Integromic analysis of genetic variation and gene expression identifies networks for cardiovascular disease phenotypes

Enhancing selection of alcohol consumption-associated genes by random forest

Machine learning methods have been used in identifying omics markers for a variety of phenotypes. We aimed to examine whether a supervised machine learning algorithm can improve identification of alcohol-associated transcriptomic markers. In this study, we analysed array-based, whole-blood derived expression data for 17 873 gene transcripts in 5508 Framingham Heart Study participants. By using the Boruta algorithm, a supervised random forest (RF)-based feature selection method, we selected twenty-five alcohol-associated transcripts. In a testing set (30 % of entire study participants), AUC (area under the receiver operating characteristics curve) of these twenty-five transcripts were 0·73, 0·69 and 0·66 for non-drinkers v. moderate drinkers, non-drinkers v. heavy drinkers and moderate drinkers v. heavy drinkers, respectively. The AUC of the selected transcripts by the Boruta method were comparable to those identified using conventional linear regression models, for example, AUC of 1958 transcripts identified by conventional linear regression models (false discovery rate < 0·2) were 0·74, 0·66 and 0·65, respectively. With Bonferroni correction for the twenty-five Boruta method-selected transcripts and three CVD risk factors (i.e. at P < 6·7e-4), we observed thirteen transcripts were associated with obesity, three transcripts with type 2 diabetes and one transcript with hypertension. For example, we observed that alcohol consumption was inversely associated with the expression of DOCK4, IL4R, and SORT1, and DOCK4 and SORT1 were positively associated with obesity, and IL4R was inversely associated with hypertension. In conclusion, using a supervised machine learning method, the RF-based Boruta algorithm, we identified novel alcohol-associated gene transcripts.

A biobank-scale test of marginal epistasis reveals genome-wide signals of polygenic epistasis

The contribution of epistasis (interactions among genes or genetic variants) to human complex trait variation remains poorly understood. Methods that aim to explicitly identify pairs of genetic variants, usually single nucleotide polymorphisms (SNPs), associated with a trait suffer from low power due to the large number of hypotheses tested while also having to deal with the computational problem of searching over a potentially large number of candidate pairs. An alternate approach involves testing whether a single SNP modulates variation in a trait against a polygenic background. While overcoming the limitation of low power, such tests of polygenic or marginal epistasis (ME) are infeasible on Biobank-scale data where hundreds of thousands of individuals are genotyped over millions of SNPs. We present a method to test for ME of a SNP on a trait that is applicable to biobank-scale data. We performed extensive simulations to show that our method provides calibrated tests of ME. We applied our method to test for ME at SNPs that are associated with 53 quantitative traits across ≈ 300 K unrelated white British individuals in the UK Biobank (UKBB). Testing 15, 601 trait-loci associations that were significant in GWAS, we identified 16 trait-loci pairs across 12 traits that demonstrate strong evidence of ME signals (p-value p < 5 × 10 - 8 53 ). We further partitioned the significant ME signals across the genome to identify 6 trait-loci pairs with evidence of local (within-chromosome) ME while 15 show evidence of distal (cross-chromosome) ME. Across the 16 trait-loci pairs, we document that the proportion of trait variance explained by ME is about 12x as large as that explained by the GWAS effects on average (range: 0.59 to 43.89). Our results show, for the first time, evidence of interaction effects between individual genetic variants and overall polygenic background modulating complex trait variation.

Genetic and Environmental Influences on Blood Pressure and Serum Lipids Across Age-Groups

Aging plays a crucial role in the mechanisms of the impacts of genetic and environmental factors on blood pressure and serum lipids. However, to our knowledge, how the influence of genetic and environmental factors on the correlation between blood pressure and serum lipids changes with age remains to be determined. In this study, data from the Chinese National Twin Registry (CNTR) were used. Resting blood pressure, including systolic and diastolic blood pressure (SBP and DBP), and fasting serum lipids, including total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C) and triglycerides (TGs) were measured in 2378 participants (1189 twin pairs). Univariate and bivariate structural equation models examined the genetic and environmental influences on blood pressure and serum lipids among three age groups. All phenotypes showed moderate to high heritability (0.37-0.59) and moderate unique environmental variance (0.30-0.44). The heritability of all phenotypes showed a decreasing trend with age. Among all phenotypes, SBP and DBP showed a significant monotonic decreasing trend. For phenotype-phenotype pairs, the phenotypic correlation (Rph) of each pair ranged from -0.04 to 0.23, and the additive genetic correlation (Ra) ranged from 0.00 to 0.36. For TC&SBP, TC&DBP, TG&SBP and TGs&DBP, both the Rph and Ra declined with age, and the Ra difference between the young group and the older adult group is statistically significant (p < .05). The unique environmental correlation (Re) of each pair did not follow any pattern with age and remained relatively stable with age. In summary, we observed that the heritability of blood pressure was affected by age. Moreover, blood pressure and serum lipids shared common genetic backgrounds, and age had an impact on the phenotypic correlation and genetic correlations.

Blood transcriptomic biomarkers of alcohol consumption and cardiovascular disease risk factors: the Framingham Heart Study

BACKGROUND

The relations of alcohol consumption and gene expression remain to be elucidated.

MATERIALS AND METHODS

We examined cross-sectional associations between alcohol consumption and whole blood derived gene expression levels and between alcohol-associated genes and obesity, hypertension, and diabetes in 5531 Framingham Heart Study (FHS) participants.

RESULTS

We identified 25 alcohol-associated genes. We further showed cross-sectional associations of 16 alcohol-associated genes with obesity, nine genes with hypertension, and eight genes with diabetes at P < 0.002. For example, we observed decreased expression of PROK2 (β = -0.0018; 95%CI: -0.0021, -0.0007; P = 6.5e - 5) and PAX5 (β = -0.0014; 95%CI: -0.0021, -0.0007; P = 6.5e - 5) per 1 g/day increase in alcohol consumption. Consistent with our previous observation on the inverse association of alcohol consumption with obesity and positive association of alcohol consumption with hypertension, we found that PROK2 was positively associated with obesity (OR = 1.42; 95%CI: 1.17, 1.72; P = 4.5e - 4) and PAX5 was negatively associated with hypertension (OR = 0.73; 95%CI: 0.59, 0.89; P = 1.6e - 3). We also observed that alcohol consumption was positively associated with expression of ABCA13 (β = 0.0012; 95%CI: 0.0007, 0.0017; P = 1.3e - 6) and ABCA13 was positively associated with diabetes (OR = 2.57; 95%CI: 1.73, 3.84; P = 3.5e - 06); this finding, however, was inconsistent with our observation of an inverse association between alcohol consumption and diabetes.

CONCLUSIONS

We showed strong cross-sectional associations between alcohol consumption and expression levels of 25 genes in FHS participants. Nonetheless, complex relationships exist between alcohol-associated genes and CVD risk factors.

Genetic susceptibility loci for Chlamydia trachomatis endometrial infection influence expression of genes involved in T cell function, tryptophan metabolism and epithelial integrity

Objectives

Identify genetic loci of enhanced susceptibility to Chlamydial trachomatis (Ct) upper genital tract infection in women.

Methods

We performed an integrated analysis of DNA genotypes and blood-derived mRNA profiles from 200 Ct-exposed women to identify expression quantitative trait loci (eQTL) and determine their association with endometrial chlamydial infection using a mediation test. We further evaluated the effect of a lead eQTL on the expression of CD151 by immune cells from women with genotypes associated with low and high whole blood expression of CD151, respectively.

