Polygenic risk scores in coronary artery disease

A perspective on genetic and polygenic risk scores-advances and limitations and overview of associated tools

Polygenetic Risk Scores are used to evaluate an individual's vulnerability to developing specific diseases or conditions based on their genetic composition, by taking into account numerous genetic variations. This article provides an overview of the concept of Polygenic Risk Scores (PRS). We elucidate the historical advancements of PRS, their advantages and shortcomings in comparison with other predictive methods, and discuss their conceptual limitations in light of the complexity of biological systems. Furthermore, we provide a survey of published tools for computing PRS and associated resources. The various tools and software packages are categorized based on their technical utility for users or prospective developers. Understanding the array of available tools and their limitations is crucial for accurately assessing and predicting disease risks, facilitating early interventions, and guiding personalized healthcare decisions. Additionally, we also identify potential new avenues for future bioinformatic analyzes and advancements related to PRS.

LPL rs264, PROCR rs867186 and PDGF rs974819 Gene Polymorphisms in Patients with Unstable Angina

BACKGROUND

Coronary artery disease is caused by changes in the coronary arteries due to the atherosclerotic process and thrombotic changes. A very important role in the development of the atherosclerotic process in the coronary vessels is played by the inflammatory process and the immune response. Due to the important role of lipids and the coagulation process in the atherosclerotic process, research has also focused on genes affecting lipid metabolism and the coagulation system. Lipoprotein lipase (LPL) is an enzyme that metabolises lipids, hydrolysing triglycerides to produce free fatty acids and glycerol. Protein C (PC) is an essential component of coagulation and fibrinolysis. It is activated on the endothelial surface by the membrane-bound thrombin-thrombomodulin complex. Platelet-derived growth factor (PDGF) has a number of important functions in processes related to fibroblast and smooth muscle cell function. Due to their influence on lipid metabolism and coagulation processes, LPL, PROCR (endothelial cell protein C receptor) and PDGF may affect the atherosclerotic process and, thus, the risk of coronary heart disease. The aim of the study was to examine the associations between the LPL rs264, PROCR rs867186 and PDGF rs974819 gene polymorphisms and the risk of unstable angina and selected clinical parameters.

METHODS

The study included 232 patients with unstable angina and 144 healthy subjects as the control group. Genotyping was performed using real-time PCR.

RESULTS

There were no statistically significant differences in the distribution of the polymorphisms tested between the patients with unstable angina and the control subjects. The results showed associations between the PROCR rs867186 and PDGF rs974819 polymorphisms and some clinical parameters in patients with unstable angina. In patients with the PDGF rs974819 CC genotype, there were increased values for cholesterol and LDL serum levels in comparison with patients with the PDGF rs974819 CT and TT genotypes. In patients with the PROCR rs867186 AA genotype, HDL serum levels were lower than in patients with the GA genotype.

CONCLUSIONS

The results of our study did not show that the LPL rs264, PROCR rs867186 and PDGF rs974819 gene polymorphisms were significant risk factors for unstable angina in our population. The results of the study suggest that PDGF rs974819 and PROCR rs867186 may be associated with some parameters of lipid metabolism.

Polygenic risk alters the penetrance of monogenic kidney disease

Chronic kidney disease (CKD) is determined by an interplay of monogenic, polygenic, and environmental risks. Autosomal dominant polycystic kidney disease (ADPKD) and COL4A-associated nephropathy (COL4A-AN) represent the most common forms of monogenic kidney diseases. These disorders have incomplete penetrance and variable expressivity, and we hypothesize that polygenic factors explain some of this variability. By combining SNP array, exome/genome sequence, and electronic health record data from the UK Biobank and All-of-Us cohorts, we demonstrate that the genome-wide polygenic score (GPS) significantly predicts CKD among ADPKD monogenic variant carriers. Compared to the middle tertile of the GPS for noncarriers, ADPKD variant carriers in the top tertile have a 54-fold increased risk of CKD, while ADPKD variant carriers in the bottom tertile have only a 3-fold increased risk of CKD. Similarly, the GPS significantly predicts CKD in COL4A-AN carriers. The carriers in the top tertile of the GPS have a 2.5-fold higher risk of CKD, while the risk for carriers in the bottom tertile is not different from the average population risk. These results suggest that accounting for polygenic risk improves risk stratification in monogenic kidney disease.

