaccessible in human studies. Methods and Results: We performed single nucleus RNA-sequencing in 78 samples in 10 distinct regions including the four chambers of the heart, ventricular septum, sinoatrial node, atrioventricular node, aorta, pulmonary artery, and pulmonary veins (PV), which produced an aggregate map of 505,835 nuclei. We identified 26 distinct cell types and additional subtypes, including a number of rare cell types such as PV cardiomyocytes and non-myelinating Schwann cells (NMSCs), and unique groups of vascular smooth muscle cells (VSMCs), endothelial cells (ECs) and fibroblasts (FBs), which gave rise to a detailed cell type distribution across tissues. We demonstrated differences in the cellular composition across different cardiac regions and tissue-specific differences in transcription for each cell type, highlighting the molecular diversity and complex tissue architecture of the cardiovascular system. Specifically, we observed great transcriptional heterogeneities among ECs and FBs. Importantly, several cell subtypes had a unique regional localization such as a subtype of VSMCs enriched in the large vasculature. We found the cellular makeup of PV tissue is closer to heart tissue than to the large arteries. We further explored the ligand-receptor repertoire across cell clusters and tissues, and observed tissue-enriched cellular communication networks, including heightened Nppa - Npr1/2/3 signaling in the sinoatrial node. Conclusions: Through a large single nucleus sequencing effort encompassing over 500,000 nuclei, we broadened our understanding of cellular transcription in the healthy cardiovascular system. The existence of tissue-restricted cellular phenotypes suggests regional regulation of cardiovascular physiology. The overall conservation in gene expression and molecular pathways across rat and human cell types, together with our detailed transcriptional characterization of each cell type, offers the potential to identify novel therapeutic targets and improve preclinical models of cardiovascular disease.

AORTA Gene: Polygenic prediction improves detection of thoracic aortic aneurysm.

Background: Thoracic aortic disease is an important cause of morbidity and mortality in the US, and aortic diameter is a heritable contributor to risk. Could a polygenic prediction of ascending aortic diameter improve detection of aortic aneurysm? Methods: Deep learning was used to measure ascending thoracic aortic diameter in 49,939 UK Biobank participants. A genome-wide association study (GWAS) was conducted in 39,524 participants and leveraged to build a 1.1 million-variant polygenic score with PRScs-auto. Aortic diameter prediction models were built with the polygenic score ("AORTA Gene") and without it. The models were tested in a held-out set of 4,962 UK Biobank participants and externally validated in 5,469 participants from Mass General Brigham Biobank (MGB), 1,298 from the Framingham Heart Study (FHS), and 610 participants from All of Us. Results: In each test set, the AORTA Gene model explained more of the variance in thoracic aortic diameter compared to clinical factors alone: 39.9% (95% CI 37.8-42.0%) vs 29.2% (95% CI 27.1-31.4%) in UK Biobank, 36.5% (95% CI 34.4-38.5%) vs 32.5% (95% CI 30.4-34.5%) in MGB, 41.8% (95% CI 37.7-45.9%) vs 33.0% (95% CI 28.9-37.2%) in FHS, and 34.9% (95% CI 28.8-41.0%) vs 28.9% (95% CI 22.9-35.0%) in All of Us. AORTA Gene had a greater AUROC for identifying diameter ≥4cm in each test set: 0.834 vs 0.765 (P=7.3E-10) in UK Biobank, 0.808 vs 0.767 in MGB (P=4.5E-12), 0.856 vs 0.818 in FHS (P=8.5E-05), and 0.827 vs 0.791 (P=7.8E-03) in All of Us. Conclusions: Genetic information improved estimation of thoracic aortic diameter when added to clinical risk factors. Larger and more diverse cohorts will be needed to develop more powerful and equitable scores.

Assessment of valvular function in over 47,000 people using deep learning-based flow measurements.

