Abstract 4358093: Clinical Utility and Transethnic Calibration of Polygenic Risk Scores for Myocardial Infarction: A Global Meta-analysis Across Diverse Genetic Ancestries

Genetic Risk Prediction for CKD: A Journey of a Thousand Miles

Do Polygenic Scores Inform Psychiatric Disease Risk After Considering Family History?

Funding Acknowledgements Type of funding sources: None. Background A polygenic risk score (PRS) is derived from a genome-wide association study (GWAS) and represents an aggregate of thousands of single-nucleotide polymorphisms (SNPs) that provide a baseline estimate of an individual’s genetic risk for a specific disease or trait at birth. Cardiometabolic disease represents a set of disease processes that historically have disproportionally affected underrepresented racial and minority groups. Furthermore, these groups represent a population generally not well captured by traditional risk scores compared to European cohorts. Since the first GWAS studying myocardial infarction was published, PRSs have increasingly been seen as a promising tool to improve risk stratification of non-European populations. However, how PRSs can be best used in clinical practice remains unclear. Purpose To provide an overview of the PRSs related to cardiometabolic disease, analyze the ancestral diversity of GWAS cohorts, and discuss the evidence supporting their clinical applications. Methods The Preferred Reporting Items For Systematic Reviews and Meta-analysis extension for Scoping Reviews protocol was used to conduct a scoping review of the MEDLINE, EMBASE, and CENTRAL databases. English studies that published a PRS related to atrial fibrillation (AF), cerebrovascular disease (CVD), coronary artery disease (CAD), dyslipidemia, heart failure, heritable cardiomyopathy, hypertension, and type 2 diabetes were reviewed. Results Across the 4,863 studies screened, 82 articles met the inclusion criteria. The most common PRS related to CAD, followed by hypertension and CVD. Limited ancestral diversity was observed as most studies (56) included only individuals of European ancestry. A smaller proportion of studies (16) published PRSs derived in multi-ancestry cohorts. Only ten studies published a PRS derived solely from a sample population of non-European ancestry (Chinese, East Asian, Japanese, and Korean). The predictive performance of most PRSs was similar to or superior to traditional risk factors. More than half of the included studies (42) reported an integrated risk model combining a PRS with traditional risk factors or a clinical risk tool (FRS, PCE, CHADS2). The integrated risk model consistently improved predictive accuracy, but few studies investigated the performance in a non-European population. Conclusion In conclusion, this scoping review is the first of its kind and reports strong evidence for the clinical use of PRSs in AF, CAD, CVD, and hypertension. However, most PRSs are derived in cohorts of European ancestry, which contributes to a lack of PRS transferability across different ancestral groups, likely exacerbating health inequities. Future prospective studies should focus on further establishing the clinical utility of PRSs. Additionally, diversity in future GWAS cohorts is essential to ensure that PRSs reflect the multi-ancestry society at large.

The First Baby Born After Polygenic Embryo Screening

BackgroundSubjective cognitive decline (SCD) and polygenic risk scores (PRS) have both been shown to be associated with the risk of developing dementia. However, it is of interest whether they can improve the established and well‐validated cardiovascular risk factors aging and dementia (CAIDE) model and how their predictive abilities compare.MethodsSCD and PRS were assessed and determined in a large, population‐based cohort study. In this study, the CAIDE model was applied to a sub‐sample of 5,360 study participants and evaluated for the outcomes of all‐cause dementia, Alzheimer’s disease (AD) and vascular dementia (VD) by calculating Akaike’s information criterion (AIC) and the area under the curve (AUC). The improvement of the CAIDE model by SCD and PRS was further examined using the net reclassification improvement (NRI) method and integrated discrimination improvement (IDI). Besides evaluations in the total sample, predictive abilities were evaluated in the mid‐life and late‐life sub‐cohort.ResultsDuring 17 years of follow‐up, 410 participants were diagnosed with dementia, including 139 AD and 152 VD diagnoses. Overall, the CAIDE model showed high discriminative ability for all outcomes reaching AUCs of 0.785, 0.793 and 0.789 for all‐cause dementia, AD, and VD, respectively. Adding information on SCD to the CAIDE model led to a significant increase in NRI for all‐cause dementia (4.4%, p = 0.0401) and VD (7.7%, p = 0.0095). In contrast, prediction models for AD further improved when PRS was added to the model (NRI, 8.4%, p = 0.0257). The latter became more substantial when applied to the mid‐life sub‐group (50‐64 years), achieving an NRI of 19.6% (p = 0.0075). When APOE ε4 carrier status was included in CAIDE model 2, the discriminative ability of the basic CAIDE model increased, and SCD and PRS did not further improve the prediction.ConclusionWhile PRS significantly improved the prediction of the CAIDE model for AD, information on SCD increased its discriminative ability for all‐cause dementia and VD. In contrast to PRS, information on SCD can be assessed more efficiently, and thus models including SCD can be more easily transferred to the clinical setting. Nevertheless, the two variables seem negligible if APOE ε4 carrier status is available.

