Genome-Wide Polygenic Score, Clinical Risk Factors, and Long-Term Trajectories of Coronary Artery Disease.
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ABSTRACT: Objective
To determine the relationship of a genome-wide polygenic score for coronary artery disease (GPSCAD) with lifetime trajectories of CAD risk, directly compare its predictive capacity to traditional risk factors, and assess its interplay with the Pooled Cohort Equations (PCE) clinical risk estimator. Approach and Results: We studied GPSCAD in 28 556 middle-aged participants of the Malmö Diet and Cancer Study, of whom 4122 (14.4%) developed CAD over a median follow-up of 21.3 years. A pronounced gradient in lifetime risk of CAD was observed-16% for those in the lowest GPSCAD decile to 48% in the highest. We evaluated the discriminative capacity of the GPSCAD-as assessed by change in the C-statistic from a baseline model including age and sex-among 5685 individuals with PCE risk estimates available. The increment for the GPSCAD (+0.045, P<0.001) was higher than for any of 11 traditional risk factors (range +0.007 to +0.032). Minimal correlation was observed between GPSCAD and 10-year risk defined by the PCE (r=0.03), and addition of GPSCAD improved the C-statistic of the PCE model by 0.026. A significant gradient in lifetime risk was observed for the GPSCAD, even among individuals within a given PCE clinical risk stratum. We replicated key findings-noting strikingly consistent results-in 325 003 participants of the UK Biobank.Conclusions
GPSCAD-a risk estimator available from birth-stratifies individuals into varying trajectories of clinical risk for CAD. Implementation of GPSCAD may enable identification of high-risk individuals early in life, decades in advance of manifest risk factors or disease.
SUBMITTER: Hindy G
PROVIDER: S-EPMC7577949 | biostudies-literature |
REPOSITORIES: biostudies-literature
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| S-EPMC7887053 | biostudies-literature
Project description:BackgroundGenome-wide polygenic scores (GPS) integrate information from many common DNA variants into a single number. Because rates of coronary artery disease (CAD) are substantially higher among South Asians, a GPS to identify high-risk individuals may be particularly useful in this population.ObjectivesThis analysis used summary statistics from a prior genome-wide association study to derive a new GPSCAD for South Asians.MethodsThis GPSCAD was validated in 7,244 South Asian UK Biobank participants and tested in 491 individuals from a case-control study in Bangladesh. Next, a static ancestry and GPSCAD reference distribution was built using whole-genome sequencing from 1,522 Indian individuals, and a framework was tested for projecting individuals onto this static ancestry and GPSCAD reference distribution using 1,800 CAD cases and 1,163 control subjects newly recruited in India.ResultsThe GPSCAD, containing 6,630,150 common DNA variants, had an odds ratio (OR) per SD of 1.58 in South Asian UK Biobank participants and 1.60 in the Bangladeshi study (p < 0.001 for each). Next, individuals of the Indian case-control study were projected onto static reference distributions, observing an OR/SD of 1.66 (p < 0.001). Compared with the middle quintile, risk for CAD was most pronounced for those in the top 5% of the GPSCAD distribution-ORs of 4.16, 2.46, and 3.22 in the South Asian UK Biobank, Bangladeshi, and Indian studies, respectively (p < 0.05 for each).ConclusionsThe new GPSCAD has been developed and tested using 3 distinct South Asian studies, and provides a generalizable framework for ancestry-specific GPS assessment.
