Cardiovascular genomics

Optimizing RNA extraction methods for high-throughput transcriptome sequencing of formalin-fixed paraffin-embedded cardiac tissue specimens

Archived FFPE cardiac tissue specimens are valuable for molecular studies aimed at identifying biomarkers linked to mortality in cardiovascular disease. Establishing a reliable and reproducible RNA extraction method is critical for generating high-quality transcriptome sequences for molecular assays. Here, the efficiency of four RNA extraction methods: Qiagen AllPrep DNA/RNA method (Method QP); Qiagen AllPrep DNA/RNA method with protocol modification on the ethanol wash step after deparaffinization (Method QE); CELLDATA RNA extraction (Method BP) and CELLDATA RNA extraction with protocol modifications on the lysis step (Method BL) was compared on 23 matching FFPE cardiac tissue specimens (n = 92).In comparing RNA quality metrics across FFPE RNA extract, nucleic acids extracted deploying Method QE and QP produced the highest RNA yield. However, Method QE outperformed Method QP as more extract from Method QE had DV 200 values above 30%. Both method BL and BP produced similar range of RNA purity and yield but more extract from Method BL had DV 200 values above 30% compared to Method BP. When accessing distribution value, Method BL outperformed Methods BP, QE, and QP as more extracts from Method BL had DV 200 values above 30% compared to other methods (PDV200<0.001; Kruskal-Wallis). Method QE outperformed other methods in terms of RNA yield. RNA extracts from Method QE, characterized by high RNA yield, achieved sequencing results comparable to those from Method BL, characterized by high DV200 values. Our findings reveal that optimizing protocols can yield higher-quality RNA, facilitating the exploration of more disease conditions with high-resolution transcriptome profiling.

Multimodal AI/ML for discovering novel biomarkers and predicting disease using multi-omics profiles of patients with cardiovascular diseases

Cardiovascular diseases (CVDs) are complex, multifactorial conditions that require personalized assessment and treatment. Advancements in multi-omics technologies, namely RNA sequencing and whole-genome sequencing, have provided translational researchers with a comprehensive view of the human genome. The efficient synthesis and analysis of this data through integrated approach that characterizes genetic variants alongside expression patterns linked to emerging phenotypes, can reveal novel biomarkers and enable the segmentation of patient populations based on personalized risk factors. In this study, we present a cutting-edge methodology rooted in the integration of traditional bioinformatics, classical statistics, and multimodal machine learning techniques. Our approach has the potential to uncover the intricate mechanisms underlying CVD, enabling patient-specific risk and response profiling. We sourced transcriptomic expression data and single nucleotide polymorphisms (SNPs) from both CVD patients and healthy controls. By integrating these multi-omics datasets with clinical demographic information, we generated patient-specific profiles. Utilizing a robust feature selection approach, we identified a signature of 27 transcriptomic features and SNPs that are effective predictors of CVD. Differential expression analysis, combined with minimum redundancy maximum relevance feature selection, highlighted biomarkers that explain the disease phenotype. This approach prioritizes both biological relevance and efficiency in machine learning. We employed Combination Annotation Dependent Depletion scores and allele frequencies to identify variants with pathogenic characteristics in CVD patients. Classification models trained on this signature demonstrated high-accuracy predictions for CVD. The best performing of these models was an XGBoost classifier optimized via Bayesian hyperparameter tuning, which was able to correctly classify all patients in our test dataset. Using SHapley Additive exPlanations, we created risk assessments for patients, offering further contextualization of these predictions in a clinical setting. Across the cohort, RPL36AP37 and HBA1 were scored as the most important biomarkers for predicting CVDs. A comprehensive literature review revealed that a substantial portion of the diagnostic biomarkers identified have previously been associated with CVD. The framework we propose in this study is unbiased and generalizable to other diseases and disorders.

