scholarly journals Risk Prediction Using Polygenic Risk Scores for Prevention of Stroke and Other Cardiovascular Diseases

Stroke ◽  
2021 ◽  
Author(s):  
Gad Abraham ◽  
Loes Rutten-Jacobs ◽  
Michael Inouye
Keyword(s):  
Risk Prediction ◽  
Risk Scores ◽  
Future Research ◽  
Cvd Risk ◽  
Polygenic Risk ◽  
Monogenic Disorders ◽  
Current State ◽  
Common Genetic Variants ◽  
Artery Disease

Early prediction of risk of cardiovascular disease (CVD), including stroke, is a cornerstone of disease prevention. Clinical risk scores have been widely used for predicting CVD risk from known risk factors. Most CVDs have a substantial genetic component, which also has been confirmed for stroke in recent gene discovery efforts. However, the role of genetics in prediction of risk of CVD, including stroke, has been limited to testing for highly penetrant monogenic disorders. In contrast, the importance of polygenic variation, the aggregated effect of many common genetic variants across the genome with individually small effects, has become more apparent in the last 5 to 10 years, and powerful polygenic risk scores for CVD have been developed. Here we review the current state of the field of polygenic risk scores for CVD including stroke, and their potential to improve CVD risk prediction. We present findings and lessons from diseases such as coronary artery disease as these will likely be useful to inform future research in stroke polygenic risk prediction.

scholarly journals Polygenic risk scores in cardiovascular risk prediction: A cohort study and modelling analyses

PLoS Medicine ◽  
2021 ◽  
Vol 18 (1) ◽  
pp. e1003498
Author(s):  
Luanluan Sun ◽  
Lisa Pennells ◽  
Stephen Kaptoge ◽  
Christopher P. Nelson ◽  
Scott C. Ritchie ◽  
...  
Keyword(s):  
Risk Factors ◽  
Primary Care ◽  
Prediction Model ◽  
Risk Prediction ◽  
Risk Scores ◽  
Cvd Risk ◽  
Polygenic Risk ◽  
History Of ◽  
Predicted Risk ◽  
Conventional Risk Factors

Background Polygenic risk scores (PRSs) can stratify populations into cardiovascular disease (CVD) risk groups. We aimed to quantify the potential advantage of adding information on PRSs to conventional risk factors in the primary prevention of CVD. Methods and findings Using data from UK Biobank on 306,654 individuals without a history of CVD and not on lipid-lowering treatments (mean age [SD]: 56.0 [8.0] years; females: 57%; median follow-up: 8.1 years), we calculated measures of risk discrimination and reclassification upon addition of PRSs to risk factors in a conventional risk prediction model (i.e., age, sex, systolic blood pressure, smoking status, history of diabetes, and total and high-density lipoprotein cholesterol). We then modelled the implications of initiating guideline-recommended statin therapy in a primary care setting using incidence rates from 2.1 million individuals from the Clinical Practice Research Datalink. The C-index, a measure of risk discrimination, was 0.710 (95% CI 0.703–0.717) for a CVD prediction model containing conventional risk predictors alone. Addition of information on PRSs increased the C-index by 0.012 (95% CI 0.009–0.015), and resulted in continuous net reclassification improvements of about 10% and 12% in cases and non-cases, respectively. If a PRS were assessed in the entire UK primary care population aged 40–75 years, assuming that statin therapy would be initiated in accordance with the UK National Institute for Health and Care Excellence guidelines (i.e., for persons with a predicted risk of ≥10% and for those with certain other risk factors, such as diabetes, irrespective of their 10-year predicted risk), then it could help prevent 1 additional CVD event for approximately every 5,750 individuals screened. By contrast, targeted assessment only among people at intermediate (i.e., 5% to <10%) 10-year CVD risk could help prevent 1 additional CVD event for approximately every 340 individuals screened. Such a targeted strategy could help prevent 7% more CVD events than conventional risk prediction alone. Potential gains afforded by assessment of PRSs on top of conventional risk factors would be about 1.5-fold greater than those provided by assessment of C-reactive protein, a plasma biomarker included in some risk prediction guidelines. Potential limitations of this study include its restriction to European ancestry participants and a lack of health economic evaluation. Conclusions Our results suggest that addition of PRSs to conventional risk factors can modestly enhance prediction of first-onset CVD and could translate into population health benefits if used at scale.

