Polygenic risk score predicts prevalence of cardiovascular disease in patients with familial hypercholesterolemia

Genetics of Hypertriglyceridemia

Hypertriglyceridemia, a commonly encountered phenotype in cardiovascular and metabolic clinics, is surprisingly complex. A range of genetic variants, from single-nucleotide variants to large-scale copy number variants, can lead to either the severe or mild-to-moderate forms of the disease. At the genetic level, severely elevated triglyceride levels resulting from familial chylomicronemia syndrome (FCS) are caused by homozygous or biallelic loss-of-function variants in LPL, APOC2, APOA5, LMF1, and GPIHBP1 genes. In contrast, susceptibility to multifactorial chylomicronemia (MCM), which has an estimated prevalence of ~1 in 600 and is at least 50-100-times more common than FCS, results from two different types of genetic variants: (1) rare heterozygous variants (minor allele frequency <1%) with variable penetrance in the five causal genes for FCS; and (2) common variants (minor allele frequency >5%) whose individually small phenotypic effects are quantified using a polygenic score. There is indirect evidence of similar complex genetic predisposition in other clinical phenotypes that have a component of hypertriglyceridemia, such as combined hyperlipidemia and dysbetalipoproteinemia. Future considerations include: (1) evaluation of whether the specific type of genetic predisposition to hypertriglyceridemia affects medical decisions or long-term outcomes; and (2) searching for other genetic contributors, including the role of genome-wide polygenic scores, novel genes, non-linear gene-gene or gene-environment interactions, and non-genomic mechanisms including epigenetics and mitochondrial DNA.

Widening the spectrum of genetic testing in familial hypercholesterolaemia: Will it translate into better patient and population outcomes?

Familial hypercholesterolaemia (FH) is caused by pathogenic variants in LDLR, APOB or PCSK9. Impaired low-density lipoprotein (LDL) receptor function leads to decreased LDL catabolism and premature atherosclerotic cardiovascular disease (ASCVD). Thousands of LDLR variants are known, but assignation of pathogenicity requires accurate phenotyping, family studies and assessment of LDL receptor function. Precise, genetic diagnosis of FH using targeted next generation sequencing allows for optimal treatment, distinguishing FH from pathogenically distinct disorders requiring different treatment. Polygenic hypercholesterolaemia resulting from an accumulation of LDL cholesterol-raising single nucleotide polymorphisms (SNPs) could also be suspected by this approach. Similarly, ASCVD risk could be estimated by broader sequencing of cholesterol and non-cholesterol-related genes. Both of these areas require further research. The clinical management of FH, focusing on the primary or secondary prevention of ASCVD, has been boosted by PCSK9 inhibitor therapy. The efficacy of PCSK9 inhibitors in homozygous FH may be partly predicted by the LDLR variants. While expanded genetic testing in FH is clinically useful in providing an accurate diagnosis and enabling cost-effective testing of relatives, further research is needed to establish its value in improving clinical outcomes.

Polygenic Susceptibility of Aortic Aneurysms Associates to the Diameter of the Aneurysm Sac: the Aneurysm-Express Biobank Cohort

Recent genome-wide association studies (GWAS) have discovered ten genetic risk variants for abdominal aortic aneurysms (AAA). To what extent these genetic variants contribute to the pathology of aneurysms is yet unknown. The present study aims to investigate whether genetic risk variants are associated with three clinical features: diameter of aneurysm sac, type of artery and aneurysm related-symptoms in aortic and peripheral aneurysm patients. Aneurysm tissue of 415 patients included in the Aneurysm-Express biobank was used. A best-fit polygenic risk score (PRS) based on previous GWAS effect estimates was modeled for each clinical phenotype. The best-fit PRS (including 272 variants at PT = 0.01015) showed a significant correlation with aneurysm diameter (R2 = 0.019, p = 0.001). No polygenic association was found with clinical symptoms or artery type. In addition, the ten genome-wide significant risk variants for AAA were tested individually, but no associations were observed with any of the clinical phenotypes. All models were corrected for confounders and data was normalized. In conclusion, a weighted PRS of AAA susceptibility explained 1.9% of the phenotypic variation (p = 0.001) in diameter in aneurysm patients. Given our limited sample size, future biobank collaborations need to confirm a potential causal role of susceptibility variants on aneurysmal disease initiation and progression.

