Biology subjects

Ellinor, P. T.

Publications and source records attributed to Ellinor, P. T..

At least 19 recordsLinked to original sources

bioRxiv2025

HeartMap: An Integrated Cell Atlas of 2.4 million cells across 209 Individuals in Health and Disease

Cardiovascular disease remains the leading cause of global mortality. Understanding its complexity requires dissecting the heart's cellular landscape. We present HeartMap, a comprehensive single-nucleus RNA sequencing atlas of the adult human heart. This resource integrates data from nine studies, encompassing over 2.4 million nuclei, 209 individuals, eight anatomical regions, and seven disease and healthy states. After rigorous data harmonization and benchmarking of batch-correction methods, we characterized transcriptional diversity across 14 cell types. To demonstrate the utility of HeartMap, we identified robust disease-associated gene signatures in dilated cardiomyopathy by comparing across multiple studies. Notably, we identified distinct activated fibroblast populations, with COL22A1 or TNC enrichment, revealing potential differences between inherited and ischemic cardiomyopathies. HeartMap provides a valuable tool for exploring cardiac disease at the single-cell level, facilitating both fundamental research and potential therapeutic development.

cell biology↗

bioRxiv2025

A role of extracellular vesicle-mediated inter-organ communication in obesity-related arrhythmia

Obesity contributes to the risk of cardiac arrhythmias, but the exact mechanism remains unclear. Here, we show visceral adipose tissue-derived extracellular vesicles (VAT EVs) from individuals with obesity prolong action potential duration (APD) and impair calcium handling in stem cell-derived cardiomyocytes, in addition to activating fibroblasts and macrophages towards a pro-fibrotic/inflammatory state, thereby creating pro-arrhythmic substrate. Adipose-derived EVs target the heart in obese mice, suggesting the potential for direct communication. Transcriptome-wide genetic association (TWAS) and epigenetic studies anchored on genes differentially expressed in cardiomyocytes, fibroblasts, and macrophages after VAT-EV exposure identified genes significantly associated with QT interval and atrial fibrillation. Finally, as a proof-of-principle, we pharmacologically blocked TRPC3 (a VAT-EV-induced ion channel) in cardiomyocytes, restoring the APD towards normality. This molecular genetic evidence supports an EV-mediated direct communication pathway between adipose tissue and the heart in arrhythmogenesis, offering a new paradigm to identify mediators of cardiovascular disease in obesity. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=192 SRC="FIGDIR/small/676027v1_ufig1.gif" ALT="Figure 1"> View larger version (74K): org.highwire.dtl.DTLVardef@114b230org.highwire.dtl.DTLVardef@1be7926org.highwire.dtl.DTLVardef@3d6b0org.highwire.dtl.DTLVardef@3ef15_HPS_FORMAT_FIGEXP M_FIG C_FIG

cell biology↗

bioRxiv2025

Single cell transcriptomic analyses of human heart failure with preserved ejection fraction