Results

We identified cis-eQTLs modulating mRNA expression of 81 genes (eGenes) associated with altered risk of ascending infection. In women with endometrial infection, eGenes involved in proinflammatory signaling were upregulated. Downregulated eGenes included genes involved in T cell functions pivotal for chlamydial control. eGenes encoding molecules linked to metabolism of tryptophan, an essential chlamydial nutrient, and formation of epithelial tight junctions were also downregulated in women with endometrial infection. A lead eSNP rs10902226 was identified regulating CD151, a tetrospanin molecule important for immune cell adhesion and migration and T cell proliferation. Further in vitro experiments showed that women with a CC genotype at rs10902226 had reduced rates of endometrial infection with increased CD151 expression in whole blood and T cells when compared to women with a GG genotype.

Conclusions

We discovered genetic variants associated with altered risk for Ct ascension. A lead eSNP for CD151 is a candidate genetic marker for enhanced CD4 T cell function and reduced susceptibility.

Theory and Applications of the (Cardio) Genomic Fabric Approach to Post-Ischemic and Hypoxia-Induced Heart Failure

The genomic fabric paradigm (GFP) characterizes the transcriptome topology by the transcripts’ abundances, the variability of the expression profile, and the inter-coordination of gene expressions in each pathophysiological condition. The expression variability analysis provides an indirect estimate of the cell capability to limit the stochastic fluctuations of the expression levels of key genes, while the expression coordination analysis determines the gene networks in functional pathways. This report illustrates the theoretical bases and the mathematical framework of the GFP with applications to our microarray data from mouse models of post ischemic, and constant and intermittent hypoxia-induced heart failures. GFP analyses revealed the myocardium priorities in keeping the expression of key genes within narrow intervals, determined the statistically significant gene interlinkages, and identified the gene master regulators in the mouse heart left ventricle under normal and ischemic conditions. We quantified the expression regulation, alteration of the expression control, and remodeling of the gene networks caused by the oxygen deprivation and determined the efficacy of the bone marrow mono-nuclear stem cell injections to restore the normal transcriptome. Through the comprehensive assessment of the transcriptome, GFP would pave the way towards the development of personalized gene therapy of cardiac diseases.

Marine Bacteria and Omic Approaches: A Novel and Potential Repository for Bioremediation Assessment

Marine environments accommodating diverse assortments of life constitute a great pool of differentiated natural resources. The cumulative need to remedy unpropitious effects of anthropogenic activities on estuaries, and coastal marine ecosystems has propelled the development of effective bioremediation strategies. Marine bacteria producing biosurfactants are promising agents for bio-remediating oil pollution in marine environments, making them prospective candidates for enhancing oil recovery. Molecular omics technologies are considered an emerging field of research in ecological and diversity assessment owing to their utility in environmental surveillance and bioremediation of polluted sites. A thorough literature review was undertaken to understand the applicability of different omic techniques employed for bioremediation assessment using marine bacteria. This review further establishes that for bioremediation of environmental pollutants (i.e., heavy metals, hydrocarbons, xenobiotic and numerous recalcitrant compounds), organisms isolated from marine environments can be better utilized for their removal. The literature survey shows that omics approaches can provide exemplary knowledge about microbial communities and their role in the bioremediation of environmental pollutants. This review centres on applications of marine bacteria in enhanced bioremediation, utilizing the omics approaches that can be a vital biological contrivance in environmental monitoring to tackle environmental degradation. The paper aims to identify the gaps in investigations involving marine bacteria to help researchers, ecologists, and decision-makers to develop a holistic understanding regarding their utility in bioremediation assessment.

Circulating PCSK7 Level is Independently Associated with Obesity, Triglycerides Level and Fatty Liver Index in a General Population without Medication

AIM

Dyslipidemia and altered iron metabolism are typical features of non-alcoholic fatty liver disease (NAFLD). Proprotein convertase subtilisin/kexin type 7 (PCSK7), a transmembrane-anchored endonuclease, is associated with triglycerides level and processing of transferrin receptor 1. However, the significance of circulating PCSK7 has not been fully addressed, though prosegment PCSK7 is secreted from cells. We investigated the associations of plasma PCSK7 level with several parameters.

METHODS

Plasma PCSK7 concentration was measured in 282 subjects (male/female: 126/156) without medication of the Tanno-Sobetsu Study, a population-based cohort study.

RESULTS

There was no significant sex difference in PCSK7 level. Current smoking habit, but not alcohol drinking habit, was associated with increased PCSK7 level. PCSK7 concentration was negatively correlated with age and blood urea nitrogen and was positively correlated with body mass index (BMI) and levels of γ-glutamyl transpeptidase (γGTP), triglycerides and fatty liver index (FLI), which is calculated by BMI, waist circumference and levels of γGTP and triglycerides, as a noninvasive and simple predictor of NAFLD. There were no significant correlations of PCSK7 level with levels of iron and plasma PCSK9, a secreted PCSK family member and a regulator of low-density lipoprotein cholesterol level. Multivariable regression analyses after adjustment of age, sex and current smoking habit showed that PCSK7 concentration was independently associated with BMI (β=0.130, P=0.035), triglycerides (β=0.141, P=0.027) or FLI (β=0.139, P=0.030).

CONCLUSIONS

Plasma PCSK7 concentration is independently associated with chronic liver disease including obesity and elevated triglycerides level in a general population of individuals who had not regularly taken any medications.

Urine peptidome analysis in cardiorenal syndrome reflects molecular processes

The cardiorenal syndrome (CRS) is defined as the confluence of heart-kidney dysfunction. This study investigates the molecular differences at the level of the urinary peptidome between CRS patients and controls and their association to disease pathophysiology. The urinary peptidome of CRS patients (n = 353) was matched for age and sex with controls (n = 356) at a 1:1 ratio. Changes in the CRS peptidome versus controls were identified after applying the Mann-Whitney test, followed by correction for multiple testing. Proteasix tool was applied to investigate predicted proteases involved in CRS-associated peptide generation. Overall, 559 differentially excreted urinary peptides were associated with CRS patients. Of these, 193 peptides were specifically found in CRS when comparing with heart failure and chronic kidney disease urinary peptide profiles. Proteasix predicted 18 proteases involved in > 1% of proteolytic cleavage events including multiple forms of MMPs, proprotein convertases, cathepsins and kallikrein 4. Forty-four percent of the cleavage events were produced by 3 proteases including MMP13, MMP9 and MMP2. Pathway enrichment analysis supported that ECM-related pathways, fibrosis and inflammation were represented. Collectively, our study describes the changes in urinary peptides of CRS patients and potential proteases involved in their generation, laying the basis for further validation.

The genetic architecture of plasma kynurenine includes cardiometabolic disease mechanisms associated with the SH2B3 gene

Inflammation increases the risk of cardiometabolic disease. Delineating specific inflammatory pathways and biomarkers of their activity could identify the mechanistic underpinnings of the increased risk. Plasma levels of kynurenine, a metabolite involved in inflammation, associates with cardiometabolic disease risk. We used genetic approaches to identify inflammatory mechanisms associated with kynurenine variability and their relationship to cardiometabolic disease. We identified single-nucleotide polymorphisms (SNPs) previously associated with plasma kynurenine, including a missense-variant (rs3184504) in the inflammatory gene SH2B3/LNK. We examined the association between rs3184504 and plasma kynurenine in independent human samples, and measured kynurenine levels in SH2B3-knock-out mice and during human LPS-evoked endotoxemia. We conducted phenome scanning to identify clinical phenotypes associated with each kynurenine-related SNP and with a kynurenine polygenic score using the UK-Biobank (n = 456,422), BioVU (n = 62,303), and Electronic Medical Records and Genetics (n = 32,324) databases. The SH2B3 missense variant associated with plasma kynurenine levels and SH2B3^−/− mice had significant tissue-specific differences in kynurenine levels.LPS, an acute inflammatory stimulus, increased plasma kynurenine in humans. Mendelian randomization showed increased waist-circumference, a marker of central obesity, associated with increased kynurenine, and increased kynurenine associated with C-reactive protein (CRP). We found 30 diagnoses associated (FDR q < 0.05) with the SH2B3 variant, but not with SNPs mapping to genes known to regulate tryptophan-kynurenine metabolism. Plasma kynurenine may be a biomarker of acute and chronic inflammation involving the SH2B3 pathways. Its regulation lies upstream of CRP, suggesting that kynurenine may be a biomarker of one inflammatory mechanism contributing to increased cardiometabolic disease risk.

