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Large-scale sequencing has enabled unparalleled opportunities to investigate the role of rare coding variation in human phenotypic variability. Here, we present a pan-ancestry analysis of sequencing data from three large biobanks, including the All of Us research program. Using mixed-effects models, we performed gene-based rare variant testing for 601 diseases across 748,879 individuals, including 155,236 with ancestry dissimilar to European. We identified 363 significant associations, which highlighted core genes for the human disease phenome and identified potential novel associations, including UBR3 for cardiometabolic disease and YLPM1 for psychiatric disease. Pan-ancestry burden testing represented an inclusive and useful approach for discovery in diverse datasets, although we also highlight the importance of ancestry-specific sensitivity analyses in this setting. Finally, we found that effect sizes for rare protein-disrupting variants were concordant between samples similar to European ancestry and other genetic ancestries (β Deming  = 0.7-1.0). Our results have implications for multi-ancestry and cross-biobank approaches in sequencing association studies for human disease., (© 2024. The Author(s), under exclusive licence to Springer Nature America, Inc.)

Droplet-based single-cell assays, including single-cell RNA sequencing (scRNA-seq), single-nucleus RNA sequencing (snRNA-seq) and cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq), generate considerable background noise counts, the hallmark of which is nonzero counts in cell-free droplets and off-target gene expression in unexpected cell types. Such systematic background noise can lead to batch effects and spurious differential gene expression results. Here we develop a deep generative model based on the phenomenology of noise generation in droplet-based assays. The proposed model accurately distinguishes cell-containing droplets from cell-free droplets, learns the background noise profile and provides noise-free quantification in an end-to-end fashion. We implement this approach in the scalable and robust open-source software package CellBender. Analysis of simulated data demonstrates that CellBender operates near the theoretically optimal denoising limit. Extensive evaluations using real datasets and experimental benchmarks highlight enhanced concordance between droplet-based single-cell data and established gene expression patterns, while the learned background noise profile provides evidence of degraded or uncaptured cell types., (© 2023. The Author(s), under exclusive licence to Springer Nature America, Inc.)

Mapping gene networks requires large amounts of transcriptomic data to learn the connections between genes, which impedes discoveries in settings with limited data, including rare diseases and diseases affecting clinically inaccessible tissues. Recently, transfer learning has revolutionized fields such as natural language understanding 1,2 and computer vision 3 by leveraging deep learning models pretrained on large-scale general datasets that can then be fine-tuned towards a vast array of downstream tasks with limited task-specific data. Here, we developed a context-aware, attention-based deep learning model, Geneformer, pretrained on a large-scale corpus of about 30 million single-cell transcriptomes to enable context-specific predictions in settings with limited data in network biology. During pretraining, Geneformer gained a fundamental understanding of network dynamics, encoding network hierarchy in the attention weights of the model in a completely self-supervised manner. Fine-tuning towards a diverse panel of downstream tasks relevant to chromatin and network dynamics using limited task-specific data demonstrated that Geneformer consistently boosted predictive accuracy. Applied to disease modelling with limited patient data, Geneformer identified candidate therapeutic targets for cardiomyopathy. Overall, Geneformer represents a pretrained deep learning model from which fine-tuning towards a broad range of downstream applications can be pursued to accelerate discovery of key network regulators and candidate therapeutic targets., (© 2023. The Author(s), under exclusive licence to Springer Nature Limited.)

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., Competing Interests: Funding Support and Author Disclosures This work was funded by a collaboration between the Broad Institute and IBM Research. Support for title page creation and format was provided by AuthorArranger, a tool developed at the National Cancer Institute. Dr Pirruccello was supported by a Sarnoff Scholar Award and National Institutes of Health (NIH) grant K08HL159346. Dr Rämö was funded by a Fellowship from the Sigrid Jusélius Foundation. Dr Chou was funded by NIH grant T32HL007208. Dr Lindsay was supported by the Fredman Fellowship for Aortic Disease, the Toomey Fund for Aortic Dissection Research, and NIH grant R01HL130113. Dr Ellinor was supported by grants from NIH (R01HL092577, R01HL157635, and R01HL139731), American Heart Association Strategically Focused Research Networks (18SFRN34110082), and the European Union (MAESTRIA 965286). Dr Pirruccello has served as a consultant for Maze Therapeutics; and has received research support to the Broad Institute from IBM Research. Dr Juric is supported by grants from Genentech, Eisai, EMD Serono, Takeda, Amgen, Celgene, Placon Therapeutics, Syros, Petra Pharma, InventisBio, Infinity Pharmaceuticals, and Novartis; and has received personal fees from Genentech, Eisai, EMD Serono, Ipsen, Syros, Relay Therapeutics, MapKure, Vibliome, Petra Pharma, and Novartis. Dr Batra has received sponsored research support from Bayer and IBM; and has consulted for Novartis and Prometheus Biosciences. Dr Ellinor has received sponsored research support from Bayer, IBM Research, Bristol Myers Squibb, and Pfizer; and has served on advisory boards or consulted for Bayer, MyoKardia, and Novartis. The Broad Institute has filed for a patent on an invention from Drs Ellinor, Lindsay, and Pirruccello related to a genetic risk predictor for aortic disease. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose., (Copyright © 2023 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.)

With the emergence of large-scale sequencing data, methods for improving power in rare variant association tests are needed. Here we show that adjusting for common variant polygenic scores improves yield in gene-based rare variant association tests across 65 quantitative traits in the UK Biobank (up to 20% increase at α = 2.6 × 10 -6 ), without marked increases in false-positive rates or genomic inflation. Benefits were seen for various models, with the largest improvements seen for efficient sparse mixed-effects models. Our results illustrate how polygenic score adjustment can efficiently improve power in rare variant association discovery., (© 2023. The Author(s), under exclusive licence to Springer Nature America, Inc.)

Ischemic cardiomyopathy (ICM) is the leading cause of heart failure worldwide, yet the cellular and molecular signature of this disease is largely unclear. Using single-nucleus RNA sequencing (snRNA-seq) and integrated computational analyses, we profile the transcriptomes of over 99,000 human cardiac nuclei from the non-infarct region of the left ventricle of 7 ICM transplant recipients and 8 non-failing (NF) controls. We find the cellular composition of the ischemic heart is significantly altered, with decreased cardiomyocytes and increased proportions of lymphatic, angiogenic, and arterial endothelial cells in patients with ICM. We show that there is increased LAMININ signaling from endothelial cells to other cell types in ICM compared with NF. Finally, we find that the transcriptional changes that occur in ICM are similar to those in hypertrophic and dilated cardiomyopathies and that the mining of these combined datasets can identify druggable genes that could be used to target end-stage heart failure., Competing Interests: Declaration of interests C.A.K. is an employee of Bayer US LLC (a subsidiary of Bayer AG) and may own stock in Bayer. S.H., S.B., and C.A.K. were full-time employees of Bayer when this work was performed. P.T.E. has received sponsored research support from Bayer AG, IBM Health, Bristol Myers Squibb, and Pfizer; he has also served on advisory boards or consulted for Bayer AG, MyoKardia, and Novartis. K.B.M. has research grant funding from Amgen, USA and has also served on advisory boards for MyoKardia, Bristol-Myers Squibb, and Pfizer., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