Valvular heart disease is associated with a high global burden of disease. Even mild aortic stenosis confers increased morbidity and mortality, prompting interest in understanding normal variation in valvular function at scale. We developed a deep learning model to study velocity-encoded magnetic resonance imaging in 47,223 UK Biobank participants. We calculated eight traits, including peak velocity, mean gradient, aortic valve area, forward stroke volume, mitral and aortic regurgitant volume, greatest average velocity, and ascending aortic diameter. We then computed sex-stratified reference ranges for these phenotypes in up to 31,909 healthy individuals. In healthy individuals, we found an annual decrement of 0.03cm 2 in the aortic valve area. Participants with mitral valve prolapse had a 1 standard deviation [SD] higher mitral regurgitant volume (P=9.6 × 10 -12 ), and those with aortic stenosis had a 4.5 SD-higher mean gradient (P=1.5 × 10 -431 ), validating the derived phenotypes' associations with clinical disease. Greater levels of ApoB, triglycerides, and Lp(a) assayed nearly 10 years prior to imaging were associated with higher gradients across the aortic valve. Metabolomic profiles revealed that increased glycoprotein acetyls were also associated with an increased aortic valve mean gradient (0.92 SD, P=2.1 x 10 -22 ). Finally, velocity-derived phenotypes were risk markers for aortic and mitral valve surgery even at thresholds below what is considered relevant disease currently. Using machine learning to quantify the rich phenotypic data of the UK Biobank, we report the largest assessment of valvular function and cardiovascular disease in the general population.

The Genetic Determinants of Aortic Distention.

BACKGROUND: As the largest conduit vessel, the aorta is responsible for the conversion of phasic systolic inflow from ventricular ejection into more continuous peripheral blood delivery. Systolic distention and diastolic recoil conserve energy and are enabled by the specialized composition of the aortic extracellular matrix. Aortic distensibility decreases with age and vascular disease. OBJECTIVES: In this study, we sought to discover epidemiologic correlates and genetic determinants of aortic distensibility and strain. METHODS: We trained a deep learning model to quantify thoracic aortic area throughout the cardiac cycle from cardiac magnetic resonance images and calculated aortic distensibility and strain in 42,342 UK Biobank participants. RESULTS: Descending aortic distensibility was inversely associated with future incidence of cardiovascular diseases, such as stroke (HR: 0.59 per SD; P = 0.00031). The heritabilities of aortic distensibility and strain were 22% to 25% and 30% to 33%, respectively. Common variant analyses identified 12 and 26 loci for ascending and 11 and 21 loci for descending aortic distensibility and strain, respectively. Of the newly identified loci, 22 were not significantly associated with thoracic aortic diameter. Nearby genes were involved in elastogenesis and atherosclerosis. Aortic strain and distensibility polygenic scores had modest effect sizes for predicting cardiovascular outcomes (delaying or accelerating disease onset by 2%-18% per SD change in scores) and remained statistically significant predictors after accounting for aortic diameter polygenic scores. CONCLUSIONS: Genetic determinants of aortic function influence risk for stroke and coronary artery disease and may lead to novel targets for medical intervention.

Genetics and mechanisms of thoracic aortic disease.

Aortic disease has many forms including aortic aneurysm and dissection, aortic coarctation or abnormalities in aortic function, such as loss of aortic distensibility. Genetic analysis in humans is one of the most important experimental approaches in uncovering disease mechanisms, but the relative infrequency of thoracic aortic disease compared with other cardiovascular conditions such as coronary artery disease has hindered large-scale identification of genetic associations. In the past decade, advances in machine learning technology coupled with large imaging datasets from biobank repositories have facilitated a rapid expansion in our capacity to measure and genotype aortic traits, resulting in the identification of dozens of genetic associations. In this Review, we describe the history of technological advances in genetic discovery and explain how newer technologies such as deep learning can rapidly define aortic traits at scale. Furthermore, we integrate novel genetic observations provided by these advances into our current biological understanding of thoracic aortic disease and describe how these new findings can contribute to strategies to prevent and treat aortic disease.

Development of a Prediction Model for Ascending Aortic Diameter Among Asymptomatic Individuals.

