Polygenic Risk Score for Coronary Heart Disease Modifies the Elevated Risk by Cigarette Smoking for Disease Incidence

Person-level contributions of bipolar polygenic risk score to the prediction of new-onset bipolar disorder in at-risk offspring

BACKGROUND

Previous work indicates that polygenic risk scores (PRS) for bipolar disorder (BD) are elevated in adults and youth with BD, but whether BD-PRS can inform person-level diagnostic prediction is unknown. Here, we test whether BD-PRS improves performance of a previously published risk calculator (RC) for BD.

METHODS

156 parents with BD-I/II and their offspring ages 6-18 were recruited and evaluated with standardized diagnostic assessments every two years for >12 years. DNA was extracted from saliva samples, genotyping performed, and BD-PRS calculated based on a 2021 meta-analysis. Using a bootstrapped and cross-validated penalized Cox regression, we assessed whether BD-PRS (alone and interacting with clinical variables) improved RC performance.

RESULTS

Of 227 offspring, 38 developed BD during follow-up. The penalized regression selected BD-PRS and interactions between BD-PRS and parental age at mood disorder onset (AAO), depression, and anxiety. The resulting RC discriminated offspring who developed BD (vs. those that did not) with good accuracy (AUC = 0.81); removing BD-PRS and its interaction terms was associated with a significant decrement to the AUC (decrement = 0.07, p = 0.039). Further exploration of selected interaction terms indicated that all were significant (p-values<0.02), indicating that BD-PRS has a larger effect on the outcome in offspring with depression and anxiety, whose affected parent had a younger AAO.

CONCLUSIONS

The addition of BD-PRS to clinical/demographic predictors in the RC significantly improved its accuracy. BD-PRS predicted BD on the person-level, particularly in offspring of parents with earlier AAO who already had symptoms of anxiety and depression at intake.

Interplay between lifestyle factors and polygenic risk for incident coronary heart disease in a large multiethnic cohort

Introduction

The objective of this study was to examine the interplay of polygenic risk and individual lifestyle factors (and a composite score of lifestyle) as antecedents of CHD in a large multiethnic cohort.

Methods

We used Genetic Epidemiology Resource in Adult Health and Aging (GERA) cohort participants free of CHD at baseline (n = 60,568; 67 % female; 18 % non-European). The individual and joint associations of smoking, Mediterranean diet pattern, level of physical activity and polygenic risk with incident CHD were assessed using Cox regression adjusting for genetic ancestry and non-mediating risk factors. Hazard ratios (HRs) and number needed to treat (NNT) were estimated according to these lifestyle factors and polygenic risk categories. Strengths included large sample size, long-follow-up, ethnic diversity, a clinically-validated polygenic risk score (PRS), and rich phenotype information.

Results

After 14 years of follow-up, there were 3159 incident CHD events. We observed no statistically significant interactions between individual lifestyle factors and polygenic risk (all p > 0.23). For individuals with a high genetic risk, moving from the worse lifestyle combination (no favorable lifestyle factors) to the best lifestyle combination (all three) is associated with 52 % lower rate of CHD. The NNT was highest in the low polygenic risk group (34), lowest in the high polygenic risk group [19] and in-between (Jin et al., 2011) [24] in the intermediate polygenic risk group.

Conclusions

Lifestyle and polygenic risk together influence the risk of incident CHD. Our results support consideration of polygenic risk in lifestyle interventions because those with high polygenic risk are likely to derive the most benefit.

The Association Between CYP2R1 rs10741657 Polymorphisms and Bone Variables, Vitamin D, and Calcium in Iranian Children and Adolescents: A Cross-Sectional Study

Osteoporosis is a common disorder with a strong genetic component. Bone mineral density (BMD), vitamin D, and calcium levels declining are a main contributor of osteoporosis and fragility fractures. This cross-sectional study designed to explore the possible link between CYP2R1 rs10741657 polymorphism and BMD of the total hip, lumbar spine and femoral neck, vitamin D, and calcium in Iranian children and adolescents. 247 children and adolescents (127 girls and 120 boys) between 9 and 18 years old from Kawar (an urban area located 50 km east of Shiraz, the capital city of the Fars province in the south of Iran) were randomly selected based on age-stratified systematic sampling and recruited for genetic analysis. The polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) method was used for genotyping CYP2R1 rs10741657. Anthropometric, biochemical, and bone mineral density (BMD) parameters were also measured. The results specified that in the dominant [P < 0.0001, - 2.943 (- 4.357-1.529)] and over-dominant [P < 0.0001, 2.789 (1.369-4.209)] models, vitamin D concentration significantly differed between genotypes. The highest vitamin D levels were displayed for participants carrying the rs10741657 AG genotype (16.47 ng/ml). In regard to calcium, in a dominant model [P = 0.012, 0.194 (0.043-0.345)] and over-dominant model [P = 0.008, 0.206 (- 0.357-0.055), there was a significant association. AG genotype displayed the highest (9.96 mg/dl) and GG genotype the lowest (9.75 mg/dl) calcium values. This study reported the association of CYP2R1 rs10741657 polymorphisms with calcium and vitamin D levels in Iranian children and adolescents.