BackgroundHeart failure with preserved ejection fraction (HFpEF) is a poorly understood, multi-system disease with high morbidity and mortality. To improve our understanding of its underlying biology, we used single-nucleus RNA sequencing (snRNA-seq) to characterize cell-specific gene expression patterns in human HFpEF myocardium. MethodsSeptal myocardial biopsies (2-3 mg) from 30 HFpEF patients and 29 non-failing donor controls were analyzed using the 10X Genomics platform, with nuclei isolated from combined samples (6 patients/pool). Genotype-based demultiplexing was performed with souporcell, and gene expression quantified with CellRanger and CellBender. After quality control, nuclei were clustered and annotated by cell types based on specific marker genes. Differential expression (DE) by cell-type in HFpEF vs controls was performed using limma-voom and functional analysis performed using Gene Set Enrichment Analysis. Data were compared to dilated cardiomyopathy (DCM) using prior snRNA-seq in DCM vs respective controls. ResultsWe successfully demultiplexed pooled myocardial biopsies, assigning >75% of droplets to individual patients. From eight pooled samples (19 HFpEF, 24 controls), we recovered 48,886 nuclei and identified 14 cell types. Cardiomyocytes (5159 differentially expressed [DE] genes, 36%) and fibroblasts (5905 DE genes, 49%) showed the most DE genes, while endothelial cells (2143), pericytes (1812), and macrophages (1405) had fewer. Enriched pathways common to multiple cell types included transcription/translation, immune activation, metabolism, and protein quality control. Of 7848 DE genes identified via pseudo-bulk snRNA-seq, 51% were DE in fibroblasts and 47% in cardiomyocytes, compared to <20% in other cell types. Unlike dilated cardiomyopathy (DCM), sub-clustering fibroblasts did not reveal an activated fibroblast population in HFpEF. Comparative analysis between HFpEF and DCM identified transcriptional differences primarily in cardiomyocytes. ConclusionsThis study demonstrates the power of genotype-based demultiplexing for single-cell transcriptomic analyses of small endomyocardial biopsies and identifies cardiomyocytes as the principal cell type with distinct transcriptional changes in HFpEF versus DCM. These findings, coupled with differential gene expression and functional pathway analyses, illuminate HFpEF pathways and may nominate compelling targets for future mechanistic studies and therapeutic efforts for HFpEF. Clinical PerspectiveWhat is new? We successfully used genotype-based demultiplexing to perform single nucleus RNA-seq from myocardial biopsies. The snRNA-seq analysis revealed distinct enrichment of pathways related to transcription/translation, immune activation, metabolism, and protein quality control across multiple cell types in HFpEF. In contrast to DCM, HFpEF is distinguished by the absence of an activated fibroblast population and a predominance of transcriptional differences within cardiomyocytes, highlighting a distinct disease mechanism. What are the clinical implications? The cell-selective HFpEF myocardial transcriptional landscape highlights altered metabolism, protein translation and quality control, immune activation and growth/matrix organization. These pathways provide compelling new targets for developing effective treatments.

bioinformatics↗

bioRxiv2024

Therapeutic Spp1 silencing in TREM2+ cardiac macrophages suppresses atrial fibrillation

Atrial fibrillation (AFib) and the risk of its lethal complications are propelled by fibrosis, which induces electrical heterogeneity and gives rise to reentry circuits. Atrial TREM2+ macrophages secrete osteopontin (encoded by Spp1), a matricellular signaling protein that engenders fibrosis and AFib. Here we show that silencing Spp1 in TREM2+ cardiac macrophages with an antibody-siRNA conjugate reduces atrial fibrosis and suppresses AFib in mice, thus offering a new immunotherapy for the most common arrhythmia.

immunology↗

bioRxiv2024

Cardiomyocyte PGC-1α enables physiological adaptations to endurance exercise through suppression of GDF15 and cardiac atrophy

Exercise training induces physiological cardiac hypertrophy, enhanced mitochondrial biogenesis and myocardial contractility. In skeletal muscle, the transcriptional coactivator PGC-1 is a key orchestrator of these responses. The heart expresses abundant and exercise-responsive PGC-1, but it is unclear whether cardiomyocyte PGC-1 is necessary for cardiac adaptation to endurance training. Here we demonstrate that cardiomyocyte PGC-1 is required for physiological cardiac hypertrophy during exercise training in mice. In the absence of cardiomyocyte PGC-1, voluntary wheel running does not improve exercise capacity and instead confers immune-fibrotic-atrophic heart failure after just 6 weeks of training. We identify cardiomyocyte PGC-1 as a negative regulator of stress-responsive senescence gene expression. The most enriched of these is the myomitokine GDF15. GDF15 is secreted locally but not systemically in PGC-1-deficient mouse hearts and reduces cardiomyocyte size. Cardiomyocyte-specific reduction of GDF15 expression preserves exercise tolerance and cardiac contractility in PGC-1-deficient mice during endurance training. Finally, we show that cardiomyocyte PPARGC1A expression correlates with cardiomyocyte number and negatively with GDF15 expression in human cardiomyopathies through single nucleus RNA sequencing. Our data implicate cardiomyocyte PGC-1 as a vital safeguard against stress-induced atrophy and local GDF15-induced dysfunction during exercise.

physiology↗

bioRxiv2023

Whole genome association testing in 333,100 individuals across three biobanks identifies rare non-coding single variant and genomic aggregate associations with height