Polygenic risk affects the penetrance of monogenic kidney disease

Background

Chronic kidney disease (CKD) is a genetically complex disease determined by an interplay of monogenic, polygenic, and environmental risks. Most forms of monogenic kidney diseases have incomplete penetrance and variable expressivity. It is presently unknown if some of the variability in penetrance can be attributed to polygenic factors.

Methods

Using the UK Biobank (N=469,835 participants) and the All of Us (N=98,622 participants) datasets, we examined two most common forms of monogenic kidney disorders, autosomal dominant polycystic kidney disease (ADPKD) caused by deleterious variants in the PKD1 or PKD2 genes, and COL4A-associated nephropathy (COL4A-AN caused by deleterious variants in COL4A3, COL4A4, or COL4A5 genes). We used the eMERGE-III electronic CKD phenotype to define cases (estimated glomerular filtration rate (eGFR) <60 mL/min/1.73m2 or kidney failure) and controls (eGFR >90 mL/min/1.73m2 in the absence of kidney disease diagnoses). The effects of the genome-wide polygenic score (GPS) for CKD were tested in monogenic variant carriers and non-carriers using logistic regression controlling for age, sex, diabetes, and genetic ancestry.

Results

As expected, the carriers of known pathogenic and rare predicted loss-of-function variants in PKD1 or PKD2 had a high risk of CKD (ORmeta=17.1, 95% CI: 11.1-26.4, P=1.8E-37). The GPS was comparably predictive of CKD in both ADPKD variant carriers (ORmeta=2.28 per SD, 95%CI: 1.55-3.37, P=2.6E-05) and non-carriers (ORmeta=1.72 per SD, 95% CI=1.69-1.76, P< E-300) independent of age, sex, diabetes, and genetic ancestry. Compared to the middle tertile of the GPS distribution for non-carriers, ADPKD variant carriers in the top tertile had a 54-fold increased risk of CKD, while ADPKD variant carriers in the bottom tertile had only a 3-fold increased risk of CKD. Similarly, the GPS was predictive of CKD in both COL4-AN variant carriers (ORmeta=1.78, 95% CI=1.22-2.58, P=2.38E-03) and non-carriers (ORmeta=1.70, 95%CI: 1.68-1.73 P

Conclusions

Variable penetrance of kidney disease in ADPKD and COL4-AN is partially explained by differences in polygenic risk profiles. Accounting for polygenic factors has the potential to improve risk stratification in monogenic kidney disease and may have implications for genetic counseling.

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Key Words

• APOL1
• GWAS
• chronic kidney disease (CKD)
• polygenic risk score (PRS)

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MESH Headings Collapse

Grants

• OT2 OD026556 NIH HHS
• U2C OD023196 NIH HHS
• OT2 OD026551 NIH HHS
• U24 OD023121 NIH HHS
• OT2 OD026552 NIH HHS
• OT2 OD026549 NIH HHS
• OT2 OD025337 NIH HHS
• OT2 OD025277 NIH HHS
• OT2 OD026555 NIH HHS
• OT2 OD026550 NIH HHS
• OT2 OD026553 NIH HHS
• OT2 OD023205 NIH HHS
• U01 HG008680 NHGRI NIH HHS
• OT2 OD025276 NIH HHS
• OT2 OD026557 NIH HHS
• U24 OD023163 NIH HHS
• OT2 OD023206 NIH HHS
• R01 LM013061 NLM NIH HHS
• U24 OD023176 NIH HHS
• OT2 OD026548 NIH HHS
• OT2 OD025315 NIH HHS
• K25 DK128563 NIDDK NIH HHS
• OT2 OD026554 NIH HHS
• UL1 TR001873 NCATS NIH HHS

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Affiliation(s)

Atlas Khan Division of Nephrology, Dept of Medicine, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY
Ning Shang Division of Nephrology, Dept of Medicine, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY
Jordan G. Nestor Division of Nephrology, Dept of Medicine, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY
Chunhua Weng Department of Biomedical Informatics, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY, USA
George Hripcsak Department of Biomedical Informatics, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY, USA
Peter C. Harris Division of Nephrology and Hypertension, Mayo Clinic, Rochester, Minnesota 55905, USA
Ali G. Gharavi Division of Nephrology, Dept of Medicine, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY
Krzysztof Kiryluk Division of Nephrology, Dept of Medicine, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY

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Stability of polygenic scores across discovery genome-wide association studies