The ability of the fracture risk assessment tool (FRAX) in discriminating fracture and non-fracture in postmenopausal women remains suboptimal. Adding a genetic profile may improve the performance of FRAX. Three genetic risk scores (GRSs) (GRS_fracture, GRS_BMD, GRS_eBMD) were calculated for each participant in the Women's Health Initiative Study (n = 23,981), based on the summary statistics of three comprehensive osteoporosis-related genome-wide association studies (GWAS). The primary outcomes were incident major osteoporotic fracture (MOF) and hip fracture (HF). The association between each GRS and fracture risk were evaluated in separate Cox Proportional Hazard models, with FRAX clinical risk factors adjusted for. The discrimination ability of each model was assessed using Area Under the Curve (AUC). The predictive improvement attributable to each GRSs was assessed using the net reclassification improvement (NRI) and the integrated discrimination improvement (IDI). GRS_BMD and GRS_eBMD were significantly associated with MOF and HF risk, independent of the base FRAX risk factors. Compare to the base FRAX model, the models with GRS_fracture, GRS_BMD, and GRS_eBMD improved the reclassification of MOF by 0.5% (95% CI, 0.2% to 0.9%, p = p < .01), 0.3% (95% CI, 0.1% to 0.6%, p = 0.01), and 2.1% (95% CI, 0.3% to 2.8%, p < .01), respectively. Similar results were also observed when using HF as an outcome. Our study suggested that the addition of genetic profiles provide limited improvements in the reclassification of FRAX for MOF and HF.

doi: medRxiv preprint NOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice.

BackgroundRisk stratification for lung cancer (LC) screening is so far mostly based on smoking history. This study aimed to assess if and to what extent such risk stratification could be enhanced by additional consideration of genetic risk scores (GRSs) and epigenetic risk scores defined by DNA methylation.MethodsWe conducted a nested case-control study of 143 incident LC cases and 1460 LC-free controls within a prospective cohort of 9949 participants aged 50–75 years with 14-year follow-up. Lifetime smoking history was obtained in detail at recruitment. We built a GRS based on 31 previously identified LC-associated single-nucleotide polymorphisms (SNPs) and a DNA methylation score (MRS) based on methylation of 151 previously identified smoking-associated cytosine-phosphate-guanine (CpG) loci. We evaluated associations of GRS and MRS with LC incidence by logistic regression models, controlling for age, sex, smoking status, and pack-years. We compared the predictive performance of models based on pack-years alone with models additionally including GRS and/or MRS using the area under the receiver operating characteristic curve (AUC), net reclassification improvement (NRI), and integrated discrimination improvement (IDI).ResultsGRS and MRS showed moderate and strong associations with LC risk even after comprehensive adjustment for smoking history (adjusted odds ratio [95% CI] comparing highest with lowest quartile 1.93 [1.05–3.71] and 5.64 [2.13–17.03], respectively). Similar associations were also observed within the risk groups of ever and heavy smokers. Addition of GRS and MRS furthermore strongly enhanced LC prediction beyond prediction by pack-years (increase of optimism-corrected AUC among heavy smokers from 0.605 to 0.654, NRI 26.7%, p = 0.0106, IDI 3.35%, p = 0.0036), the increase being mostly attributable to the inclusion of MRS.ConclusionsConsideration of MRS, by itself or in combination with GRS, may strongly enhance LC risk stratification.

Polygenic Risk Scores in Schizophrenia: Ready for the Real World?

This study aimed to investigate environmental factors and genetic variant loci associated with hepatocellular carcinoma (HCC) in Chinese population and construct a weighted genetic risk score (wGRS) and polygenic risk score (PRS). A case-control study was applied to confirm the single nucleotide polymorphisms (SNPs) and environmental variables linked to HCC in the Chinese population, which had been screened by meta-analyses. wGRS and PRS were built in training sets and validation sets. Area under the curve (AUC), net reclassification improvement (NRI), integrated discrimination improvement (IDI), Akaike information criterion (AIC), and Bayesian information criterion (BIC) were applied to evaluate the performance of the models. A total of 13 SNPs were included in both risk prediction models. Compared with wGRS, PRS had better accuracy and discrimination ability in predicting HCC risk. The AUC for PRS in combination with drinking history, cirrhosis, HBV infection, and family history of HCC in training sets and validation sets (AUC: 0.86, 95% CI: 0.84-0.89; AUC: 0.85, 95% CI: 0.81-0.89) increased at least 20% than the AUC for PRS alone (AUC: 0.63, 95% CI: 0.60-0.67; AUC: 0.65, 95% CI: 0.60-0.71). A novel model combining PRS with alcohol history, HBV infection, cirrhosis, and family history of HCC could be applied as an effective tool for risk prediction of HCC, which could discriminate at-risk individuals for precise prevention.