Polygenic Risk Score for Cardiovascular Diseases in Artificial Intelligence Paradigm: A Review

Cardiovascular disease (CVD) related mortality and morbidity heavily strain society. The relationship between external risk factors and our genetics have not been well established. It is widely acknowledged that environmental influence and individual behaviours play a significant role in CVD vulnerability, leading to the development of polygenic risk scores (PRS). We employed the PRISMA search method to locate pertinent research and literature to extensively review artificial intelligence (AI)-based PRS models for CVD risk prediction. Furthermore, we analyzed and compared conventional vs. AI-based solutions for PRS. We summarized the recent advances in our understanding of the use of AI-based PRS for risk prediction of CVD. Our study proposes three hypotheses: i) Multiple genetic variations and risk factors can be incorporated into AI-based PRS to improve the accuracy of CVD risk predicting. ii) AI-based PRS for CVD circumvents the drawbacks of conventional PRS calculators by incorporating a larger variety of genetic and non-genetic components, allowing for more precise and individualised risk estimations. iii) Using AI approaches, it is possible to significantly reduce the dimensionality of huge genomic datasets, resulting in more accurate and effective disease risk prediction models. Our study highlighted that the AI-PRS model outperformed traditional PRS calculators in predicting CVD risk. Furthermore, using AI-based methods to calculate PRS may increase the precision of risk predictions for CVD and have significant ramifications for individualized prevention and treatment plans.

Functional mutation, splice, distribution, and divergence analysis of impactful genes associated with heart failure and other cardiovascular diseases

Cardiovascular disease (CVD) is caused by a multitude of complex and largely heritable conditions. Identifying key genes and understanding their susceptibility to CVD in the human genome can assist in early diagnosis and personalized treatment of the relevant patients. Heart failure (HF) is among those CVD phenotypes that has a high rate of mortality. In this study, we investigated genes primarily associated with HF and other CVDs. Achieving the goals of this study, we built a cohort of thirty-five consented patients, and sequenced their serum-based samples. We have generated and processed whole genome sequence (WGS) data, and performed functional mutation, splice, variant distribution, and divergence analysis to understand the relationships between each mutation type and its impact. Our variant and prevalence analysis found FLNA, CST3, LGALS3, and HBA1 linked to many enrichment pathways. Functional mutation analysis uncovered ACE, MME, LGALS3, NR3C2, PIK3C2A, CALD1, TEK, and TRPV1 to be notable and potentially significant genes. We discovered intron, 5' Flank, 3' UTR, and 3' Flank mutations to be the most common among HF and other CVD genes. Missense mutations were less common among HF and other CVD genes but had more of a functional impact. We reported HBA1, FADD, NPPC, ADRB2, ADBR1, MYH6, and PLN to be consequential based on our divergence analysis.

RNA-seq driven expression and enrichment analysis to investigate CVD genes with associated phenotypes among high-risk heart failure patients

Background

Heart failure (HF) is one of the most common complications of cardiovascular diseases (CVDs) and among the leading causes of death in the US. Many other CVDs can lead to increased mortality as well. Investigating the genetic epidemiology and susceptibility to CVDs is a central focus of cardiology and biomedical life sciences. Several studies have explored expression of key CVD genes specially in HF, yet new targets and biomarkers for early diagnosis are still missing to support personalized treatment. Lack of gender-specific cardiac biomarker thresholds in men and women may be the reason for CVD underdiagnosis in women, and potentially increased morbidity and mortality as a result, or conversely, an overdiagnosis in men. In this context, it is important to analyze the expression and enrichment of genes with associated phenotypes and disease-causing variants among high-risk CVD populations.

Methods

We performed RNA sequencing focusing on key CVD genes with a great number of genetic associations to HF. Peripheral blood samples were collected from a broad age range of adult male and female CVD patients. These patients were clinically diagnosed with CVDs and CMS/HCC HF, as well as including cardiomyopathy, hypertension, obesity, diabetes, asthma, high cholesterol, hernia, chronic kidney, joint pain, dizziness and giddiness, osteopenia of multiple sites, chest pain, osteoarthritis, and other diseases.

Results

We report RNA-seq driven case–control study to analyze patterns of expression in genes and differentiating the pathways, which differ between healthy and diseased patients. Our in-depth gene expression and enrichment analysis of RNA-seq data from patients with mostly HF and other CVDs on differentially expressed genes and CVD annotated genes revealed 4,885 differentially expressed genes (DEGs) and regulation of 41 genes known for HF and 23 genes related to other CVDs, with 15 DEGs as significantly expressed including four genes already known (FLNA, CST3, LGALS3, and HBA1) for HF and CVDs with the enrichment of many pathways. Furthermore, gender and ethnic group specific analysis showed shared and unique genes between the genders, and among different races. Broadening the scope of the results in clinical settings, we have linked the CVD genes with ICD codes.