Cardiovascular disease risk and pathophysiology in South Asians: can longitudinal multi-omics shed light?

Cardiovascular disease (CVD) is the leading cause of mortality in South Asia, with rapidly increasing prevalence of hypertension, type 2 diabetes (T2DM) and hyperlipidemia over the last two decades. Atherosclerotic CVD (ASCVD) affects South Asians earlier in life and at lower body weights, which is not fully explained by differential burden of conventional risk factors. Heart failure (HF) is a complex clinical syndrome of heterogeneous structural phenotypes including two major clinical subtypes, HF with preserved (HFpEF) and reduced ejection fraction (HFrEF). The prevalence of HF in South Asians is also rising with other metabolic diseases, and HFpEF develops at younger age and leaner body mass index in South Asians than in Whites. Recent genome-wide association studies, epigenome-wide association studies and metabolomic studies of ASCVD and HF have identified genes, metabolites and pathways associated with CVD traits. However, these findings were mostly driven by samples of European ancestry, which may not accurately represent the CVD risk at the molecular level, and the unique risk profile of CVD in South Asians. Such bias, while formulating hypothesis-driven research studies, risks missing important causal or predictive factors unique to South Asians. Importantly, a longitudinal design of multi-omic markers can capture the life-course risk and natural history related to CVD, and partially disentangle putative causal relationship between risk factors, multi-omic markers and subclinical and clinical ASCVD and HF. In conclusion, combining high-resolution untargeted metabolomics with epigenomics of rigorous, longitudinal design will provide comprehensive unbiased molecular characterization of subclinical and clinical CVD among South Asians. A thorough understanding of CVD-associated metabolomic profiles, together with advances in epigenomics and genomics, will lead to more accurate estimates of CVD progression and stimulate new strategies for improving cardiovascular health.

TRPM2, PDLIM5, BCL3, CD14, GBA Genes as Feasible Markers for Premature Coronary Heart Disease Risk

Background: Beyond non-genetic risk factors, familial hypercholesterolemia (FH) plays a major role in the development of CHD. FH is a genetic disorder characterized by heritable and severely elevated levels of low-density lipoprotein (LDL) cholesterol, which can lead to premature cardiovascular disease, particularly familial coronary heart disease (FH-CHD). Method: To explore genes indicating a risk of familial (premature) coronary heart disease (FH-CHD) development in FH, 30 Thai male volunteers were enrolled: 7 healthy controls (N), 6 patients with hypercholesterolemia (H), 4 with FH, 10 with CHD, and 3 with FH-CHD. Transcriptome data were investigated using next-generation sequencing analysis in whole blood (n = 3). Genes that were significantly expressed in both FH and FH-CHD, but not in N, H, and CHD groups, were selected and functionally analyzed. Results: The findings revealed that 55 intersecting genes were differentially expressed between FH and FH-CHD groups. Ten of the 55 genes (MAPK14, TRPM2, STARD8, PDLIM5, BCL3, BLOC1S5, GBA, RBMS1, CD14, and CD36 were selected for validation. These 10 genes play potential roles in chronic inflammation and are involved in pathways related to pathogenesis of CHD. Using quantitative real-time PCR, we evaluated the mRNA expression of the selected genes in all 30 volunteers. TRPM2, PDLIM5, BCL3 were significantly upregulated and GBA was significantly downregulated in both FH and FH-CHD compared with the N, H, and CHD groups. Conclusion: our preliminary investigation reveals that the TRPM2, PDLIM5, BCL3, and GBA genes may have potential for further development as predictive markers for FH-CHD.

Risk Prediction in Patients With Heart Failure With Preserved Ejection Fraction Using Gene Expression Data and Machine Learning

Heart failure with preserved ejection fraction (HFpEF) has become a major health issue because of its high mortality, high heterogeneity, and poor prognosis. Using genomic data to classify patients into different risk groups is a promising method to facilitate the identification of high-risk groups for further precision treatment. Here, we applied six machine learning models, namely kernel partial least squares with the genetic algorithm (GA-KPLS), the least absolute shrinkage and selection operator (LASSO), random forest, ridge regression, support vector machine, and the conventional logistic regression model, to predict HFpEF risk and to identify subgroups at high risk of death based on gene expression data. The model performance was evaluated using various criteria. Our analysis was focused on 149 HFpEF patients from the Framingham Heart Study cohort who were classified into good-outcome and poor-outcome groups based on their 3-year survival outcome. The results showed that the GA-KPLS model exhibited the best performance in predicting patient risk. We further identified 116 differentially expressed genes (DEGs) between the two groups, thus providing novel therapeutic targets for HFpEF. Additionally, the DEGs were enriched in Gene Ontology terms and Kyoto Encyclopedia of Genes and Genomes pathways related to HFpEF. The GA-KPLS-based HFpEF model is a powerful method for risk stratification of 3-year mortality in HFpEF patients.

Penalized partial least squares for pleiotropy

BACKGROUND

The increasing number of genome-wide association studies (GWAS) has revealed several loci that are associated to multiple distinct phenotypes, suggesting the existence of pleiotropic effects. Highlighting these cross-phenotype genetic associations could help to identify and understand common biological mechanisms underlying some diseases. Common approaches test the association between genetic variants and multiple traits at the SNP level. In this paper, we propose a novel gene- and a pathway-level approach in the case where several independent GWAS on independent traits are available. The method is based on a generalization of the sparse group Partial Least Squares (sgPLS) to take into account groups of variables, and a Lasso penalization that links all independent data sets. This method, called joint-sgPLS, is able to convincingly detect signal at the variable level and at the group level.

RESULTS

Our method has the advantage to propose a global readable model while coping with the architecture of data. It can outperform traditional methods and provides a wider insight in terms of a priori information. We compared the performance of the proposed method to other benchmark methods on simulated data and gave an example of application on real data with the aim to highlight common susceptibility variants to breast and thyroid cancers.

CONCLUSION

The joint-sgPLS shows interesting properties for detecting a signal. As an extension of the PLS, the method is suited for data with a large number of variables. The choice of Lasso penalization copes with architectures of groups of variables and observations sets. Furthermore, although the method has been applied to a genetic study, its formulation is adapted to any data with high number of variables and an exposed a priori architecture in other application fields.

The Molecular Basis of Predicting Atherosclerotic Cardiovascular Disease Risk

Atherosclerotic cardiovascular disease (ASCVD) proceeds through a series of stages: initiation, progression (or regression), and complications. By integrating known biology regarding molecular signatures of each stage with recent advances in high-dimensional molecular data acquisition platforms (to assay the genome, epigenome, transcriptome, proteome, metabolome, and gut microbiome), snapshots of each phase of atherosclerotic cardiovascular disease development can be captured. In this review, we will summarize emerging approaches for assessment of atherosclerotic cardiovascular disease risk in humans using peripheral blood molecular signatures and molecular imaging approaches. We will then discuss the potential (and challenges) for these snapshots to be integrated into a personalized movie providing dynamic readouts of an individual's atherosclerotic cardiovascular disease risk status throughout the life course.