scholarly journals Polygenic Risk Scores Predict Hypertension Onset and Cardiovascular Risk

Hypertension ◽  
2021 ◽  
Vol 77 (4) ◽  
pp. 1119-1127 ◽  
Author(s):  
Felix Vaura ◽  
Anni Kauko ◽  
Karri Suvila ◽  
Aki S. Havulinna ◽  
Nina Mars ◽  
...  
Keyword(s):  
Risk Prediction ◽  
Early Onset ◽  
Late Onset ◽  
Risk Scores ◽  
Event Analysis ◽  
Cvd Risk ◽  
Polygenic Risk ◽  
Clinical Risk ◽  
Traditional Risk Factors ◽  
Association Data

Although genetic risk scores have been used to predict hypertension, their utility in the clinical setting remains uncertain. Our study comprised N=218 792 FinnGen participants (mean age 58 years, 56% women) and N=22 624 well-phenotyped FINRISK participants (mean age 50 years, 53% women). We used public genome-wide association data to compute polygenic risk scores (PRSs) for systolic and diastolic blood pressure (BP). Using time-to-event analysis, we then assessed (1) the association of BP PRSs with hypertension and cardiovascular disease (CVD) in FinnGen and (2) the improvement in model discrimination when combining BP PRSs with the validated 4- and 10-year clinical risk scores for hypertension and CVD in FINRISK. In FinnGen, compared with having a 20 to 80 percentile range PRS, a PRS in the highest 2.5% conferred 2.3-fold (95% CI, 2.2–2.4) risk of hypertension and 10.6 years (95% CI, 9.9–11.4) earlier hypertension onset. In subgroup analyses, this risk was only 1.6-fold (95% CI, 1.5–1.7) for late-onset hypertension (age ≥55 years) but 2.8-fold (95% CI, 2.6–2.9) for early-onset hypertension (age <55 years). Elevated systolic BP PRS also conferred 1.3-fold (95% CI, 1.2–1.4) risk of CVD and 2.3 years (95% CI, 1.6–3.1) earlier onset. In FINRISK, systolic and diastolic BP PRSs improved clinical risk prediction of hypertension (but not CVD), increasing the C statistics by 0.7% (95% CI, 0.3–1.1). We demonstrate that genetic information improves hypertension risk prediction. BP PRSs together with traditional risk factors could improve prediction of hypertension and particularly early-onset hypertension, which confers substantial CVD risk.

scholarly journals Use of polygenic risk scores and other molecular markers to enhance cardiovascular risk prediction: prospective cohort study and modelling analysis

2019 ◽  
Author(s):  
Luanluan Sun ◽  
Lisa Pennells ◽  
Stephen Kaptoge ◽  
Christopher P Nelson ◽  
Gad Abraham ◽  
...  
Keyword(s):  
Primary Care ◽  
Molecular Markers ◽  
Prediction Model ◽  
Risk Prediction ◽  
Risk Scores ◽  
Intermediate Risk ◽  
Cvd Risk ◽  
Polygenic Risk ◽  
History Of ◽  
Risk Predictors