Background: A key goal of precision medicine is to disaggregate common, complex diseases into discrete molecular subtypes. Rare coding variants in the low-density lipoprotein receptor gene ( LDLR ) are identified in 1% to 2% of coronary artery disease (CAD) patients, defining a molecular subtype with risk driven by hypercholesterolemia., Methods: To search for additional subtypes, we compared the frequency of rare, predicted loss-of-function and damaging missense variants aggregated within a given gene in 41 081 CAD cases versus 217 115 controls., Results: Rare variants in LDLR were most strongly associated with CAD, present in 1% of cases and associated with 4.4-fold increased CAD risk. A second subtype was characterized by variants in endothelial nitric oxide synthase gene ( NOS3 ), a key enzyme regulating vascular tone, endothelial function, and platelet aggregation. A rare predicted loss-of-function or damaging missense variants in NOS3 was present in 0.6% of cases and associated with 2.42-fold increased risk of CAD (95% CI, 1.80-3.26; P =5.50×10 -9 ). These variants were associated with higher systolic blood pressure (+3.25 mm Hg; [95% CI, 1.86-4.65]; P =5.00×10 -6 ) and increased risk of hypertension (adjusted odds ratio 1.31; [95% CI, 1.14-1.51]; P =2.00×10 -4 ) but not circulating cholesterol concentrations, suggesting that, beyond lipid pathways, nitric oxide synthesis is a key nonlipid driver of CAD risk., Conclusions: Beyond LDLR , we identified an additional nonlipid molecular subtype of CAD characterized by rare variants in the NOS3 gene.

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.

Aims: Genetic studies have implicated the ARHGEF26 locus in the risk of coronary artery disease (CAD). However, the causal pathways by which DNA variants at the ARHGEF26 locus confer risk for CAD are incompletely understood. We sought to elucidate the mechanism responsible for the enhanced risk of CAD associated with the ARHGEF26 locus., Methods and Results: In a conditional analysis of the ARHGEF26 locus, we show that the sentinel CAD-risk signal is significantly associated with various non-lipid vascular phenotypes. In human endothelial cell (EC), ARHGEF26 promotes the angiogenic capacity, and interacts with known angiogenic factors and pathways. Quantitative mass spectrometry showed that one CAD-risk coding variant, rs12493885 (p.Val29Leu), resulted in a gain-of-function ARHGEF26 that enhances proangiogenic signalling and displays enhanced interactions with several proteins partially related to the angiogenic pathway. ARHGEF26 is required for endothelial angiogenesis by promoting macropinocytosis of Vascular Endothelial Growth Factor Receptor 2 (VEGFR2) on cell membrane and is crucial to Vascular Endothelial Growth Factor (VEGF)-dependent murine vessel sprouting ex vivo. In vivo, global or tissue-specific deletion of ARHGEF26 in EC, but not in vascular smooth muscle cells, significantly reduced atherosclerosis in mice, with enhanced plaque stability., Conclusions: Our results demonstrate that ARHGEF26 is involved in angiogenesis signaling, and that DNA variants within ARHGEF26 that are associated with CAD risk could affect angiogenic processes by potentiating VEGF-dependent angiogenesis., Competing Interests: Conflict of interest: P.E. has received sponsored research support from Bayer AG and IBM Health. He has also served on advisory boards or consulted for Bayer AG, Quest Diagnostics, MyoKardia and Novartis. T.M. and S.K. are employees of Verve Therapeutics. S.K. holds equity in Verve Therapeutics, Maze Therapeutics, Catabasis, and San Therapeutics. He is a member of the scientific advisory boards for Regeneron Genetics Center and Corvidia Therapeutics; he has served as a consultant for Acceleron, Eli Lilly, Novartis, Merck, Novo Nordisk, Novo Ventures, Ionis, Alnylam, Aegerion, Haug Partners, Noble Insights, Leerink Partners, Bayer Healthcare, Illumina, Colour Genomics, MedGenome, Quest, and Medscape; he reports patents related to a method of identifying and treating a person having a predisposition to or afflicted with cardiometabolic disease (20180010185) and a genetics risk predictor (20190017119)., (Published on behalf of the European Society of Cardiology. All rights reserved. © The Author(s) 2021. For permissions, please email: journals.permissions@oup.com.)

For Alzheimer's disease-a leading cause of dementia and global morbidity-improved identification of presymptomatic high-risk individuals and identification of new circulating biomarkers are key public health needs. Here, we tested the hypothesis that a polygenic predictor of risk for Alzheimer's disease would identify a subset of the population with increased risk of clinically diagnosed dementia, subclinical neurocognitive dysfunction, and a differing circulating proteomic profile. Using summary association statistics from a recent genome-wide association study, we first developed a polygenic predictor of Alzheimer's disease comprised of 7.1 million common DNA variants. We noted a 7.3-fold (95% CI 4.8 to 11.0; p < 0.001) gradient in risk across deciles of the score among 288,289 middle-aged participants of the UK Biobank study. In cross-sectional analyses stratified by age, minimal differences in risk of Alzheimer's disease and performance on a digit recall test were present according to polygenic score decile at age 50 years, but significant gradients emerged by age 65. Similarly, among 30,541 participants of the Mass General Brigham Biobank, we again noted no significant differences in Alzheimer's disease diagnosis at younger ages across deciles of the score, but for those over 65 years we noted an odds ratio of 2.0 (95% CI 1.3 to 3.2; p = 0.002) in the top versus bottom decile of the polygenic score. To understand the proteomic signature of inherited risk, we performed aptamer-based profiling in 636 blood donors (mean age 43 years) with very high or low polygenic scores. In addition to the well-known apolipoprotein E biomarker, this analysis identified 27 additional proteins, several of which have known roles related to disease pathogenesis. Differences in protein concentrations were consistent even among the youngest subset of blood donors (mean age 33 years). Of these 28 proteins, 7 of the 8 proteins with concentrations available were similarly associated with the polygenic score in participants of the Multi-Ethnic Study of Atherosclerosis. These data highlight the potential for a DNA-based score to identify high-risk individuals during the prolonged presymptomatic phase of Alzheimer's disease and to enable biomarker discovery based on profiling of young individuals in the extremes of the score distribution., Competing Interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: SK is an employee of Verve Therapeutics; holds equity in Verve Therapeutics, Maze Therapeutics, Catabasis, and San Therapeutics; has served on scientific advisory boards for Regeneron Genetics Center and Corvidia Therapeutics; has served as a consultant for Acceleron, Eli Lilly, Novartis, Merck, Novo Nordisk, Novo Ventures, Ionis, Alnylam, Aegerion, Haug Partners, Noble Insights, Leerink Partners, Bayer Healthcare, Illumina, Color Genomics, MedGenome, Quest, Pfizer, and Medscape; and has patents related to a method of identifying and treating a person having a predisposition to or afflicted with cardiometabolic disease (20180010185) and a genetics risk predictor (20190017119). ASB has received grants from AstraZeneca, Bayer, Biogen, Bioverativ, Novartis and Sanofi. A.V.K. is an employee and holds equity in Verve Therapeutics; has served as a scientific advisor to Amgen, Maze Therapeutics, Navitor Pharmaceuticals, Sarepta Therapeutics, Novartis, Silence Therapeutics, Korro Bio, Veritas International, Color Health, Third Rock Ventures, Illumina, Foresite Labs, and Columbia University (NIH); received speaking fees from Illumina, MedGenome, Amgen, and the Novartis Institute for Biomedical Research; received a sponsored research agreement from IBM Research, and is listed as a co-inventor on a patent application for use of imaging data in assessing body fat distribution and associated cardiometabolic risk.

