Deep learning enables genetic analysis of the human thoracic aorta

Pirruccello, James P.; Chaffin, Mark; Chou, Elizabeth L.; Fleming, Stephen J.; Lin, Honghuang; Nekoui, Mahan; Khurshid, Shaan; Friedman, Samuel; Bick, Alexander; Arduini, Alessandro; Weng, Lu‐Chen; Choi, Seung Hoan; Akkad, Amer-Denis; Batra, Puneet; Tucker, Nathan R; Hall, Amelia Weber; Roselli, Carolina; Benjamin, Emelia J.; Vellarikkal, Shamsudheen Karuthedath; Gupta, Rajat M.; Stegmann, C.; Juric, Dejan; Stone, James R.; Vasan, Ramachandran S.; Ho, Jennifer E.; Hoffmann, Udo; Lubitz, Steven A.; Philippakis, Anthony; Lindsay, Mark E.; Ellinor, Patrick T.

doi:10.1038/s41588-021-00962-4

Cited by 131 publications

(150 citation statements)

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“…In addition, summary data from GWAS of ascending aortic (AA) and descending aortic (DA) diameters were obtained. AA and DA diameters were evaluated in cardiac magnetic resonance images from the UK Biobank by a deep learning model ( 33 ).…”

Section: Methodsmentioning

confidence: 99%

Association of Circulating Cathepsin B Levels With Blood Pressure and Aortic Dilation

Chai

Tian

Yang

et al. 2022

Front. Cardiovasc. Med.

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Hypertension is a key risk factor for spontaneous coronary artery dissection (SCAD) and aortic dilation. Circulating proteins play key roles in a range of biological processes and represent a major source of druggable targets. The aim of this study was to identify circulating proteins that were associated with blood pressure (BP), SCAD and aortic dilation. We identified shared genetic variants of BP and SCAD in genome-wide association studies, searched for circulating protein affected by these variants and examined the association of circulating protein levels with BP, aortic aneurysm and dissection (AAD) and aortic diameters by integrating data from circulating protein quantitative trait loci (pQTL) studies and genome wide association study (GWAS) in individuals from the UK Biobank using two-sample Mendelian randomization analysis methods. Single nucleotide polymorphisms (SNPs) in JAG1, ERI1, ULK4, THSD4, CMIP, COL4A2, FBN1, FAM76B, FGGY, NUS1, and HNF4G, which were related to extracellular matrix components, were associated with both BP and SCAD. We found 49 significant pQTL signals among these SNPs. The regulated proteins were encoded by MMP10, IL6R, FIGF, MMP1, CTSB, IGHG1, DSG2, TTC17, RETN, POMC, SCARF2, RELT, and GALNT16, which were enriched in biological processes such as collagen metabolic process and multicellular organism metabolic process. Causal associations between BP and AAD and aortic diameters were detected. Significant associations between circulating levels of cathepsin B, a well-known prorenin processing enzyme, and BP and aortic diameters were identified by using several Mendelian randomization analysis methods and were validated by independent data.ConclusionThe present study identified the association between circulating cathepsin B and BP and aortic diameters. The findings indicated that BP-associated genetic variants may influence aortic dilation risk by circulating proteins that regulate BP.

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Section: Methodsmentioning

confidence: 99%

Association of Circulating Cathepsin B Levels With Blood Pressure and Aortic Dilation

Chai

Tian

Yang

et al. 2022

Front. Cardiovasc. Med.

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“…Of note, FBN1 was present both in our GWAS gene set and the HTAAD gene set. We then tested whether the 21 putative causal TAAD risk genes were enriched in speci c cell types identi ed from publicly available single nuclear RNA expression data (snRNA) from ascending and descending thoracic aorta 33 . Using the fast gene set enrichment software, we observed that the causal TAAD gene set was signi cantly enriched in VSMC (P = 0.0078), and a suggestive enrichment was also observed with broblasts (P = 0.02), though this was no longer signi cant after Bonferroni correction (P < 0.01 = 0.05/5 cell types; Supplementary Table 17).…”