Polygenic scores (PGS) are commonly evaluated in terms of their predictive accuracy at the population level by the proportion of phenotypic variance they explain. To be useful for precision medicine applications, they also need to be evaluated at the individual level when phenotypes are not necessarily already known. We investigated the stability of PGS in European American (EUR) and African American (AFR)-ancestry individuals from the Philadelphia Neurodevelopmental Cohort and the Adolescent Brain Cognitive Development study using different discovery genome-wide association study (GWAS) results for post-traumatic stress disorder (PTSD), type 2 diabetes (T2D), and height. We found that pairs of EUR-ancestry GWAS for the same trait had genetic correlations >0.92. However, PGS calculated from pairs of same-ancestry and different-ancestry GWAS had correlations that ranged from <0.01 to 0.74. PGS stability was greater for height than for PTSD or T2D. A series of height GWAS in the UK Biobank suggested that correlation between PGS is strongly dependent on the extent of sample overlap between the discovery GWAS. Focusing on the upper end of the PGS distribution, different discovery GWAS do not consistently identify the same individuals in the upper quantiles, with the best case being 60% of individuals above the 80th percentile of PGS overlapping from one height GWAS to another. The degree of overlap decreases sharply as higher quantiles, less heritable traits, and different-ancestry GWAS are considered. PGS computed from different discovery GWAS have only modest correlation at the individual level, underscoring the need to proceed cautiously with integrating PGS into precision medicine applications.

Polygenic risk scores for the diagnosis and management of dyslipidemia

PURPOSE OF REVIEW

To review current progress in the use of polygenic risk scores for lipid traits and their use in the diagnosis and treatment of lipid disorders.

RECENT FINDINGS

Inherited lipid disorders, including those causing extremes of low-density lipoprotein (LDL) cholesterol, high-density lipoprotein (HDL) cholesterol, or triglycerides were initially identified as monogenic traits, in which a single rare variant with large effect size is responsible for the phenotype. More recently, a polygenic basis for many lipid traits has also been identified. Patients with polygenic dyslipidemia can be identified through the use of polygenic risk scores (PRSs), which collapse information from a handful to several million genetic variants into a single metric.

SUMMARY

PRSs for lipid traits may aid in the identification of the genetic basis for the lipid phenotype in individual patients, may provide additional information regarding the risk of cardiovascular disease, and could help in guiding therapeutic decision making.

Polygenic risk scores for cardiovascular diseases and type 2 diabetes

Polygenic risk scores (PRSs) are a promising approach to accurately predict an individual's risk of developing disease. The area under the receiver operating characteristic curve (AUC) of PRSs in their population are often only reported for models that are adjusted for age and sex, which are known risk factors for the disease of interest and confound the association between the PRS and the disease. This makes comparison of PRS between studies difficult because the genetic effects cannot be disentangled from effects of age and sex (which have a high AUC without the PRS). In this study, we used data from the UK Biobank and applied the stacked clumping and thresholding method and a variation called maximum clumping and thresholding method to develop PRSs to predict coronary artery disease, hypertension, atrial fibrillation, stroke and type 2 diabetes. We created case-control training datasets in which age and sex were controlled by design. We also excluded prevalent cases to prevent biased estimation of disease risks. The maximum clumping and thresholding PRSs required many fewer single-nucleotide polymorphisms to achieve almost the same discriminatory ability as the stacked clumping and thresholding PRSs. Using the testing datasets, the AUCs for the maximum clumping and thresholding PRSs were 0.599 (95% confidence interval [CI]: 0.585, 0.613) for atrial fibrillation, 0.572 (95% CI: 0.560, 0.584) for coronary artery disease, 0.585 (95% CI: 0.564, 0.605) for type 2 diabetes, 0.559 (95% CI: 0.550, 0.569) for hypertension and 0.514 (95% CI: 0.494, 0.535) for stroke. By developing a PRS using a dataset in which age and sex are controlled by design, we have obtained true estimates of the discriminatory ability of the PRSs alone rather than estimates that include the effects of age and sex.