Heart failure encompasses a heterogeneous set of clinical features that converge on impaired cardiac contractile function 1,2 and presents a growing public health concern. Previous work has highlighted changes in both transcription and protein expression in failing hearts 3,4 , but may overlook molecular changes in less prevalent cell types. Here we identify extensive molecular alterations in failing hearts at single-cell resolution by performing single-nucleus RNA sequencing of nearly 600,000 nuclei in left ventricle samples from 11 hearts with dilated cardiomyopathy and 15 hearts with hypertrophic cardiomyopathy as well as 16 non-failing hearts. The transcriptional profiles of dilated or hypertrophic cardiomyopathy hearts broadly converged at the tissue and cell-type level. Further, a subset of hearts from patients with cardiomyopathy harbour a unique population of activated fibroblasts that is almost entirely absent from non-failing samples. We performed a CRISPR-knockout screen in primary human cardiac fibroblasts to evaluate this fibrotic cell state transition; knockout of genes associated with fibroblast transition resulted in a reduction of myofibroblast cell-state transition upon TGFβ1 stimulation for a subset of genes. Our results provide insights into the transcriptional diversity of the human heart in health and disease as well as new potential therapeutic targets and biomarkers for heart failure., (© 2022. The Author(s), under exclusive licence to Springer Nature Limited.)

Congenital heart diseases often involve maldevelopment of the evolutionarily recent right heart chamber. To gain insight into right heart structure and function, we fine-tuned deep learning models to recognize the right atrium, right ventricle and pulmonary artery, measuring right heart structures in 40,000 individuals from the UK Biobank with magnetic resonance imaging. Genome-wide association studies identified 130 distinct loci associated with at least one right heart measurement, of which 72 were not associated with left heart structures. Loci were found near genes previously linked with congenital heart disease, including NKX2-5, TBX5/TBX3, WNT9B and GATA4. A genome-wide polygenic predictor of right ventricular ejection fraction was associated with incident dilated cardiomyopathy (hazard ratio, 1.33 per standard deviation; P = 7.1 × 10 -13 ) and remained significant after accounting for a left ventricular polygenic score. Harnessing deep learning to perform large-scale cardiac phenotyping, our results yield insights into the genetic determinants of right heart structure and function., (© 2022. The Author(s), under exclusive licence to Springer Nature America, Inc.)

Cardiometabolic diseases are the leading cause of death worldwide. Despite a known genetic component, our understanding of these diseases remains incomplete. Here, we analyzed the contribution of rare variants to 57 diseases and 26 cardiometabolic traits, using data from 200,337 UK Biobank participants with whole-exome sequencing. We identified 57 gene-based associations, with broad replication of novel signals in Geisinger MyCode. There was a striking risk associated with mutations in known Mendelian disease genes, including MYBPC3, LDLR, GCK, PKD1 and TTN. Many genes showed independent convergence of rare and common variant evidence, including an association between GIGYF1 and type 2 diabetes. We identified several large effect associations for height and 18 unique genes associated with blood lipid or glucose levels. Finally, we found that between 1.0% and 2.4% of participants carried rare potentially pathogenic variants for cardiometabolic disorders. These findings may facilitate studies aimed at therapeutics and screening of these common disorders., (© 2022. The Author(s), under exclusive licence to Springer Nature America, Inc.)

Large-scale gene sequencing studies for complex traits have the potential to identify causal genes with therapeutic implications. We performed gene-based association testing of blood lipid levels with rare (minor allele frequency < 1%) predicted damaging coding variation by using sequence data from >170,000 individuals from multiple ancestries: 97,493 European, 30,025 South Asian, 16,507 African, 16,440 Hispanic/Latino, 10,420 East Asian, and 1,182 Samoan. We identified 35 genes associated with circulating lipid levels; some of these genes have not been previously associated with lipid levels when using rare coding variation from population-based samples. We prioritize 32 genes in array-based genome-wide association study (GWAS) loci based on aggregations of rare coding variants; three (EVI5, SH2B3, and PLIN1) had no prior association of rare coding variants with lipid levels. Most of our associated genes showed evidence of association among multiple ancestries. Finally, we observed an enrichment of gene-based associations for low-density lipoprotein cholesterol drug target genes and for genes closest to GWAS index single-nucleotide polymorphisms (SNPs). Our results demonstrate that gene-based associations can be beneficial for drug target development and provide evidence that the gene closest to the array-based GWAS index SNP is often the functional gene for blood lipid levels., Competing Interests: Declaration of interests The authors declare no competing interests for the present work. P.N. reports investigator-initiated grants from Amgen, Apple, and Boston Scientific; is a scientific advisor to Apple, Blackstone Life Sciences, and Novartis; and has spousal employment at Vertex, all unrelated to the present work. A.V.K. has served as a scientific advisor to Sanofi, Medicines Company, Maze Pharmaceuticals, Navitor Pharmaceuticals, Verve Therapeutics, Amgen, and Color; received speaking fees from Illumina, MedGenome, Amgen, and the Novartis Institute for Biomedical Research; received sponsored research agreements from the Novartis Institute for Biomedical Research and IBM Research; and reports a patent related to a genetic risk predictor (20190017119). C.J.W.’s spouse is employed at Regeneron. L.E.S. is currently an employee of Celgene/Bristol Myers Squibb. Celgene/Bristol Myers Squibb had no role in the funding, design, conduct, and interpretation of this study. M.E.M. receives funding from Regeneron unrelated to this work. E.E.K. has received speaker honoraria from Illumina, Inc and Regeneron Pharmaceuticals. B.M.P. serves on the Steering Committee of the Yale Open Data Access Project funded by Johnson & Johnson. L.A.C. has consulted with the Dyslipidemia Foundation on lipid projects in the Framingham Heart Study. P.T.E. is supported by a grant from Bayer AG to the Broad Institute focused on the genetics and therapeutics of cardiovascular disease. P.T.E. has consulted for Bayer AG, Novartis, MyoKardia, and Quest Diagnostics. S.A.L. receives sponsored research support from Bristol Myers Squibb/Pfizer, Bayer AG, Boehringer Ingelheim, Fitbit, and IBM and has consulted for Bristol Myers Squibb/Pfizer, Bayer AG, and Blackstone Life Sciences. The views expressed in this article are those of the author(s) and not necessarily those of the NHS, the NIHR, or the Department of Health. M.I.M. has served on advisory panels for Pfizer, NovoNordisk, and Zoe Global and has received honoraria from Merck, Pfizer, Novo Nordisk, and Eli Lilly and research funding from Abbvie, Astra Zeneca, Boehringer Ingelheim, Eli Lilly, Janssen, Merck, NovoNordisk, Pfizer, Roche, Sanofi Aventis, Servier, and Takeda. As of June 2019, M.I.M. is an employee of Genentech and a holder of Roche stock. M.E.J. holds shares in Novo Nordisk A/S. H.M.K. is an employee of Regeneron Pharmaceuticals; he owns stock and stock options for Regeneron Pharmaceuticals. M.E.J. has received research grants form Astra Zeneca, Boehringer Ingelheim, Amgen, and Sanofi. S.K. is founder of Verve Therapeutics., (Copyright © 2021 American Society of Human Genetics. All rights reserved.)