Network Medicine Approach in Prevention and Personalized Treatment of Dyslipidemias

Dyslipidemias can affect molecular networks underlying the metabolic homeostasis and vascular function leading to atherogenesis at early stages of development. Since disease-related proteins often interact with each other in functional modules, many advanced network-oriented algorithms were applied to patient-derived big data to identify the complex gene-environment interactions underlying the early pathophysiology of dyslipidemias and atherosclerosis. Both the proprotein convertase subtilisin/kexin type 7 (PCSK7) and collagen type 1 alpha 1 chain (COL1A1) genes arose from the application of TFfit and WGCNA algorithms, respectively, as potential useful therapeutic targets in prevention of dyslipidemias. Moreover, the Seed Connector algorithm (SCA) algorithm suggested a putative role of the neuropilin-1 (NRP1) protein as drug target, whereas a regression network analysis reported that niacin may provide benefits in mixed dyslipidemias. Dyslipidemias are highly heterogeneous at the clinical level; thus, it would be helpful to overcome traditional evidence-based paradigm toward a personalized risk assessment and therapy. Network Medicine uses omics data, artificial intelligence (AI), imaging tools, and clinical information to design personalized therapy of dyslipidemias and atherosclerosis. Recently, a novel non-invasive AI-derived biomarker, named Fat Attenuation Index (FAI™) has been established to early detect clinical signs of atherosclerosis. Moreover, an integrated AI-radiomics approach can detect fibrosis and microvascular remodeling improving the customized risk assessment. Here, we offer a network-based roadmap ranging from novel molecular pathways to digital therapeutics which can improve personalized therapy of dyslipidemias.

Gut microbiota metabolites as integral mediators in cardiovascular diseases (Review)

Cardiovascular diseases (CVDs), such as atherosclerosis, hypertension, myocardial infarction and diabetic heart disease, are associated with high morbidity and mortality rates worldwide, and may also induce multiple organ failure in their later stages, greatly reducing the long-term survival of the patients. There are several causes of CVDs, but after nearly a decade of investigation, researchers have found that CVDs are usually accompanied by an imbalance of gut microbiota and a decreased abundance of flora. More importantly, the metabolites produced by intestinal flora, such as trimethylamine and trimethylamine N-oxide, bile acids, short-chain fatty acids and aromatic amino acids, exert different effects on the occurrence and development of CVDs, as observed in the relevant pathways in the cells, which may either promote or protect against CVD occurrence. It is known that changes in the intestinal flora following antibiotic administration, diet supplementation with probiotics, or exercise, can interfere with the composition of the intestinal flora and may represent an effective approach to preventing or treating CVDs. The focus of this review was the analysis of gut microbiota metabolites to elucidate their effects on CVDs and to identify the most cost-effective and beneficial methods for treating CVDs with minimal side effects.

Network Medicine: A Clinical Approach for Precision Medicine and Personalized Therapy in Coronary Heart Disease

Early identification of coronary atherosclerotic pathogenic mechanisms is useful for predicting the risk of coronary heart disease (CHD) and future cardiac events. Epigenome changes may clarify a significant fraction of this "missing hereditability", thus offering novel potential biomarkers for prevention and care of CHD. The rapidly growing disciplines of systems biology and network science are now poised to meet the fields of precision medicine and personalized therapy. Network medicine integrates standard clinical recording and non-invasive, advanced cardiac imaging tools with epigenetics into deep learning for in-depth CHD molecular phenotyping. This approach could potentially explore developing novel drugs from natural compounds (i.e. polyphenols, folic acid) and repurposing current drugs, such as statins and metformin. Several clinical trials have exploited epigenetic tags and epigenetic sensitive drugs both in primary and secondary prevention. Due to their stability in plasma and easiness of detection, many ongoing clinical trials are focused on the evaluation of circulating miRNAs (e.g. miR-8059 and miR-320a) in blood, in association with imaging parameters such as coronary calcifications and stenosis degree detected by coronary computed tomography angiography (CCTA), or functional parameters provided by FFR/CT and PET/CT. Although epigenetic modifications have also been prioritized through network based approaches, the whole set of molecular interactions (interactome) in CHD is still under investigation for primary prevention strategies.

Powerful and efficient SNP-set association tests across multiple phenotypes using GWAS summary data

MOTIVATION

Many GWAS conducted in the past decade have identified tens of thousands of disease related variants, which in total explained only part of the heritability for most traits. There remain many more genetics variants with small effect sizes to be discovered. This has motivated the development of sequencing studies with larger sample sizes and increased resolution of genotyped variants, e.g., the ongoing NHLBI Trans-Omics for Precision Medicine (TOPMed) whole genome sequencing project. An alternative approach is the development of novel and more powerful statistical methods. The current dominating approach in the field of GWAS analysis is the "single trait single variant" association test, despite the fact that most GWAS are conducted in deeply-phenotyped cohorts with many correlated traits measured. In this paper, we aim to develop rigorous methods that integrate multiple correlated traits and multiple variants to improve the power to detect novel variants. In recognition of the difficulty of accessing raw genotype and phenotype data due to privacy and logistic concerns, we develop methods that are applicable to publicly available GWAS summary data.

RESULTS

We build rigorous statistical models for GWAS summary statistics to motivate novel multi-trait SNP-set association tests, including variance component test, burden test and their adaptive test, and develop efficient numerical algorithms to quickly compute their analytical P-values. We implement the proposed methods in an open source R package. We conduct thorough simulation studies to verify the proposed methods rigorously control type I errors at the genome-wide significance level, and further demonstrate their utility via comprehensive analysis of GWAS summary data for multiple lipids traits and glycemic traits. We identified many novel loci that were not detected by the individual trait based GWAS analysis.

AVAILABILITY AND IMPLEMENTATION

We have implemented the proposed methods in an R package freely available at http://www.github.com/baolinwu/MSKAT.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

iFunMed: Integrative functional mediation analysis of GWAS and eQTL studies

Genome-wide association studies (GWAS) have successfully identified thousands of genetic variants contributing to disease and other phenotypes. However, significant obstacles hamper our ability to elucidate causal variants, identify genes affected by causal variants, and characterize the mechanisms by which genotypes influence phenotypes. The increasing availability of genome-wide functional annotation data is providing unique opportunities to incorporate prior information into the analysis of GWAS to better understand the impact of variants on disease etiology. Although there have been many advances in incorporating prior information into prioritization of trait-associated variants in GWAS, functional annotation data have played a secondary role in the joint analysis of GWAS and molecular (i.e., expression) quantitative trait loci (eQTL) data in assessing evidence for association. To address this, we develop a novel mediation framework, iFunMed, to integrate GWAS and eQTL data with the utilization of publicly available functional annotation data. iFunMed extends the scope of standard mediation analysis by incorporating information from multiple genetic variants at a time and leveraging variant-level summary statistics. Data-driven computational experiments convey how informative annotations improve single-nucleotide polymorphism (SNP) selection performance while emphasizing robustness of iFunMed to noninformative annotations. Application to Framingham Heart Study data indicates that iFunMed is able to boost detection of SNPs with mediation effects that can be attributed to regulatory mechanisms.