Associations of maternal blood pressure-raising polygenic risk scores with fetal weight

Maternal blood pressure (BP) is associated with variations in fetal weight, an important determinant of neonatal and adult health. However, the association of BP-raising genetic risk with fetal weight is unknown. We tested the associations of maternal BP-raising polygenic risk scores (PRS) with estimated fetal weights (EFWs) at 13, 20, 27, and 40 weeks of gestation. This study included 622 White, 637 Black, 568 Hispanic, and 238 Asian pregnant women with genotype data from the NICHD Fetal Growth Studies. PRS of systolic (SBP) and diastolic BP (DBP) were calculated for each participant based on summary statistics from a recent genome-wide association study. Linear regression models were used to compare mean EFW differences between the highest versus lowest tertile of PRS, adjusting for maternal age, education, parity, genetic principal components and fetal sex. Hispanics in the highest DBP PRS tertile, compared to those in the lowest, had 8.1 g (95% CI: -15.1, -1.1), 32.4 g (-58.4, -6.4) and 119.4 g (-218.1, -20.7) lower EFW at 20, 27 and 40 weeks, respectively. Similarly, Asians in the highest DBP PRS tertile had 137.2 g (-263.5, -10.8) lower EFW at week 40, and those in the highest tertile of SBP PRS had 3.2 g (-5.8, -0.7), 12.9 g (-23.5, -2.4), and 39.8 g (-76.9, -2.7) lower EFWs at 13, 20, and 27 weeks. The findings showed that pregnant women's genetic susceptibility to high BP contributes to reduced fetal growth, suggesting a potential future clinical application in perinatal health.

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.

Understanding the genetics of adult-onset dilated cardiomyopathy: what a clinician needs to know

There is increasing understanding of the genetic basis to dilated cardiomyopathy and in this review, we offer a practical primer for the practising clinician. We aim to help all clinicians involved in the care of patients with dilated cardiomyopathy to understand the clinical relevance of the genetic basis of dilated cardiomyopathy, introduce key genetic concepts, explain which patients and families may benefit from genetic testing, which genetic tests are commonly performed, how to interpret genetic results, and the clinical applications of results. We conclude by reviewing areas for future research in this dynamic field.

rs629301 CELSR2 polymorphism confers a ten-year equivalent risk of critical stenosis assessed by coronary angiography

BACKGROUND AND AIMS

Novel genetic determinants associated with coronary artery disease (CAD) have been discovered by genome wide association studies. Variants encompassing the CELSR2- PSRC1-SORT1 gene cluster have been associated with CAD. This study is aimed to investigate the rs629301 polymorphism association with the extent of CAD evaluated by coronary angiography (CAG), and to evaluate its associations with an extensive panel of lipid and lipoprotein measurements in a large Italian cohort of 2429 patients.

METHODS AND RESULTS

The patients were collected by four Intensive Care Units located in Palermo and Verona (Italy). Clinical Records were filed, blood samples were collected, lipids and apolipoproteins (apo) were measured in separate laboratories. CAD was defined by the presence of stenotic arteries (>50% lumen diameter) by CAG. The presence of CAD was associated with the rs629301 genotype. Patients with CAD were 78% and 73% (p = 0.007) of the T/T vs. T/G + G/G genotype carriers respectively. T/T genotype was also correlated with the number of stenotic arteries, with a 1.29 (1.04-1.61) risk to have a three-arteries disease. T/T genotype correlated with higher levels of LDL-, non-HDL cholesterol, apoB, apoE and apoCIII, and lower HDL-cholesterol. Logistic Regression confirmed that rs629301was associated with CAD independently from the common risk factors, with a risk similar to that conferred by ten years of age [odds ratios were 1.43 (1.04-1.96) and 1.39 (1.22-1.58) respectively].

CONCLUSIONS

rs629301 risk allele was independently associated with the extension and severity of CAD and positively with apoE and apoB containing lipoproteins.

African genetic diversity and adaptation inform a precision medicine agenda

The deep evolutionary history of African populations, since the emergence of modern humans more than 300,000 years ago, has resulted in high genetic diversity and considerable population structure. Selected genetic variants have increased in frequency due to environmental adaptation, but recent exposures to novel pathogens and changes in lifestyle render some of them with properties leading to present health liabilities. The unique discoverability potential from African genomic studies promises invaluable contributions to understanding the genomic and molecular basis of health and disease. Globally, African populations are understudied, and precision medicine approaches are largely based on data from European and Asian-ancestry populations, which limits the transferability of findings to the continent of Africa. Africa needs innovative precision medicine solutions based on African data that use knowledge and implementation strategies aligned to its climatic, cultural, economic and genomic diversity.

Genetics of Cardiovascular Disease: How Far Are We from Personalized CVD Risk Prediction and Management?