Modification of coronary artery disease clinical risk factors by coronary artery disease polygenic risk score

BACKGROUND

The extent to which the relationships between clinical risk factors and coronary artery disease (CAD) are altered by CAD polygenic risk score (PRS) is not well understood. Here, we determine whether the interactions between clinical risk factors and CAD PRS further explain risk for incident CAD.

METHODS

Participants were of European ancestry from the UK Biobank without prevalent CAD. An externally trained genome-wide CAD PRS was generated and then applied. Clinical risk factors were ascertained at baseline. Cox proportional hazards models were fitted to examine the incident CAD effects of CAD PRS, risk factors, and their interactions. Next, the PRS and risk factors were stratified to investigate the attributable risk of clinical risk factors.

FINDINGS

A total of 357,144 individuals of European ancestry without prevalent CAD were included. During a median of 11.1 years of follow-up (interquartile range 10.4-14.1 years), CAD PRS was associated with 1.35-fold (95% confidence interval [CI] 1.332-1.368) risk per SD for incident CAD. The prognostic relevance of the following risk factors was relatively diminished for those with high CAD PRS on a continuous scale: type 2 diabetes (hazard ratio [HR]interaction 0.91, 95% CIinteraction 0.88-0.94), increased body mass index (HRinteraction 0.97, 95% CIinteraction 0.96-0.98), and increased C-reactive protein (HRinteraction 0.98, 95% CIinteraction 0.96-0.99). However, a high CAD PRS yielded joint risk increases with low-density lipoprotein cholesterol (HRinteraction 1.05, 95% CIinteraction 1.04-1.06) and total cholesterol (HRinteraction 1.05, 95% CIinteraction 1.03-1.06).

CONCLUSION

The CAD PRS is associated with incident CAD, and its application improves the prognostic relevance of several clinical risk factors.

FUNDING

P.N. (R01HL127564, R01HL151152, and U01HG011719) is supported by the National Institutes of Health.

Evidence relating cigarettes, cigars and pipes to cardiovascular disease and stroke: Meta-analysis of recent data from three regions

BACKGROUND

More recent data are required relating to disease risk for use of various smoked products and of other products containing nicotine. Earlier we published meta-analyses of recent results for chronic obstructive pulmonary disease and lung cancer on the relative risk (RR) of current compared to never product use for cigarettes, cigars and pipes based on evidence from North America, Europe and Japan. We now report corresponding up-to-date evidence for acute myocardial infarction (AMI), ischaemic heart disease (IHD) and stroke.

AIM

To estimate, using recent data, AMI, IHD and stroke RRs by region for current smoking of cigarettes, cigars and pipes.

METHODS

Publications in English from 2015 to 2020 were considered that, based on epidemiological studies in the three regions, estimated the current smoking RR of AMI, IHD or stroke for one or more of the three products. The studies should involve at least 100 cases of stroke or cardiovascular disease (CVD), not be restricted to populations with specific medical conditions, and should be of cohort or nested case-control study design or randomized controlled trials. A literature search was conducted on MEDLINE, examining titles and abstracts initially, and then full texts. Additional papers were sought from reference lists of selected papers, reviews and meta-analyses. For each study identified, we entered the most recent available data on current smoking of each product, as well as the characteristics of the study and the RR estimates. Combined RR estimates were derived using random-effects meta-analysis for stroke and, in the case of CVD, separately for IHD and AMI. For cigarette smoking, where far more data were available, heterogeneity was studied by a wide range of factors. For cigar and pipe smoking, a more limited heterogeneity analysis was carried out. A more limited assessment of variation in risk by daily number of cigarettes smoked was also conducted. Results were compared with those from previous meta-analyses published since 2000.

RESULTS

Current cigarette smoking: Ten studies gave a random-effects RR for AMI of 2.72 [95% confidence interval (CI): 2.40-3.08], derived from 13 estimates between 1.47 and 4.72. Twenty-three studies gave an IHD RR of 2.01 (95%CI: 1.84-2.21), using 28 estimates between 0.81 and 4.30. Thirty-one studies gave a stroke RR of 1.62 (95%CI: 1.48-1.77), using 37 estimates from 0.66 to 2.91. Though heterogeneous, only two of the overall 78 RRs were below 1.0, 71 significantly (P < 0.05) exceeding 1.0. The heterogeneity was only partly explicable by the factors studied. Estimates were generally higher for females and for later-starting studies. They were significantly higher for North America than Europe for AMI, but not the other diseases. For stroke, the only endpoint with multiple Japanese studies, RRs were lower there than for Western studies. Adjustment for multiple factors tended to increase RRs. Our RR estimates and the variations by sex and region are consistent with earlier meta-analyses. RRs generally increased with amount smoked. Current cigar and pipe smoking: No AMI data were available. One North American study reported reduced IHD risk for non-exclusive cigar or pipe smoking, but considered few cases. Two North American studies found no increased stroke risk with exclusive cigar smoking, one reporting reduced risk for exclusive pipe smoking (RR 0.24, 95%CI: 0.06-0.91). The cigar results agree with an earlier review showing no clear risk increase for IHD or stroke.

