In a recent study published in Nature Communications, a group of researchers investigated the genetics behind resting heart rate (RHR) and its impact on cardiovascular disease, correcting previous biases and providing novel insights into disease development.
Studies have linked RHR to cardiovascular disease and mortality, but these are possibly influenced by disease status and various confounding factors. Mendelian randomization (MR) uses genetic variants associated with RHR as proxies, reducing the risk of confounders and reverse causation.
Few studies also found a positive correlation between genetically predicted RHR and mortality, but not cardiovascular disease risk. Interestingly, higher genetically predicted RHR appeared to lower the risks of atrial fibrillation and cardio-embolic stroke. Genome-wide association studies (GWAS) have pinpointed RHR-related genetic variants. However, the two largest GWAS in the United Kingdom (UK) Biobank had limitations, like being done in subcohorts or lacking replication.
About the study
In the present study, an extensive RHR meta-analysis included 835,465 individuals from 100 different sources. RHR data were gathered through various means including electrocardiogram (ECG), pulse rate, blood pressure monitors, and electronic medical records.
Detailed genomic analysis was done, with each cohort subjected to rigorous quality control procedures. Genetic correlation analyses identified significant links with previous traits.
Further investigations prioritized candidate genes from the final meta-analysis, followed by functional prediction and annotation of all potential non-synonymous genetic variants. The expression quantitative trait loci (eQTL) analyses and data-driven expression-prioritized integration for complex traits (DEPICT) analyses focused on gene expression and associations with identified variants.
The study also involved tissue enrichment, ECG morphology, pathway analyses, and single-nucleus ribonucleic acid (RNA) expression for a comprehensive understanding.
The UK Biobank tracks disease prevalence and functional outcomes through the Assessment Centre and National Health Service data. Disease prevalence was also self-reported through nurse interviews. Data was available until different periods for English, Welsh, and Scottish participants.
Definitions of health conditions and longevity were collected, and exceptions were applied in certain cases. Blood pressure values were obtained and corrected for medication use. Detailed statistical analysis was carried out for functional outcomes, external cohort definitions, and genome-wide association studies. Replication analysis, quality control, regression analyses, and additional exclusions were done to enhance reliability.
The authors then conducted an MR analysis using 493 unique genetic variants from a meta-analysis. These variants' effect sizes were used to examine relationships within UK Biobank and other independent cohorts. Substitutions were considered if original genetic variants could not be found in the given data.
They also evaluated possible weak instrument bias through a formula that involved sample size and the variance of the exposure explained by the SNP. Other analytic steps included adjusting summary statistics, filtering for pleiotropic effects, and exploring potential reversed causation.
The authors also investigated linear and non-linear associations of genetically predicted RHR with various health outcomes using different models and tests. The relationship between RHR and stroke was also explored using a multivariable MR. They used R for their analyses and considered a two-sided P-value of <0.05 significant for their results.
In the present study, the IC-RHR conducted a meta-analysis of RHR using 99 cohorts with 351,158 individuals and a GWAS on 484,307 UK Biobank subjects. This resulted in the analysis of 30,458,884 genotyped and imputed autosomal variants in 835,464 individuals, identifying 493 independent genetic variants.
These variants, showing a significant correlation with physical activity and anthropometric measurements, increase the understanding of RHR's heritability, estimated to be 10%. Findings were internally replicated, and discrepancies in replication with previous studies were mostly attributed to more stringent clumping criteria and a lack of genome-wide significance.
The IC-RHR explored the biology of 352 RHR-related loci, identifying 407 unique genes proximate to the lead variant and 52 genes containing coding genetic variants. They used a variety of analysis methods to identify 670 unique causal genes, prioritizing 33. Phosphatase And Actin Regulator 4 (PHACTR4), Enolase 3 (ENO3), and sentrin-specific protease 2 (SENP2) were highly prioritized.
Pathway analysis revealed RHR connections to primarily cardiac biology, muscle cell differentiation, cardiac tissue development, and pro-arrhythmogenic pathways. Tissue enrichment analysis implicated the cardiovascular system as critical, though some associations with non-cardiovascular tissues were also found.
The ECGenetics browser was used to explore the electrophysiological impact of RHR genetic variants, identifying 86 variants associated with a minimum of one ECG time point.
Genes acetylcholinesterase (ACHE), Ankyrin repeat domain 1 (ANKRD1), and sodium voltage-gated channel alpha subunit 5 (SCN5A) primarily influenced atrial depolarization, while titin (TTN) affected ventricular depolarization and regulator of G protein signaling 6 (RGS6), and synaptotagmin 10 (SYT10) influenced ventricular repolarization.
Some loci, previously unassociated with RHR or cardiac rhythm and structure, showed substantial effects. Single-nucleus RNA sequencing data revealed that RHR gene expression was maximum in ventricular cardiomyocytes, followed by atrial cardiomyocytes.
Two-sample MR analyses were carried out to investigate the relationship between genetically predicted RHR and all-cause mortality and cardiovascular diseases. The initial model, assuming balanced pleiotropy, found no significant associations between genetically predicted RHR and all-cause mortality, parental longevity, or the leading causes of death in the UK Biobank over a median follow-up of 8.9 years.
MR analysis also revealed no significant associations between genetically predicted RHR and prevalent cardiovascular diseases like coronary artery disease or myocardial infarction. However, a higher genetically predicted RHR was suggestively linked with a lower risk of atrial fibrillation, with the strongest effects seen in individuals with significantly higher RHRs.
An association was found between a higher genetically predicted RHR and risk of stroke, particularly ischemic and cardio-embolic stroke, in the MEGASTROKE consortium, though this was not replicated in the UK Biobank. Genetically predicted RHR was associated with an increased risk of dilated cardiomyopathy.
The study identified potential new risk loci for certain cardiovascular diseases not previously expected to be associated with RHR. These findings provide new insights into the genetic and mechanistic underpinnings of RHR and associated health outcomes.
- van de Vegte YJ, Eppinga RN, van der Ende MY, et al. (2023). Genetic insights into resting heart rate and its role in cardiovascular disease. Nature Communications. doi: 10.1038/s41467-023-39521-2. https://www.nature.com/articles/s41467-023-39521-2
Posted in: Medical Research News | Medical Condition News | Disease/Infection News
Tags: Actin, Atrial Fibrillation, Autosomal, Blood, Blood Pressure, Cardiomyopathy, Cardiovascular Disease, Cell, Coronary Artery Disease, Electronic Medical Records, Gene, Gene Expression, Genes, Genetic, Genetics, Genome, Genomic, Heart, Heart Rate, Morphology, Mortality, Muscle, Myocardial Infarction, Phosphatase, Physical Activity, Protein, Ribonucleic Acid, RNA, RNA Sequencing, Stroke, UK Biobank
Vijay Kumar Malesu
Vijay holds a Ph.D. in Biotechnology and possesses a deep passion for microbiology. His academic journey has allowed him to delve deeper into understanding the intricate world of microorganisms. Through his research and studies, he has gained expertise in various aspects of microbiology, which includes microbial genetics, microbial physiology, and microbial ecology. Vijay has six years of scientific research experience at renowned research institutes such as the Indian Council for Agricultural Research and KIIT University. He has worked on diverse projects in microbiology, biopolymers, and drug delivery. His contributions to these areas have provided him with a comprehensive understanding of the subject matter and the ability to tackle complex research challenges.