AbstractBackgroundThere is debate about the value of adding information on genetic and other molecular markers to conventional cardiovascular disease (CVD) risk predictors.MethodsUsing data on 306,654 individuals without a history of CVD from UK Biobank, we calculated measures of risk-discrimination and reclassification upon addition of polygenic risk scores (PRS) and a panel of 27 clinical biochemistry markers to a conventional risk prediction model (i.e., including age, sex, systolic blood pressure, smoking status, history of diabetes, total cholesterol and HDL cholesterol). We then modelled implications of initiating guideline-recommended statin therapy after the assessment of molecular markers for a UK primary-care setting.FindingsThe C-index was 0.710 (95% CI, 0.703-0.717) for a CVD prediction model containing conventional risk predictors alone. The C-index increased by similar amounts when adding information on PRS or biochemistry markers (0.011 and 0.014, respectively; P<0.001), and it increased still further (0.022; P<0.001) when information on both was combined. Among cases and controls, continuous net reclassification improvements were about 12% and 19%, respectively, when both PRS and biochemistry markers were added. If PRS and biochemistry markers were to be assessed in the entire primary care population aged 40-75, then it could help prevent one additional CVD event for every 893 individuals screened. By contrast, targeted assessment only among people at intermediate (i.e., 5-10%) 10-year CVD risk could help prevent one additional CVD event for every 233 individuals screened. This targeted strategy could help reclassify 16% of the intermediate-risk group to the high-risk (i.e., ≥10%) category, preventing 11% more CVD events than conventional risk prediction.InterpretationAdding information on both PRS and selected biochemistry markers moderately enhanced CVD predictive accuracy and could improve primary prevention of CVD. However, our modelling suggested that targeted assessment of molecular markers among individuals at intermediate-risk would be more efficient than blanket approaches.

scholarly journals Improving Machine Learning Prediction of ADHD Using Gene Set Polygenic Risk Scores and Risk Scores from Genetically Correlated Phenotypes

2022 ◽  
Author(s):  
Eric J Barnett ◽  
Yanli Zhang-James ◽  
Stephen V Faraone
Keyword(s):  
Machine Learning ◽  
Risk Prediction ◽  
Genetic Risk ◽  
Characteristic Curve ◽  
Genetic Correlations ◽  
Learning Curves ◽  
Risk Scores ◽  
Polygenic Risk ◽  
Gene Set ◽  
Common Genetic Variants

Background: Polygenic risk scores (PRSs), which sum the effects of SNPs throughout the genome to measure risk afforded by common genetic variants, have improved our ability to estimate disorder risk for Attention-Deficit/Hyperactivity Disorder (ADHD) but the accuracy of risk prediction is rarely investigated. Methods: With the goal of improving risk prediction, we performed gene set analysis of GWAS data to select gene sets associated with ADHD within a training subset. For each selected gene set, we generated gene set polygenic risk scores (gsPRSs), which sum the effects of SNPs for each selected gene set. We created gsPRS for ADHD and for phenotypes having a high genetic correlation with ADHD. These gsPRS were added to the standard PRS as input to machine learning models predicting ADHD. We used feature importance scores to select gsPRS for a final model and to generate a ranking of the most consistently predictive gsPRS. Results: For a test subset that had not been used for training or validation, a random forest (RF) model using PRSs from ADHD and genetically correlated phenotypes and an optimized group of 20 gsPRS had an area under the receiving operating characteristic curve (AUC) of 0.72 (95% CI: 0.70 to 0.74). This AUC was a statistically significant improvement over logistic regression models and RF models using only PRS from ADHD and genetically correlated phenotypes. Conclusions: Summing risk at the gene set level and incorporating genetic risk from disorders with high genetic correlations with ADHD improved the accuracy of predicting ADHD. Learning curves suggest that additional improvements would be expected with larger study sizes. Our study suggests that better accounting of genetic risk and the genetic context of allelic differences results in more predictive models.

scholarly journals The role of coronary artery calcification testing in incident coronary artery disease risk prediction in type 1 diabetes

Diabetologia ◽  
2018 ◽  
Vol 62 (2) ◽  
pp. 259-268 ◽  
Author(s):  
Jingchuan Guo ◽  
Sebhat A. Erqou ◽  
Rachel G. Miller ◽  
Daniel Edmundowicz ◽  
Trevor J. Orchard ◽  
...  
Keyword(s):  
Coronary Artery Disease ◽  
Type 1 Diabetes ◽  
Coronary Artery ◽  
Risk Prediction ◽  
Coronary Artery Calcification ◽  
Disease Risk ◽  
Coronary Artery Disease Risk ◽  
Artery Disease

The Advancement of Neuroimaging Research Investigating Developmental Stuttering

2011 ◽  
Vol 21 (3) ◽  
pp. 88-95 ◽  
Author(s):  
Deryk S. Beal
Keyword(s):  
Speech Production ◽  
Fast Rate ◽  
Neural Correlates ◽  
Future Research ◽  
Current State ◽  
Developmental Stuttering ◽  
The Brain ◽  
State Of The Field