The role of rare non-coding variation in complex human phenotypes is still largely unknown. To elucidate the impact of rare variants in regulatory elements, we performed a whole-genome sequencing association analysis for height using 333,100 individuals from three datasets: UK Biobank (N=200,003), TOPMed (N=87,652) and All of Us (N=45,445). We performed rare (<0.1% minor-allele-frequency) single-variant and aggregate testing of non-coding variants in regulatory regions based on proximal, intergenic and deep-intronic annotation. We observed 29 independent variants associated with height at P < 6 x 10-10 after conditioning on previously reported variants, with effect sizes ranging from -7cm to +4.7cm. We also identified and replicated non-coding aggregate-based associations proximal to HMGA1 containing variants associated with a 5cm taller height and of highly-conserved variants in MIR497HG on chromosome 17. We have developed a novel approach for identifying non-coding rare variants in regulatory regions with large effects from whole-genome sequencing data associated with complex traits.

genetics↗

bioRxiv2023

Transcriptional profile of the rat cardiovascular system at single cell resolution

BackgroundDespite 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 ResultsWe 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. ConclusionsThrough 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.

cell biology↗

bioRxiv2023

Fusion Neural Network (FusNet) for predicting protein-mediated loops

The organization of the three-dimensional (3D) genome is a complex, and requires a plethora of proteins to ensure the proper formation and regulation of chromatin loops as well as higher order structures. Studying protein-mediated loop regulation can help unravel the intricate interplay between these loops and their crucial roles in modulating gene expression across different cellular contexts. However, current targeted chromatin conformation capture experiments face limitations in capturing protein-mediated loops across various cell types, and existing computational methods fail to predict diverse protein-mediated loops. To address these issues, we propose a fusion neural network (FusNet) designed for predicting protein-mediated loops. FusNet leverages genome sequence information, open chromatin, and ChIP-seq data to efficiently represent and analyze the positions of loop anchors. To extract informative features and reduce the complexity of FusNet, we constructed a convolutional neural network, which compresses the dimensionality of the features while also preserving the most significant ones. To enhance the accuracy and generalization capacity of FusNet, we built a fusion layer by stacking the prediction of fundamental models with a meta-model. FusNet demonstrated its effectiveness in predicting protein-mediated loops, exhibiting high consistency with Hi-C data. Moreover, we find that the loops output from FusNet are highly associated with regulatory functions. Through association analysis with genetic risk variants, FusNet further revealed its potential for unraveling disease-related mechanisms. In conclusion, our study offers a novel computational approach for predicting various protein-mediated chromatin loops, which could substantially enhance research on the functional significance of protein-mediated loop structures in diverse cellular contexts. Significance StatementThe intricate spatial organization of the three-dimensional (3D) genome involves functional proteins critically contributing to chromatin loop formation and regulation. Understanding these protein-mediated loops is vital for elucidating their influence on 3D genome architecture and gene regulation across different cellular types and disease-related contexts. In this study, we propose a Fusion Neural Network (FusNet) for predicting protein-mediated loops. FusNet can concurrently capture and analyze multiple protein-mediated loops in various cell types to advance our understanding of the multitude of protein-mediated loop structures and their functional significance. Importantly, through association analysis with risk variants, FusNet manifests potential in revealing disease-related mechanisms.

bioinformatics↗

bioRxiv2023

Identification of a new Corin atrial natriuretic peptide-converting enzyme substrate: Agouti-signaling protein (ASIP)

Corin is a transmembrane tethered enzyme best known for processing the hormone atrial natriuretic peptide (ANP) in cardiomyocytes to control electrolyte balance and blood pressure. Loss of function mutations in Corin prevent ANP processing and lead to hypertension. Curiously, Corin loss of function variants also result in lighter coat color pigmentation in multiple species. Corin pigmentation effects are dependent on a functional Agouti locus encoding the agouti-signaling protein (ASIP) based on a genetic interaction. However, the nature of this conserved role of Corin has not been defined. Here we report that ASIP is a direct proteolytic substrate of the Corin enzyme.

biochemistry↗

bioRxiv2021

TAILS identifies candidate substrates and biomarkers of ADAMTS7, a therapeutic protease target in coronary artery disease