Systematic review regulatory principles of non-coding RNAs in cardiovascular diseases

Cardiovascular diseases (CVDs) continue to be a major cause of morbidity and mortality, and non-coding RNAs (ncRNAs) play critical roles in CVDs. With the recent emergence of high-throughput technologies, including small RNA sequencing, investigations of CVDs have been transformed from candidate-based studies into genome-wide undertakings, and a number of ncRNAs in CVDs were discovered in various studies. A comprehensive review of these ncRNAs would be highly valuable for researchers to get a complete picture of the ncRNAs in CVD. To address these knowledge gaps and clinical needs, in this review, we first discussed dysregulated ncRNAs and their critical roles in cardiovascular development and related diseases. Moreover, we reviewed >28 561 published papers and documented the ncRNA-CVD association benchmarking data sets to summarize the principles of ncRNA regulation in CVDs. This data set included 13 249 curated relationships between 9503 ncRNAs and 139 CVDs in 12 species. Based on this comprehensive resource, we summarized the regulatory principles of dysregulated ncRNAs in CVDs, including the complex associations between ncRNA and CVDs, tissue specificity and ncRNA synergistic regulation. The highlighted principles are that CVD microRNAs (miRNAs) are highly expressed in heart tissue and that they play central roles in miRNA-miRNA functional synergistic network. In addition, CVD-related miRNAs are close to one another in the functional network, indicating the modular characteristic features of CVD miRNAs. We believe that the regulatory principles summarized here will further contribute to our understanding of ncRNA function and dysregulation mechanisms in CVDs.

A generally conserved response to hypoxia in iPSC-derived cardiomyocytes from humans and chimpanzees

Despite anatomical similarities, there are differences in susceptibility to cardiovascular disease (CVD) between primates; humans are prone to myocardial ischemia, while chimpanzees are prone to myocardial fibrosis. Induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) allow for direct inter-species comparisons of the gene regulatory response to CVD-relevant perturbations such as oxygen deprivation, a consequence of ischemia. To gain insight into the evolution of disease susceptibility, we characterized gene expression levels in iPSC-CMs in humans and chimpanzees, before and after hypoxia and re-oxygenation. The transcriptional response to hypoxia is generally conserved across species, yet we were able to identify hundreds of species-specific regulatory responses including in genes previously associated with CVD. The 1,920 genes that respond to hypoxia in both species are enriched for loss-of-function intolerant genes; but are depleted for expression quantitative trait loci and cardiovascular-related genes. Our results indicate that response to hypoxic stress is highly conserved in humans and chimpanzees.

Searching for the Genetic Determinants of Peripheral Arterial Disease: A Review of the Literature and Future Directions

Peripheral arterial disease (PAD) is a significant but under-recognized disease that is poorly understood despite population-scale genetic studies. To address this morbid disease, clinicians need additional tools to identify, prevent, and treat patients at risk for PAD. Genetic studies of coronary artery disease have yielded promising results for clinical application, which have thus far been lacking in PAD. In this article, we review recent findings, discuss limitations, and propose future directions of genomic study and clinical application. However, despite many studies, we still lack definitive genetic markers for PAD. This can be attributed to the heterogeneity of PAD's pathogenesis and clinical manifestations, as well as inconsistencies in study methodologies, limitations of current genetic assessment techniques, incompletely comprehended molecular pathophysiology, and confounding generalized atherosclerotic risk factors. The goals of this review are to evaluate the limitations of our current genetic knowledge of PAD and to propose approaches to expedite the identification of valuable markers of PAD.

Genome, transcriptome and proteome: the rise of omics data and their integration in biomedical sciences

Advances in the technologies and informatics used to generate and process large biological data sets (omics data) are promoting a critical shift in the study of biomedical sciences. While genomics, transcriptomics and proteinomics, coupled with bioinformatics and biostatistics, are gaining momentum, they are still, for the most part, assessed individually with distinct approaches generating monothematic rather than integrated knowledge. As other areas of biomedical sciences, including metabolomics, epigenomics and pharmacogenomics, are moving towards the omics scale, we are witnessing the rise of inter-disciplinary data integration strategies to support a better understanding of biological systems and eventually the development of successful precision medicine. This review cuts across the boundaries between genomics, transcriptomics and proteomics, summarizing how omics data are generated, analysed and shared, and provides an overview of the current strengths and weaknesses of this global approach. This work intends to target students and researchers seeking knowledge outside of their field of expertise and fosters a leap from the reductionist to the global-integrative analytical approach in research.

Rare Copy Number Variants Identify Novel Genes in Sporadic Total Anomalous Pulmonary Vein Connection

Total anomalous pulmonary venous connection (TAPVC) is a rare congenital heart anomaly. Several genes have been associated TAPVC but the mechanisms remain elusive. To search novel CNVs and candidate genes, we screened a cohort of 78 TAPVC cases and 100 healthy controls for rare copy number variants (CNVs) using whole exome sequencing (WES). Then we identified pathogenic CNVs by statistical comparisons between case and control groups. After that, we identified altogether eight pathogenic CNVs of seven candidate genes (PCSK7, RRP7A, SERHL, TARP, TTN, SERHL2, and NBPF3). All these seven genes have not been described previously to be related to TAPVC. After network analysis of these candidate genes and 27 known pathogenic genes derived from the literature and publicly database, PCSK7 and TTN were the most important genes for TAPVC than other genes. Our study provides novel candidate genes potentially related to this rare congenital birth defect (CHD) which should be further fundamentally researched and discloses the possible molecular pathogenesis of TAPVC.

Multiple genotype-phenotype association study reveals intronic variant pair on SIDT2 associated with metabolic syndrome in a Korean population

Background

Metabolic syndrome is a risk factor for type 2 diabetes and cardiovascular disease. We identified common genetic variants that alter the risk for metabolic syndrome in the Korean population. To isolate these variants, we conducted a multiple-genotype and multiple-phenotype genome-wide association analysis using the family-based quasi-likelihood score (MFQLS) test. For this analysis, we used 7211 and 2838 genotyped study subjects for discovery and replication, respectively. We also performed a multiple-genotype and multiple-phenotype analysis of a gene-based single-nucleotide polymorphism (SNP) set.

Results

We found an association between metabolic syndrome and an intronic SNP pair, rs7107152 and rs1242229, in SIDT2 gene at 11q23.3. Both SNPs correlate with the expression of SIDT2 and TAGLN, whose products promote insulin secretion and lipid metabolism, respectively. This SNP pair showed statistical significance at the replication stage.

Conclusions

Our findings provide insight into an underlying mechanism that contributes to metabolic syndrome.

Electronic supplementary material

The online version of this article (10.1186/s40246-018-0180-4) contains supplementary material, which is available to authorized users.

GLANET: genomic loci annotation and enrichment tool

Motivation

Genomic studies identify genomic loci representing genetic variations, transcription factor (TF) occupancy, or histone modification through next generation sequencing (NGS) technologies. Interpreting these loci requires evaluating them with known genomic and epigenomic annotations.

Results

We present GLANET as a comprehensive annotation and enrichment analysis tool which implements a sampling-based enrichment test that accounts for GC content and/or mappability biases, jointly or separately. GLANET annotates and performs enrichment analysis on these loci with a rich library. We introduce and perform novel data-driven computational experiments for assessing the power and Type-I error of its enrichment procedure which show that GLANET has attained high statistical power and well-controlled Type-I error rate. As a key feature, users can easily extend its library with new gene sets and genomic intervals. Other key features include assessment of impact of single nucleotide variants (SNPs) on TF binding sites and regulation based pathway enrichment analysis.

Availability and implementation

GLANET can be run using its GUI or on command line. GLANET's source code is available at https://github.com/burcakotlu/GLANET . Tutorials are provided at https://glanet.readthedocs.org .

Contact

burcak@ceng.metu.edu.tr or oznur.tastan@cs.bilkent.edu.tr.

Supplementary information

Supplementary data are available at Bioinformatics online.