Importance: Ascending thoracic aortic disease is an important cause of sudden death in the US, yet most aortic aneurysms are identified incidentally. Objective: To develop and validate a clinical score to estimate ascending aortic diameter. Design, Setting, and Participants: Using an ongoing magnetic resonance imaging substudy of the UK Biobank cohort study, which had enrolled participants from 2006 through 2010, score derivation was performed in 30 018 participants and internal validation in an additional 6681. External validation was performed in 1367 participants from the Framingham Heart Study (FHS) offspring cohort who had undergone computed tomography from 2002 through 2005, and in 50 768 individuals who had undergone transthoracic echocardiography in the Community Care Cohort Project, a retrospective hospital-based cohort of longitudinal primary care patients in the Mass General Brigham (MGB) network between 2001-2018. Exposures: Demographic and clinical variables (11 covariates that would not independently prompt thoracic imaging). Main Outcomes and Measures: Ascending aortic diameter was modeled with hierarchical group least absolute shrinkage and selection operator (LASSO) regression. Correlation between estimated and measured diameter and performance for identifying diameter 4.0 cm or greater were assessed. Results: The 30 018-participant training cohort (52% women), were a median age of 65.1 years (IQR, 58.6-70.6 years). The mean (SD) ascending aortic diameter was 3.04 (0.31) cm for women and 3.32 (0.34) cm for men. A score to estimate ascending aortic diameter explained 28.2% of the variance in aortic diameter in the UK Biobank validation cohort (95% CI, 26.4%-30.0%), 30.8% in the FHS cohort (95% CI, 26.8%-34.9%), and 32.6% in the MGB cohort (95% CI, 31.9%-33.2%). For detecting individuals with an ascending aortic diameter of 4 cm or greater, the score had an area under the receiver operator characteristic curve of 0.770 (95% CI, 0.737-0.803) in the UK Biobank, 0.813 (95% CI, 0.772-0.854) in the FHS, and 0.766 (95% CI, 0.757-0.774) in the MGB cohorts, although the model significantly overestimated or underestimated aortic diameter in external validation. Using a fixed-score threshold of 3.537, 9.7 people in UK Biobank, 1.8 in the FHS, and 4.6 in the MGB cohorts would need imaging to confirm 1 individual with an ascending aortic diameter of 4 cm or greater. The sensitivity at that threshold was 8.9% in the UK Biobank, 11.3% in the FHS, and 18.8% in the MGB cohorts, with specificities of 98.1%, 99.2%, and 96.2%, respectively. Conclusions and Relevance: A prediction model based on common clinically available data was derived and validated to predict ascending aortic diameter. Further research is needed to optimize the prediction model and to determine whether its use is associated with improved outcomes.

Review of the Pathologic Characteristics in Myhre Syndrome: Gain-of-Function Pathogenic Variants in SMAD4 cause a Multisystem Fibroproliferative Response.

Background: Myhre syndrome, caused by pathogenic variants in SMAD4, is characterized by compact body habitus with short stature, distinctive craniofacial appearance, stiff skin, cardiovascular abnormalities (valve stenosis, coarctation, hypoplasia, or stenosis of aorta), effusions of potential spaces (pericardium, pleura, peritoneum), restricted movement of the joints (including thorax), and hearing loss. Lung and airway disease has been reported, but not always well-defined, to include interstitial lung disease, large airway obstruction, and pulmonary arterial hypertension. Excessive fibroproliferation of tissues especially following trauma or surgical instrumentation has been recognized, although these may also present spontaneously. Method: We report the pathologic features of 1 new patient with progressive choanal stenosis, and 22 literature cases, including the expanded history of 5 patients (3 who died). Results: Examination of patient tissues documents cellular fibroproliferation and deposition of excessive extracellular matrix explaining some of the observed clinical features of Myhre syndrome. Conclusion: Excessive fibrosis is noted in multiple tissues, especially heart, lung, and upper and lower airways. Our research provides the first systematic review to provide a knowledge base of gross and pathologic findings in Myhre syndrome.

Aortic Cellular Diversity and Quantitative Genome-Wide Association Study Trait Prioritization Through Single-Nuclear RNA Sequencing of the Aneurysmal Human Aorta.