Conclusions

Many pathways were found to be enriched, and gender-specific analysis showed shared and unique genes between the genders. Additional testing of these genes may lead to the development of new clinical tools to improve diagnosis and prognosis of CVD patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40246-021-00367-8.

Low HDL concentration in rs2048327-G carriers can predispose men to develop coronary heart disease: Tehran Cardiometabolic genetic study (TCGS)

Recent genome-wide association studies (GWAS) highlighted the importance of genetic variations on SLC22A3 and MIA3 genes in developing coronary heart disease (CHD) among different ethnicities. However, the influence of these variations is not recognized within the Iranian population. Hence, in the present study, we aim to investigate two key single nucleotide polymorphisms (SNPs) on CHD incidence in this population. For this purpose, from Tehran Cardiometabolic Genetic Study (TCGS), 453 individuals with CHD were selected as a case and 453 individuals as a control that matched their age and gender. After quality control of two selected SNPs, rs2048327 (SLC22A3) and rs17465637 (MIA3), we used genotyps resulted from chip-typing technology and conducted the logistic regression analysis adjusted for non-genetic risk factors to detect the possible association of these SNPs with the CHD development. Our findings demonstrated the rs2048327-G and rs17465637-C can significantly increase the risk of CHD development about two times in only males and females, respectively. Interestingly, in the male carriers of the risk allele (G) of rs2048327, the low high-density lipoprotein (HDL) level can significantly predispose them to develop coronary heart disease in the future. According to our results, paying more attention to gender and genetic markers can help more efficient coronary heart disease screening and diagnosis.

Genomics and Precision Medicine: Implications for Critical Care

A new paradigm for disease diagnosis and treatment is emerging that will bring about changes in health care delivery in and out of the hospital setting. Over the past several decades, genomic medicine has been one of the fastest growing fields in acute and chronic health care. This quick growth has created a lag in genomics knowledge and preparation among nurses and health care providers. Genomic medicine may lead to more precise evaluation, diagnosis, and management of selected acute care conditions. This article reviews the current state of genetic and genomics science and looks at the expanding field of genomic medicine's integration into precision medicine. The aim of this article is to raise awareness and spark further inquiry to the remarkable field of genomics and precision medicine.

Impact of genetic polymorphisms on platelet function and response to anti platelet drugs

Cardiovascular genomic consists in the identification of polymorphic genes responsible for the susceptibility to cardiovascular disease including coronary artery disease (CAD). Genes involved in platelet activation and aggregation play a key role in the predisposition to CAD. A considerable inter-variability of platelet response to agonists and to drugs exists and in particular the hyper-reactivity phenotype seems to be heritable. Besides glycoproteins and receptors expressed on platelets surface whose mutations significantly impact on platelet function, moreover researchers in the last decades have paid great attention to the genes involved in the response to anti-platelet drugs, considering their pivotal role in the treatment and outcomes of CAD patients especially those undergoing PCI. With the outbreak of advanced techniques developed to analyse human genetic footprints, researchers nowadays have shifted from genetic linkage analysis and a candidate gene approach toward genome-wide association (GWAS) studies and the analysis of miRNA-mRNA expression profiles.

How American Nurses Association Code of Ethics informs genetic/genomic nursing

Members of the Ethics and Public Policy Committee of the International Society of Nurses in Genetics prepared this article to assist nurses in interpreting the American Nurses Association (2015) Code of Ethics for Nurses with Interpretive Statements (Code) within the context of genetics/genomics. The Code explicates the nursing profession's norms and responsibilities in managing ethical issues. The nearly ubiquitous application of genetic/genomic technologies in healthcare poses unique ethical challenges for nursing. Therefore, authors conducted literature searches that drew from various professional resources to elucidate implications of the code in genetic/genomic nursing practice, education, research, and public policy. We contend that the revised Code coupled with the application of genomic technologies to healthcare creates moral obligations for nurses to continually refresh their knowledge and capacities to translate genetic/genomic research into evidence-based practice, assure the ethical conduct of scientific inquiry, and continually develop or revise national/international guidelines that protect the rights of individuals and populations within the context of genetics/genomics. Thus, nurses have an ethical responsibility to remain knowledgeable about advances in genetics/genomics and incorporate emergent evidence into their work.