Network Analysis of Gene Expression

Studies have pointed out that the expression of genes are highly regulated, which result in a cascade of distinct patterns of coexpression forming a network. Identifying and understanding such patterns is crucial in deciphering molecular mechanisms that underlie the pathophysiology of diseases. With the advance of high throughput assay of messenger RNA (mRNA) and high performance computing, reconstructing such network from molecular data such as gene expression is now possible. This chapter discusses an overview of methods of constructing such networks, practical considerations, and an example.

Network-directed cis-mediator analysis of normal prostate tissue expression profiles reveals downstream regulatory associations of prostate cancer susceptibility loci

Large-scale genome-wide association studies have identified multiple single-nucleotide polymorphisms associated with risk of prostate cancer. Many of these genetic variants are presumed to be regulatory in nature; however, follow-up expression quantitative trait loci (eQTL) association studies have to-date been restricted largely to cis-acting associations due to study limitations. While trans-eQTL scans suffer from high testing dimensionality, recent evidence indicates most trans-eQTL associations are mediated by cis-regulated genes, such as transcription factors. Leveraging a data-driven gene co-expression network, we conducted a comprehensive cis-mediator analysis using RNA-Seq data from 471 normal prostate tissue samples to identify downstream regulatory associations of previously identified prostate cancer risk variants. We discovered multiple trans-eQTL associations that were significantly mediated by cis-regulated transcripts, four of which involved risk locus 17q12, proximal transcription factor HNF1B, and target trans-genes with known HNF response elements (MIA2, SRC, SEMA6A, KIF12). We additionally identified evidence of cis-acting down-regulation of MSMB via rs10993994 corresponding to reduced co-expression of NDRG1. The majority of these cis-mediator relationships demonstrated trans-eQTL replicability in 87 prostate tissue samples from the Gene-Tissue Expression Project. These findings provide further biological context to known risk loci and outline new hypotheses for investigation into the etiology of prostate cancer.

Transcriptomic Signature of Atherosclerosis in the Peripheral Blood: Fact or Fiction?

The notion that gene expression signatures in blood can serve as biomarkers of disease states is not new. In the case of atherosclerosis, and coronary artery disease in particular, whether changes in gene expression in peripheral blood mononuclear cells reflects disease processes occurring in the vessel wall remains controversial. When comparing 15 studies that identified 706 differentially expressed genes, only 23 genes were replicated in 2 to 3 studies, at most. This low level of replication may reflect sample sizes too small to overcome heterogeneity in the response to disease. Genetic differences affect how each person responds to disease and what genes are altered. Recent studies with larger cohorts (over 5000 individuals) that considered the effect of common genetic variants still could not claim disease signature genes as biomarkers suggesting that even larger case-control studies will be required to achieve the required statistical power. On the other hand, out of 7 studies that identified 58 microRNAs, 12 were concordant in 2 or more studies, suggesting that microRNAs may be less affected by genetic differences and more accurately reflect the disease process. Here, we review the current state of knowledge on expression profiling and its utility for predicting coronary artery disease status and mortality.

Contribution of independent and pleiotropic genetic effects in the metabolic syndrome in a hypertensive rat

Hypertension is a major risk factor for cardiovascular disease, Type 2 diabetes, and end organ failure, and is often found concomitant with disorders characteristic of the Metabolic Syndrome (MetS), including obesity, dyslipidemia, and insulin resistance. While the associated features often occur together, the pathway(s) or mechanism(s) linking hypertension in MetS are not well understood. Previous work determined that genetic variation on rat chromosome 17 (RNO17) contributes to several MetS-defining traits (including hypertension, obesity, and dyslipidemia) in the Lyon Hypertensive (LH) rat, a genetically determined MetS model. We hypothesized that at least some of the traits on RNO17 are controlled by a single gene with pleiotropic effects. To address this hypothesis, consomic and congenic strains were developed, whereby a defined fragment of RNO17 from the LH rat was substituted with the control Lyon Normotensive (LN) rat, and MetS phenotypes were measured in the resultant progeny. Compared to LH rats, LH-17^LN consomic rats have significantly reduced body weight, blood pressure, and lipid profiles. A congenic strain (LH-17^LNc), with a substituted fragment at the distal end of RNO17 (17q12.3; 74–97 Mb; rn4 assembly), showed differences from the LH rat in blood pressure and serum total cholesterol and triglycerides. Interestingly, there was no difference in body weight between the LH-17^LNc and the parental LH rat. These data indicate that blood pressure and serum lipids are regulated by a gene(s) in the distal congenic interval, and could be due to pleiotropy. The data also indicate that body weight is not determined by the same gene(s) at this locus. Interestingly, only two small haplotypes spanning a total of approximately 0.5 Mb differ between the LH and LN genomes in the congenic interval. Genes in these haplotypes are strong candidate genes for causing dyslipidemia in the LH rat. Overall, MetS, even in a simplified genetic model such as the LH-17^LN rat, is likely due to both independent and pleiotropic gene effects.

Early Head Start, Pediatric Dental Use, and Oral Health-Related Quality of Life

The objective of the study was to examine the mediating effect of child dental use on the effectiveness of North Carolina Early Head Start (EHS) in improving oral health-related quality of life (OHRQoL). In total, 479 parents of children enrolled in EHS and 699 parents of Medicaid-matched children were interviewed at baseline when children were approximately 10 mo old and 24 mo later. In this quasi-experimental study, mediation analysis was performed using the counterfactual framework analysis, which employed 2 logit models with random effects: 1) for the mediator as a function of the treatment and covariates and 2) for the outcome as a function of the treatment, mediator, and covariates. The covariates were baseline dental OHRQoL, dental need, survey language, and a propensity score. We used in-person computer-assisted, structured interviews to collect information on demographic characteristics and dental use and to administer the Early Childhood Oral Health Impact Scale, a measure of OHRQoL. Dental use had a mediation effect in the undesired direction with a 2-percentage point increase in the probability of any negative impact to OHRQoL (95% confidence interval [CI], 0.3%-3.9%). Even with higher dental use by EHS participants, the probability of any negative impact to OHRQoL was approximately 8 percentage points lower if an individual were moved from the non-EHS group to the EHS group (95% CI, -13.9% to -1.2%). EHS increases child dental use, which worsens family OHRQoL. However, EHS is associated with improved OHRQoL overall. Knowledge Transfer Statement: Study results can inform policy makers that comprehensive early childhood education programs improve oral health-related quality of life (OHRQoL) for disadvantaged families with young children in pathways outside of clinical dental care. This awareness and its promotion can lead to greater resource investments in early childhood education programs. Information about the negative impacts of dental use on OHRQoL should lead to the development and testing of strategies in dentistry and Early Head Start to improve dental care experiences.

Dynamic Role of trans Regulation of Gene Expression in Relation to Complex Traits

Identifying causal genetic variants and understanding their mechanisms of effect on traits remains a challenge in genome-wide association studies (GWASs). In particular, how genetic variants (i.e., trans-eQTLs) affect expression of remote genes (i.e., trans-eGenes) remains unknown. We hypothesized that some trans-eQTLs regulate expression of distant genes by altering the expression of nearby genes (cis-eGenes). Using published GWAS datasets with 39,165 single-nucleotide polymorphisms (SNPs) associated with 1,960 traits, we explored whole blood gene expression associations of trait-associated SNPs in 5,257 individuals from the Framingham Heart Study. We identified 2,350 trans-eQTLs (at p < 10^-7); more than 80% of them were found to have cis-associated eGenes. Mediation testing suggested that for 35% of trans-eQTL-trans-eGene pairs in different chromosomes and 90% pairs in the same chromosome, the disease-associated SNP may alter expression of the trans-eGene via cis-eGene expression. In addition, we identified 13 trans-eQTL hotspots, affecting from ten to hundreds of genes, suggesting the existence of master genetic regulators. Using causal inference testing, we searched causal variants across eight cardiometabolic traits (BMI, systolic and diastolic blood pressure, LDL cholesterol, HDL cholesterol, total cholesterol, triglycerides, and fasting blood glucose) and identified several cis-eGenes (ALDH2 for systolic and diastolic blood pressure, MCM6 and DARS for total cholesterol, and TRIB1 for triglycerides) that were causal mediators for the corresponding traits, as well as examples of trans-mediators (TAGAP for LDL cholesterol). The finding of extensive evidence of genome-wide mediation effects suggests a critical role of cryptic gene regulation underlying many disease traits.