Familial Hypercholesterolaemia in 2020: A Leading Tier 1 Genomic Application

Familial hypercholesterolaemia (FH) is caused by a major genetic defect in the low-density lipoprotein (LDL) clearance pathway. Characterised by LDL-cholesterol elevation from birth, FH confers a significant risk for premature coronary artery disease (CAD) if overlooked and untreated. With risk exposure beginning at birth, early detection and intervention is crucial for the prevention of CAD. Lowering LDL-cholesterol with lifestyle and statin therapy can reduce the risk of CAD. However, most individuals with FH will not reach guideline recommended LDL-cholesterol targets. FH has an estimated prevalence of approximately 1:250 in the community. Multiple strategies are required for screening, diagnosing and treating FH. Recent publications on FH provide new data for developing models of care, including new therapies. This review provides an overview of FH and outlines some recent advances in the care of FH for the prevention of CAD in affected families. The future care of FH in Australia should be developed within the context of the National Health Genomics Policy Framework.

New Case Detection by Cascade Testing in Familial Hypercholesterolemia: A Systematic Review of the Literature

BACKGROUND

The prevalence of familial hypercholesterolemia is 1 in 250, but <10% of patients are diagnosed. Cascade testing enables early detection of cases through systematic family tracing. Establishment of familial hypercholesterolemia cascade testing programs in the US could be informed by approaches used elsewhere.

METHODS

We conducted a systematic review of published studies in the English language of cascade testing for familial hypercholesterolemia, which reported the number of index cases and number of relatives tested and specified methods of contacting relatives and testing modalities methods utilized. For each study, we calculated yield (proportion of relatives who test positive) and new cases per index case, to facilitate comparison.

RESULTS

We identified 10 studies from the literature that met inclusion criteria; the mean number of probands and relatives per study was 242 and 826, respectively. The average yield was 44.76% with a range of 30% to 60.5%, and the mean new cases per index case was 1.65 with a range of 0.22 to 8.0. New cases per index case tended to be greater in studies that used direct contact versus indirect contact (2.06 versus 0.86), tested beyond first-degree relatives versus only first-degree relatives (3.65 versus 0.80), used active sample collection versus collection at clinic (4.11 versus 1.06), and utilized genetic testing versus biochemical testing (2.47 versus 0.42).

CONCLUSIONS

New case detection in familial hypercholesterolemia cascade testing programs tended to be higher with direct contact of relatives, testing beyond first-degree relatives, in-home-based sample collection, and genetic testing. These findings should be helpful for establishing cascade testing programs in the United States.

Quantifying the polygenic contribution to variable expressivity in eleven rare genetic disorders

Rare genetic disorders (RGDs) often exhibit significant clinical variability among affected individuals, a disease characteristic termed variable expressivity. Recently, the aggregate effect of common variation, quantified as polygenic scores (PGSs), has emerged as an effective tool for predictions of disease risk and trait variation in the general population. Here, we measure the effect of PGSs on 11 RGDs including four sex-chromosome aneuploidies (47,XXX; 47,XXY; 47,XYY; 45,X) that affect height; two copy-number variant (CNV) disorders (16p11.2 deletions and duplications) and a Mendelian disease (melanocortin 4 receptor deficiency (MC4R)) that affect BMI; and two Mendelian diseases affecting cholesterol: familial hypercholesterolemia (FH; LDLR and APOB) and familial hypobetalipoproteinemia (FHBL; PCSK9 and APOB). Our results demonstrate that common, polygenic factors of relevant complex traits frequently contribute to variable expressivity of RGDs and that PGSs may be a useful metric for predicting clinical severity in affected individuals and for risk stratification.