CONCLUSION

Current cigarette smoking increases risk of AMI, IHD and stroke, RRs being 2.72, 2.01 and 1.62. The stroke risk is lower in Japan, no increase was seen for cigars/pipes.

Polygenic Risk, Rare Variants, and Family History: Independent and Additive Effects on Coronary Heart Disease

Background

Genetic factors are not included in prediction models for coronary heart disease (CHD).

Objectives

The authors assessed the predictive utility of a polygenic risk score (PRS) for CHD (defined as myocardial infarction, coronary revascularization, or cardiovascular death) and whether the risks due to monogenic familial hypercholesterolemia (FH) and family history (FamHx) are independent of and additive to the PRS.

Methods

In UK-biobank participants, PRSCHD was calculated using metaGRS, and 10-year risk for incident CHD was estimated using the pooled cohort equations (PCE). The area under the curve (AUC) of the receiver operator curve and net reclassification improvement (NRI) were assessed. FH was defined as the presence of a pathogenic or likely pathogenic variant in LDLR, APOB, or PCSK9. FamHx was defined as a diagnosis of CHD in first-degree relatives. Independent and additive effects of PRSCHD, FH, and FamHx were evaluated in stratified analyses.

Results

In 323,373 participants with genotype data, the addition of PRSCHD to PCE increased the AUC from 0.759 (95% CI: 0.755-0.763) to 0.773 (95% CI: 0.769-0.777). The AUC and NRIEvent for PRSCHD were higher before the age of 55 years. Of 199,997 participants with exome sequence data, 10,000 had a PRSCHD ≥95th percentile (PRSP95), 673 had FH, and 46,163 had FamHx. The CHD risk associated with PRSP95 was independent of FH and FamHx. The risks associated with combinations of PRSCHD, FH, and FamHx were additive and comprehensive estimates could be obtained by multiplying the risk from each genetic factor.

Conclusions

Incorporating PRSCHD into the PCE improves risk prediction for CHD, especially at younger ages. The associations of PRSCHD, FH, and FamHx with CHD were independent and additive.

Prediction of Preeclampsia from Clinical and Genetic Risk Factors in Early and Late Pregnancy Using Machine Learning and Polygenic Risk Scores

Background

Preeclampsia, a pregnancy-specific condition associated with new-onset hypertension after 20 weeks gestation, is a leading cause of maternal and neonatal morbidity and mortality. Predictive tools to understand which individuals are most at risk are needed.

Methods

We identified a cohort of N=1,125 pregnant individuals who delivered between 05/2015-05/2022 at Mass General Brigham hospitals with available electronic health record (EHR) data and linked genetic data. Using clinical EHR data and systolic blood pressure polygenic risk scores (SBP PRS) derived from a large genome-wide association study, we developed machine learning (xgboost) and linear regression models to predict preeclampsia risk.

Results

Pregnant individuals with an SBP PRS in the top quartile had higher blood pressures throughout pregnancy compared to patients within the lowest quartile SBP PRS. In the first trimester, the most predictive model was xgboost, with an area under the curve (AUC) of 0.73. Adding the SBP PRS to the models improved the performance only of the linear regression model from AUC 0.70 to 0.71; the predictive power of other models remained unchanged. In late pregnancy, with data obtained up to the delivery admission, the best performing model was xgboost using clinical variables, which achieved an AUC of 0.91.

Conclusions

Integrating clinical and genetic factors into predictive models can inform personalized preeclampsia risk and achieve higher predictive power than the current practice. In the future, personalized tools can be implemented in clinical practice to identify high-risk patients for preventative therapies and timely intervention to improve adverse maternal and neonatal outcomes.

Elucidating effects of environmental exposure using human-induced pluripotent stem cell disease modeling

Induced pluripotent stem cells (iPSCs) are a powerful modeling system for medical discovery and translational research. To date, most studies have focused on the potential for iPSCs for regenerative medicine, drug discovery, and disease modeling. However, iPSCs are also a powerful modeling system to investigate the effects of environmental exposure on the cardiovascular system. With the emergence of e-cigarettes, air pollution, marijuana use, opioids, and microplastics as novel cardiovascular risk factors, iPSCs have the potential for elucidating the effects of these toxins on the body using conventional two-dimensional (2D) arrays and more advanced tissue engineering approaches with organoid and other three-dimensional (3D) models. The effects of these environmental factors may be enhanced by genetic polymorphisms that make some individuals more susceptible to the effects of toxins. iPSC disease modeling may reveal important gene-environment interactions that exacerbate cardiovascular disease and predispose some individuals to adverse outcomes. Thus, iPSCs and gene-editing techniques could play a pivotal role in elucidating the mechanisms of gene-environment interactions and understanding individual variability in susceptibility to environmental effects.