Enlargement or aneurysm of the aorta predisposes to dissection, an important cause of sudden death. We trained a deep learning model to evaluate the dimensions of the ascending and descending thoracic aorta in 4.6 million cardiac magnetic resonance images from the UK Biobank. We then conducted genome-wide association studies in 39,688 individuals, identifying 82 loci associated with ascending and 47 with descending thoracic aortic diameter, of which 14 loci overlapped. Transcriptome-wide analyses, rare-variant burden tests and human aortic single nucleus RNA sequencing prioritized genes including SVIL, which was strongly associated with descending aortic diameter. A polygenic score for ascending aortic diameter was associated with thoracic aortic aneurysm in 385,621 UK Biobank participants (hazard ratio = 1.43 per s.d., confidence interval 1.32-1.54, P = 3.3 × 10 -20 ). Our results illustrate the potential for rapidly defining quantitative traits with deep learning, an approach that can be broadly applied to biomedical images., (© 2021. The Author(s), under exclusive licence to Springer Nature America, Inc.)

Carotid plaque instability contributes to large vessel ischemic stroke. Although vascular smooth muscle cells (VSMCs) affect atherosclerotic growth and instability, no treatments aimed at improving VSMC function are available. Large genetic studies investigating atherosclerosis and carotid disease in relation to the risk of stroke have implicated polymorphisms at the HDAC9 locus. The HDAC9 protein has been shown to affect the VSMC phenotype; however, how this might affect carotid disease is unknown. We conducted a pilot investigation using single nuclei RNA sequencing of human carotid tissue to identify cells expressing HDAC9 and specifically investigate the role of the HDAC9 in carotid atherosclerosis. We found that carotid VSMCs express HDAC9 and genes typically associated with immune characteristics. Using cellular assays, we have demonstrated that recruitment of macrophages can be modulated by HDAC9 expression. HDAC9 expression might affect carotid plaque stability and progression through its effects on the VSMC phenotype and recruitment of immune cells., (© 2021 by the Society for Vascular Surgery. Published by Elsevier Inc.)

Aims: The HERMES (HEart failure Molecular Epidemiology for Therapeutic targetS) consortium aims to identify the genomic and molecular basis of heart failure., Methods and Results: The consortium currently includes 51 studies from 11 countries, including 68 157 heart failure cases and 949 888 controls, with data on heart failure events and prognosis. All studies collected biological samples and performed genome-wide genotyping of common genetic variants. The enrolment of subjects into participating studies ranged from 1948 to the present day, and the median follow-up following heart failure diagnosis ranged from 2 to 116 months. Forty-nine of 51 individual studies enrolled participants of both sexes; in these studies, participants with heart failure were predominantly male (34-90%). The mean age at diagnosis or ascertainment across all studies ranged from 54 to 84 years. Based on the aggregate sample, we estimated 80% power to genetic variant associations with risk of heart failure with an odds ratio of ≥1.10 for common variants (allele frequency ≥ 0.05) and ≥1.20 for low-frequency variants (allele frequency 0.01-0.05) at P < 5 × 10 -8 under an additive genetic model., Conclusions: HERMES is a global collaboration aiming to (i) identify the genetic determinants of heart failure; (ii) generate insights into the causal pathways leading to heart failure and enable genetic approaches to target prioritization; and (iii) develop genomic tools for disease stratification and risk prediction., (© 2021 The Authors. ESC Heart Failure published by John Wiley & Sons Ltd on behalf of European Society of Cardiology.)

Cardiometabolic diseases are frequently polygenic in architecture, comprising a large number of risk alleles with small effects spread across the genome 1-3 . Polygenic scores (PGS) aggregate these into a metric representing an individual's genetic predisposition to disease. PGS have shown promise for early risk prediction 4-7 and there is an open question as to whether PGS can also be used to understand disease biology 8 . Here, we demonstrate that cardiometabolic disease PGS can be used to elucidate the proteins underlying disease pathogenesis. In 3,087 healthy individuals, we found that PGS for coronary artery disease, type 2 diabetes, chronic kidney disease and ischaemic stroke are associated with the levels of 49 plasma proteins. Associations were polygenic in architecture, largely independent of cis and trans protein quantitative trait loci and present for proteins without quantitative trait loci. Over a follow-up of 7.7 years, 28 of these proteins associated with future myocardial infarction or type 2 diabetes events, 16 of which were mediators between polygenic risk and incident disease. Twelve of these were druggable targets with therapeutic potential. Our results demonstrate the potential for PGS to uncover causal disease biology and targets with therapeutic potential, including those that may be missed by approaches utilizing information at a single locus., (© 2021. The Author(s), under exclusive licence to Springer Nature Limited.)

Extracellular vesicles (EVs) mediate intercellular signaling by transferring their cargo to recipient cells, but the functional consequences of signaling are not fully appreciated. RBC-derived EVs are abundant in circulation and have been implicated in regulating immune responses. Here, we use a transgenic mouse model for fluorescence-based mapping of RBC-EV recipient cells to assess the role of this intercellular signaling mechanism in heart disease. Using fluorescent-based mapping, we detected an increase in RBC-EV-targeted cardiomyocytes in a murine model of ischemic heart failure. Single cell nuclear RNA sequencing of the heart revealed a complex landscape of cardiac cells targeted by RBC-EVs, with enrichment of genes implicated in cell proliferation and stress signaling pathways compared with non-targeted cells. Correspondingly, cardiomyocytes targeted by RBC-EVs more frequently express cellular markers of DNA synthesis, suggesting the functional significance of EV-mediated signaling. In conclusion, our mouse model for mapping of EV-recipient cells reveals a complex cellular network of RBC-EV-mediated intercellular communication in ischemic heart failure and suggests a functional role for this mode of intercellular signaling., (© 2021 Valkov et al.)

