Investigating The Role of Nuclear And Mitochondrial Variation in Cardiovascular Disease

Cardiovascular disease (CVD) refers to a complex heterogeneous cluster of diseases of the heart and blood vessels that often manifest clinically as outcomes of myocardial infarction and stroke. CVD is a major public health burden that affects 85 million people just in the United States. As a complex disease of high prevalence, CVD has been poised to benefit from large scale sequencing and genotyping studies. Therefore, understanding the underlying pathophysiology of CVD to identify biomarkers for diagnosis and drug targets for prevention and treatment are active areas of research. We attempt to approach this problem from a genetics perspective. First, we utilize large-scale genotyping in cohorts of patients with sudden cardiac arrest, a specific form of CVD, to carry out the largest genome-wide association study (GWAS) for SCA to date and identify genetic loci associated with SCA. Additionally, we used genetic risk score approach to determine the effect of traditional SCA risk factors on the genetically attributable risk for SCA, and hence identify QT interval and BMI as putative causal risk factors for SCA. The second part of this work focuses on inter-individual variation in the number of copies of mitochondrial DNA (mtDNA), a measure we refer to as the mtDNA copy number (mtDNA CN). We assess the association of mtDNA CN with age and age related phenotypes like frailty and mortality, establishing mtDNA CN as significant predictor of all-cause mortality. Finally, we focus on determining the relationship between mtDNA CN and CVD and assessing the clinical utility of mtDNA CN as a predictor of different forms of cardiovascular disease

RNA-Seq optimization with eQTL gold standards.

BackgroundRNA-Sequencing (RNA-Seq) experiments have been optimized for library preparation, mapping, and gene expression estimation. These methods, however, have revealed weaknesses in the next stages of analysis of differential expression, with results sensitive to systematic sample stratification or, in more extreme cases, to outliers. Further, a method to assess normalization and adjustment measures imposed on the data is lacking.ResultsTo address these issues, we utilize previously published eQTLs as a novel gold standard at the center of a framework that integrates DNA genotypes and RNA-Seq data to optimize analysis and aid in the understanding of genetic variation and gene expression. After detecting sample contamination and sequencing outliers in RNA-Seq data, a set of previously published brain eQTLs was used to determine if sample outlier removal was appropriate. Improved replication of known eQTLs supported removal of these samples in downstream analyses. eQTL replication was further employed to assess normalization methods, covariate inclusion, and gene annotation. This method was validated in an independent RNA-Seq blood data set from the GTEx project and a tissue-appropriate set of eQTLs. eQTL replication in both data sets highlights the necessity of accounting for unknown covariates in RNA-Seq data analysis.ConclusionAs each RNA-Seq experiment is unique with its own experiment-specific limitations, we offer an easily-implementable method that uses the replication of known eQTLs to guide each step in one's data analysis pipeline. In the two data sets presented herein, we highlight not only the necessity of careful outlier detection but also the need to account for unknown covariates in RNA-Seq experiments

A comprehensive evaluation of the genetic architecture of sudden cardiac arrest

Aims: Sudden cardiac arrest (SCA) accounts for 10% of adult mortality in Western populations. We aim to identify potential loci associated with SCA and to identify risk factors causally associated with SCA. Methods and results: We carried out a large genome-wide association study (GWAS) for SCA (n = 3939 cases, 25 989 non-cases) to examine common variation genome-wide and in candidate arrhythmia genes. We also exploited Mendelian randomization (MR) methods using cross-trait multi-variant genetic risk score associations (GRSA) to assess causal relationships of 18 risk factors with SCA. No variants were associated with SCA at genome-wide significance, nor were common variants in candidate arrhythmia genes associated with SCA at nominal significance. Using cross-trait GRSA, we established genetic correlation between SCA and (i) coronary artery disease (CAD) and traditional CAD risk factors (blood pressure, lipids, and diabetes), (ii) height and BMI, and (iii) electrical instability traits (QT and atrial fibrillation), suggesting aetiologic roles for these traits in SCA risk. Conclusions: Our findings show that a comprehensive approach to the genetic architecture of SCA can shed light on the determinants of a complex life-threatening condition with multiple influencing factors in the general population. The results of this genetic analysis, both positive and negative findings, have implications for evaluating the genetic architecture of patients with a family history of SCA, and for efforts to prevent SCA in high-risk populations and the general community

RNA-Seq optimization with eQTL gold standards.

