Genetic basis of cardiovascular diseases

Cardiovascular diseases are a major global health issue. This thesis focusses on the genetic basis of three cardiovascular conditions: atrial fibrillation (AF), heart failure (HF), and mitral valve prolapse (MVP). AF is widespread and mainly manifests in an irregular heartbeat, HF is a complex condition that occurs when the heart fails to pumps blood effectively, and MVP is a heart valve disorder. Genome-wide association studies (GWAS) have been instrumental in understanding the genetic basis of cardiovascular diseases. Extensive research was conducted for AF, HF, and MVP by utilizing large international consortia and analyzing data from various biobanks and cohorts. First, 250 new AF-related genetic regions were discovered in the largest genetic study for AF to date. Second, rare deleterious variants within the gene TTN, were linked to AF susceptibility. Additionally, we increased the number of HF cases studied from 6.8k to 47k, uncovering 10 new genetic regions. We expanded our analysis on MVP from 1.4k cases to 4.8k, identifying 12 new genetic regions. We carefully evaluated potential effector genes at GWAS locations by generating new functional data and using existing datasets. Overall, this research identified potential effector genes for AF and MVP and shed light on interesting biological pathways. Additionally, we developed improved and new genetic risk scores for AF and MVP, using state of the art computational methods. Although we didn't pinpoint the exact pathophysiology between every genetic variant and AF, our work demonstrated the value of genetic risk profiles in predicting disease risk, offering an improvement over clinical risk factors alone

Genetics of Atrial Fibrillation in 2020 GWAS, Genome Sequencing, Polygenic Risk, and Beyond

Atrial fibrillation is a common heart rhythm disorder that leads to an increased risk for stroke and heart failure. Atrial fibrillation is a complex disease with both environmental and genetic risk factors that contribute to the arrhythmia. Over the last decade, rapid progress has been made in identifying the genetic basis for this common condition. In this review, we provide an overview of the primary types of genetic analyses performed for atrial fibrillation, including linkage studies, genome-wide association studies, and studies of rare coding variation. With these results in mind, we aim to highlighting the existing knowledge gaps and future directions for atrial fibrillation genetics research

Genetic Risk and Atrial Fibrillation in Patients with Heart Failure

Aims: To study the association between an atrial fibrillation (AF) genetic risk score with prevalent AF and all-cause mortality in patients with heart failure. Methods and results: An AF genetic risk score was calculated in 3759 European ancestry individuals (1783 with sinus rhythm, 1976 with AF) from the BIOlogy Study to TAilored Treatment in Chronic Heart Failure (BIOSTAT-CHF) by summing 97 single nucleotide polymorphism (SNP) alleles (ranging from 0–2) weighted by the natural logarithm of the relative SNP risk from the latest AF genome-wide association study. Further, we assessed AF risk variance explained by additive SNP variation, and performance of clinical or genetic risk factors, and the combination in classifying AF prevalence. AF was classified as AF or atrial flutter (AFL) at baseline electrocardiogram and/or a history of AF or AFL. The genetic risk score was associated with AF after multivariable adjustment. Odds ratio for AF prevalence per 1-unit increase genetic risk score was 2.12 (95% confidence interval 1.84–2.45, P = 2.15 × 10−24) in the total cohort, 2.08 (1.72–2.50, P = 1.30 × 10−14) in heart failure with reduced ejection fraction (HFrEF) and 2.02 (1.37–2.99, P = 4.37 × 10−4) in heart failure with preserved ejection fraction (HFpEF). AF-associated loci explained 22.9% of overall AF SNP heritability. Addition of the genetic risk score to clinical risk factors increased the C-index by 2.2% to 0.721. Conclusions: The AF genetic risk score was associated with increased AF prevalence in HFrEF and HFpEF. Genetic variation accounted for 22.9% of overall AF SNP heritability. Addition of genetic risk to clinical risk improved model performance in classifying AF prevalence