Loss-of-function mutations in the secreted enzyme ADAMTS7 (a disintegrin and metalloproteinase with thrombospondin motifs 7) are associated with protection for coronary artery disease (CAD). ADAMTS7 catalytic inhibition has been proposed as a therapeutic strategy for treating CAD; however, the lack of an endogenous substrate has hindered the development of activity-based biomarkers. To identify ADAMTS7 extracellular substrates and their cleavage sites relevant to vascular disease, we used TAILS (terminal amine isotopic labeling of substrates), a method for identifying protease-generated neo-N termini. We compared the secreted proteome of vascular smooth muscle and endothelial cells expressing either full-length mouse ADAMTS7 WT, catalytic mutant ADAMTS7 E373Q or a control luciferase adenovirus. Significantly enriched N-terminal cleavage sites in ADAMTS7 WT samples were compared to the negative control conditions and filtered for stringency, resulting in catalogs of high confidence candidate ADAMTS7 cleavage sites from our three independent TAILS experiments. Within the overlap of these discovery sets, we identified 24 unique cleavage sites from 16 protein substrates, including cleavage sites in EFEMP1 (EGF-containing fibulin-like extracellular matrix protein 1/Fibulin-3). The ADAMTS7 TAILS preference for EMEMP1 cleavage at the amino acids 123.124 over the adjacent 124.125 site was validated using both endogenous EFEMP1 and purified EFEMP1 in a binary in vitro cleavage assay. Collectively our TAILS discovery experiments have uncovered hundreds of potential substrates and cleavage sites to explore disease related biological substrates and facilitate activity-based ADAMTS7 biomarker development. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=146 SRC="FIGDIR/small/469331v1_ufig1.gif" ALT="Figure 1"> View larger version (37K): org.highwire.dtl.DTLVardef@1520ad4org.highwire.dtl.DTLVardef@144f148org.highwire.dtl.DTLVardef@13cdbc1org.highwire.dtl.DTLVardef@7f7b4f_HPS_FORMAT_FIGEXP M_FIG Graphical Abstract C_FIG

molecular biology↗

bioRxiv2021

Adjusting for Common Variant Polygenic Scores Improves Yield in Rare Variant Association Analyses

With the emergence of large-scale sequencing data, methods for improving power in rare variant analyses (RVAT) are needed. Here, we show that adjusting for common variant polygenic scores improves the yield in gene-based RVAT across 65 quantitative traits in the UK Biobank (up to 20% increase at =2.6x10-6), without a marked increase in false-positive rates or genomic inflation. Our results illustrate how adjusting for common variant effects can aid in rare variant association discovery.

genomics↗

bioRxiv2021

Genetic Analysis of Right Heart Structure and Function in 40,000 People

The heart evolved hundreds of millions of years ago. During mammalian evolution, the cardiovascular system developed with complete separation between pulmonary and systemic circulations incorporated into a single pump with chambers dedicated to each circulation. A lower pressure right heart chamber supplies deoxygenated blood to the lungs, while a high pressure left heart chamber supplies oxygenated blood to the rest of the body. Due to the complexity of morphogenic cardiac looping and septation required to form these two chambers, congenital heart diseases often involve maldevelopment of the evolutionarily recent right heart chamber. Additionally, some diseases predominantly affect structures of the right heart, including arrhythmogenic right ventricular cardiomyopathy (ARVC) and pulmonary hypertension. To gain insight into right heart structure and function, we fine-tuned deep learning models to recognize the right atrium, the right ventricle, and the pulmonary artery, and then used those models to measure right heart structures in over 40,000 individuals from the UK Biobank with magnetic resonance imaging. We found associations between these measurements and clinical disease including pulmonary hypertension and dilated cardiomyopathy. We then conducted genome-wide association studies, identifying 104 distinct loci associated with at least one right heart measurement. Several of these loci were found near genes previously linked with congenital heart disease, such as NKX2-5, TBX3, WNT9B, and GATA4. We also observed interesting commonalities and differences in association patterns at genetic loci linked with both right and left ventricular measurements. Finally, we found that a polygenic predictor of right ventricular end systolic volume was associated with incident dilated cardiomyopathy (HR 1.28 per standard deviation; P = 2.4E-10), and remained a significant predictor of disease even after accounting for a left ventricular polygenic score. Harnessing deep learning to perform large-scale cardiac phenotyping, our results yield insights into the genetic and clinical determinants of right heart structure and function.