| S-EPMC7592606 | biostudies-literature
Project description:Background Adverse cardiovascular outcomes such as coronary artery disease (CAD) are the leading noncancer causes of morbidity and mortality among childhood cancer survivors. Objectives The aim of this study was to assess the role of a genome-wide polygenic score (GPS) for CAD, well validated in the general population, and its interplay with cancer-related risk factors among childhood cancer survivors. Methods In a cohort study of 2,472 5-year childhood cancer survivors from the St. Jude Lifetime Cohort, the association between the GPS and the risk of CAD was performed using Cox regression models adjusted for age at cancer diagnosis, sex, cumulative dose of anthracyclines, and mean heart radiation dose. Results Among survivors of European ancestry, the GPS was significantly associated with the risk of CAD (HR per 1 SD of the GPS: 1.25; 95% CI: 1.04-1.49; P = 0.014). Compared with the first tertile, survivors in the upper tertile had a greater risk of CAD (1.51-fold higher HR of CAD [95% CI: 0.96-2.37; P = 0.074]), although the difference was not statistically significant. The GPS-CAD association was stronger among survivors diagnosed with cancer at age <10 years exposed to >25 Gy heart radiation (HR top vs. bottom tertile of GPS: 15.49; 95% CI: 5.24-45.52; Ptrend = 0.005) but not among those diagnosed at age ≥10 years (Ptrend ≥ 0.77) and not among those diagnosed at age <10 years exposed to ≤25 Gy heart radiation (Ptrend = 0.23). Among high-risk survivors, defined by an estimated relative hazard ≥3.0 from fitted Cox models including clinical risk factors alone, the cumulative incidence of CAD at 40 years from diagnosis was 29% (95% CI: 13%-45%). After incorporating the GPS into the model, the cumulative incidence increased to 48% (95% CI: 26%-69%). Conclusions Childhood cancer survivors are at risk for premature CAD. A GPS may help identify those who may benefit from targeted screening and personalized preventive interventions. Central Illustration
| S-EPMC9270604 | biostudies-literature
Project description:BackgroundThe value of polygenic risk scores (PRSs) towards improving guideline-recommended clinical risk models for coronary artery disease (CAD) prediction is controversial. Here we examine whether an integrated polygenic risk score improves the prediction of CAD beyond pooled cohort equations. METHODS: An observation study of 291,305 unrelated White British UK Biobank participants enrolled from 2006 to 2010 was conducted. A case-control sample of 9499 prevalent CAD cases and an equal number of randomly selected controls was used for tuning and integrating of the polygenic risk scores. A separate cohort of 272,307 individuals (with follow-up to 2020) was used to examine the risk prediction performance of pooled cohort equations, integrated polygenic risk score, and PRS-enhanced pooled cohort equation for incident CAD cases. The performance of each model was analyzed by discrimination and risk reclassification using a 7.5% threshold.ResultsIn the cohort of 272,307 individuals (mean age, 56.7 years) used to analyze predictive accuracy, there were 7036 incident CAD cases over a 12-year follow-up period. Model discrimination was tested for integrated polygenic risk score, pooled cohort equation, and PRS-enhanced pooled cohort equation with reported C-statistics of 0.640 (95% CI, 0.634-0.646), 0.718 (95% CI, 0.713-0.723), and 0.753 (95% CI, 0.748-0.758), respectively. Risk reclassification for the addition of the integrated polygenic risk score to the pooled cohort equation at a 7.5% risk threshold resulted in a net reclassification improvement of 0.117 (95% CI, 0.102 to 0.129) for cases and - 0.023 (95% CI, - 0.025 to - 0.022) for noncases [overall: 0.093 (95% CI, 0.08 to 0.104)]. For incident CAD cases, this represented 14.2% correctly reclassified to the higher-risk category and 2.6% incorrectly reclassified to the lower-risk category.ConclusionsAddition of the integrated polygenic risk score for CAD to the pooled cohort questions improves the predictive accuracy for incident CAD and clinical risk classification in the White British from the UK Biobank. These findings suggest that an integrated polygenic risk score may enhance CAD risk prediction and screening in the White British population.
| S-EPMC9639312 | biostudies-literature
Project description:BackgroundDespite moderate heritability estimates for depression-related phenotypes, few robust genetic predictors have been identified. Potential explanations for this discrepancy include the use of phenotypic measures taken from a single time point, rather than integrating information over longer time periods via multiple assessments, and the possibility that genetic risk is shaped by multiple loci with small effects.MethodsWe developed a 14-year long-term average depression measure based on 14 years of follow-up in the Nurses' Health Study (NHS; N = 6989 women). We estimated polygenic scores (PS) with internal whole-genome scoring (NHS-GWAS-PS). We also constructed PS by applying two external PS weighting algorithms from independent samples, one previously shown to predict depression (GAIN-MDD-PS) and another from the largest genome-wide analysis currently available (PGC-MDD-PS). We assessed the association of all three PS with our long-term average depression phenotype using linear, logistic, and quantile regressions.ResultsIn this study, the three PS approaches explained at most 0.2% of variance in the long-term average phenotype. Quantile regressions indicated PS had larger impacts at higher quantiles of depressive symptoms. Quantile regression coefficients at the 75th percentile were at least 40% larger than at the 25th percentile in all three polygenic scoring algorithms. The interquartile range comparison suggested the effects of PS significantly differed at the 25th and 75th percentiles of the long-term depressive phenotype for the PGC-MDD-PS (P = 0.03), and this difference also reached borderline statistical significance for the GAIN-MDD-PS (P = 0.05).ConclusionsIntegrating multiple phenotype assessments spanning 14 years and applying different polygenic scoring approaches did not substantially improve genetic prediction of depression. Quantile regressions suggested the effects of PS may be largest at high quantiles of depressive symptom scores, presumably among people with additional, unobserved sources of vulnerability to depression.