Despite the rapid progress in diagnosis and treatment of cardiovascular disease (CVD), this disease remains a major cause of mortality and morbidity. Recent progress over the last two decades in the field of molecular genetics, especially with new tools such as genome-wide association studies, has helped to identify new genes and their variants, which can be used for calculations of risk, prediction of treatment efficacy, or detection of subjects prone to drug side effects. Although the use of genetic risk scores further improves CVD prediction, the significance is not unambiguous, and some subjects at risk remain undetected. Further research directions should focus on the "second level" of genetic information, namely, regulatory molecules (miRNAs) and epigenetic changes, predominantly DNA methylation and gene-environment interactions.

Assessing COVID-19 susceptibility through analysis of the genetic and epigenetic diversity of ACE2-mediated SARS-CoV-2 entry

There is considerable variation in disease course among individuals infected with SARS-CoV-2. Many of them do not exhibit any symptoms, while some others proceed to develop COVID-19; however, severity of COVID-19 symptoms greatly differs among individuals. Focusing on the early events related to SARS-CoV-2 entry to cells through the ACE2 pathway, we describe how variability in (epi)genetic factors can conceivably explain variability in disease course. We specifically focus on variations in ACE2, TMPRSS2 and FURIN genes, as central components for SARS-CoV-2 infection, and on other molecules that modulate their expression such as CALM, ADAM-17, AR and ESRs. We propose a genetic classifier for predicting SARS-CoV-2 infectivity potential as a preliminary tool for identifying the at-risk-population. This tool can serve as a dynamic scaffold being updated and adapted to validated (epi)genetic data. Overall, the proposed approach holds potential for better personalization of COVID-19 handling.

Biomarkers for Heart Failure Prognosis: Proteins, Genetic Scores and Non-coding RNAs

Heart failure (HF) is a complex disease in which cardiomyocyte injury leads to a cascade of inflammatory and fibrosis pathway activation, thereby causing decrease in cardiac function. As a result, several biomolecules are released which can be identified easily in circulating body fluids. The complex biological processes involved in the development and worsening of HF require an early treatment strategy to stop deterioration of cardiac function. Circulating biomarkers provide not only an ideal platform to detect subclinical changes, their clinical application also offers the opportunity to monitor disease treatment. Many of these biomarkers can be quantified with high sensitivity; allowing their clinical application to be evaluated beyond diagnostic purposes as potential tools for HF prognosis. Though the field of biomarkers is dominated by protein molecules, non-coding RNAs (microRNAs, long non-coding RNAs, and circular RNAs) are novel and promising biomarker candidates that encompass several ideal characteristics required in the biomarker field. The application of genetic biomarkers as genetic risk scores in disease prognosis, albeit in its infancy, holds promise to improve disease risk estimation. Despite the multitude of biomarkers that have been available and identified, the majority of novel biomarker candidates are not cardiac-specific, and instead may simply be a readout of systemic inflammation or other pathological processes. Thus, the true value of novel biomarker candidates in HF prognostication remains unclear. In this article, we discuss the current state of application of protein, genetic as well as non-coding RNA biomarkers in HF risk prognosis.

Taking the next steps to implement polygenic risk scoring for improved risk stratification and primary prevention of coronary artery disease

Coronary artery disease (CAD) remains the leading cause of death worldwide. The role of hypertension, cholesterol, diabetes mellitus, and smoking in driving disease has been well recognized at a population level and has been the target of primary prevention strategies for over 50 years with substantial impact. However, in many cases, these factors alone do not provide enough precision at the individual level to allow physicians and patients to take appropriate preventive measures and many patients continue to suffer acute coronary syndromes in the absence of these risk factors. Recent advances in user-friendly chip designs, high speed throughput, and economic efficiency of genome-wide association studies complemented by advances in statistical analytical approaches have facilitated the rapid development of polygenic risk scores (PRSs). The latest PRSs combine data regarding hundreds of thousands of single-nucleotide polymorphisms to predict chronic diseases including CAD. Novel CAD PRSs are strong predictors of risk and may have application, in a complementary manner with existing risk prediction algorithms. However, there remain substantial controversies, and ultimately, we need to move forward from observational studies to prospectively and rigorously assess the potential impact if widespread implementation is to be aspired to. Consideration needs to be made of ethnicity, sex, as well as age, and risk estimate based on existing non-genomic algorithms. We provide an overview and commentary on the important advances in deriving and validating PRSs, as well as pragmatic considerations that will be required for implementation of the new knowledge into clinical practice.