BACKGROUND: Mural cells in ascending aortic aneurysms undergo phenotypic changes that promote extracellular matrix destruction and structural weakening. To explore this biology, we analyzed the transcriptional features of thoracic aortic tissue. METHODS: Single-nuclear RNA sequencing was performed on 13 samples from human donors, 6 with thoracic aortic aneurysm, and 7 without aneurysm. Individual transcriptomes were then clustered based on transcriptional profiles. Clusters were used for between-disease differential gene expression analyses, subcluster analysis, and analyzed for intersection with genetic aortic trait data. RESULTS: We sequenced 71 689 nuclei from human thoracic aortas and identified 14 clusters, aligning with 11 cell types, predominantly vascular smooth muscle cells (VSMCs) consistent with aortic histology. With unbiased methodology, we found 7 vascular smooth muscle cell and 6 fibroblast subclusters. Differentially expressed genes analysis revealed a vascular smooth muscle cell group accounting for the majority of differential gene expression. Fibroblast populations in aneurysm exhibit distinct behavior with almost complete disappearance of quiescent fibroblasts. Differentially expressed genes were used to prioritize genes at aortic diameter and distensibility genome-wide association study loci highlighting the genes JUN, LTBP4 (latent transforming growth factor beta-binding protein 1), and IL34 (interleukin 34) in fibroblasts, ENTPD1, PDLIM5 (PDZ and LIM domain 5), ACTN4 (alpha-actinin-4), and GLRX in vascular smooth muscle cells, as well as LRP1 in macrophage populations. CONCLUSIONS: Using nuclear RNA sequencing, we describe the cellular diversity of healthy and aneurysmal human ascending aorta. Sporadic aortic aneurysm is characterized by differential gene expression within known cellular classes rather than by the appearance of novel cellular forms. Single-nuclear RNA sequencing of aortic tissue can be used to prioritize genes at aortic trait loci.

An additional patient with SMAD4-Juvenile Polyposis-Hereditary hemorrhagic telangiectasia and connective tissue abnormalities: SMAD4 loss-of-function and gain-of-function pathogenic variants result in contrasting phenotypes.

Loss-of-function pathogenic variants in somatic and germline cells in SMAD4 may cause cancer and juvenile polyposis-Hereditary Hemorrhagic Telangiectasia (SMAD4-JP-HHT), respectively. In a similar manner, gain-of-function somatic and germline pathogenic variants in SMAD4 can cause various forms of cancer as well as Myhre syndrome. The different SMAD4 molecular mechanisms result in contrasting clinical phenotypes demonstrated by SMAD4-JP-HHT and Myhre syndrome. We report an additional patient with SMAD4-JP-HHT and aortopathy, and expand the phenotype to include severe valvulopathy, cutaneous, ophthalmologic, and musculoskeletal features consistent with an inherited disorder of connective tissue. We compared this 70-year-old man with SMAD4-JP-HHT to 18 additional literature cases, and also compared patients with SMAD4-JP-HHT to those with Myhre syndrome. In contrast to aorta dilation, hypermobility, and loose skin in SMAD4-JP-HHT, Myhre syndrome has aorta hypoplasia, stiff joints, and firm skin representing an intriguing phenotypic contrast, which can be attributed to different molecular mechanisms involving SMAD4. We remind clinicians about the possibility of significant cardiac valvulopathy and aortopathy, as well as connective tissue disease in SMAD4-JP-HHT. Additional patients and longer follow-up will help determine if more intensive surveillance improves care amongst these patients.

Spatially Distinct Genetic Determinants of Aortic Dimensions Influence Risks of Aneurysm and Stenosis.

BACKGROUND: The left ventricular outflow tract (LVOT) and ascending aorta are spatially complex, with distinct pathologies and embryologic origins. Prior work examined the genetics of thoracic aortic diameter in a single plane. OBJECTIVES: We sought to elucidate the genetic basis for the diameter of the LVOT, aortic root, and ascending aorta. METHODS: Using deep learning, we analyzed 2.3 million cardiac magnetic resonance images from 43,317 UK Biobank participants. We computed the diameters of the LVOT, the aortic root, and at 6 locations of ascending aorta. For each diameter, we conducted a genome-wide association study and generated a polygenic score. Finally, we investigated associations between these scores and disease incidence. RESULTS: A total of 79 loci were significantly associated with at least 1 diameter. Of these, 35 were novel, and most were associated with 1 or 2 diameters. A polygenic score of aortic diameter approximately 13 mm from the sinotubular junction most strongly predicted thoracic aortic aneurysm (n = 427,016; mean HR: 1.42 per SD; 95% CI: 1.34-1.50; P = 6.67 × 10-21). A polygenic score predicting a smaller aortic root was predictive of aortic stenosis (n = 426,502; mean HR: 1.08 per SD; 95% CI: 1.03-1.12; P = 5 × 10-6). CONCLUSIONS: We detected distinct genetic loci underpinning the diameters of the LVOT, aortic root, and at several segments of ascending aorta. We spatially defined a region of aorta whose genetics may be most relevant to predicting thoracic aortic aneurysm. We further described a genetic signature that may predispose to aortic stenosis. Understanding genetic contributions to proximal aortic diameter may enable identification of individuals at risk for aortic disease and facilitate prioritization of therapeutic targets.