Background: Alterations in electrocardiographic (ECG) intervals are well-known markers for arrhythmia and sudden cardiac death (SCD) risk. While the genetics of arrhythmia syndromes have been studied, relations between electrocardiographic intervals and rare genetic variation at a population level are poorly understood., Methods: Using a discovery sample of 29 000 individuals with whole-genome sequencing from Trans-Omics in Precision Medicine and replication in nearly 100 000 with whole-exome sequencing from the UK Biobank and MyCode, we examined associations between low-frequency and rare coding variants with 5 routinely measured electrocardiographic traits (RR, P-wave, PR, and QRS intervals and corrected QT interval)., Results: We found that rare variants associated with population-based electrocardiographic intervals identify established monogenic SCD genes ( KCNQ1 , KCNH2 , and SCN5A ), a controversial monogenic SCD gene ( KCNE1 ), and novel genes ( PAM and MFGE8 ) involved in cardiac conduction. Loss-of-function and pathogenic SCN5A variants, carried by 0.1% of individuals, were associated with a nearly 6-fold increased odds of the first-degree atrioventricular block ( P =8.4×10 -5 ). Similar variants in KCNQ1 and KCNH2 (0.2% of individuals) were associated with a 23-fold increased odds of marked corrected QT interval prolongation ( P =4×10 -25 ), a marker of SCD risk. Incomplete penetrance of such deleterious variation was common as over 70% of carriers had normal electrocardiographic intervals., Conclusions: Our findings indicate that large-scale high-depth sequence data and electrocardiographic analysis identifies monogenic arrhythmia susceptibility genes and rare variants with large effects. Known pathogenic variation in conventional arrhythmia and SCD genes exhibited incomplete penetrance and accounted for only a small fraction of marked electrocardiographic interval prolongation.

Angiotensin-converting enzyme 2 (ACE2) and accessory proteases (TMPRSS2 and CTSL) are needed for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) cellular entry, and their expression may shed light on viral tropism and impact across the body. We assessed the cell-type-specific expression of ACE2, TMPRSS2 and CTSL across 107 single-cell RNA-sequencing studies from different tissues. ACE2, TMPRSS2 and CTSL are coexpressed in specific subsets of respiratory epithelial cells in the nasal passages, airways and alveoli, and in cells from other organs associated with coronavirus disease 2019 (COVID-19) transmission or pathology. We performed a meta-analysis of 31 lung single-cell RNA-sequencing studies with 1,320,896 cells from 377 nasal, airway and lung parenchyma samples from 228 individuals. This revealed cell-type-specific associations of age, sex and smoking with expression levels of ACE2, TMPRSS2 and CTSL. Expression of entry factors increased with age and in males, including in airway secretory cells and alveolar type 2 cells. Expression programs shared by ACE2 + TMPRSS2 + cells in nasal, lung and gut tissues included genes that may mediate viral entry, key immune functions and epithelial-macrophage cross-talk, such as genes involved in the interleukin-6, interleukin-1, tumor necrosis factor and complement pathways. Cell-type-specific expression patterns may contribute to the pathogenesis of COVID-19, and our work highlights putative molecular pathways for therapeutic intervention.

Background: Atrial fibrillation (AF) often arises from structural abnormalities in the left atria (LA). Annotation of the noncoding genome in human LA is limited, as are effects on gene expression and chromatin architecture. Many AF-associated genetic variants reside in noncoding regions; this knowledge gap impairs efforts to understand the molecular mechanisms of AF and cardiac conduction phenotypes., Methods: We generated a model of the LA noncoding genome by profiling 7 histone post-translational modifications (active: H3K4me3, H3K4me2, H3K4me1, H3K27ac, H3K36me3; repressive: H3K27me3, H3K9me3), CTCF binding, and gene expression in samples from 5 individuals without structural heart disease or AF. We used MACS2 to identify peak regions ( P <0.01), applied a Markov model to classify regulatory elements, and annotated this model with matched gene expression data. We intersected chromatin states with expression quantitative trait locus, DNA methylation, and HiC chromatin interaction data from LA and left ventricle. Finally, we integrated genome-wide association data for AF and electrocardiographic traits to link disease-related variants to genes., Results: Our model identified 21 epigenetic states, encompassing regulatory motifs, such as promoters, enhancers, and repressed regions. Genes were regulated by proximal chromatin states; repressive states were associated with a significant reduction in gene expression ( P <2×10 -16 ). Chromatin states were differentially methylated, promoters were less methylated than repressed regions ( P <2×10 -16 ). We identified over 15 000 LA-specific enhancers, defined by homeobox family motifs, and annotated several cardiovascular disease susceptibility loci. Intersecting AF and PR genome-wide association studies loci with long-range chromatin conformation data identified a gene interaction network dominated by NKX2-5 , TBX3 , ZFHX3 , and SYNPO2L ., Conclusions: Profiling the noncoding genome provides new insights into the gene expression and chromatin regulation in human LA tissue. These findings enabled identification of a gene network underlying AF; our experimental and analytic approach can be extended to identify molecular mechanisms for other cardiac diseases and traits.

Objective: To determine the relationship of a genome-wide polygenic score for coronary artery disease (GPS CAD ) with lifetime trajectories of CAD risk, directly compare its predictive capacity to traditional risk factors, and assess its interplay with the Pooled Cohort Equations (PCE) clinical risk estimator. Approach and Results: We studied GPS CAD in 28 556 middle-aged participants of the Malmö Diet and Cancer Study, of whom 4122 (14.4%) developed CAD over a median follow-up of 21.3 years. A pronounced gradient in lifetime risk of CAD was observed-16% for those in the lowest GPS CAD decile to 48% in the highest. We evaluated the discriminative capacity of the GPS CAD -as assessed by change in the C-statistic from a baseline model including age and sex-among 5685 individuals with PCE risk estimates available. The increment for the GPS CAD (+0.045, P <0.001) was higher than for any of 11 traditional risk factors (range +0.007 to +0.032). Minimal correlation was observed between GPS CAD and 10-year risk defined by the PCE ( r =0.03), and addition of GPS CAD improved the C-statistic of the PCE model by 0.026. A significant gradient in lifetime risk was observed for the GPS CAD , even among individuals within a given PCE clinical risk stratum. We replicated key findings-noting strikingly consistent results-in 325 003 participants of the UK Biobank., Conclusions: GPS CAD -a risk estimator available from birth-stratifies individuals into varying trajectories of clinical risk for CAD. Implementation of GPS CAD may enable identification of high-risk individuals early in life, decades in advance of manifest risk factors or disease.

Background: Genome-wide polygenic scores (GPS) integrate information from many common DNA variants into a single number. Because rates of coronary artery disease (CAD) are substantially higher among South Asians, a GPS to identify high-risk individuals may be particularly useful in this population., Objectives: This analysis used summary statistics from a prior genome-wide association study to derive a new GPS CAD for South Asians., Methods: This GPS CAD was validated in 7,244 South Asian UK Biobank participants and tested in 491 individuals from a case-control study in Bangladesh. Next, a static ancestry and GPS CAD reference distribution was built using whole-genome sequencing from 1,522 Indian individuals, and a framework was tested for projecting individuals onto this static ancestry and GPS CAD reference distribution using 1,800 CAD cases and 1,163 control subjects newly recruited in India., Results: The GPS CAD , containing 6,630,150 common DNA variants, had an odds ratio (OR) per SD of 1.58 in South Asian UK Biobank participants and 1.60 in the Bangladeshi study (p < 0.001 for each). Next, individuals of the Indian case-control study were projected onto static reference distributions, observing an OR/SD of 1.66 (p < 0.001). Compared with the middle quintile, risk for CAD was most pronounced for those in the top 5% of the GPS CAD distribution-ORs of 4.16, 2.46, and 3.22 in the South Asian UK Biobank, Bangladeshi, and Indian studies, respectively (p < 0.05 for each)., Conclusions: The new GPS CAD has been developed and tested using 3 distinct South Asian studies, and provides a generalizable framework for ancestry-specific GPS assessment., (Copyright © 2020 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.)

