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Background: Polygenic risk score (PRS) quantifies the cumulative effects of common genetic variants across the genome, including both coding and non-coding regions, to predict the risk of developing common diseases. In cardiovascular medicine, PRS enhances risk stratification beyond traditional clinical risk factors, offering a precision medicine approach to coronary artery disease (CAD) prevention. This study evaluates the predictive performance of a multi-ancestry PRS framework for cardiovascular risk assessment using the All of Us (AoU) short-read whole-genome sequencing dataset comprising over 225,000 participants. Methods: We developed PRSs for lipid traits (LDL-C, HDL-C, triglycerides) and cardiometabolic conditions (type 2 diabetes, hypertension, atrial fibrillation) and constructed two metaPRSs: one integrating lipid and cardiometabolic PRSs (risk factor metaPRS) and another incorporating CAD PRSs in addition to these risk factors (risk factor + CAD metaPRS). Predictive performance was evaluated separately for each trait-specific PRS and for both metaPRSs to assess their effectiveness in CAD risk prediction across diverse ancestries. Model predictive performance, including calibration, was assessed separately for each ancestry group, ensuring that all metrics were ancestry-specific and that PRSs remain generalizable across diverse populations Results: PRSs for lipids and cardiometabolic conditions demonstrated strong predictive performance across ancestries. The risk factors metaPRS predicted CAD risk across multiple ancestries. The addition of a CAD-specific PRS to the risk factors metaPRS improved predictive performance, highlighting a genetic component in CAD etiopathology that is not fully captured by traditional risk factors, whether clinically measured or genetically inferred. Model calibration and validation across ancestries confirmed the broad applicability of PRS-based approaches in multi-ethnic populations. Conclusion: PRS-based risk stratification provides a reliable, ancestry-inclusive framework for personalized cardiovascular disease prevention, enabling better targeted interventions such as pharmacological therapy and lifestyle modifications. By incorporating genetic information from both coding and non-coding regions, PRSs refine risk prediction across diverse populations, advancing the integration of genomics into precision medicine for common diseases

Background Elevated triglyceride (TG) levels are a heritable and modifiable risk factor for cardiovascular disease and have well-established associations with common genetic variation captured in a polygenic risk score (PRS). In young adulthood, the 22q11.2 microdeletion conveys a 2-fold increased risk for mild-moderate hypertriglyceridemia. This study aimed to assess the role of the TG-PRS in individuals with this elevated baseline risk for mild-moderate hypertriglyceridemia. Methods We studied a deeply phenotyped cohort of adults ( n  = 157, median age 34 years) with a 22q11.2 microdeletion and available genome sequencing, lipid level, and other clinical data. The association between a previously developed TG-PRS and TG levels was assessed using a multivariable regression model adjusting for effects of sex, BMI, and other covariates. We also constructed receiver operating characteristic (ROC) curves using logistic regression models to assess the ability of TG-PRS and significant clinical variables to predict mild-moderate hypertriglyceridemia status. Results The TG-PRS was a significant predictor of TG-levels ( p  = 1.52E-04), along with male sex and BMI, in a multivariable model (p model  = 7.26E-05). The effect of TG-PRS appeared to be slightly stronger in individuals with obesity (BMI ≥ 30) (beta = 0.4617) than without (beta = 0.1778), in a model unadjusted for other covariates ( p -interaction = 0.045). Among ROC curves constructed, the inclusion of TG-PRS, sex, and BMI as predictor variables produced the greatest area under the curve (0.749) for classifying those with mild-moderate hypertriglyceridemia, achieving an optimal sensitivity and specificity of 0.746 and 0.707, respectively. Conclusions These results demonstrate that in addition to significant effects of sex and BMI, genome-wide common variation captured in a PRS also contributes to the variable expression of the 22q11.2 microdeletion with respect to elevated TG levels.

Background Polygenic risk scores (PRS) quantify an individual’s genetic predisposition for different traits and are expected to play an increasingly important role in personalized medicine. A crucial challenge in clinical practice is the generalizability and transferability of PRS models to populations with different ancestries. When assessing the generalizability of PRS models for continuous traits, the R 2 is a commonly used measure to evaluate prediction accuracy. While the R 2 is a well-defined goodness-of-fit measure for statistical linear models, there exist different definitions for its application on test data, which complicates interpretation and comparison of results. Methods Based on large-scale genotype data from the UK Biobank, we compare three definitions of the R 2 on test data for evaluating the generalizability of PRS models to different populations. Polygenic models for several phenotypes, including height, BMI and lipoprotein A, are derived based on training data with European ancestry using state-of-the-art regression methods and are evaluated on various test populations with different ancestries. Results Our analysis shows that the choice of the R 2  definition can lead to considerably different results on test data, making the comparison of R 2  values from the literature problematic. While the definition as the squared correlation between predicted and observed phenotypes solely addresses the discriminative performance and always yields values between 0 and 1, definitions of the R 2 based on the mean squared prediction error (MSPE) with reference to intercept-only models assess both discrimination and calibration. These MSPE-based definitions can yield negative values indicating miscalibrated predictions for out-of-target populations. We argue that the choice of the most appropriate definition depends on the aim of PRS analysis — whether it primarily serves for risk stratification or also for individual phenotype prediction. Moreover, both correlation-based and MSPE-based definitions of R 2 can provide valuable complementary information. Conclusions Awareness of the different definitions of the R 2 on test data is necessary to facilitate the reporting and interpretation of results on PRS generalizability. It is recommended to explicitly state which definition was used when reporting R 2 values on test data. Further research is warranted to develop and evaluate well-calibrated polygenic models for diverse populations.