A robust and adaptive framework for interaction testing in quantitative traits between multiple genetic loci and exposure variables

The identification and understanding of gene-environment interactions can provide insights into the pathways and mechanisms underlying complex diseases. However, testing for gene-environment interaction remains a challenge since a.) statistical power is often limited and b.) modeling of environmental effects is nontrivial and such model misspecifications can lead to false positive interaction findings. To address the lack of statistical power, recent methods aim to identify interactions on an aggregated level using, for example, polygenic risk scores. While this strategy can increase the power to detect interactions, identifying contributing genes and pathways is difficult based on these relatively global results. Here, we propose RITSS (Robust Interaction Testing using Sample Splitting), a gene-environment interaction testing framework for quantitative traits that is based on sample splitting and robust test statistics. RITSS can incorporate sets of genetic variants and/or multiple environmental factors. Based on the user's choice of statistical/machine learning approaches, a screening step selects and combines potential interactions into scores with improved interpretability. In the testing step, the application of robust statistics minimizes the susceptibility to main effect misspecifications. Using extensive simulation studies, we demonstrate that RITSS controls the type 1 error rate in a wide range of scenarios, and we show how the screening strategy influences statistical power. In an application to lung function phenotypes and human height in the UK Biobank, RITSS identified highly significant interactions based on subcomponents of genetic risk scores. While the contributing single variant interaction signals are weak, our results indicate interaction patterns that result in strong aggregated effects, providing potential insights into underlying gene-environment interaction mechanisms.

Prognostic Modelling Studies of Coronary Heart Disease—A Systematic Review of Conventional and Genetic Risk Factor Studies

This study aims to provide an overview of multivariable prognostic modelling studies developed for coronary heart disease (CHD) in the general population and to explore the optimal prognostic model by comparing the models’ performance. A systematic review was performed using Embase, PubMed, Cochrane, Web of Science, and Scopus databases until 30 November 2019. In this work, only prognostic studies describing conventional risk factors alone or a combination of conventional and genomic risk factors, being developmental and/or validation prognostic studies of a multivariable model, were included. A total of 4021 records were screened by titles and abstracts, and 72 articles were eligible. All the relevant studies were checked by comparing the discrimination, reclassification, and calibration measures. Most of the models were developed in the United States and Canada and targeted the general population. The models included a set of similar predictors, such as age, sex, smoking, cholesterol level, blood pressure, BMI, and diabetes mellitus. In this study, many articles were identified and screened for consistency and reliability using CHARM and GRIPS statements. However, the usefulness of most prognostic models was not demonstrated; only a limited number of these models supported clinical evidence. Unfortunately, substantial heterogeneity was recognized in the definition and outcome of CHD events. The inclusion of genetic risk scores in addition to conventional risk factors might help in predicting the incidence of CHDs; however, the generalizability of the existing prognostic models remains open. Validation studies for the existing developmental models are needed to ensure generalizability, improve the research quality, and increase the transparency of the study.

Associations of Genome-Wide Polygenic Risk Score and Risk Factors With Hypertension in a Japanese Population

Background:

Although many polygenic risk scores (PRS) for cardiovascular traits have been developed in European populations, it is an urgent task to construct a PRS and to evaluate its ability in non-European populations. We developed a genome-wide PRS for blood pressure in a Japanese population and examined the associations between this PRS and hypertension prevalence.

Methods:

We performed a cross-sectional study in 11 252 Japanese individuals who participated in the J-MICC (Japan Multi-Institutional Collaborative Cohort) study. Using publicly available GWAS summary statistics from Biobank Japan, we developed the PRS in the target data (n=7876). With >30 000 single nucleotide polymorphisms, we evaluated PRS performance in the test data (n=3376). Hypertension was defined as systolic blood pressure of 130 mm Hg or more, or diastolic blood pressure of 85 mm Hg or more, or taking an antihypertensive drug.

Results:

Compared with the middle PRS quintile, the prevalence of hypertension at the top PRS quintile was higher independently from traditional risk factors (odds ratio, 1.73 [95% CI, 1.32–2.27]). The difference of mean systolic blood pressure and diastolic blood pressure between the middle and the top PRS quintile was 4.55 (95% CI, 2.26–6.85) and 2.32 (95% CI, 0.86–3.78) mm Hg, respectively. Subgroups reflecting combinations of Japanese PRS and modifiable lifestyles and factors (smoking, alcohol intake, sedentary time, and obesity) were associated with the prevalence of hypertension. A European-derived PRS was not associated with hypertension in our participants.