Nursing genetics and genomics: The International Society of Nurses in Genetics (ISONG) survey

BACKGROUND

The International Society of Nursing in Genetics (ISONG) fosters scientific and professional development in the discovery, interpretation, and application of genomic information in nursing research, education, and clinical practice.

OBJECTIVES

Assess genomic-related activities of ISONG members in research, education and practice, and competencies to serve as global leaders in genomics.

DESIGN

Cross-sectional survey (21-items) assessing genomic-related training, knowledge, and practice.

SETTINGS

An email invitation included a link to the anonymous online survey.

PARTICIPANTS

All ISONG members (n = 350 globally) were invited to partake.

METHODS

Descriptive statistics and Wilcoxon Rank Sum Test for between-group comparisons.

RESULTS

Respondents (n = 231, 66%), were mostly Caucasian, female, with a master's degree or higher. Approximately 70% wanted to incorporate genomics in research, teaching, and practice. More than half reported high genomic competency, and over 95% reported that genomics is relevant the next 5 years.

CONCLUSIONS

Findings provide a foundation for developing additional educational programs for an international nursing workforce in genomics.

Association of the HNF1A polymorphisms and serum lipid traits, the risk of coronary artery disease and ischemic stroke

BACKGROUND

Hepatocyte nuclear factor-1α gene (HNF1A) single nucleotide polymorphisms (SNPs) have been associated with serum lipid traits in several previous genome-wide association studies. However, little is known about such associations in the Chinese populations. The present study aimed to determine the association of the HNF1A rs1169288, rs2259820, rs2464196 and rs2650000 SNPs and serum lipid traits, the risk of coronary artery disease (CAD) and ischemic stroke (IS).

METHODS

The genotypes of the four SNPs in 562 CAD and 521 IS patients, as well as 594 healthy controls, were detected using the Snapshot technology.

RESULTS

The genotype and allele distribution of the four SNPs was not different between controls and CAD or IS patients (p > 0.05 for all). rs1169288, rs2259820 and rs2464196 SNPs were significantly associated with serum lipid levels in both controls and CAD patients (p < 0.004-0.009). rs2259820 and rs2464196 SNPs were significantly associated with a lower risk of CAD [odds ratio (OR) = 0.64, 95% confidence interval (CI) = 0.44-0.91, p = 0.015 and OR =0.62, 95% CI = 0.43-0.89, p = 0.010, respectively]. Significant linkage disequilibrium was noted among the four SNPs (r²  > 0.5, D' > 0.8). The haplotype of rs1169288A-rs2259820C-rs2464196G-rs2650000A was associated with an increased risk of CAD (OR =1.95, 95% CI: 1.13-3.37, p = 0.015). Interactions of SNP-SNP (rs1169288-rs2464196-rs2650000) and haplotype-environment on the risk of CAD (A-C-G-A-smoking) or IS (A-C-G-A-sex and A-T-A-C-alcohol consumption) were also observed among these SNPs.

CONCLUSIONS

These findings suggest that the HNF1A polymorphisms may be the genetic risk factors for CAD and IS.

Improving -Omics-Based Research and Precision Health in Minority Populations: Recommendations for Nurse Scientists

PURPOSE

The purpose of this article is to provide an overview of the role of nurse scientists in -omics-based research and to promote discussion around the conduct of -omics-based nursing research in minority communities. Nurses are advocates, educators, practitioners, scientists, and researchers, and are crucial to the design and successful implementation of -omics studies, particularly including minority communities. The contribution of nursing in this area of research is crucial to reducing health disparities.

METHODS

In this article, challenges in the conduct of -omics-based research in minority communities are discussed, and recommendations for improving diversity among nurse scientists, study participants, and utilization of training and continuing education programs in -omics are provided.

FINDINGS AND CONCLUSIONS

Many opportunities exist for nurses to increase their knowledge in -omics and to continue to build the ranks of nurse scientists as leaders in -omics-based research. In order to work successfully with communities of color, nurse scientists must advocate for participation in the Precision Medicine Initiative, improve representation of nurse faculty of color, and increase utilization of training programs in -omics and lead such initiatives.