A genetic risk score predicts coronary artery disease in familial hypercholesterolaemia: enhancing the precision of risk assessment

Familial hypercholesterolaemia (FH) is associated with increased risk of coronary artery disease (CAD); however, risk prediction and stratification remain a challenge. Genetic risk scores (GRS) may have utility in identifying FH patients at high CAD risk. The study included 811 patients attending the lipid disorders clinic at Royal Perth Hospital with mutation-positive (n = 251) and mutation-negative (n = 560) FH. Patients were genotyped for a GRS previously associated with CAD. Associations between the GRS, clinical characteristics, and CAD were assessed using regression analyses. The average age of patients was 49.6 years, and 44.1% were male. The GRS was associated with increased odds of a CAD event in mutation-positive [odds ratio (OR) = 3.3; 95% confidence interval (CI) = 1.3-8.2; P = .009] and mutation-negative FH patients (OR = 1.8; 95% CI = 1.0-3.3; P = .039) after adjusting for established predictors of CAD risk. The GRS was associated with greater subclinical atherosclerosis as assessed by coronary artery calcium score (P = .039). A high GRS was associated with CAD defined clinically and angiographically in FH patients. High GRS patients may benefit from more intensive management including lifestyle modification and aggressive lipid-lowering therapy. Further assessment of the utility of the GRS requires investigation in prospective cohorts, including its role in influencing the management of FH patients in the clinic.

Evaluation of the role of STAP1 in Familial Hypercholesterolemia

Familial hypercholesterolemia (FH) is characterised by elevated serum levels of low-density lipoprotein cholesterol (LDL-C) and a substantial risk for cardiovascular disease. The autosomal-dominant FH is mostly caused by mutations in LDLR (low density lipoprotein receptor), APOB (apolipoprotein B), and PCSK9 (proprotein convertase subtilisin/kexin). Recently, STAP1 has been suggested as a fourth causative gene. We analyzed STAP1 in 75 hypercholesterolemic patients from Berlin, Germany, who are negative for mutations in canonical FH genes. In 10 patients with negative family history, we additionally screened for disease causing variants in LDLRAP1 (low density lipoprotein receptor adaptor protein 1), associated with autosomal-recessive hypercholesterolemia. We identified one STAP1 variant predicted to be disease causing. To evaluate association of serum lipid levels and STAP1 carrier status, we analyzed 20 individuals from a population based cohort, the Cooperative Health Research in South Tyrol (CHRIS) study, carrying rare STAP1 variants. Out of the same cohort we randomly selected 100 non-carriers as control. In the Berlin FH cohort STAP1 variants were rare. In the CHRIS cohort, we obtained no statistically significant differences between carriers and non-carriers of STAP1 variants with respect to lipid traits. Until such an association has been verified in more individuals with genetic variants in STAP1, we cannot estimate whether STAP1 generally is a causative gene for FH.

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.

Predicting Polygenic Risk of Psychiatric Disorders

Genetics provides two major opportunities for understanding human disease-as a transformative line of etiological inquiry and as a biomarker for heritable diseases. In psychiatry, biomarkers are very much needed for both research and treatment, given the heterogenous populations identified by current phenomenologically based diagnostic systems. To date, however, useful and valid biomarkers have been scant owing to the inaccessibility and complexity of human brain tissue and consequent lack of insight into disease mechanisms. Genetic biomarkers are therefore especially promising for psychiatric disorders. Genome-wide association studies of common diseases have matured over the last decade, generating the knowledge base for increasingly informative individual-level genetic risk prediction. In this review, we discuss fundamental concepts involved in computing genetic risk with current methods, strengths and weaknesses of various approaches, assessments of utility, and applications to various psychiatric disorders and related traits. Although genetic risk prediction has become increasingly straightforward to apply and common in published studies, there are important pitfalls to avoid. At present, the clinical utility of genetic risk prediction is still low; however, there is significant promise for future clinical applications as the ancestral diversity and sample sizes of genome-wide association studies increase. We discuss emerging data and methods aimed at improving the value of genetic risk prediction for disentangling disease mechanisms and stratifying subjects for epidemiological and clinical studies. For all applications, it is absolutely critical that polygenic risk prediction is applied with appropriate methodology and control for confounding to avoid repeating some mistakes of the candidate gene era.