Assessing, quantifying and valuing the ecosystem services of coastal lagoons

The natural conservation of coastal lagoons is important not only for their ecological importance, but also because of the valuable ecosystem services they provide for human welfare and wellbeing. Coastal lagoons are shallow semi-enclosed systems that support important habitats such as wetlands, mangroves, salt-marshes and seagrass meadows, as well as a rich biodiversity. Coastal lagoons are also complex social-ecological systems with ecosystem services that provide livelihoods, wellbeing and welfare to humans. This study assessed, quantified and valued the ecosystem services of 32 coastal lagoons. The main findings of the study are: (i) the definitions of ecosystem services are still not generally accepted; (ii) the quantification of ecosystem services is made in many different ways, using different units; (iii) the evaluation in monetary terms of some ecosystem service is problematic, often relying on non-monetary evaluation methods; (iv) when ecosystem services are valued in monetary terms, this may represent very different human benefits; and, (v) different aspects of climate change, including increasing temperature, sea-level rise and changes in rainfall patterns threaten the valuable ecosystem services of coastal lagoons.DEVOTES project, from the European Union's Seventh Framework Programme for research, technological development and demonstration [308392]; networks and communities of Eurolag; Future Earth Coasts; SCOR; Fundacao para a Ciencia e a Tecnologia (FCT) Investigador Programme [IF/00331/2013]; Fundacao para a Ciencia e a Tecnologia [UID/MAR/04292/2013]; CESAM by FCT/MEC national funds (PIDDAC) [UID/AMB/50017/2013 - POCI-01-0145-FEDER-007638]; FEDER; European Commission, under the 7th Framework Programme through the collaborative research project LAGOONS [283157]; FCT [SFRH/BPD/107823/2015, SFRH/BPD/91494/2012

Atrial fibrillation genetic risk differentiates cardioembolic stroke from other stroke subtypes

Objective We sought to assess whether genetic risk factors for atrial fibrillation (AF) can explain cardioembolic stroke risk. Methods We evaluated genetic correlations between a previous genetic study of AF and AF in the presence of cardioembolic stroke using genome-wide genotypes from the Stroke Genetics Network (N = 3,190 AF cases, 3,000 cardioembolic stroke cases, and 28,026 referents). We tested whether a previously validated AF polygenic risk score (PRS) associated with cardioembolic and other stroke subtypes after accounting for AF clinical risk factors. Results We observed a strong correlation between previously reported genetic risk for AF, AF in the presence of stroke, and cardioembolic stroke (Pearson r = 0.77 and 0.76, respectively, across SNPs with p 0.1). Conclusion: s Genetic risk of AF is associated with cardioembolic stroke, independent of clinical risk factors. Studies are warranted to determine whether AF genetic risk can serve as a biomarker for strokes caused by AF

Genetic risk prediction of atrial fibrillation

Background—Atrial fibrillation (AF) has a substantial genetic basis. Identification of individuals at greatest AF risk could minimize the incidence of cardioembolic stroke. Methods—To determine whether genetic data can stratify risk for development of AF, we examined associations between AF genetic risk scores and incident AF in five prospective studies comprising 18,919 individuals of European ancestry. We examined associations between AF genetic risk scores and ischemic stroke in a separate study of 509 ischemic stroke cases (202 cardioembolic [40%]) and 3,028 referents. Scores were based on 11 to 719 common variants (≥5%) associated with AF at P-values ranging from <1x10-3 to <1x10-8 in a prior independent genetic association study. Results—Incident AF occurred in 1,032 (5.5%) individuals. AF genetic risk scores were associated with new-onset AF after adjusting for clinical risk factors. The pooled hazard ratio for incident AF for the highest versus lowest quartile of genetic risk scores ranged from 1.28 (719 variants; 95%CI, 1.13-1.46; P=1.5x10-4) to 1.67 (25 variants; 95%CI, 1.47-1.90; P=9.3x10-15). Discrimination of combined clinical and genetic risk scores varied across studies and scores (maximum C statistic, 0.629-0.811; maximum ΔC statistic from clinical score alone, 0.009-0.017). AF genetic risk was associated with stroke in age- and sex-adjusted models. For example, individuals in the highest versus lowest quartile of a 127-variant score had a 2.49-fold increased odds of cardioembolic stroke (95%CI, 1.39-4.58; P=2.7x10-3). The effect persisted after excluding individuals (n=70) with known AF (odds ratio, 2.25; 95%CI, 1.20-4.40; P=0.01). Conclusions—Comprehensive AF genetic risk scores were associated with incident AF beyond associations for clinical AF risk factors, though offered small improvements in discrimination. AF genetic risk was also associated with cardioembolic stroke in age- and sex-adjusted analyses. Efforts are warranted to determine whether AF genetic risk may improve identification of subclinical AF or help distinguish between stroke mechanisms