Section: Gene Expression Analyses Reveal Taad-relevant Cell Typesmentioning

confidence: 99%

“…Finally, we sought to identify relevant cell types for the individual putative causal TAAD risk genes leveraging scRNA sequencing data from normal and aneurysmal ascending aortic aneurysm tissue 34 and snRNA sequencing data from normal ascending and descending thoracic aorta 33 . For the 9 genes we hypothesized promote TAAD risk through changes in aortic gene expression ("candidate causal genes through changes in expression" in Supplementary Table 14), we rst qualitatively assessed for a prioritized cell type through: 1) the percentage of cells expressing the gene in a given cell type cluster, and 2) the magnitude of average gene expression in each cell type (Figure 4a,b,c).…”

Section: Gene Expression Analyses Reveal Taad-relevant Cell Typesmentioning

confidence: 99%

“…Enrichment Analysis with Human Thoracic Aorta snRNA-seq Data We generated a gene set of 21 putative causal TAAD risk genes: the 11 candidate causal genes identi ed from our GWAS, as well as 10 additional previously identi ed de nitive or strong HTAAD genes ("Category A") 6 , and overlapped our gene set with publicly available single nucleus RNA-sequencing data from ascending and descending thoracic aorta specimens 33 . We downloaded single nucleus expression data and existing tSNE cluster annotations for each cell from the Broad Institute single cell portal (https://singlecell.broadinstitute.org/single_cell), and combined the clusters into 5 overarching cell categorizations -VSMCs, broblasts, endothelial cells, leukocytes, and other cells (Supplementary Table 17).…”

Section: Strati Ed Ld Score Regression Analysismentioning

confidence: 99%

“…Within each cluster, differential gene expression between clusters based on case/control status was performed with FindAllMarkers (group.by = 'stim'), and a Bonferroni P < 0.01 was set for statistical signi cance. Previously published single nucleus RNA sequencing from thoracic aorta was downloaded from the Broad Single Cell Portal 33 . The cells were processed in R Studio with Seurat (v4) according to the pipeline above, with metadata of the original clustering added.…”

Section: Strati Ed Ld Score Regression Analysismentioning

confidence: 99%

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Genome-wide Association Study of Thoracic Aortic Aneurysm and Dissection in the Million Veteran Program

Klarin

Devineni

Sendamarai

et al. 2022

Preprint

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Current understanding of the genetic contribution to thoracic aortic aneurysms and dissections (TAAD) has largely been informed through studies focusing on rare, Mendelian forms of disease. In the current analysis we performed the largest genome-wide association study (GWAS) of TAAD to date, testing ~25 million DNA sequence variants in 8,626 participants with (7,050 European, 1,266 African, and 310 Hispanic ancestry individuals) and 453,043 participants without TAAD in the Million Veteran Program. The results were replicated in an independent sample of 4,459 individuals with and 512,463 without TAAD from 6 cohorts. We identified 21 TAAD loci, 17 of which have not been previously reported. We leverage phenome-wide scanning and causal inference methods to provide evidence that TAAD is a non-atherosclerotic aortic disorder distinct form other forms of vascular disease; we subsequently use Bayesian techniques and single cell/nucleus RNA sequencing data to identify causal risk genes and cell types. Finally, we generate a TAAD polygenic risk score and demonstrate that those at the top 5% of polygenic risk are over 4-fold more likely to experience an incident, fatal TAAD event. Our results demonstrate that the genetic architecture of TAAD mirrors that of other complex traits and is not solely inherited through protein altering variants of large effect size. These data provide new mechanistic insights into TAAD risk and improve our understanding of thoracic aortic disease.

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

Lin

Khurshid

Nekoui

et al. 2022

JAMA

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ImportanceAscending thoracic aortic disease is an important cause of sudden death in the US, yet most aortic aneurysms are identified incidentally.ObjectiveTo develop and validate a clinical score to estimate ascending aortic diameter.Design, Setting, and ParticipantsUsing 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.ExposuresDemographic and clinical variables (11 covariates that would not independently prompt thoracic imaging).Main Outcomes and MeasuresAscending 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.ResultsThe 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 RelevanceA 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.

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Deep learning enables genetic analysis of the human thoracic aorta

Cited by 131 publications

References 82 publications

Association of Circulating Cathepsin B Levels With Blood Pressure and Aortic Dilation

Association of Circulating Cathepsin B Levels With Blood Pressure and Aortic Dilation

Genome-wide Association Study of Thoracic Aortic Aneurysm and Dissection in the Million Veteran Program

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

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