Background: The human heart requires a complex ensemble of specialized cell types to perform its essential function. A greater knowledge of the intricate cellular milieu of the heart is critical to increase our understanding of cardiac homeostasis and pathology. As recent advances in low-input RNA sequencing have allowed definitions of cellular transcriptomes at single-cell resolution at scale, we have applied these approaches to assess the cellular and transcriptional diversity of the nonfailing human heart., Methods: Microfluidic encapsulation and barcoding was used to perform single nuclear RNA sequencing with samples from 7 human donors, selected for their absence of overt cardiac disease. Individual nuclear transcriptomes were then clustered based on transcriptional profiles of highly variable genes. These clusters were used as the basis for between-chamber and between-sex differential gene expression analyses and intersection with genetic and pharmacologic data., Results: We sequenced the transcriptomes of 287 269 single cardiac nuclei, revealing 9 major cell types and 20 subclusters of cell types within the human heart. Cellular subclasses include 2 distinct groups of resident macrophages, 4 endothelial subtypes, and 2 fibroblast subsets. Comparisons of cellular transcriptomes by cardiac chamber or sex reveal diversity not only in cardiomyocyte transcriptional programs but also in subtypes involved in extracellular matrix remodeling and vascularization. Using genetic association data, we identified strong enrichment for the role of cell subtypes in cardiac traits and diseases. Intersection of our data set with genes on cardiac clinical testing panels and the druggable genome reveals striking patterns of cellular specificity., Conclusions: Using large-scale single nuclei RNA sequencing, we defined the transcriptional and cellular diversity in the normal human heart. Our identification of discrete cell subtypes and differentially expressed genes within the heart will ultimately facilitate the development of new therapeutics for cardiovascular diseases.

Rationale: Genome-wide association studies have identified a large number of common variants (single-nucleotide polymorphisms) associated with atrial fibrillation (AF). These variants are located mainly in noncoding regions of the genome and likely include variants that modulate the function of transcriptional regulatory elements (REs) such as enhancers. However, the actual REs modulated by variants and the target genes of such REs remain to be identified. Thus, the biological mechanisms by which genetic variation promotes AF has thus far remained largely unexplored., Objective: To identify REs in genome-wide association study loci that are influenced by AF-associated variants., Methods and Results: We screened 2.45 Mbp of human genomic DNA containing 12 strongly AF-associated loci for RE activity using self-transcribing active regulatory region sequencing and a recently generated monoclonal line of conditionally immortalized rat atrial myocytes. We identified 444 potential REs, 55 of which contain AF-associated variants ( P <10 -8 ). Subsequently, using an adaptation of the self-transcribing active regulatory region sequencing approach, we identified 24 variant REs with allele-specific regulatory activity. By mining available chromatin conformation data, the possible target genes of these REs were mapped. To define the physiological function and target genes of such REs, we deleted the orthologue of an RE containing noncoding variants in the Hcn4 (potassium/sodium hyperpolarization-activated cyclic nucleotide-gated channel 4) locus of the mouse genome. Mice heterozygous for the RE deletion showed bradycardia, sinus node dysfunction, and selective loss of Hcn4 expression., Conclusions: We have identified REs at multiple genetic loci for AF and found that loss of an RE at the HCN4 locus results in sinus node dysfunction and reduced gene expression. Our approach can be broadly applied to facilitate the identification of human disease-relevant REs and target genes at cardiovascular genome-wide association studies loci.

Background: Polygenic risk scores (PRS) for coronary artery disease (CAD) identify high-risk individuals more likely to benefit from primary prevention statin therapy. Whether polygenic CAD risk is captured by conventional paradigms for assessing clinical cardiovascular risk remains unclear., Objectives: This study sought to intersect polygenic risk with guideline-based recommendations and management patterns for CAD primary prevention., Methods: A genome-wide CAD PRS was applied to 47,108 individuals across 3 U.S. health care systems. The authors then assessed whether primary prevention patients at high polygenic risk might be distinguished on the basis of greater guideline-recommended statin eligibility and higher rates of statin therapy., Results: Of 47,108 study participants, the mean age was 60 years, and 11,020 (23.4%) had CAD. The CAD PRS strongly associated with prevalent CAD (odds ratio: 1.4 per SD increase in PRS; p < 0.0001). High polygenic risk (top 20% of PRS) conferred 1.9-fold odds of developing CAD (p < 0.0001). However, among primary prevention patients (n = 33,251), high polygenic risk did not correspond with increased recommendations for statin therapy per the American College of Cardiology/American Heart Association (46.2% for those with high PRS vs. 46.8% for all others, p = 0.54) or U.S. Preventive Services Task Force (43.7% vs. 43.7%, p = 0.99) or higher rates of statin prescriptions (25.0% vs. 23.8%, p = 0.04). An additional 4.1% of primary prevention patients may be recommended for statin therapy if high CAD PRS were considered a guideline-based risk-enhancing factor., Conclusions: Current paradigms for primary cardiovascular prevention incompletely capture a polygenic susceptibility to CAD. An opportunity may exist to improve CAD prevention efforts by integrating both genetic and clinical risk., (Copyright © 2020 American College of Cardiology Foundation. All rights reserved.)

The electrocardiographic PR interval reflects atrioventricular conduction, and is associated with conduction abnormalities, pacemaker implantation, atrial fibrillation (AF), and cardiovascular mortality. Here we report a multi-ancestry (N = 293,051) genome-wide association meta-analysis for the PR interval, discovering 202 loci of which 141 have not previously been reported. Variants at identified loci increase the percentage of heritability explained, from 33.5% to 62.6%. We observe enrichment for cardiac muscle developmental/contractile and cytoskeletal genes, highlighting key regulation processes for atrioventricular conduction. Additionally, 8 loci not previously reported harbor genes underlying inherited arrhythmic syndromes and/or cardiomyopathies suggesting a role for these genes in cardiovascular pathology in the general population. We show that polygenic predisposition to PR interval duration is an endophenotype for cardiovascular disease, including distal conduction disease, AF, and atrioventricular pre-excitation. These findings advance our understanding of the polygenic basis of cardiac conduction, and the genetic relationship between PR interval duration and cardiovascular disease.

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 report a genome-wide association study of cardiac magnetic resonance imaging (MRI)-derived left ventricular measurements in 36,041 UK Biobank participants, with replication in 2184 participants from the Multi-Ethnic Study of Atherosclerosis. We identify 45 previously unreported loci associated with cardiac structure and function, many near well-established genes for Mendelian cardiomyopathies. A polygenic score of MRI-derived left ventricular end systolic volume strongly associates with incident DCM in the general population. Even among carriers of TTN truncating mutations, this polygenic score influences the size and function of the human heart. These results further implicate common genetic polymorphisms in the pathogenesis of DCM.