BackgroundRNA-Sequencing (RNA-Seq) experiments have been optimized for library preparation, mapping, and gene expression estimation. These methods, however, have revealed weaknesses in the next stages of analysis of differential expression, with results sensitive to systematic sample stratification or, in more extreme cases, to outliers. Further, a method to assess normalization and adjustment measures imposed on the data is lacking.ResultsTo address these issues, we utilize previously published eQTLs as a novel gold standard at the center of a framework that integrates DNA genotypes and RNA-Seq data to optimize analysis and aid in the understanding of genetic variation and gene expression. After detecting sample contamination and sequencing outliers in RNA-Seq data, a set of previously published brain eQTLs was used to determine if sample outlier removal was appropriate. Improved replication of known eQTLs supported removal of these samples in downstream analyses. eQTL replication was further employed to assess normalization methods, covariate inclusion, and gene annotation. This method was validated in an independent RNA-Seq blood data set from the GTEx project and a tissue-appropriate set of eQTLs. eQTL replication in both data sets highlights the necessity of accounting for unknown covariates in RNA-Seq data analysis.ConclusionAs each RNA-Seq experiment is unique with its own experiment-specific limitations, we offer an easily-implementable method that uses the replication of known eQTLs to guide each step in one's data analysis pipeline. In the two data sets presented herein, we highlight not only the necessity of careful outlier detection but also the need to account for unknown covariates in RNA-Seq experiments

Association between mitochondrial DNA copy number and sudden cardiac death: Findings from the Atherosclerosis Risk in Communities study (ARIC)

Aims Sudden cardiac death (SCD) is a major public health burden. Mitochondrial dysfunction has been implicated in a wide range of cardiovascular diseases including cardiomyopathy, heart failure, and arrhythmias, but it is unknown if it also contributes to SCD risk. We sought to examine the prospective association between mtDNA copy number (mtDNA-CN), a surrogate marker of mitochondrial function, and SCD risk. Methods and results We measured baseline mtDNA-CN in 11 093 participants from the Atherosclerosis Risk in Communities (ARIC) study. mtDNA copy number was calculated from probe intensities of mitochondrial single nucleotide polymorphisms (SNP) on the Affymetrix Genome-Wide Human SNP Array 6.0. Sudden cardiac death was defined as a sudden pulseless condition presumed due to a ventricular tachyarrhythmia in a previously stable individual without evidence of a non-cardiac cause of cardiac arrest. Sudden cardiac death cases were reviewed and adjudicated by an expert committee. During a median follow-up of 20.4 years, we observed 361 SCD cases. After adjusting for age, race, sex, and centre, the hazard ratio for SCD comparing the 1st to the 5th quintiles of mtDNA-CN was 2.24 (95% confidence interval 1.58-3.19; P-trend \u3c0.001). When further adjusting for traditional cardiovascular disease risk factors, prevalent coronary heart disease, heart rate, QT interval, and QRS duration, the association remained statistically significant. Spline regression models showed that the association was approximately linear over the range of mtDNA-CN values. No apparent interaction by race or by sex was detected. Conclusion In this community-based prospective study, mtDNA-CN in peripheral blood was inversely associated with the risk of SCD

Association of Mitochondrial DNA Copy Number With Cardiovascular Disease.