CLINICAL RELEVANCE

All nursing care has the potential to be affected by the era of -omics and precision health. By taking an inclusive approach to diversity in nursing and -omics research, nurses will be well placed to be leaders in reducing health disparities through research, practice, and education.

Meta-analysis of the association of the CYP2J2 G-50T polymorphism with coronary artery disease

The association of the CYP2J2 G-50T polymorphism with coronary artery disease has been explored, but the results remain controversial. Thus, a meta-analysis was conducted to provide a comprehensive estimate of this association. We selected ten articles encompassing 12 independent case-control studies with 7063 cases and 10,453 controls for this meta-analysis. Overall, we found significant associations between the CYP2J2 G-50T polymorphism and coronary artery disease risk in three genetic models (allele model: odds ratio (OR) = 1.19, 95% confidence interval (CI) = 1.05–1.34; homozygote model: OR = 2.25, 95% CI = 1.27–4.01; recessive model: OR = 2.17, 95% CI = 1.22–3.86). In these three genetic models, a significant association was observed in Caucasians but not in Asians when the data were stratified by ethnicity. However, no significant associations were found between the CYP2J2 polymorphism G-50T and coronary artery disease risk in heterozygote model and dominant model. In conclusion, our meta-analysis suggested that the CYP2J2 G-50T polymorphism was associated with coronary artery disease risk in the allele, homozygote and recessive models in Caucasians.

The Intergenerational Impact of Genetic and Psychological Factors on Blood Pressure Study (InterGEN): Design and Methods for Recruitment and Psychological Measures

BACKGROUND

Although studies show that genomics and environmental stressors affect blood pressure, few studies have examined their combined effects, especially in African Americans.

OBJECTIVE

We present the recruitment methods and psychological measures of the Intergenerational Impact of Genetic and Psychological Factors on Blood Pressure (InterGEN) study, which seeks to investigate the individual and combined effects of genetic (G) and environmental (E) (psychological) stressors on blood pressure in African American mother-child dyads. Genetic methods are presented elsewhere, but here we present the recruitment methods, psychological measures, and analysis plan for these environmental stressors.

METHODS

This longitudinal study will enroll 250 mother-child dyads (N = 500). Study participation is restricted to women who (a) are ≤21 years of age, (b) self-identify as African American or Black, (c) speak English, (d) do not have an identified mental illness or cognitive impairment, and (e) have a biological child between 3 and 5 years old. The primary environmental stressors assessed are parenting stress, perceived racism and discrimination, and maternal mental health. Covariates include age, cigarette smoking (for mothers), and gender (for children). The study outcome variables are systolic and diastolic blood pressure.

ANALYSIS

The main analytic outcome is genetic-by-environment interaction analyses (G × E); however, main effects (G) and (E) will be individually assessed first. Genetic (G) and interaction analyses (G × E) are described in a companion paper and will include laboratory procedures. Statistical modeling of environmental stressors on blood pressure will be done using descriptive statistics and generalized estimating equation models.

IMPLICATIONS

The methodology presented here includes the study rationale, community engagement and recruitment protocol, psychological variable measurement, and analysis plan for assessing the association of environmental stressors and blood pressure. This study may provide the foundation for other studies and development of interventions to reduce the risk for hypertension and to propose targeted health promotion programs for this high-risk population.

A perspective for sequencing familial hypercholesterolaemia in African Americans

African Americans suffer disproportionately from poor cardiovascular health outcomes despite similar proportions of African Americans and Americans of European ancestry experiencing elevated cholesterol levels. Some of the variation in cardiovascular outcomes is due to confounding effects of other risk factors, such as hypertension and genetic influence. However, genetic variants found to contribute to variation in serum cholesterol levels in populations of European ancestry are less likely to replicate in populations of African ancestry. To date, there has been limited follow-up on variant discrepancies or on identifying variants that exist in populations of African ancestry. African and African-American populations have the highest levels of genetic heterogeneity, which is a factor that must be considered when evaluating genetic variants in the burgeoning era of personalised medicine. Many of the large published studies identifying genetic variants associated with disease risk have evaluated populations of mostly European ancestry and estimated risk in other populations based on these findings. The purpose of this paper is to provide a perspective, using familial hypercholesterolaemia as an exemplar, that studies evaluating genetic variation focused within minority populations are necessary to identify factors that contribute to disparities in health outcomes and realise the full utility of personalised medicine.