Conclusions:

A PRS for blood pressure was significantly associated with hypertension and BP traits in a general Japanese population. Our findings also highlighted the importance of a combination of PRS and risk factors for identifying high-risk subgroups.

Interaction between genetics and smoking in determining risk of coronary artery diseases

Coronary artery disease (CAD) is a preeminent cause of death, and smoking is a strong risk factor for CAD. Genetic factors contribute to the development of CAD, but the interplay between genetic predisposition and smoking history in CAD remains unclear. Using data from the UK Biobank, we constructed several genetic risk scores (GRSs) based on known CAD loci and assessed their interactions with smoking for the development of incident CAD in 307,147 participants of European ancestry who were free of CAD. We fitted Cox proportional hazard models and assessed gene-smoking interaction on both multiplicative and additive scales. Overall, we found no multiplicative interactions, but observed a synergistic additive interaction of GRS with both smoking status and pack-years of smoking, finding that the absolute CAD risk due to smoking was higher for those with high genetic risk. Trait-based sub-GRSs suggested smoking status and smoking intensity measured by pack-years might confer gene-smoking interaction effects with different intermediate risk factors for CAD. Our study results suggest that genetics could modify the effects of smoking on CAD and highlight the value of addressing gene-lifestyle interactions on both additive and multiplicative scales.

Appraisal of Gene-Environment Interactions in GWAS for Evidence-Based Precision Nutrition Implementation

PURPOSE OF REVIEW

This review aims to analyse the currently reported gene-environment (G × E) interactions in genome-wide association studies (GWAS), involving environmental factors such as lifestyle and dietary habits related to metabolic syndrome phenotypes. For this purpose, the present manuscript reviews the available GWAS registered on the GWAS Catalog reporting the interaction between environmental factors and metabolic syndrome traits.

RECENT FINDINGS

Advances in omics-related analytical and computational approaches in recent years have led to a better understanding of the biological processes underlying these G × E interactions. A total of 42 GWAS were analysed, reporting over 300 loci interacting with environmental factors. Alcohol consumption, sleep time, smoking habit and physical activity were the most studied environmental factors with significant G × E interactions. The implementation of more comprehensive GWAS will provide a better understanding of the metabolic processes that determine individual responses to environmental exposures and their association with the development of chronic diseases such as obesity and the metabolic syndrome. This will facilitate the development of precision approaches for better prevention, management and treatment of these diseases.

Interaction of Cigarette Smoking and Polygenic Risk Score on Reduced Lung Function

IMPORTANCE

The risk of airflow limitation and chronic obstructive pulmonary disease (COPD) is influenced by combinations of cigarette smoking and genetic susceptibility, yet it remains unclear whether gene-by-smoking interactions are associated with quantitative measures of lung function.

OBJECTIVE

To assess the interaction of cigarette smoking and polygenic risk score in association with reduced lung function.

DESIGN, SETTING, AND PARTICIPANTS

This UK Biobank cohort study included UK citizens of European ancestry aged 40 to 69 years with genetic and spirometry data passing quality control metrics. Data was analyzed from July 2020 to March 2021.

EXPOSURES

PRS of combined forced expiratory volume in 1 second (FEV1) and percent of forced vital capacity exhaled in the first second (FEV1/FVC), self-reported pack-years of smoking, ever- vs never-smoking status, and current- vs former- or never-smoking status.

MAIN OUTCOMES AND MEASURES

FEV1/FVC was the primary outcome. Models were used to test for interactions with models, including the main effects of PRS, different smoking variables, and their cross-product terms. The association between pack-years of smoking and FEV1/FVC were compared for those in the highest vs lowest decile of estimated genetic risk for low lung function.

RESULTS

We included 319 730 individuals, of whom 24 915 (8%) had moderate-to-severe COPD cases, and 44.4% were men. Participants had a mean (SD) age 56.5 of (8.02) years. The PRS and pack-years were significantly associated with lower FEV1/FVC (PRS: β, -0.03; 95% CI, -0.031 to -0.03; pack-years: β, -0.0064; 95% CI, -0.0064 to -0.0063) and the interaction term (β, -0.0028; 95% CI, -0.0029 to -0.0026). A stepwise increment in estimated effect sizes for these interaction terms was observed per 10 pack-years of smoking exposure. The interaction of PRS with 11 to 20, 31 to 40, and more than 50 pack-years categories were β (interaction) -0.0038 (95% CI, -0.0046 to -0.0031); -0.013 (95% CI, -0.014 to -0.012); and -0.017 (95% CI, -0.019 to -0.016), respectively. There was evidence of significant interaction between PRS with ever- or never- smoking status (β, interaction; -0.0064; 95% CI, -0.0068 to -0.0060) and current or not-current smoking (β, interaction; -0.0091; 95% CI, -0.0097 to -0.0084). For any given level of pack-years of smoking exposure, FEV1/FVC was significantly lower for individuals in the tenth decile (ie, highest risk) than the first decile (ie, lowest risk) of genetic risk. For every 20 pack-years of smoking, those in the tenth decile compared with the first decile of genetic risk showed nearly a 2-fold reduction in FEV1/FVC.