ImportanceMitochondrial dysfunction is a core component of the aging process and may play a key role in atherosclerotic cardiovascular disease. Mitochondrial DNA copy number (mtDNA-CN), which represents the number of mitochondria per cell and number of mitochondrial genomes per mitochondrion, is an indirect biomarker of mitochondrial function.ObjectiveTo determine whether mtDNA-CN, measured in an easily accessible tissue (buffy coat/circulating leukocytes), can improve risk classification for cardiovascular disease (CVD) and help guide initiation of statin therapy for primary prevention of CVD.Design, setting, and participantsProspective, population-based cohort analysis including 21 870 participants (20 163 free from CVD at baseline) from 3 studies: Cardiovascular Health Study (CHS), Atherosclerosis Risk in Communities Study (ARIC), and Multiethnic Study of Atherosclerosis (MESA). The mean follow-up was 13.5 years. The study included 11 153 participants from ARIC, 4830 from CHS, and 5887 from MESA. Analysis of the data was conducted from March 10, 2014, to January 29, 2017.ExposuresMitochondrial DNA-CN measured from buffy coat/circulating leukocytes.Main outcomes and measuresIncident CVD, which combines coronary heart disease, defined as the first incident myocardial infarction or death owing to coronary heart disease, and stroke, defined as the first nonfatal stroke or death owing to stroke.ResultsOf the 21 870 participants, the mean age was 62.4 years (ARIC, 57.9 years; MESA, 62.4 years; and CHS, 72.5 years), and 54.7% of participants were women. The hazard ratios for incident coronary heart disease, stroke, and CVD associated with a 1-SD decrease in mtDNA-CN were 1.29 (95% CI, 1.24-1.33), 1.11 (95% CI, 1.06-1.16), and 1.23 (95% CI, 1.19-1.26). The associations persisted after adjustment for traditional CVD risk factors. Addition of mtDNA-CN to the 2013 American College of Cardiology/American Heart Association Pooled Cohorts Equations for estimating 10-year hard atherosclerosis CVD risk was associated with improved risk classification (continuous net reclassification index, 0.194; 95% CI, 0.130-0.258; P < .001). Mitochondrial DNA-CN further improved sensitivity and specificity for the 2013 American College of Cardiology/American Heart Association recommendations on initiating statin therapy for primary prevention of ASCVD (net 221 individuals appropriately downclassified and net 15 individuals appropriately upclassified).Conclusions and relevanceMitochondrial DNA-CN was independently associated with incident CVD in 3 large prospective studies and may have potential clinical utility in improving CVD risk classification

Effect of sex and underlying disease on the genetic association of QT interval and sudden cardiac death

Abstract Background: Sudden cardiac death (SCD) accounts for ≈300 000 deaths annually in the United States. Men have a higher risk of SCD and are more likely to have underlying coronary artery disease, while women are more likely to have arrhythmic events in the setting of inherited or acquired QT prolongation. Moreover, there is evidence of sex differences in the genetics of QT interval duration. Using sex‐ and coronary artery disease–stratified analyses, we assess differences in genetic association between longer QT interval and SCD risk. Methods and Results: We examined 2282 SCD subjects and 3561 Finnish controls. The SCD subjects were stratified by underlying disease (ischemic versus nonischemic) and by sex. We used logistic regression to test for association between the top QT interval–associated single‐nucleotide polymorphism, rs12143842 (in the NOS1AP locus), and SCD risk. We also performed Mendelian randomization to test for causal association of QT interval in the various subgroups. No statistically significant differences were observed between the sexes for associations with rs12143842, despite the odds ratio being higher in females across all subgroup analyses. Consistent with our hypothesis, female non‐ischemics had the highest odds ratio point estimate for association between rs12143842 and SCD risk and male ischemics the lowest odds ratio point estimate (P=0.036 for difference). Similar trends were observed for the Mendelian randomization analysis. Conclusions: While individual subgroup comparisons did not achieve traditional criteria for statistical significance, this study is consistent with the hypothesis that the causal association of longer QT interval on SCD risk is stronger in women and nonischemic individuals

A comprehensive evaluation of the genetic architecture of sudden cardiac arrest

AimsSudden cardiac arrest (SCA) accounts for 10% of adult mortality in Western populations. We aim to identify potential loci associated with SCA and to identify risk factors causally associated with SCA.Methods and resultsWe carried out a large genome-wide association study (GWAS) for SCA (n = 3939 cases, 25 989 non-cases) to examine common variation genome-wide and in candidate arrhythmia genes. We also exploited Mendelian randomization (MR) methods using cross-trait multi-variant genetic risk score associations (GRSA) to assess causal relationships of 18 risk factors with SCA. No variants were associated with SCA at genome-wide significance, nor were common variants in candidate arrhythmia genes associated with SCA at nominal significance. Using cross-trait GRSA, we established genetic correlation between SCA and (i) coronary artery disease (CAD) and traditional CAD risk factors (blood pressure, lipids, and diabetes), (ii) height and BMI, and (iii) electrical instability traits (QT and atrial fibrillation), suggesting aetiologic roles for these traits in SCA risk.ConclusionsOur findings show that a comprehensive approach to the genetic architecture of SCA can shed light on the determinants of a complex life-threatening condition with multiple influencing factors in the general population. The results of this genetic analysis, both positive and negative findings, have implications for evaluating the genetic architecture of patients with a family history of SCA, and for efforts to prevent SCA in high-risk populations and the general community