genetics↗

bioRxiv2021

Transcriptome variation in human tissues revealed by long-read sequencing

Regulation of transcript structure generates transcript diversity and plays an important role in human disease. The advent of long-read sequencing technologies offers the opportunity to study the role of genetic variation in transcript structure. In this paper, we present a large human long-read RNA-seq dataset using the Oxford Nanopore Technologies platform from 88 samples from GTEx tissues and cell lines, complementing the GTEx resource. We identified just under 100,000 new transcripts for annotated genes, and validated the protein expression of a similar proportion of novel and annotated transcripts. We developed a new computational package, LORALS, to analyze genetic effects of rare and common variants on the transcriptome via allele-specific analysis of long reads. We called allele-specific expression and transcript structure events, providing novel insights into the specific transcript alterations caused by common and rare genetic variants and highlighting the resolution gained from long-read data. We were able to perturb transcript structure upon knockdown of PTBP1, an RNA binding protein that mediates splicing, thereby finding genetic regulatory effects that are modified by the cellular environment. Finally, we use this dataset to enhance variant interpretation and study rare variants leading to aberrant splicing patterns.

genomics↗

bioRxiv2020

Rare Genetic Variation Underlying Human Diseases and Traits: Results from 200,000 Individuals in the UK Biobank

BackgroundMany human diseases are known to have a genetic contribution. While genome-wide studies have identified many disease-associated loci, it remains challenging to elucidate causal genes. In contrast, exome sequencing provides an opportunity to identify new disease genes and large-effect variants of clinical relevance. We therefore sought to determine the contribution of rare genetic variation in a curated set of human diseases and traits using a unique resource of 200,000 individuals with exome sequencing data from the UK Biobank. Methods and ResultsWe included 199,832 participants with a mean age of 68 at follow-up. Exome-wide gene-based tests were performed for 64 diseases and 23 quantitative traits using a mixed-effects model, testing rare loss-of-function and damaging missense variants. We identified 51 known and 23 novel associations with 26 diseases and traits at a false-discovery-rate of 1%. There was a striking risk associated with many Mendelian disease genes including: MYPBC3 with over a 100-fold increased odds of hypertrophic cardiomyopathy, PKD1 with a greater than 25-fold increased odds of chronic kidney disease, and BRCA2, BRCA1, ATM and PALB2 with 3 to 10-fold increased odds of breast cancer. Notable novel findings included an association between GIGYF1 and type 2 diabetes (OR 5.6, P=5.35x10-8), elevated blood glucose, and lower insulin-like-growth-factor-1 levels. Rare variants in CCAR2 were also associated with diabetes risk (OR 13, P=8.5x10-8), while COL9A3 was associated with cataract (OR 3.4, P=6.7x10-8). Notable associations for blood lipids and hypercholesterolemia included NR1H3, RRBP1, GIGYF1, SCGN, APH1A, PDE3B and ANGPTL8. A number of novel genes were associated with height, including DTL, PIEZO1, SCUBE3, PAPPA and ADAMTS6, while BSN was associated with body-mass-index. We further assessed putatively pathogenic variants in known Mendelian cardiovascular disease genes and found that between 1.3 and 2.3% of the population carried likely pathogenic variants in known cardiomyopathy, arrhythmia or hypercholesterolemia genes. ConclusionsLarge-scale population sequencing identifies known and novel genes harboring high-impact variation for human traits and diseases. A number of novel findings, including GIGYF1,represent interesting potential therapeutic targets. Exome sequencing at scale can identify a meaningful proportion of the population that carries a pathogenic variant underlying cardiovascular disease.

genetics↗

bioRxiv2020

Robust, flexible, and scalable tests for Hardy-Weinberg Equilibrium across diverse ancestries

Traditional Hardy-Weinberg equilibrium (HWE) tests (the {chi}2 test and the exact test) have long been used as a metric for evaluating genotype quality, as technical artifacts leading to incorrect genotype calls often can be identified as deviations from HWE. However, in datasets comprised of individuals from diverse ancestries, HWE can be violated even without genotyping error, complicating the use of HWE testing to assess genotype data quality. In this manuscript, we present the Robust Unified Test for HWE (RUTH) to test for HWE while accounting for population structure and genotype uncertainty, and evaluate the impact of population heterogeneity and genotype uncertainty on the standard HWE tests and alternative methods using simulated and real sequence datasets. Our results demonstrate that ignoring population structure or genotype uncertainty in HWE tests can inflate false positive rates by many orders of magnitude. Our evaluations demonstrate different tradeoffs between false positives and statistical power across the methods, with RUTH consistently amongst the best across all evaluations. RUTH is implemented as a practical and scalable software tool to rapidly perform HWE tests across millions of markers and hundreds of thousands of individuals while supporting standard VCF/BCF formats. RUTH is publicly available at https://www.github.com/statgen/ruth.