CONCLUSIONS AND RELEVANCE

COPD is characterized by diminished lung function, and our analyses suggest there is substantial interaction between genome-wide PRS and smoking exposures. While smoking was associated with decreased lung function across all genetic risk categories, the associations were strongest in individuals with higher estimated genetic risk.

Gene-Environment Interactions for Cardiovascular Disease

PURPOSE OF REVIEW

We provide an overview of recent findings with respect to gene-environment (GxE) interactions for cardiovascular disease (CVD) risk and discuss future opportunities for advancing the field.

RECENT FINDINGS

Over the last several years, GxE interactions for CVD have mostly been identified for smoking and coronary artery disease (CAD) or related risk factors. By comparison, there is more limited evidence for GxE interactions between CVD outcomes and other exposures, such as physical activity, air pollution, diet, and sex. The establishment of large consortia and population-based cohorts, in combination with new computational tools and mouse genetics platforms, can potentially overcome some of the limitations that have hindered human GxE interaction studies and reveal additional association signals for CVD-related traits. The identification of novel GxE interactions is likely to provide a better understanding of the pathogenesis and genetic liability of CVD, with significant implications for healthy lifestyles and therapeutic strategies.

Clinical Conditions and Their Impact on Utility of Genetic Scores for Prediction of Acute Coronary Syndrome

BACKGROUND

Acute coronary syndrome (ACS) is a clinically significant presentation of coronary heart disease. Genetic information has been proposed to improve prediction beyond well-established clinical risk factors. While polygenic scores (PS) can capture an individual's genetic risk for ACS, its prediction performance may vary in the context of diverse correlated clinical conditions. Here, we aimed to test whether clinical conditions impact the association between PS and ACS.

METHODS

We explored the association between 405 clinical conditions diagnosed before baseline and 9080 incident cases of ACS in 387 832 individuals from the UK Biobank. Results were replicated in 6430 incident cases of ACS in 177 876 individuals from FinnGen.

RESULTS

We identified 80 conventional (eg, stable angina pectoris and type 2 diabetes) and unconventional (eg, diaphragmatic hernia and inguinal hernia) associations with ACS. The association between PS and ACS was consistent in individuals with and without most clinical conditions. However, a diagnosis of stable angina pectoris yielded a differential association between PS and ACS. PS was associated with a significantly reduced (interaction P=2.87×10^-8) risk for ACS in individuals with stable angina pectoris (hazard ratio, 1.163 [95% CI, 1.082-1.251]) compared with individuals without stable angina pectoris (hazard ratio, 1.531 [95% CI, 1.497-1.565]). These findings were replicated in FinnGen (interaction P=1.38×10^-6).

CONCLUSIONS

In summary, while most clinical conditions did not impact utility of PS for prediction of ACS, we found that PS was substantially less predictive of ACS in individuals with prevalent stable coronary heart disease. PS may be more appropriate for prediction of ACS in asymptomatic individuals than symptomatic individuals with clinical suspicion for coronary heart disease.

Monogenic and Polygenic Models of Coronary Artery Disease

PURPOSE OF THE REVIEW

Coronary artery disease (CAD) is a common disease globally attributable to the interplay of complex genetic and lifestyle factors. Here, we review how genomic sequencing advances have broadened the fundamental understanding of the monogenic and polygenic contributions to CAD and how these insights can be utilized, in part by creating polygenic risk estimates, for improved disease risk stratification at the individual patient level.

RECENT FINDINGS

Initial studies linking premature CAD with rare familial cases of elevated blood lipids highlighted high-risk monogenic contributions, predominantly presenting as familial hypercholesterolemia (FH). More commonly CAD genetic risk is a function of multiple, higher frequency variants each imparting lower magnitude of risk, which can be combined to form polygenic risk scores (PRS) conveying significant risk to individuals at the extremes. However, gaps remain in clinical validation of PRSs, most notably in non-European populations. With improved and more broadly utilized genomic sequencing technologies, the genetic underpinnings of coronary artery disease are being unraveled. As a result, polygenic risk estimation is poised to become a widely used and powerful tool in the clinical setting. While the use of PRSs to augment current clinical risk stratification for optimization of cardiovascular disease risk by lifestyle change or therapeutic targeting is promising, we await adequately powered, prospective studies, demonstrating the clinical utility of polygenic risk estimation in practice.

Effect of Cytochrome P450 Family 2 Subfamily R Member 1 Variants on the Predisposition of Coronary Heart Disease in the Chinese Han Population

Propose: Cytochrome P450 family 2 subfamily R member 1 (CYP2R1) variations can affect the activity of 25-hydroxylase, resulting in the deficiency of 25(OH)D, which leads to an increased incidence and mortality of coronary heart disease (CHD). The purpose is to assess the influence of CYP2R1 variants on CHD risk among the Chinese Han population.