Association of variants in CELSR2-PSRC1-SORT1 with risk of serum lipid traits, coronary artery disease and ischemic stroke

Recent genome-wide association studies (GWAS) have identified genetic variants associated with coronary artery disease (CAD), ischemic stroke (IS) and serum lipid traits in different ethnic groups. Some loci were found to affect the risk of CAD and IS. However, there were no data in the southern Chinese populations. Our study was to assess the association of CELSR2-PSRC1-SORT1 rs599839, rs464218 and rs6698443 SNPs and serum lipid levels and the risk of CAD and IS. The genotypes of 3 SNPs were detected in 561 CAD and 527 IS patients, and in 590 healthy controls. The genotypic and allelic frequencies of the rs599839 SNP were different between the controls and IS patients (P < 0.05). The minor G alleles of rs599839 and rs464218 SNPs were associated with higher high-density lipoprotein cholesterol concentrations in CAD and IS patients (P < 0.05); respectively. No association was found between the SNPs of rs599839, rs464218 and rs6698843 at the CELSR2-PSRC1-SORT1 and the risk of CAD or IS. These results will be replicated in the other Chinese populations.

Cardiac genetic testing: a single-center pilot study of a Dominican population

The impact of undergoing genetic testing in a Dominican population is not well understood. The objective of this investigation was to evaluate the psychological well-being and perceived cardiac risk among Dominicans who underwent genetic testing. Participants completed a qualitative interview and the Short Form-36 (SF-36) questionnaire after cardiac genetic testing. There were 31 subjects evaluated (mean age 42 ± 11 years). Participants revealed three common themes: (a) fear of dying prematurely, (b) guilt of possibly passing on a mutation to their children, and (c) fear of having an implantable cardioverter defibrillator (ICD) shock. Physical components of the SF-36 were within normal limits (46.2 ± 6.6) but elevated for mental components (59.9 ± 5.3). The quality of life and specific themes results determined in this investigation warrant further research in the Dominican population.

An overview of epigenetics in nursing

Epigenetic changes to the genome are biochemical alterations to the DNA that do not change an individual's genome but do change and influence gene expression. The nursing profession is qualified to conduct and integrate epigenetic-focused nursing research into practice. This article discusses current epigenetic nursing research, provides an overview of how epigenetic research relates to nursing practice, makes recommendations, and provides epigenetic online resources for nursing research. An overview of major epigenetic studies in nursing (specific to childbirth studies, preeclampsia, metabolic syndrome, immunotherapy cancer, and pain) is provided, with recommendations on next steps.

Evaluating the role of Cardiac Genetics Nurses in inherited cardiac conditions services using a Maturity Matrix

Background:

Cardiovascular disease is a leading cause of death worldwide and genetic risk factors play a role in nearly all such cases. In the UK, health service capacity to meet either current or future estimated needs of people affected by inherited cardiac conditions (ICCs) is inadequate. In 2008 the British Heart Foundation funded nine three-year Cardiac Genetics Nurse (CGN) posts across England and Wales to enhance ICC services. The CGNs were experienced cardiac nurses who had additional training in genetics and acted to coordinate cardiac and genetics service activities.

Aim:

To create and apply a framework against which progress in ICC service improvement could be measured over time following the CGN appointments.

Methods:

A performance grid (Maturity Matrix, MM) articulating standards in five domains against stages of ICC service development was created by stakeholders through a consensus approach. The MM was used to guide staged self-assessments by the CGNs between 2009 and 2011. A six-point scale was used to locate progress from ‘emerging’ to ‘established’, represented graphically by spider diagrams.

Results:

Progress in all domains was significant for new, emerging and established services. It was most notable for effective utilisation of care pathways and efficient running of clinics. Commitment to family-centred care was evident.

Conclusion:

The ICC-MM provided a comprehensive framework for assessing ICC services and has merit in providing guidance on development. CGNs can help integrate care across specialisms, facilitating the development of effective and sustainable ICC services at new, developing, and more established ICC service locations.