We are amassing information about the role of the brain in speech production and the potential neural limitations that coincide with developmental stuttering at a fast rate. As such, it is difficult for many clinician-scientists who are interested in the neural correlates of stuttering to stay informed of the current state of the field. In this paper, I aim to inspire clinician-scientists to tackle hypothesis-driven research that is grounded in neurobiological theory. To this end, I will review the neuroanatomical structures, and their functions, which are implicated in speech production and then describe the relevant differences identified in these structures in people who stutter relative to their fluently speaking peers. I will conclude the paper with suggestions on directions of future research to facilitate the evolution of the field of neuroimaging of stuttering.

scholarly journals Improved prediction of schizophrenia by leveraging genetic overlap with brain morphology

2020 ◽  
Author(s):  
Dennis van der Meer ◽  
Alexey A Shadrin ◽  
Kevin O'Connell ◽  
Francesco Bettella ◽  
Srdjan Djurovic ◽  
...  
Keyword(s):  
State Of The Art ◽  
Imaging Genetics ◽  
Brain Morphology ◽  
Risk Scores ◽  
Brain Disorders ◽  
Polygenic Risk ◽  
Current State ◽  
Genetic Overlap

Schizophrenia is a complex, polygenic disorder associated with subtle, distributed abnormalities in brain morphology. Here, we report large genetic overlap between schizophrenia and brain morphology, which enabled derivation of polygenic risk scores more predictive of schizophrenia diagnosis than the current state-of-the-art. Our results illustrate the potential of exploiting genetic overlap in imaging genetics studies, and how pleiotropy-enriched risk scores may improve prediction of polygenic brain disorders.

scholarly journals Polygenic risk scores for coronary artery disease and subsequent event risk amongst established cases

2020 ◽  
Vol 29 (8) ◽  
pp. 1388-1395
Author(s):  
Laurence J Howe ◽  
Frank Dudbridge ◽  
Amand F Schmidt ◽  
Chris Finan ◽  
Spiros Denaxas ◽  
...  
Keyword(s):  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Risk Scores ◽  
Effect Estimate ◽  
Inverse Association ◽  
Uk Biobank ◽  
Case Control Studies ◽  
Polygenic Risk ◽  
Increased Risk ◽  
Artery Disease

Abstract Background There is growing evidence that polygenic risk scores (PRSs) can identify individuals with elevated lifetime risk of coronary artery disease (CAD). Whether they can also be used to stratify the risk of subsequent events among those surviving a first CAD event remain uncertain, with possible biological differences between CAD onset and progression, and the potential for index event bias. Methods Using two baseline subsamples of UK Biobank: prevalent CAD cases (N = 10 287) and individuals without CAD (N = 393 108), we evaluated associations between a CAD PRS and incident cardiovascular and fatal outcomes. Results A 1 SD higher PRS was associated with an increased risk of incident myocardial infarction (MI) in participants without CAD (OR 1.33; 95% CI 1.29, 1.38), but the effect estimate was markedly attenuated in those with prevalent CAD (OR 1.15; 95% CI 1.06, 1.25) and heterogeneity P = 0.0012. Additionally, among prevalent CAD cases, we found an evidence of an inverse association between the CAD PRS and risk of all-cause death (OR 0.91; 95% CI 0.85, 0.98) compared with those without CAD (OR 1.01; 95% CI 0.99, 1.03) and heterogeneity P = 0.0041. A similar inverse association was found for ischaemic stroke [prevalent CAD (OR 0.78; 95% CI 0.67, 0.90); without CAD (OR 1.09; 95% CI 1.04, 1.15), heterogeneity P &lt; 0.001]. Conclusions Bias induced by case stratification and survival into UK Biobank may distort the associations of PRS derived from case-control studies or populations initially free of disease. Differentiating between effects of possible biases and genuine biological heterogeneity is a major challenge in disease progression research.

Sign in / Sign up

Export Citation Format

Share Document