The Role of Genetics in Cardiovascular Risk Reduction: Findings From a Single Lipid Clinic and Review of the Literature

BACKGROUND

Genetic information is not routinely obtained in the management of most lipid disorders or in primary or secondary prevention of cardiovascular disease (CVD). We sought to determine the prevalence of pathogenic variants associated with lipoprotein metabolism or coronary artery disease (CAD) in a single lipid clinic and discuss the future use of genetic information in CVD prevention.

METHODS

Genetic testing was offered to patients with hypertriglyceridemia (defined as pre-treatment fasting triglycerides ≥150 mg/dL), elevated LDL-C (defined as pre-treatment ≥190 mg/dL), low HDL-C (defined as ≤40 mg/dL), elevated lipoprotein (a) (defined as ≥50 mg/dL or 100 nmol/L) or premature CAD (defined as an acute coronary syndrome or revascularization before age 40 years in men and 50 years in women) using next-generation DNA sequencing of 327 exons and selected variants in 129 genes known or suspected to be associated with lipoprotein metabolism or CAD.

RESULTS

82 of 84 patients (97.6%) were found to have a variant associated with abnormal lipid metabolism or CAD. The most common pathogenic or likely pathogenic variants included those of the LDL receptor (15 patients) and lipoprotein lipase (9 patients). Other common variants included those of apolipoprotein A5 (14 patients) and variants associated with elevated lipoprotein (a) (25 patients).

CONCLUSIONS

The majority of patients presenting to a single lipid clinic were found to have at least one variant associated with abnormal lipoprotein metabolism or CAD. Incorporating genetic information, including the use of genetic risk scores, is anticipated in the future care of lipid disorders and CVD prevention.

The evolution of genetic-based risk scores for lipids and cardiovascular disease

PURPOSE OF REVIEW

With improved next-generation sequencing technology, open-access genetic databases and increased awareness of complex trait genetics, we are entering a new era of risk assessment in which genetic-based risk scores (GRSs) will play a clinical role. We review the concepts underlying polygenic models of disease susceptibility and challenges in clinical implementation.

RECENT FINDINGS

Polygenic risk scores are currently used in genetic research on dyslipidemias and cardiovascular disease (CVD). Although the underlying principles for constructing polygenic scores for lipids are established, the lack of consensus on which score to use is indicated by the large number - about 50 - that have been published. Recently, large-scale polygenic scores for CVD appear to afford superior risk prediction compared to small-scale scores. Despite the potential benefits of GRSs, certain biases towards ethnicity and sex need to be worked through.

SUMMARY

We are on the verge of clinical application of GRSs to provide incremental information on dyslipidemia and CVD risk above and beyond traditional clinical variables. Additional work is required to develop a consensus of how such scores will be constructed and measured in a validated manner, as well as clinical indications for their use.

Clinical use of current polygenic risk scores may exacerbate health disparities

Polygenic risk scores (PRS) are poised to improve biomedical outcomes via precision medicine. However, the major ethical and scientific challenge surrounding clinical implementation of PRS is that those available today are several times more accurate in individuals of European ancestry than other ancestries. This disparity is an inescapable consequence of Eurocentric biases in genome-wide association studies, thus highlighting that-unlike clinical biomarkers and prescription drugs, which may individually work better in some populations but do not ubiquitously perform far better in European populations-clinical uses of PRS today would systematically afford greater improvement for European-descent populations. Early diversifying efforts show promise in leveling this vast imbalance, even when non-European sample sizes are considerably smaller than the largest studies to date. To realize the full and equitable potential of PRS, greater diversity must be prioritized in genetic studies, and summary statistics must be publically disseminated to ensure that health disparities are not increased for those individuals already most underserved.