Analyzing 12,361 all-cause cirrhosis cases and 790,095 controls from eight cohorts, we identify a common missense variant in the Mitochondrial Amidoxime Reducing Component 1 gene (MARC1 p.A165T) that associates with protection from all-cause cirrhosis (OR 0.91, p = 2.3*10-11). This same variant also associates with lower levels of hepatic fat on computed tomographic imaging and lower odds of physician-diagnosed fatty liver as well as lower blood levels of alanine transaminase (-0.025 SD, 3.7*10-43), alkaline phosphatase (-0.025 SD, 1.2*10-37), total cholesterol (-0.030 SD, p = 1.9*10-36) and LDL cholesterol (-0.027 SD, p = 5.1*10-30) levels. We identified a series of additional MARC1 alleles (low-frequency missense p.M187K and rare protein-truncating p.R200Ter) that also associated with lower cholesterol levels, liver enzyme levels and reduced risk of cirrhosis (0 cirrhosis cases for 238 R200Ter carriers versus 17,046 cases of cirrhosis among 759,027 non-carriers, p = 0.04) suggesting that deficiency of the MARC1 enzyme may lower blood cholesterol levels and protect against cirrhosis., Competing Interests: CAE reports consulting fees from Navitor Pharma, Novartis and Deerfield Management. AVK has received research grants from IBM Research and the Novartis Institute for Biomedical Research and has served as a consultant to or received honoraria from Color Genomics, Illumina, Novartis, Maze Therapeutics, and Navitor Pharmaceuticals; and has a patent related to a genetic risk predictor (20190017119). SK is an employee of Verve Therapeutics and has received a research grant from Bayer Healthcare; and consulting fees from Merck, Novartis, Sanofi, AstraZeneca, Alnylam Pharmaceuticals, Leerink Partners, Noble Insights, MedGenome, Aegerion Pharmaceuticals, Regeneron Pharmaceuticals, Quest Diagnostics, Color Genomics, Genomics PLC, and Eli Lilly and Company; and holds equity in San Therapeutics, Catabasis Pharmaceuticals, Verve Therapeutics and Maze Therapeutics. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose.

Rationale: Genome-wide association studies have identified over 100 genetic loci for atrial fibrillation (AF); recent work described an association between loss-of-function (LOF) variants in TTN and early-onset AF., Objective: We sought to determine the contribution of rare and common genetic variation to AF risk in the general population., Methods: The UK Biobank is a population-based study of 500 000 individuals including a subset with genome-wide genotyping and exome sequencing. In this case-control study, we included AF cases and controls of genetically determined white-European ancestry; analyses were performed using a logistic mixed-effects model adjusting for age, sex, the first 4 principal components of ancestry, empirical relationships, and case-control imbalance. An exome-wide, gene-based burden analysis was performed to examine the relationship between AF and rare, high-confidence LOF variants in genes with ≥10 LOF carriers. A polygenic risk score for AF was estimated using the LDpred algorithm. We then compared the contribution of AF polygenic risk score and LOF variants to AF risk., Results: The study included 1546 AF cases and 41 593 controls. In an analysis of 9099 genes with sufficient LOF variant carriers, a significant association between AF and rare LOF variants was observed in a single gene, TTN (odds ratio, 2.71, P =2.50×10 -8 ). The association with AF was more significant (odds ratio, 6.15, P =3.26×10 -14 ) when restricting to LOF variants located in exons highly expressed in cardiac tissue ( TTN LOF ). Overall, 0.44% of individuals carried TTN LOF variants, of whom 14% had AF. Among individuals in the highest 0.44% of the AF polygenic risk score only 9.3% had AF. In contrast, the AF polygenic risk score explained 4.7% of the variance in AF susceptibility, while TTN LOF variants only accounted for 0.2%., Conclusions: Both monogenic and polygenic factors contribute to AF risk in the general population. While rare TTN LOF variants confer a substantial AF penetrance, the additive effect of many common variants explains a larger proportion of genetic susceptibility to AF.

Heart failure (HF) is a leading cause of morbidity and mortality worldwide. A small proportion of HF cases are attributable to monogenic cardiomyopathies and existing genome-wide association studies (GWAS) have yielded only limited insights, leaving the observed heritability of HF largely unexplained. We report results from a GWAS meta-analysis of HF comprising 47,309 cases and 930,014 controls. Twelve independent variants at 11 genomic loci are associated with HF, all of which demonstrate one or more associations with coronary artery disease (CAD), atrial fibrillation, or reduced left ventricular function, suggesting shared genetic aetiology. Functional analysis of non-CAD-associated loci implicate genes involved in cardiac development (MYOZ1, SYNPO2L), protein homoeostasis (BAG3), and cellular senescence (CDKN1A). Mendelian randomisation analysis supports causal roles for several HF risk factors, and demonstrates CAD-independent effects for atrial fibrillation, body mass index, and hypertension. These findings extend our knowledge of the pathways underlying HF and may inform new therapeutic strategies.

Background and Purpose- Coagulation factor XI (FXI) is a novel target for antithrombotic therapy addressed by various therapeutic modalities currently in clinical development. The expected magnitude of thrombotic event reduction mediated by targeting FXI is unclear. Methods- We analyzed the association of 2 common genetic variants, which alter levels of FXI, with a range of human phenotypes. We combined variants into a genetic score standardized to a 30% increase in relative activated partial thromboplastin time, equivalent to what can be achieved with pharmacological FXI reduction. Using data from 371 695 participants in the United Kingdom Biobank and 2 large-scale genome-wide association studies, we examined the effect of this FXI score on thrombotic and bleeding end points. Results- Genetic disposition to lower FXI levels was associated with reduced risks of venous thrombosis (odds ratio, 95% CI; P value; odds ratio=0.1, 0.07-0.14; P =3×10 -43 ) and ischemic stroke (odds ratio=0.47, 0.36-0.61; P =2×10 -8 ) but not with major bleeding (odds ratio=0.7, 0.45-1.04; P =0.0739). The observed relative risk reductions were consistent within a range of subgroups that were at high risk for thrombosis. Consistently, we observed higher absolute risk reductions conferred by genetically lower FXI levels in high-risk subgroups, such as patients with atrial fibrillation. Conclusions- Human genetic data suggest that pharmacological inhibition of FXI may achieve considerable reductions in ischemic stroke risk without clear evidence for an associated risk of major bleeding. The quantitative framework developed can be used to support the estimation of achievable risk reductions with pharmacological modulation of FXI.