| S-EPMC3967544 | biostudies-literature
Project description:The relationship between lifestyles and cardiometabolic outcomes varies between individuals. In 382,275 UK Biobank Europeans, we investigate how lifestyles interact with polygenic scores (PGS) of cardiometabolic risk factors. We identify six interactions (PGS for body mass index with meat diet, physical activity, sedentary behaviour and insomnia; PGS for high-density lipoprotein cholesterol with sedentary behaviour; PGS for triglycerides with meat diet) in multivariable linear regression models including an interaction term and show stronger associations between lifestyles and cardiometabolic risk factors among individuals with high PGSs than those with low PGSs. Genome-wide interaction analyses pinpoint three genetic variants (FTO rs72805613 for BMI; CETP rs56228609 for high-density lipoprotein cholesterol; TRIB2 rs4336630 for triglycerides; PInteraction < 5 × 10-8). The associations between lifestyles and cardiometabolic risk factors differ between individuals grouped by the genotype of these variants, with the degree of differences being similar to that between individuals with high and low values for the corresponding PGSs. This study demonstrates that associations between lifestyles and cardiometabolic risk factors can differ between individuals based upon their genetic profiles. It further suggests that genetic variants with interaction effects contribute more to such differences compared to those without interaction effects, which has potential implications for developing PGSs for personalised intervention.
| S-EPMC10675788 | biostudies-literature
Project description:Introduction: Cardiometabolic factors are implicated in the aetiology of Alzheimer's disease and may lie on the pathways linking genetic variants to Alzheimer's disease across the life course. We examined whether polygenic risk scores (PRS) were associated with cardiometabolic health indicators through childhood and adolescence. Methods: In 7,977 participants from the Avon Longitudinal Study of Parents and Children, we tested whether a PRS for Alzheimer's disease was associated with trajectories of cardiometabolic risk factors. We examined trajectories for height at 1-18 years; lean and fat mass at 9-18 years; systolic and diastolic blood pressure at 7-18 years; glucose and C-reactive protein at 9-18 years; insulin at 10-18 years; and high and low-density lipoproteins and triglycerides birth at 18 years. We also examined birthweight and interleukin-6 (IL-6) at age 9 years and physical activity at ages 11, 12, and 15 years. Results: No consistent associations were observed between the PRS excluding genetic variants in the apolipoprotein E gene region and cardiometabolic factors trajectories across childhood and adolescence. Conclusions: We did not detect evidence to suggest that the PRS for Alzheimer's disease acts through childhood and adolescent cardiometabolic risk factors. Further studies should examine whether these associations emerge later in adulthood when variation in cardiometabolic risk factors is likely to be greater.
| S-EPMC6971849 | biostudies-literature
Project description:Accurate colorectal cancer (CRC) risk prediction models are critical for identifying individuals at low and high risk of developing CRC, as they can then be offered targeted screening and interventions to address their risks of developing disease (if they are in a high-risk group) and avoid unnecessary screening and interventions (if they are in a low-risk group). As it is likely that thousands of genetic variants contribute to CRC risk, it is clinically important to investigate whether these genetic variants can be used jointly for CRC risk prediction. In this paper, we derived and compared different approaches to generating predictive polygenic risk scores (PRS) from genome-wide association studies (GWASs) including 55,105 CRC-affected case subjects and 65,079 control subjects of European ancestry. We built the PRS in three ways, using (1) 140 previously identified and validated CRC loci; (2) SNP selection based on linkage disequilibrium (LD) clumping followed by machine-learning approaches; and (3) LDpred, a Bayesian approach for genome-wide risk prediction. We tested the PRS in an independent cohort of 101,987 individuals with 1,699 CRC-affected case subjects. The discriminatory accuracy, calculated by the age- and sex-adjusted area under the receiver operating characteristics curve (AUC), was highest for the LDpred-derived PRS (AUC = 0.654) including nearly 1.2 M genetic variants (the proportion of causal genetic variants for CRC assumed to be 0.003), whereas the PRS of the 140 known variants identified from GWASs had the lowest AUC (AUC = 0.629). Based on the LDpred-derived PRS, we are able to identify 30% of individuals without a family history as having risk for CRC similar to those with a family history of CRC, whereas the PRS based on known GWAS variants identified only top 10% as having a similar relative risk. About 90% of these individuals have no family history and would have been considered average risk under current screening guidelines, but might benefit from earlier screening. The developed PRS offers a way for risk-stratified CRC screening and other targeted interventions.