Learning from Longitudinal Data in Electronic Health Record and Genetic Data to Improve Cardiovascular Event Prediction

Current approaches to predicting a cardiovascular disease (CVD) event rely on conventional risk factors and cross-sectional data. In this study, we applied machine learning and deep learning models to 10-year CVD event prediction by using longitudinal electronic health record (EHR) and genetic data. Our study cohort included 109, 490 individuals. In the first experiment, we extracted aggregated and longitudinal features from EHR. We applied logistic regression, random forests, gradient boosting trees, convolutional neural networks (CNN) and recurrent neural networks with long short-term memory (LSTM) units. In the second experiment, we applied a late-fusion approach to incorporate genetic features. We compared the performance with approaches currently utilized in routine clinical practice - American College of Cardiology and the American Heart Association (ACC/AHA) Pooled Cohort Risk Equation. Our results indicated that incorporating longitudinal feature lead to better event prediction. Combining genetic features through a late-fusion approach can further improve CVD prediction, underscoring the importance of integrating relevant genetic data whenever available.

Genomic Predictors of Asthma Phenotypes and Treatment Response

Asthma is a complex respiratory disease considered as the most common chronic condition in children. A large genetic contribution to asthma susceptibility is predicted by the clustering of asthma and allergy symptoms among relatives and the large disease heritability estimated from twin studies, ranging from 55 to 90%. Genetic basis of asthma has been extensively investigated in the past 40 years using linkage analysis and candidate-gene association studies. However, the development of dense arrays for polymorphism genotyping has enabled the transition toward genome-wide association studies (GWAS), which have led the discovery of several unanticipated asthma genes in the last 11 years. Despite this, currently known risk variants identified using many thousand samples from distinct ethnicities only explain a small proportion of asthma heritability. This review examines the main findings of the last 2 years in genomic studies of asthma using GWAS and admixture mapping studies, as well as the direction of studies fostering integrative perspectives involving omics data. Additionally, we discuss the need for assessing the whole spectrum of genetic variation in association studies of asthma susceptibility, severity, and treatment response in order to further improve our knowledge of asthma genes and predictive biomarkers. Leveraging the individual's genetic information will allow a better understanding of asthma pathogenesis and will facilitate the transition toward a more precise diagnosis and treatment.

Interpreting polygenic scores, polygenic adaptation, and human phenotypic differences

Recent analyses of polygenic scores have opened new discussions concerning the genetic basis and evolutionary significance of differences among populations in distributions of phenotypes. Here, we highlight limitations in research on polygenic scores, polygenic adaptation and population differences. We show how genetic contributions to traits, as estimated by polygenic scores, combine with environmental contributions so that differences among populations in trait distributions need not reflect corresponding differences in genetic propensity. Under a null model in which phenotypes are selectively neutral, genetic propensity differences contributing to phenotypic differences among populations are predicted to be small. We illustrate this null hypothesis in relation to health disparities between African Americans and European Americans, discussing alternative hypotheses with selective and environmental effects. Close attention to the limitations of research on polygenic phenomena is important for the interpretation of their relationship to human population differences.

Predicting cardiovascular disease in familial hypercholesterolemia

PURPOSE OF REVIEW

Familial hypercholesterolemia is a frequent genetic disease associated with a high lifetime risk of cardiovascular disease (CVD). Statins are the cornerstone of treatment of familial hypercholesterolemia; however, with the advent of novel LDL-cholesterol lowering therapies, it has become necessary to identify familial hypercholesterolemia subjects presenting a significant residual CVD risk. The aim of this review is to provide an update on the recent literature concerning cardiovascular risk stratification in familial hypercholesterolemia.

RECENT FINDINGS

Recently, several clinical and genetic factors have been shown to be independent predictors of CVD in familial hypercholesterolemia. These include clinical scores such as the Montreal-FH-SCORE, novel protein biomarkers, carotid plaque score and genetic predictors such as genetic risk scores as well as single-nucleotide polymorphisms.