Venous thromboembolism is a significant cause of mortality 1 , yet its genetic determinants are incompletely defined. We performed a discovery genome-wide association study in the Million Veteran Program and UK Biobank, with testing of approximately 13 million DNA sequence variants for association with venous thromboembolism (26,066 cases and 624,053 controls) and meta-analyzed both studies, followed by independent replication with up to 17,672 venous thromboembolism cases and 167,295 controls. We identified 22 previously unknown loci, bringing the total number of venous thromboembolism-associated loci to 33, and subsequently fine-mapped these associations. We developed a genome-wide polygenic risk score for venous thromboembolism that identifies 5% of the population at an equivalent incident venous thromboembolism risk to carriers of the established factor V Leiden p.R506Q and prothrombin G20210A mutations. Our data provide mechanistic insights into the genetic epidemiology of venous thromboembolism and suggest a greater overlap among venous and arterial cardiovascular disease than previously thought.

Peripheral artery disease (PAD) is a leading cause of cardiovascular morbidity and mortality; however, the extent to which genetic factors increase risk for PAD is largely unknown. Using electronic health record data, we performed a genome-wide association study in the Million Veteran Program testing ~32 million DNA sequence variants with PAD (31,307 cases and 211,753 controls) across veterans of European, African and Hispanic ancestry. The results were replicated in an independent sample of 5,117 PAD cases and 389,291 controls from the UK Biobank. We identified 19 PAD loci, 18 of which have not been previously reported. Eleven of the 19 loci were associated with disease in three vascular beds (coronary, cerebral, peripheral), including LDLR, LPL and LPA, suggesting that therapeutic modulation of low-density lipoprotein cholesterol, the lipoprotein lipase pathway or circulating lipoprotein(a) may be efficacious for multiple atherosclerotic disease phenotypes. Conversely, four of the variants appeared to be specific for PAD, including F5 p.R506Q, highlighting the pathogenic role of thrombosis in the peripheral vascular bed and providing genetic support for Factor Xa inhibition as a therapeutic strategy for PAD. Our results highlight mechanistic similarities and differences among coronary, cerebral and peripheral atherosclerosis and provide therapeutic insights.

Background Familial hypobetalipoproteinemia is a genetic disorder caused by rare protein-truncating variants (PTV) in the gene encoding APOB (apolipoprotein B), the major protein component of LDL (low-density lipoprotein) and triglyceride-rich lipoprotein particles. Whether heterozygous APOB deficiency is associated with decreased risk for coronary heart disease (CHD) is uncertain. We combined family-based and large scale gene-sequencing to characterize the association of rare PTVs in APOB with circulating LDL-C (LDL cholesterol), triglycerides, and risk for CHD. Methods We sequenced the APOB gene in 29 Japanese hypobetalipoproteinemia families, as well as 57 973 individuals derived from 12 CHD case-control studies-18 442 with early-onset CHD and 39 531 controls. We defined PTVs as variants that lead to a premature stop, disrupt canonical splice-sites, or lead to insertions/deletions that shift reading frame. We tested the association of rare APOB PTV carrier status with blood lipid levels and CHD. Results Among 29 familial hypobetalipoproteinemia families, 8 families harbored APOB PTVs. Carrying 1 APOB PTV was associated with 55 mg/dL lower LDL-C ( P=3×10 -5 ) and 53% lower triglyceride level ( P=2×10 -4 ). Among 12 case-control studies, an APOB PTV was present in 0.038% of CHD cases as compared to 0.092% of controls. APOB PTV carrier status was associated with a 43 mg/dL lower LDL-C ( P=2×10 -7 ), a 30% decrease in triglycerides ( P=5×10 -4 ), and a 72% lower risk for CHD (odds ratio, 0.28; 95% CI, 0.12-0.64; P=0.002). Conclusions Rare PTV mutations in APOB which are associated with lower LDL-C and reduced triglycerides also confer protection against CHD.

Severe obesity is a rapidly growing global health threat. Although often attributed to unhealthy lifestyle choices or environmental factors, obesity is known to be heritable and highly polygenic; the majority of inherited susceptibility is related to the cumulative effect of many common DNA variants. Here we derive and validate a new polygenic predictor comprised of 2.1 million common variants to quantify this susceptibility and test this predictor in more than 300,000 individuals ranging from middle age to birth. Among middle-aged adults, we observe a 13-kg gradient in weight and a 25-fold gradient in risk of severe obesity across polygenic score deciles. In a longitudinal birth cohort, we note minimal differences in birthweight across score deciles, but a significant gradient emerged in early childhood and reached 12 kg by 18 years of age. This new approach to quantify inherited susceptibility to obesity affords new opportunities for clinical prevention and mechanistic assessment., (Copyright © 2019. Published by Elsevier Inc.)

Background: The relative prevalence and clinical importance of monogenic mutations related to familial hypercholesterolemia and of high polygenic score (cumulative impact of many common variants) pathways for early-onset myocardial infarction remain uncertain. Whole-genome sequencing enables simultaneous ascertainment of both monogenic mutations and polygenic score for each individual., Methods: We performed deep-coverage whole-genome sequencing of 2081 patients from 4 racial subgroups hospitalized in the United States with early-onset myocardial infarction (age ≤55 years) recruited with a 2:1 female-to-male enrollment design. We compared these genomes with those of 3761 population-based control subjects. We first identified individuals with a rare, monogenic mutation related to familial hypercholesterolemia. Second, we calculated a recently developed polygenic score of 6.6 million common DNA variants to quantify the cumulative susceptibility conferred by common variants. We defined high polygenic score as the top 5% of the control distribution because this cutoff has previously been shown to confer similar risk to that of familial hypercholesterolemia mutations., Results: The mean age of the 2081 patients presenting with early-onset myocardial infarction was 48 years, and 66% were female. A familial hypercholesterolemia mutation was present in 36 of these patients (1.7%) and was associated with a 3.8-fold (95% CI, 2.1-6.8; P<0.001) increased odds of myocardial infarction. Of the patients with early-onset myocardial infarction, 359 (17.3%) carried a high polygenic score, associated with a 3.7-fold (95% CI, 3.1-4.6; P<0.001) increased odds. Mean estimated untreated low-density lipoprotein cholesterol was 206 mg/dL in those with a familial hypercholesterolemia mutation, 132 mg/dL in those with high polygenic score, and 122 mg/dL in those in the remainder of the population. Although associated with increased risk in all racial groups, high polygenic score demonstrated the strongest association in white participants ( P for heterogeneity=0.008)., Conclusions: Both familial hypercholesterolemia mutations and high polygenic score are associated with a >3-fold increased odds of early-onset myocardial infarction. However, high polygenic score has a 10-fold higher prevalence among patients presents with early-onset myocardial infarction., Clinical Trial Registration: URL: https://www.clinicaltrials.gov . Unique identifier: NCT00597922.

High-throughput metabolomics using liquid chromatography and mass spectrometry (LC/MS) provides a useful method to identify biomarkers of disease and explore biological systems. However, the majority of metabolic features detected from untargeted metabolomics experiments have unknown ion signatures, making it critical that data should be thoroughly quality controlled to avoid analyzing false signals. Here, we present a postalignment method relying on intermittent pooled study samples to separate genuine metabolic features from potential measurement artifacts. We apply the method to lipid metabolite data from the PREDIMED (PREvención con DIeta MEDi-terránea) study to demonstrate clear removal of measurement artifacts. The method is publicly available as the R package MetProc, available on CRAN under the GPL-v2 license.