A genetic risk score of 45 coronary artery disease risk variants associates with increased risk of myocardial infarction in 6041 Danish individuals

Polygenic risk score for atopic dermatitis in the Canadian population

We have previously shown that polygenic risk scores (PRS) can improve risk stratification of peripheral artery disease (PAD) in a large, retrospective cohort. Here, we evaluate the potential of PRS in improving the detection of PAD and prediction of major adverse cardiovascular and cerebrovascular events (MACCE) and adverse events (AE) in an institutional patient cohort. We created a cohort of 278 patients (52 cases and 226 controls) and fit a PAD-specific PRS based on the weighted sum of risk alleles. We built traditional clinical risk models and machine learning (ML) models using clinical and genetic variables to detect PAD, MACCE, and AE. The models' performances were measured using the area under the curve (AUC), net reclassification index (NRI), integrated discrimination improvement (IDI), and Brier score. We also evaluated the clinical utility of our PAD model using decision curve analysis (DCA). We found a modest, but not statistically significant improvement in the PAD detection model's performance with the inclusion of PRS from 0.902 (95% CI: 0.846-0.957) (clinical variables only) to 0.909 (95% CI: 0.856-0.961) (clinical variables with PRS). The PRS inclusion significantly improved risk re-classification of PAD with an NRI of 0.07 (95% CI: 0.002-0.137), p = 0.04. For our ML model predicting MACCE, the addition of PRS did not significantly improve the AUC, however, NRI analysis demonstrated significant improvement in risk re-classification (p = 2e-05). Decision curve analysis showed higher net benefit of our combined PRS-clinical model across all thresholds of PAD detection. Including PRS to a clinical PAD-risk model was associated with improvement in risk stratification and clinical utility, although we did not see a significant change in AUC. This result underscores the potential clinical utility of incorporating PRS data into clinical risk models for prevalent PAD and the need for use of evaluation metrics that can discern the clinical impact of using new biomarkers in smaller populations.

Should preimplantation genetic testing for polygenic disease be offered to all – or none?

Simple SummaryVarious genomic variants that are statistically associated with breast cancer (BC) have been discovered and robustly replicated as a result of different genome-wide association studies. Such findings have led to the development of a different risk classification with the Polygenic Risk Score (PRS). In this paper, we have calculated the PRS of the Norwegian samples using various PRS models, compared their performances, and then evaluated the PRS-based lifetime risk of developing BC. The best performing PRS model includes 3820 SNPs (AUC = 0.625 and OR = 1.567), and the other studied models also provide closer performances. The results show that the PRS can be a useful instrument for lifetime risk stratification of developing BC in the Norwegian population, and can thus be utilized in the BC screening program.Background: Statistical associations of numerous single nucleotide polymorphisms with breast cancer (BC) have been identified in genome-wide association studies (GWAS). Recent evidence suggests that a Polygenic Risk Score (PRS) can be a useful risk stratification instrument for a BC screening strategy, and a PRS test has been developed for clinical use. The performance of the PRS is yet unknown in the Norwegian population. Aim: To evaluate the performance of PRS models for BC in a Norwegian dataset. Methods: We investigated a sample of 1053 BC cases and 7094 controls from different regions of Norway. PRS values were calculated using four PRS models, and their performance was evaluated by the area under the curve (AUC) and the odds ratio (OR). The effect of the PRS on the age of onset of BC was determined by a Cox regression model, and the lifetime absolute risk of developing BC was calculated using the iCare tool. Results: The best performing PRS model included 3820 SNPs, which yielded an AUC = 0.625 and an OR = 1.567 per one standard deviation increase. The PRS values of the samples correlate with an increased risk of BC, with a hazard ratio of 1.494 per one standard deviation increase (95% confidence interval of 1.406–1.588). The individuals in the highest decile of the PRS have at least twice the risk of developing BC compared to the individuals with a median PRS. The results in this study with Norwegian samples are coherent with the findings in the study conducted using Estonian and UK Biobank samples. Conclusion: The previously validated PRS models have a similar observed accuracy in the Norwegian data as in the UK and Estonian populations. A PRS provides a meaningful association with the age of onset of BC and lifetime risk. Therefore, as suggested in Estonia, a PRS may also be integrated into the screening strategy for BC in Norway.