bioinformatics↗

bioRxiv2020

A system for phenotype harmonization in the NHLBI Trans-Omics for Precision Medicine (TOPMed) Program

Genotype-phenotype association studies often combine phenotype data from multiple studies to increase power. Harmonization of the data usually requires substantial effort due to heterogeneity in phenotype definitions, study design, data collection procedures, and data set organization. Here we describe a centralized system for phenotype harmonization that includes input from phenotype domain and study experts, quality control, documentation, reproducible results, and data sharing mechanisms. This system was developed for the National Heart, Lung and Blood Institutes Trans-Omics for Precision Medicine (TOPMed) program, which is generating genomic and other omics data for >80 studies with extensive phenotype data. To date, 63 phenotypes have been harmonized across thousands of participants from up to 17 TOPMed studies per phenotype. We discuss the challenges faced in this undertaking and how they were addressed. The harmonized phenotype data and associated documentation have been submitted to National Institutes of Health data repositories for controlled-access by the scientific community. We also provide materials to facilitate future harmonization efforts by the community, which include (1) the code used to generate the 63 harmonized phenotypes, enabling others to reproduce, modify or extend these harmonizations to additional studies; and (2) results of labeling thousands of phenotype variables with controlled vocabulary terms.

genetics↗

bioRxiv2020

Deep learning enables genetic analysis of the human thoracic aorta

The aorta is the largest blood vessel in the body, and enlargement or aneurysm of the aorta can predispose to dissection, an important cause of sudden death. While rare syndromes have been identified that predispose to aortic aneurysm, the common genetic basis for the size of the aorta remains largely unknown. By leveraging a deep learning architecture that was originally developed to recognize natural images, we trained a model to evaluate the dimensions of the ascending and descending thoracic aorta in cardiac magnetic resonance imaging. After manual annotation of just 116 samples, we applied this model to 3,840,140 images from the UK Biobank. We then conducted a genome-wide association study in 33,420 individuals, revealing 68 loci associated with ascending and 35 with descending thoracic aortic diameter, of which 10 loci overlapped. Integration of common variation with transcriptome-wide analyses, rare-variant burden tests, and single nucleus RNA sequencing prioritized SVIL, a gene highly expressed in vascular smooth muscle, that was significantly associated with the diameter of the ascending and descending aorta. A polygenic score for ascending aortic diameter was associated with a diagnosis of thoracic aortic aneurysm in the remaining 391,251 UK Biobank participants who did not undergo imaging (HR = 1.44 per standard deviation; P = 3.7{middle dot}10-12). Defining the genetic basis of the diameter of the aorta may enable the identification of asymptomatic individuals at risk for aneurysm or dissection and facilitate the prioritization of potential therapeutic targets for the prevention or treatment of aortic aneurysm. Finally, our results illustrate the potential for rapidly defining novel quantitative traits derived from a deep learning model, an approach that can be more broadly applied to biomedical imaging data.

genomics↗

bioRxiv2020

Analysis of cardiac magnetic resonance imaging traits in 29,000 individuals reveals shared genetic basis with dilated cardiomyopathy

Dilated cardiomyopathy (DCM) is an important cause of heart failure and the leading indication for heart transplantation. Many rare genetic variants have been associated with DCM, but common variant studies of the disease have yielded few associated loci. As structural changes in the heart are a defining feature of DCM, we conducted a genome-wide association study (GWAS) of cardiac magnetic resonance imaging (MRI)-derived left ventricular measurements in 29,041 UK Biobank participants. 26 novel loci were associated with cardiac structure and function. These loci were found near 17 genes previously shown to cause Mendelian cardiomyopathies. A polygenic score of left ventricular end systolic volume was associated with incident DCM in previously disease-free individuals (hazard ratio = 1.54 per one standard deviation increase in the polygenic score, P = 2.1x10-16). Even among carriers of truncating mutations in TTN, the polygenic score influenced the size and function of the heart. These results further implicate common genetic polymorphisms in DCM pathogenesis.

genomics↗