Genetics of myocardial interstitial fibrosis in the human heart and association with disease

Myocardial interstitial fibrosis is associated with cardiovascular disease and adverse prognosis. Here, to investigate the biological pathways that underlie fibrosis in the human heart, we developed a machine learning model to measure native myocardial T1 time, a marker of myocardial fibrosis, in 41,505 UK Biobank participants who underwent cardiac magnetic resonance imaging. Greater T1 time was associated with diabetes mellitus, renal disease, aortic stenosis, cardiomyopathy, heart failure, atrial fibrillation, conduction disease and rheumatoid arthritis. Genome-wide association analysis identified 11 independent loci associated with T1 time. The identified loci implicated genes involved in glucose transport (SLC2A12), iron homeostasis (HFE, TMPRSS6), tissue repair (ADAMTSL1, VEGFC), oxidative stress (SOD2), cardiac hypertrophy (MYH7B) and calcium signaling (CAMK2D). Using a transforming growth factor β1-mediated cardiac fibroblast activation assay, we found that 9 of the 11 loci consisted of genes that exhibited temporal changes in expression or open chromatin conformation supporting their biological relevance to myofibroblast cell state acquisition. By harnessing machine learning to perform large-scale quantification of myocardial interstitial fibrosis using cardiac imaging, we validate associations between cardiac fibrosis and disease, and identify new biologically relevant pathways underlying fibrosis.</p

Transcriptional and Cellular Diversity of the Human Heart

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

Genetic Risk Score to Identify Risk of Venous Thromboembolism in Patients With Cardiometabolic Disease

BACKGROUND –: Venous thromboembolism (VTE) is a major cause of cardiovascular morbidity and mortality with a known genetic contribution. We tested the performance of a genetic risk score (GRS) for its ability to predict VTE in three cohorts of patients with cardiometabolic disease. METHODS –: We included patients from the FOURIER, PEGASUS-TIMI 54, and SAVOR-TIMI 53 trials (history of atherosclerosis, myocardial infarction, and diabetes, respectively) who consented for genetic testing and were not on baseline anticoagulation. We calculated a VTE GRS based on 297 SNPs with established genome-wide significance. Patients were divided into tertiles of genetic risk. Cox proportional hazards models were used to calculate hazard ratios for VTE across genetic risk groups. The polygenic risk score was compared to available clinical risk factors (age, obesity, smoking, history of heart failure, diabetes) and common monogenic mutations. RESULTS –: A total of 29,663 patients were included in the analysis with a median follow-up of 2.4 years, of whom 174 had a VTE event. There was a significantly increased gradient of risk across VTE genetic risk tertiles (p-trend <0.0001). After adjustment for clinical risk factors, patients in the intermediate and high genetic risk groups had a 1.88-fold (95% CI 1.23–2.89, p=0.004) and 2.70-fold (95% CI 1.81–4.06, p<0.0001) higher risk of VTE compared to patients with low genetic risk. In a continuous model adjusted for clinical risk factors, each standard deviation increase in the GRS was associated with a 47% (95% CI 29–68) increased risk of VTE (p<0.0001). CONCLUSIONS –: In a broad spectrum of patients with cardiometabolic disease, a polygenic risk score is a strong, independent predictor of VTE after accounting for available clinical risk factors, identifying 1/3 of patients who have a risk of VTE comparable to that seen with established monogenic thrombophilia

Deep learning enables genetic analysis of the human thoracic aorta

Genome-wide association analyses identify variants associated with thoracic aortic diameter. A polygenic score for ascending aortic diameter was associated with a diagnosis of thoracic aortic aneurysm in independent samples. 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 x 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