Methods: A total of 508 CHD patients and 510 healthy controls were enrolled. The MassARRAY platform completed genotyping of CYP2R1 variants. Odds ratios (ORs) with 95% confidence intervals (CI) were calculated using logistic regression analysis.

Results: Rs6486205 (OR = 1.25, 95% CI: 1.05–1.50, p = 0.014), rs10741657 (OR = 1.29, 95% CI: 1.08–1.54, p = 0.005), and rs2060793 (OR = 1.27, 95% CI: 1.06–1.51, p = 0.009) were associated with the increased susceptibility to CHD in the whole subjects. Interestingly, the relationships between these variants and CHD risk were observed in the subjects with age >60 years, males or non-smoker. Additionally, the haplotypes A_rs10741657A_rs2060793 and G_rs10741657G_rs2060793 had the higher risk of CHD, and the combination (rs6486205 and rs10741657) was the best multi-locus model.

Conclusion: Our study suggested the contribution of CYP2R1 polymorphisms to the increased CHD predisposition in the Chinese Han population. Furthermore, the risk association was related to confounding factors for CHD, including age, sex, and smoking. These findings might help to strengthen the understanding of the CYP2R1 gene in the occurrence of CHD.

Interactions Between Enhanced Polygenic Risk Scores and Lifestyle for Cardiovascular Disease, Diabetes, and Lipid Levels

BACKGROUND

Both lifestyle and genetic factors confer risk for cardiovascular diseases, type 2 diabetes, and dyslipidemia. However, the interactions between these 2 groups of risk factors were not comprehensively understood due to previous poor estimation of genetic risk. Here we set out to develop enhanced polygenic risk scores (PRS) and systematically investigate multiplicative and additive interactions between PRS and lifestyle for coronary artery disease, atrial fibrillation, type 2 diabetes, total cholesterol, triglyceride, and LDL-cholesterol.

METHODS

Our study included 276 096 unrelated White British participants from the UK Biobank. We investigated several PRS methods (P+T, LDpred, PRS continuous shrinkage, and AnnoPred) and showed that AnnoPred achieved consistently improved prediction accuracy for all 6 diseases/traits. With enhanced PRS and combined lifestyle status categorized by smoking, body mass index, physical activity, and diet, we investigated both multiplicative and additive interactions between PRS and lifestyle using regression models.

RESULTS

We observed that healthy lifestyle reduced disease incidence by similar multiplicative magnitude across different PRS groups. The absolute risk reduction from lifestyle adherence was, however, significantly greater in individuals with higher PRS. Specifically, for type 2 diabetes, the absolute risk reduction from lifestyle adherence was 12.4% (95% CI, 10.0%-14.9%) in the top 1% PRS versus 2.8% (95% CI, 2.3%-3.3%) in the bottom PRS decile, leading to a ratio of >4.4. We also observed a significant interaction effect between PRS and lifestyle on triglyceride level.

CONCLUSIONS

By leveraging functional annotations, AnnoPred outperforms state-of-the-art methods on quantifying genetic risk through PRS. Our analyses based on enhanced PRS suggest that individuals with high genetic risk may derive similar relative but greater absolute benefit from lifestyle adherence.

Nicotine induces cardiac toxicity through blocking mitophagic clearance in young adult rat

Since an outbreak of vaping-related deaths in the US has been reported as a public health crisis, the cardiovascular safety of nicotine nowadays receives increasing attention due to use of tobacco cigarette alternatives, such as electronic cigarettes. However, whether and how nicotine contributes to cardiac detrimental effects are in great controversy, especially less understood in young adult population. We report that chronic nicotine exposure, a major component of Electronic cigarettes, resulted in directly inhibited cardiomyocytes viability, increased cardiac fibrosis, and markedly suppressed cardiac function compared with sham. Gene array combined with bioinformatics analysis identified cardiac apoptosis and mitophagy were the key signals responsible for nicotine induced cardiac detrimental effect. Mechanistically, nicotine exposure markedly increased cleaved Caspase 3 and cleaved Caspase 9 indicating the involvement of intrinsic apoptotic pathway (mitochondrial cell death pathway). Meanwhile, nicotine-induced ROS outbreak promoted lysomal alkalization, furthermore blocked mitophagic degradation, thereby disrupted mitophagic flux promoted mitochondrial cell death cascade. Taken together, these findings indicate that nicotine confers cardiotoxicity via ROS-induced mitophagic flux blockage and provide the first demonstration of a causative link between nicotine and cardiac toxicity in young adult rat which may suggest nicotine induces cardiomyocytes impairment leading to cardiotoxicity in young adult population.