Admixture mapping in two Mexican samples identifies significant associations of locus ancestry with triglyceride levels in the BUD13/ZNF259/APOA5 region and fine mapping points to rs964184 as the main driver of the association signal

We carried out an admixture mapping study of lipid traits in two samples from Mexico City. Native American locus ancestry was significantly associated with triglyceride levels in a broad region of chromosome 11 overlapping the BUD13, ZNF259 and APOA5 genes. In our fine-mapping analysis of this region using dense genome-wide data, rs964184 is the only marker included in the 99% credible set of SNPs, providing strong support for rs964184 as the causal variant within this region. The frequency of the allele associated with increased triglyceride concentrations (rs964184-G) is between 30–40% higher in Native American populations from Mexico than in European populations. The evidence currently available for this variant indicates that it may be exerting its effect through three potential mechanisms: 1) modification of enhancer activity, 2) regulation of the expression of several genes in cis and/or trans, or 3) modification of the methylation patterns of the promoter of the APOA5 gene.

Multidimensional Integrative Genomics Approaches to Dissecting Cardiovascular Disease

Elucidating the mechanisms of complex diseases such as cardiovascular disease (CVD) remains a significant challenge due to multidimensional alterations at molecular, cellular, tissue, and organ levels. To better understand CVD and offer insights into the underlying mechanisms and potential therapeutic strategies, data from multiple omics types (genomics, epigenomics, transcriptomics, metabolomics, proteomics, microbiomics) from both humans and model organisms have become available. However, individual omics data types capture only a fraction of the molecular mechanisms. To address this challenge, there have been numerous efforts to develop integrative genomics methods that can leverage multidimensional information from diverse data types to derive comprehensive molecular insights. In this review, we summarize recent methodological advances in multidimensional omics integration, exemplify their applications in cardiovascular research, and pinpoint challenges and future directions in this incipient field.

Biomarkers in cardiovascular disease: Statistical assessment and section on key novel heart failure biomarkers

Cardiovascular disease (CVD) is a leading cause of death worldwide and continues to increase in prevalence compared to previous decades, in part because of the aging of the world population. Atherosclerotic CVD starts at a very young age and progresses over time allowing sufficient time for screening and early detection of the condition. Advances in biomarker research and developments related to CVD over the past 30 years have led to more sensitive screening methods, a greater emphasis on its early detection and diagnosis, and improved treatments resulting in more favorable clinical outcomes in the community. However, the use of biomarkers for different purposes in CVD remains an important area of research that has been explored by scientists over the years and many new developments are still underway. Therefore, a detailed description of all CVD biomarkers that are currently been used or investigated for future use in the field of cardiovascular medicine is out of scope for any review article. In the present review, we do not intend to replicate the information from previous exhaustive review on biomarkers, but highlight key statistical and clinical issues with an emphasis on methods to evaluate the incremental yield of biomarkers, including their clinical utility, a prerequisite before any putative novel biomarker is utilized in clinical practice. In addition, we will summarize information regarding recent novel heart failure biomarkers in current practice, which are undergoing scrutiny before they can be available for clinical use, and their impact on clinical outcomes.

Disease variants alter transcription factor levels and methylation of their binding sites

Most disease-associated genetic variants are noncoding, making it challenging to design experiments to understand their functional consequences. Identification of expression quantitative trait loci (eQTLs) has been a powerful approach to infer the downstream effects of disease-associated variants, but most of these variants remain unexplained. The analysis of DNA methylation, a key component of the epigenome, offers highly complementary data on the regulatory potential of genomic regions. Here we show that disease-associated variants have widespread effects on DNA methylation in trans that likely reflect differential occupancy of trans binding sites by cis-regulated transcription factors. Using multiple omics data sets from 3,841 Dutch individuals, we identified 1,907 established trait-associated SNPs that affect the methylation levels of 10,141 different CpG sites in trans (false discovery rate (FDR) < 0.05). These included SNPs that affect both the expression of a nearby transcription factor (such as NFKB1, CTCF and NKX2-3) and methylation of its respective binding site across the genome. Trans methylation QTLs effectively expose the downstream effects of disease-associated variants.

Cohort Profile: The Framingham Heart Study (FHS): overview of milestones in cardiovascular epidemiology

The Framingham Heart Study (FHS) has conducted seminal research defining cardiovascular disease (CVD) risk factors and fundamentally shaping public health guidelines for CVD prevention over the past five decades. The success of the Original Cohort, initiated in 1948, paved the way for further epidemiological research in preventive cardiology. Due to the keen observations suggesting the role of shared familial factors in the development of CVD, in 1971 the FHS began enroling the second generation cohort, comprising the children of the Original Cohort and the spouses of the children. In 2002, the third generation cohort, comprising the grandchildren of the Original Cohort, was initiated to additionally explore genetic contributions to CVD in greater depth. Additionally, because of the predominance of White individuals of European descent in the three generations of FHS participants noted above, the Heart Study enrolled the OMNI1 and OMNI2 cohorts in 1994 and 2003, respectively, aimed to reflect the current greater racial and ethnic diversity of the town of Framingham. All FHS cohorts have been examined approximately every 2-4 years since the initiation of the study. At these periodic Heart Study examinations, we obtain a medical history and perform a cardiovascular-focused physical examination, 12-lead electrocardiography, blood and urine samples testing and other cardiovascular imaging studies reflecting subclinical disease burden.The FHS has continually evolved along the cutting edge of cardiovascular science and epidemiological research since its inception. Participant studies now additionally include study of cardiovascular imaging, serum and urine biomarkers, genetics/genomics, proteomics, metabolomics and social networks. Numerous ancillary studies have been established, expanding the phenotypes to encompass multiple organ systems including the lungs, brain, bone and fat depots, among others. Whereas the FHS was originally conceived and designed to study the epidemiology of cardiovascular disease, it has evolved over the years with staggering expanded breadth and depth that have far greater implications in the study of the epidemiology of a wide spectrum of human diseases. The FHS welcomes research collaborations using existing or new collection of data. Detailed information regarding the procedures for research application submission and review are available at [http://www.framinghamheartstudy.org/researchers/index.php].

Tensor decomposition for multiple-tissue gene expression experiments

Genome-wide association studies of gene expression traits and other cellular phenotypes have successfully identified links between genetic variation and biological processes. The majority of discoveries have uncovered cis-expression quantitative trait locus (eQTL) effects via mass univariate testing of SNPs against gene expression in single tissues. Here we present a Bayesian method for multiple-tissue experiments focusing on uncovering gene networks linked to genetic variation. Our method decomposes the 3D array (or tensor) of gene expression measurements into a set of latent components. We identify sparse gene networks that can then be tested for association against genetic variation across the genome. We apply our method to a data set of 845 individuals from the TwinsUK cohort with gene expression measured via RNA-seq analysis in adipose, lymphoblastoid cell lines (LCLs) and skin. We uncover several gene networks with a genetic basis and clear biological and statistical significance. Extensions of this approach will allow integration of different omics, environmental and phenotypic data sets.