| S-EPMC7477007 | biostudies-literature
Project description:Diabetic retinopathy (DR) is a common consequence in type 2 diabetes (T2D) and a leading cause of blindness in working-age adults. Yet, its genetic predisposition is largely unknown. Here, we examined the polygenic architecture underlying DR by deriving and assessing a genome-wide polygenic risk score (PRS) for DR. We evaluated the PRS in 6079 individuals with T2D of European, Hispanic, African and other ancestries from a large-scale multi-ethnic biobank. Main outcomes were PRS association with DR diagnosis, symptoms and complications, and time to diagnosis, and transferability to non-European ancestries. We observed that PRS was significantly associated with DR. A standard deviation increase in PRS was accompanied by an adjusted odds ratio (OR) of 1.12 [95% confidence interval (CI) 1.04-1.20; P = 0.001] for DR diagnosis. When stratified by ancestry, PRS was associated with the highest OR in European ancestry (OR = 1.22, 95% CI 1.02-1.41; P = 0.049), followed by African (OR = 1.15, 95% CI 1.03-1.28; P = 0.028) and Hispanic ancestries (OR = 1.10, 95% CI 1.00-1.10; P = 0.050). Individuals in the top PRS decile had a 1.8-fold elevated risk for DR versus the bottom decile (P = 0.002). Among individuals without DR diagnosis, the top PRS decile had more DR symptoms than the bottom decile (P = 0.008). The PRS was associated with retinal hemorrhage (OR = 1.44, 95% CI 1.03-2.02; P = 0.03) and earlier DR presentation (10% probability of DR by 4 years in the top PRS decile versus 8 years in the bottom decile). These results establish the significant polygenic underpinnings of DR and indicate the need for more diverse ancestries in biobanks to develop multi-ancestral PRS.
| S-EPMC8165647 | biostudies-literature
Project description:ImportanceThe incremental value of polygenic risk scores in addition to well-established risk prediction models for coronary artery disease (CAD) is uncertain.ObjectiveTo examine whether a polygenic risk score for CAD improves risk prediction beyond pooled cohort equations.Design, setting, and participantsObservational study of UK Biobank participants enrolled from 2006 to 2010. A case-control sample of 15 947 prevalent CAD cases and equal number of age and sex frequency-matched controls was used to optimize the predictive performance of a polygenic risk score for CAD based on summary statistics from published genome-wide association studies. A separate cohort of 352 660 individuals (with follow-up to 2017) was used to evaluate the predictive accuracy of the polygenic risk score, pooled cohort equations, and both combined for incident CAD.ExposuresPolygenic risk score for CAD, pooled cohort equations, and both combined.Main outcomes and measuresCAD (myocardial infarction and its related sequelae). Discrimination, calibration, and reclassification using a risk threshold of 7.5% were assessed.ResultsIn the cohort of 352 660 participants (mean age, 55.9 years; 205 297 women [58.2%]) used to evaluate the predictive accuracy of the examined models, there were 6272 incident CAD events over a median of 8 years of follow-up. CAD discrimination for polygenic risk score, pooled cohort equations, and both combined resulted in C statistics of 0.61 (95% CI, 0.60 to 0.62), 0.76 (95% CI, 0.75 to 0.77), and 0.78 (95% CI, 0.77 to 0.79), respectively. The change in C statistic between the latter 2 models was 0.02 (95% CI, 0.01 to 0.03). Calibration of the models showed overestimation of risk by pooled cohort equations, which was corrected after recalibration. Using a risk threshold of 7.5%, addition of the polygenic risk score to pooled cohort equations resulted in a net reclassification improvement of 4.4% (95% CI, 3.5% to 5.3%) for cases and -0.4% (95% CI, -0.5% to -0.4%) for noncases (overall net reclassification improvement, 4.0% [95% CI, 3.1% to 4.9%]).Conclusions and relevanceThe addition of a polygenic risk score for CAD to pooled cohort equations was associated with a statistically significant, yet modest, improvement in the predictive accuracy for incident CAD and improved risk stratification for only a small proportion of individuals. The use of genetic information over the pooled cohort equations model warrants further investigation before clinical implementation.
| S-EPMC7042853 | biostudies-literature