Whole-Genome Approach Discovers Novel Genetic and Nongenetic Variance Components Modulated by Lifestyle for Cardiovascular Health

Background

Both genetic and nongenetic factors can predispose individuals to cardiovascular risk. Finding ways to alter these predispositions is important for cardiovascular disease prevention.

Methods and Results

We used a novel whole‐genome approach to estimate the genetic and nongenetic effects on—and hence their predispositions to—cardiovascular risk and determined whether they vary with respect to lifestyle factors such as physical activity, smoking, alcohol consumption, and dietary intake. We performed analyses on the ARIC (Atherosclerosis Risk in Communities) Study (N=6896–7180) and validated findings using the UKBB (UK Biobank, N=14 076–34 538). Lifestyle modulation was evident for many cardiovascular traits such as body mass index and resting heart rate. For example, alcohol consumption modulated both genetic and nongenetic effects on body mass index, whereas smoking modulated nongenetic effects on heart rate, pulse pressure, and white blood cell count. We also stratified individuals according to estimated genetic and nongenetic effects that are modulated by lifestyle factors and showed distinct phenotype–lifestyle relationships across the stratified groups. Finally, we showed that neglecting lifestyle modulations of cardiovascular traits would on average reduce single nucleotide polymorphism heritability estimates of these traits by a small yet significant amount, primarily owing to the overestimation of residual variance.

Conclusions

Lifestyle changes are relevant to cardiovascular disease prevention. Individual differences in the genetic and nongenetic effects that are modulated by lifestyle factors, as shown by the stratified group analyses, implies a need for personalized lifestyle interventions. In addition, single nucleotide polymorphism–based heritability of cardiovascular traits without accounting for lifestyle modulations could be underestimated.

2018 George Lyman Duff Memorial Lecture: Genetics and Genomics of Coronary Artery Disease: A Decade of Progress

Recent studies have led to a broader understanding of the genetic architecture of coronary artery disease and demonstrate that it largely derives from the cumulative effect of multiple common risk alleles individually of small effect size rather than rare variants with large effects on coronary artery disease risk. The tools applied include genome-wide association studies encompassing over 200 000 individuals complemented by bioinformatic approaches including imputation from whole-genome data sets, expression quantitative trait loci analyses, and interrogation of ENCODE (Encyclopedia of DNA Elements), Roadmap Epigenetic Project, and other data sets. Over 160 genome-wide significant loci associated with coronary artery disease risk have been identified using the genome-wide association studies approach, 90% of which are situated in intergenic regions. Here, I will describe, in part, our research over the last decade performed in collaboration with a series of bright trainees and an extensive number of groups and individuals around the world as it applies to our understanding of the genetic basis of this complex disease. These studies include computational approaches to better understand missing heritability and identify causal pathways, experimental approaches, and progress in understanding at the molecular level the function of the multiple risk loci identified and potential applications of these genomic data in clinical medicine and drug discovery.

Towards clinical utility of polygenic risk scores

Prediction of disease risk is an essential part of preventative medicine, often guiding clinical management. Risk prediction typically includes risk factors such as age, sex, family history of disease and lifestyle (e.g. smoking status); however, in recent years, there has been increasing interest to include genomic information into risk models. Polygenic risk scores (PRS) aggregate the effects of many genetic variants across the human genome into a single score and have recently been shown to have predictive value for multiple common diseases. In this review, we summarize the potential use cases for seven common diseases (breast cancer, prostate cancer, coronary artery disease, obesity, type 1 diabetes, type 2 diabetes and Alzheimer’s disease) where PRS has or could have clinical utility. PRS analysis for these diseases frequently revolved around (i) risk prediction performance of a PRS alone and in combination with other non-genetic risk factors, (ii) estimation of lifetime risk trajectories, (iii) the independent information of PRS and family history of disease or monogenic mutations and (iv) estimation of the value of adding a PRS to specific clinical risk prediction scenarios. We summarize open questions regarding PRS usability, ancestry bias and transferability, emphasizing the need for the next wave of studies to focus on the implementation and health-economic value of PRS testing. In conclusion, it is becoming clear that PRS have value in disease risk prediction and there are multiple areas where this may have clinical utility.

Progress in Polygenic Composite Scores in Alzheimer's and Other Complex Diseases

Advances in high-throughput genotyping and next-generation sequencing (NGS) coupled with larger sample sizes brings the realization of precision medicine closer than ever. Polygenic approaches incorporating the aggregate influence of multiple genetic variants can contribute to a better understanding of the genetic architecture of many complex diseases and facilitate patient stratification. This review addresses polygenic concepts, methodological developments, hypotheses, and key issues in study design. Polygenic risk scores (PRSs) have been applied to many complex diseases and here we focus on Alzheimer's disease (AD) as a primary exemplar. This review was designed to serve as a starting point for investigators wishing to use PRSs in their research and those interested in enhancing clinical study designs through enrichment strategies.