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Importance: The population prevalence of cardiac transthyretin amyloidosis (ATTR) caused by pathogenic variation in the TTR gene (vATTR) is unknown. Objective: To estimate the population prevalence of disease-causing TTR variants and evaluate associated phenotypes and outcomes. Design, Setting, and Participants: This population-based cohort study analyzed UK Biobank (UKB) participants with whole-exome sequencing, electrocardiogram, and cardiovascular magnetic resonance data. Participants were enrolled from 2006 to 2010, with a median follow-up of 12 (IQR, 11-13) years (cutoff date for the analysis, March 12, 2024). Sixty-two candidate TTR variants were extracted based on rarity (minor allele frequency ≤0.0001) and/or previously described associations with amyloidosis if more frequent. Exposure: Carrier status for TTR variants. Main Outcomes and Measures: Associations of TTR carrier status with vATTR prevalence and cardiovascular imaging and electrocardiogram traits were explored using descriptive statistics. Associations between TTR carrier status and atrial fibrillation, conduction disease, heart failure, and all-cause mortality were evaluated using adjusted Cox proportional hazards models. Genotypic and diagnostic concordance was examined using International Statistical Classification of Diseases, Tenth Revision codes from the hospital record. Results: The overall cohort included 469â¯789 UKB participants (mean [SD] age, 56.5 [8.1] years; 54.2% female and 45.8% male). A likely pathogenic/pathogenic (LP/P) TTR variant was detected in 473 (0.1%) participants, with Val142Ile being the most prevalent (367 [77.6%]); 91 individuals (0.02%) were carriers of a variant of unknown significance . The overall prevalence of LP/P variants was 0.02% (105 of 444â¯243) in participants with European ancestry and 4.3% (321 of 7533) in participants with African ancestry. The LP/P variants were associated with higher left ventricular mass indexed to body surface area (ß = 4.66; 95% CI, 1.87-7.44), and Val142Ile was associated with a longer PR interval (ß = 18.34; 95% CI, 5.41-31.27). The LP/P carrier status was associated with a higher risk of heart failure (hazard ratio [HR], 2.68; 95% CI, 1.75-4.12) and conduction disease (HR, 1.88; 95% CI, 1.25-2.83). Higher all-cause mortality risk was observed for non-Val142Ile LP/P variants (HR, 1.98; 95% CI, 1.06-3.67). Thirteen participants (2.8%) with LP/P variants had diagnostic codes compatible with cardiac or neurologic amyloidosis. Variants of unknown significance were not associated with outcomes. Conclusions and Relevance: This study found that approximately 1 in 1000 UKB participants were LP/P TTR variant carriers, exceeding previously reported prevalence. The findings emphasize the need for clinical vigilance in identifying individuals at risk of developing vATTR and associated poor outcomes.
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Essential hypertension is a complex trait where the underlying aetiology is not completely understood. Left untreated it increases the risk of severe health complications including cardiovascular and renal disease. It is almost 15 years since the first genome-wide association study for hypertension, and after a slow start there are now over 1000 blood pressure (BP) loci explaining â¼6% of the single nucleotide polymorphism-based heritability. Success in discovery of hypertension genes has provided new pathological insights and drug discovery opportunities and translated to the development of BP genetic risk scores (GRSs), facilitating population disease risk stratification. Comparing highest and lowest risk groups shows differences of 12.9 mm Hg in systolic-BP with significant differences in risk of hypertension, stroke, cardiovascular disease and myocardial infarction. GRSs are also being trialled in antihypertensive drug responses. Drug targets identified include NPR1, for which an agonist drug is currently in clinical trials. Identification of variants at the PHACTR1 locus provided insights into regulation of EDN1 in the endothelin pathway, which is aiding the development of endothelin receptor EDNRA antagonists. Drug re-purposing opportunities, including SLC5A1 and canagliflozin (a type-2 diabetes drug), are also being identified. In this review, we present key studies from the past, highlight current avenues of research and look to the future focusing on gene discovery, epigenetics, gene-environment interactions, GRSs and drug discovery. We evaluate limitations affecting BP genetics, including ancestry bias and discuss streamlining of drug target discovery and applications for treating and preventing hypertension, which will contribute to tailored precision medicine for patients.
Assuntos
Hipertensão , Infarto do Miocárdio , Anti-Hipertensivos/uso terapêutico , Pressão Sanguínea/efeitos dos fármacos , Estudo de Associação Genômica Ampla , Humanos , Hipertensão/tratamento farmacológico , Hipertensão/genética , Infarto do Miocárdio/tratamento farmacológico , Polimorfismo de Nucleotídeo ÚnicoRESUMO
Genome-wide association studies (GWAS) have revealed thousands of genetic loci that underpin the complex biology of many human traits. However, the strength of GWAS - the ability to detect genetic association by linkage disequilibrium (LD) - is also its limitation. Whilst the ever-increasing study size and improved design have augmented the power of GWAS to detect effects, differentiation of causal variants or genes from other highly correlated genes associated by LD remains the real challenge. This has severely hindered the biological insights and clinical translation of GWAS findings. Although thousands of disease susceptibility loci have been reported, causal genes at these loci remain elusive. Machine learning (ML) techniques offer an opportunity to dissect the heterogeneity of variant and gene signals in the post-GWAS analysis phase. ML models for GWAS prioritization vary greatly in their complexity, ranging from relatively simple logistic regression approaches to more complex ensemble models such as random forests and gradient boosting, as well as deep learning models, i.e., neural networks. Paired with functional validation, these methods show important promise for clinical translation, providing a strong evidence-based approach to direct post-GWAS research. However, as ML approaches continue to evolve to meet the challenge of causal gene identification, a critical assessment of the underlying methodologies and their applicability to the GWAS prioritization problem is needed. This review investigates the landscape of ML applications in three parts: selected models, input features, and output model performance, with a focus on prioritizations of complex disease associated loci. Overall, we explore the contributions ML has made towards reaching the GWAS end-game with consequent wide-ranging translational impact.
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High blood pressure (BP) remains the major heritable and modifiable risk factor for cardiovascular disease. Persistent high BP, or hypertension, is a complex trait with both genetic and environmental interactions. Despite swift advances in genomics, translating new discoveries to further our understanding of the underlying molecular mechanisms remains a challenge. More than 500 loci implicated in the regulation of BP have been revealed by genome-wide association studies (GWAS) in 2018 alone, taking the total number of BP genetic loci to over 1000. Even with the large number of loci now associated to BP, the genetic variance explained by all loci together remains low (~5.7%). These genetic associations have elucidated mechanisms and pathways regulating BP, highlighting potential new therapeutic and drug repurposing targets. A large proportion of the BP loci were discovered and reported simultaneously by multiple research groups, creating a knowledge gap, where the reported loci to date have not been investigated in a harmonious way. Here, we review the BP-associated genetic variants reported across GWAS studies and investigate their potential impact on the biological systems using in silico enrichment analyses for pathways, tissues, gene ontology and genetic pleiotropy.