THE FUTURE OF MEDICINE, healthcare innovation through precision medicine: policy case study of Qatar

In 2016, the World Innovation Summit for Health (WISH) published its Forum Report on precision medicine "PRECISION MEDICINE - A GLOBAL ACTION PLAN FOR IMPACT". Healthcare is undergoing a transformation, and it is imperative to leverage new technologies to generate new data and support the advent of precision medicine (PM). Recent scientific breakthroughs and technological advancements have improved our disease knowledge and altered diagnosis and treatment approaches resulting in a more precise, predictive, preventative and personalized health care that is customized for the individual patient. Consequently, the big data revolution has provided an opportunity to apply artificial intelligence and machine learning algorithms to mine such a vast data set. Additionally, personalized medicine promises to revolutionize healthcare, with its key goal of providing the right treatment to the right patient at the right time and dose, and thus the potential of improving quality of life and helping to bring down healthcare costs.This policy briefing will look in detail at the issues surrounding continued development, sustained investment, risk factors, testing and approval of innovations for better strategy and faster process. The paper will serve as a policy bridge that is required to enhance a conscious decision among the powers-that-be in Qatar in order to find a way to harmonize multiple strands of activity and responsibility in the health arena. The end goal will be for Qatar to enhance public awareness and engagement and to integrate effectively the incredible advances in research into healthcare systems, for the benefit of all patients.The PM policy briefing provides concrete recommendations on moving forward with PM initiatives in Qatar and internationally. Equally important, integration of PM within a primary care setting, building a coalition of community champions through awareness and advocacy, finally, communicating PM value, patient engagement/empowerment and education/continued professional development programs of the healthcare workforce.Key recommendations for implementation of precision medicine inside and outside Qatar: 1. Create Community Awareness and PM Education Programs 2. Engage and Empower Patients 3. Communicate PM Value 4. Develop appropriate Infrastructure and Information Management Systems 5. Integrate PM into standard Healthcare System and Ensure Access to Care PM is no longer futuristic. It is here. Implementing PM in routine clinical care does require some investment and infrastructure development. Invariably, cost and lack of expertise are cited as barriers to PM implementation. Equally consequential, are the curriculum and professional development of medical care experts.Policymakers need to lead and coordinate effort among stakeholders and consider cultural and faith perspectives to ensure success. It is essential that policymakers integrate PM approaches into national strategies to improve health and health care for all, and to drive towards the future of medicine precision health.

Review article: the emerging role of genetics in precision medicine for patients with non-alcoholic steatohepatitis

BACKGROUND

Non-alcoholic steatohepatitis (NASH) is a severe form of non-alcoholic fatty liver disease (NAFLD) characterised by liver fat accumulation, inflammation and progressive fibrosis. Emerging data indicate that genetic susceptibility increases risks of NAFLD, NASH and NASH-related cirrhosis.

AIMS

To review NASH genetics and discuss the potential for precision medicine approaches to treatment.

METHOD

PubMed search and inclusion of relevant literature.

RESULTS

Single-nucleotide polymorphisms in PNPLA3, TM6SF2, GCKR, MBOAT7 and HSD17B13 are clearly associated with NASH development or progression. These genetic variants are common and have moderate-to-large effect sizes for development of NAFLD, NASH and hepatocellular carcinoma (HCC). The genes play roles in lipid remodelling in lipid droplets, hepatic very low-density lipoprotein (VLDL) secretion and de novo lipogenesis. The PNPLA3 I148M variant (rs738409) has large effects, with approximately twofold increased odds of NAFLD and threefold increased odds of NASH and HCC per allele. Obesity interacts with PNPLA3 I148M to elevate liver fat content and increase rates of NASH. Although the isoleucine-to-methionine substitution at amino acid position 148 of the PNPLA3 enzyme inactivates its lipid remodelling activity, the effect of PNPLA3 I148M results from trans-repression of another lipase (ATGL/PNPLA2) by sequestration of a shared cofactor (CGI-58/ABHD5), leading to decreased hepatic lipolysis and VLDL secretion. In homozygous Pnpla3 I148M knock-in rodent models of NAFLD, targeted PNPLA3 mRNA knockdown reduces hepatic steatosis, inflammation and fibrosis.