SUMMARY

Although there has been recent progress in cardiovascular risk stratification in familial hypercholesterolemia, there is still a need to further refine our knowledge concerning phenotype modifiers in this disease. Indeed, current known predictors do not explain the entirety of cardiovascular risk. More precise individual risk stratification in familial hypercholesterolemia could help to better tailor the proper therapy for each patient.

Familial hypercholesterolemia: experience from the French-Canadian population

PURPOSE OF REVIEW

There has recently been renewed interest in the study of the various facets of familial hypercholesterolemia, a severe monogenic disease associated with elevated LDL-cholesterol and premature cardiovascular disease (CVD). In the present review, novel data presenting the frequency of familial hypercholesterolemia as well as factors modulating the cardiovascular risk in familial hypercholesterolemia will be discussed.

RECENT FINDINGS

Recent studies have showed that familial hypercholesterolemia is much more prevalent than initially thought. Classically, it was estimated that familial hypercholesterolemia affected one in 500 people worldwide, but a recent large-scale meta-analysis has shown a prevalence closer to one in 250. In the French-Canadian population, this disease is even more frequent reaching one in 81 in certain regions of the Province of Quebec. Several novel studies in the French-Canadian population have shown that the clinical outcomes in familial hypercholesterolemia seem to be greatly influenced by risk factors other than LDL-cholesterol. Also, scores to predict CVD in familial hypercholesterolemia have been recently proposed.

SUMMARY

Familial hypercholesterolemia is more frequent than initially thought and the phenotype of this disease can be variable. Indeed, both clinical and genetic variables can modulate the CVD risk in this population.

Usefulness of the genetic risk score to identify phenocopies in families with familial hypercholesterolemia?

Familial hypercholesterolemia (FH) is caused by mutations in LDLR (low-density lipoprotein receptor), APOB (apolipoprotein B), PCSK9 (proprotein convertase subtilisin/kexin type 9), or APOE (apolipoprotein E) genes in approximately 80% of the cases. Polygenic forms of hypercholesterolemia may be present among patients clinically diagnosed with FH but with no identified mutation (FH mutation-negative (FH/M-)). To address whether polygenic forms may explain phenocopies in FH families, we calculated a 6-single-nucleotide polymorphism (SNP) genetic risk score (GRS) in all members from five French FH families where a mutation was identified (FH/M+) as well as some phenocopies (FH/M-). In two families, three FH/M- patients present a high GRS suggesting a polygenic hypercholesterolemia for these phenocopies. However, a high GRS is also observed in nine FH/M+ patients and in four unaffected relatives from three families. These observations indicate that the GRS does not seem to be a good diagnostic tool at the individual level. Nevertheless, the GRS seems to be a contributor of the severity of hypercholesterolemia since patients who cumulate a mutation and a high GRS exhibit higher low-density lipoprotein cholesterol levels when compared to patients with only FH (p = 0.054) or only polygenic hypercholesterolemia (p = 0.0039). In conclusion, the GRS can be used as a marker of the severity of hypercholesterolemia but does not seem to be a reliable tool to distinguish phenocopies within FH families.

Knowns and unknowns in the care of pediatric familial hypercholesterolemia

Familial hypercholesterolemia (FH) is a common genetic disorder that causes elevated LDL cholesterol levels from birth. Untreated FH accelerates atherosclerosis and predisposes individuals to premature coronary artery disease (CAD) in adulthood. Mendelian randomization studies have demonstrated that LDL cholesterol has both a causal and cumulative effect on the risk of CAD. This supports clinical recommendations that children with FH commence pharmacological treatment from the age of 8 to 10 years, to reduce the burden of hypercholesterolemia. Worldwide, the majority of children with FH remain undiagnosed. Recent evidence suggests that the frequency of FH is at least 1 in 250 and this constitutes a public health issue. We review and identify the knowns and unknowns concerning the detection and management of pediatric FH that impact on the developing model of care for this condition.