Associations between the Framingham Risk Score and coronary plaque characteristics as assessed by three-vessel optical coherence tomography

OBJECTIVES

This study sought to explore the association between the Framingham Risk Score (FRS) and coronary plaque characteristics assessed by optical coherence tomography (OCT) imaging.

BACKGROUND

Clinical prediction models are useful for identifying high-risk patients. However, coronary events often occur in individuals estimated to be at low risk.

METHODS

A total of 254 patients with coronary artery disease who underwent three-vessel OCT were divided into tertiles according to FRS. Nonculprit plaque characteristics were compared among the three groups.

RESULTS

A total of 663 plaques were analyzed. FRS was significantly associated with calcification [37% (low FRS) vs. 46% (intermediate FRS) vs. 70% (high FRS); P<0.001] and neovascularization [39% (low FRS) vs. 41% (intermediate FRS) vs. 56% (high FRS); P<0.001], but not with lipid-rich plaques or thin-cap fibroatheroma (TCFA). On multivariate analysis, FRS was an independent predictor of the presence of both calcification and neovascularization. There were no deaths, two acute myocardial infarctions, and 15 nontarget lesion revascularizations at the 1-year follow-up. The event rate increased progressively across FRS tertiles [2.4% (low FRS) vs. 7.1% (intermediate FRS) vs. 8.6% (high FRS); P=0.186]. The c-statistic for FRS to predict future clinical events was 0.628 (95% confidence interval, 0.500-0.757). The addition of both calcification and TCFA to FRS provided incremental prognostic value [c-statistics: 0.761 (95% confidence interval, 0.631-0.890)].

CONCLUSION

The present study showed significant associations between FRS and the presence of coronary calcification and neovascularization in nonculprit plaques. The combination of FRS and OCT-detected calcifications and TCFA provides improved prognostic ability in identifying patients with known coronary artery disease who are at risk of recurrent events.

Network-Based Identification and Prioritization of Key Regulators of Coronary Artery Disease Loci

OBJECTIVE

Recent genome-wide association studies of coronary artery disease (CAD) have revealed 58 genome-wide significant and 148 suggestive genetic loci. However, the molecular mechanisms through which they contribute to CAD and the clinical implications of these findings remain largely unknown. We aim to retrieve gene subnetworks of the 206 CAD loci and identify and prioritize candidate regulators to better understand the biological mechanisms underlying the genetic associations.

APPROACH AND RESULTS

We devised a new integrative genomics approach that incorporated (1) candidate genes from the top CAD loci, (2) the complete genetic association results from the 1000 genomes-based CAD genome-wide association studies from the Coronary Artery Disease Genome Wide Replication and Meta-Analysis Plus the Coronary Artery Disease consortium, (3) tissue-specific gene regulatory networks that depict the potential relationship and interactions between genes, and (4) tissue-specific gene expression patterns between CAD patients and controls. The networks and top-ranked regulators according to these data-driven criteria were further queried against literature, experimental evidence, and drug information to evaluate their disease relevance and potential as drug targets. Our analysis uncovered several potential novel regulators of CAD such as LUM and STAT3, which possess properties suitable as drug targets. We also revealed molecular relations and potential mechanisms through which the top CAD loci operate. Furthermore, we found that multiple CAD-relevant biological processes such as extracellular matrix, inflammatory and immune pathways, complement and coagulation cascades, and lipid metabolism interact in the CAD networks.

CONCLUSIONS

Our data-driven integrative genomics framework unraveled tissue-specific relations among the candidate genes of the CAD genome-wide association studies loci and prioritized novel network regulatory genes orchestrating biological processes relevant to CAD.

Genetics paired with CT angiography in the setting of atherosclerosis

Coronary artery disease (CAD) continues to be the leading cause of morbidity and mortality globally. Although the etiological mechanisms for CAD have not been fully elucidated, however, most would agree that atherosclerotic plaques progressively narrow the coronary arteries are the earliest manifestations and the principal cause of CAD. The emergence of revolutionary imaging technologies such as cardiac CT angiography, noninvasive computed fractional flow reserve and intravascular ultrasound provided the possibility of detecting and monitoring phenotypes associated with subclinical atherosclerosis. Meanwhile, with the widespread use of high-throughput genotyping pipeline such as next-generation sequencing, combined with big data-driven solutions in bioinformatics, translating the emerging genetic technologies into clinical practice and, therefore, provide valuable insight into the CAD study. In this review, we briefly describe the latest noninvasive cardiac imaging techniques for atherosclerosis-related phenotypes' detection, mainly focusing on the coronary artery calcification, plaque burden and stenosis. Furthermore, we highlight the state-of-the-art genotyping techniques and its application in the field of CAD translational study. Finally, we discuss the clinical relevance of genetics paired with noninvasive imaging in the setting of coronary artery atherosclerosis.

Translational Bioinformatics: Past, Present, and Future

Though a relatively young discipline, translational bioinformatics (TBI) has become a key component of biomedical research in the era of precision medicine. Development of high-throughput technologies and electronic health records has caused a paradigm shift in both healthcare and biomedical research. Novel tools and methods are required to convert increasingly voluminous datasets into information and actionable knowledge. This review provides a definition and contextualization of the term TBI, describes the discipline’s brief history and past accomplishments, as well as current foci, and concludes with predictions of future directions in the field.

Multiple susceptibility loci at chromosome 11q23.3 are associated with plasma triglyceride in East Asians

Genetic studies of plasma TG levels have identified associations with multiple candidate loci on chromosome11q23.3, which harbors a number of genes, including BUD13, ZNF259, and APOA5-A4-C3-A1. This study aimed to examine whether these multiple candidate genes on the 11q23.3 regions exert independent effects on TG levels or whether their effects are confounded by linkage disequilibrium (LD). We performed a genome-wide association study and consequent fine-mapping analyses on TG levels in two Korean population-based cohorts: the Korea Association Resource study (n = 8,223) and the Healthy Twin study (n = 1,735). A total of 301 loci reached genome-wide significance level in pooled analysis, including 10 SNPs with weak LD (r(2) < 0.06) clustered on 11q23.3: ApoA5 (rs651821, rs2075291); ZNF259 (rs964184, rs603446); BUD13 (rs11216126); Apoa4 (rs7396851); SIK3 (rs12292858); PCSK7 (rs199890178); PAFAH1B2 (rs12420127), and SIDT2 (rs2269399). When the inter-dependence between alleles was examined using conditional models, five loci on BUD13, ZNF259, and ApoA5 showed possible independent associations. A haplotype analysis using five SNPs revealed both hyper- and hypotriglyceridemic haplotypes, which are relatively common in Koreans (haplotype frequency 0.08-0.22). Our findings suggest the presence of multiple functional loci on 11q23.3, which might exert their effects on plasma TG level independently or through complex interactions between functional loci.

GWAS as a Driver of Gene Discovery in Cardiometabolic Diseases

Cardiometabolic diseases represent a common complex disorder with a strong genetic component. Currently, genome-wide association studies (GWAS) have yielded some 755 single-nucleotide polymorphisms (SNPs) encompassing 366 independent loci that may help to decipher the molecular basis of cardiometabolic diseases. Going from a disease SNP to the underlying disease mechanisms is a huge challenge because the associated SNPs rarely disrupt protein function. Many disease SNPs are located in noncoding regions, and therefore attention is now focused on linking genetic SNP variation to effects on gene expression levels. By integrating genetic information with large-scale gene expression data, and with data from epigenetic roadmaps revealing gene regulatory regions, we expect to be able to identify candidate disease genes and the regulatory potential of disease SNPs.