CONCLUSION

The emerging genetic and molecular understanding of NASH paves the way for novel interventions, including precision medicines that can modulate the activity of specific genes associated with NASH.

Polygenic risk for coronary heart disease acts through atherosclerosis in type 2 diabetes

Background

Type 2 diabetes increases the risk of coronary heart disease (CHD), yet the mechanisms involved remain poorly described. Polygenic risk scores (PRS) provide an opportunity to understand risk factors since they reflect etiologic pathways from the entire genome. We therefore tested whether a PRS for CHD influenced risk of CHD in individuals with type 2 diabetes and which risk factors were associated with this PRS.

Methods

We tested the association of a CHD PRS with CHD and its traditional clinical risk factors amongst individuals with type 2 diabetes in UK Biobank (N = 21,102). We next tested the association of the CHD PRS with atherosclerotic burden in a cohort of 352 genome-wide genotyped participants with type 2 diabetes who had undergone coronary angiograms.

Results

In the UK Biobank we found that the CHD PRS was strongly associated with CHD amongst individuals with type 2 diabetes (OR per standard deviation increase = 1.50; p = 1.5 × 10^− 59). But this CHD PRS was, at best, only weakly associated with traditional clinical risk factors, such as hypertension, hyperlipidemia, glycemic control, obesity and smoking. Conversely, in the angiographic cohort, the CHD PRS was strongly associated with multivessel stenosis (OR = 1.65; p = 4.9 × 10^− 4) and increased number of major stenotic lesions (OR = 1.35; p = 9.4 × 10^− 3).

Conclusions

Polygenic predisposition to CHD is strongly associated with atherosclerotic burden in individuals with type 2 diabetes and this effect is largely independent of traditional clinical risk factors. This suggests that genetic risk for CHD acts through atherosclerosis with little effect on most traditional risk factors, providing the opportunity to explore new biological pathways.

Biomarkers in primary prevention : Meaningful diagnosis based on biomarker scores?

Cardiovascular (CV) risk assessment is based on the utilization of risk scores, enabling clinicians to estimate an individual's risk to develop CV pathologies and events. Such risk scores comprise classic CV risk factors such as smoking, diabetes, hypertension, and blood cholesterol levels. Recently, other CV biomarkers such as cardiac troponins have been suggested and evaluated as alternative biomarkers not only in the acute diagnostic setting of myocardial infarction, but also as markers for risk stratification in the general population. In this review, we summarize the current knowledge on biomarkers in the field of primary prevention in cardiovascular disease (CVD). Furthermore, we present potential alternative biomarker-based strategies for CV risk assessment. In this respect we provide an outlook on the potential use of genomic variation as well as circulating non-coding RNAs to complement current risk assessment strategies so as to further personalize risk stratification in CVD.

Genetic Testing in Inherited Heart Diseases

Inherited heart diseases include numerous conditions, from the more prevalent hypertrophic cardiomyopathy (HCM) and familial hypercholesterolaemia (FH), to the comparatively less common inherited arrhythmia syndromes, such as long QT syndrome (LQTS), catecholaminergic polymorphic ventricular tachycardia (CPVT) and Brugada syndrome (BrS). Genetic testing has evolved rapidly over the last decade and is now considered a mainstream component of clinical management of inherited heart diseases. Cardiac manifestations can also be part of wider syndromes, and genetic testing can play a critical role in clarifying the underlying aetiological basis of disease in some cases. The greatest utility of a genetic diagnosis, however, comes from the ability to elucidate disease risk amongst asymptomatic at-risk family members. Given the nuances and challenges, cardiac genetic testing is best performed in a multidisciplinary specialised clinic with access to cardiac genetic counselling.

Predicting Phenotypic Diversity from Molecular and Genetic Data

Despite the importance of complex phenotypes, an in-depth understanding of the combined molecular and genetic effects on a phenotype has yet to be achieved. Here, we introduce InPhenotype, a novel computational approach for complex phenotype prediction, where gene-expression data and genotyping data are integrated to yield quantitative predictions of complex physiological traits. Unlike existing computational methods, InPhenotype makes it possible to model potential regulatory interactions between gene expression and genomic loci without compromising the continuous nature of the molecular data. We applied InPhenotype to synthetic data, exemplifying its utility for different data parameters, as well as its superiority compared to current methods in both prediction quality and the ability to detect regulatory interactions of genes and genomic loci. Finally, we show that InPhenotype can provide biological insights into both mouse and yeast datasets.