The study examined (a) whether alcohol use subgroups could be identified among African Americans assessed from adolescence through early adulthood, and (b) whether subgroup membership was associated with the interaction between internalizing symptoms and antisocial behavior polygenic risk scores (PRSs) and environmental characteristics (i.e., parental monitoring, community disadvantage). Participants ( N = 436) were initially recruited for an elementary school-based prevention trial in a Mid-Atlantic city. Youths reported on the frequency of their past year alcohol use from ages 14–26. DNA was obtained from participants at age 21. Internalizing symptoms and antisocial behavior PRSs were created based on a genome-wide association study (GWAS) conducted by Benke et al. (2014) and Tielbeek et al. (2017), respectively. Parental monitoring and community disadvantage were assessed at age 12. Four classes of past year alcohol use were identified: (a) early-onset, increasing; (b) late-onset, moderate use; (c) low steady; and (d) early-onset, decreasing. In high community disadvantaged settings, participants with a higher internalizing symptoms PRS were more likely to be in the early-onset, decreasing class than the low steady class. When exposed to elevated community disadvantage, participants with a higher antisocial behavior PRS were more likely to be in the early-onset, increasing class than the early-onset, decreasing and late-onset, moderate use classes.

Mendelian randomisation and polygenic risk score analysis have become increasingly popular in the last decade due to the advent of large-scale genome-wide association studies. Each approach has valuable applications, some of which are overlapping, yet there are important differences which we describe here.

Polygenic risk scores (PRSs) are emerging as powerful tools for predicting individual susceptibility to various diseases and traits based on genetic variants. These scores integrate information from multiple genetic markers associated with the trait or disease of interest, offering personalized risk assessment and enhancing disease management strategies. PRS is an active area of research and is being studied in various fields, such as disease prediction. This review explores the advancement of PRS research, focusing on methodological approaches, software tools, and applications across diverse disciplines. A systematic literature review identified 40 relevant articles classified based on PRS methods and software. Key methods for PRS computation, including penalized regression and threshold-based approaches, Bayesian approaches, and machine learning approaches, are discussed, along with notable software and their features. Applications of PRS in disease prevention are highlighted. Challenges and future directions, such as increasing diversity in genetic data, integrating environmental factors, and evaluating clinical implications, are also discussed to guide future research and implementation efforts.

Objective The TLR3/cGAS‐STING‐IFN signaling has recently been reported to be disturbed in colorectal cancer due to deregulated expression of the genes involved. Our study aimed to investigate the influence of potential regulatory variants in these genes on the risk of sporadic colorectal cancer (CRC) in a Czech cohort of 1424 CRC patients and 1114 healthy controls. Methods The variants in the TLR3, CGAS, TMEM173, IKBKE, and TBK1 genes were selected using various online bioinformatic tools, such as UCSC browser, HaploReg, Regulome DB, Gtex Portal, SIFT, PolyPhen2, and miRNA prediction tools. Results Logistic regression analysis adjusted for age and sex detected a nominal association between CRC risk and three variants, CGAS rs72960018 (OR: 1.68, 95% CI: 1.11‐2.53, P‐value = .01), CGAS rs9352000 (OR: 2.02, 95% CI: 1.07‐3.84, P‐value = .03) and TMEM173 rs13153461 (OR: 1.53, 95% CI: 1.03‐2.27, P‐value = .03). Their cumulative effect revealed a threefold increased CRC risk in carriers of 5‐6 risk alleles compared to those with 0‐2 risk alleles. Epistatic interactions between these genes and the previously genotyped IFNAR1, IFNAR2, IFNA, IFNB, IFNK, IFNW, IRF3, and IRF7 genes, were computed to test their effect on CRC risk. Overall, we obtained nine pair‐wise interactions within and between the CGAS, TMEM173, IKBKE, and TBK1 genes. Two of them remained statistically significant after Bonferroni correction. Additional 52 interactions were observed when IFN variants were added to the analysis. Conclusions Our data suggest that epistatic interactions and a high number of risk alleles may play an important role in CRC carcinogenesis, offering novel biological understanding for the CRC management. Our data suggest that epistatic interactions and a high number of risk alleles may play an important role in CRC carcinogenesis, offering novel biological understanding for the CRC management.

Polygenic factors are estimated to account for an additional 18% of the familial relative risk of breast cancer, with those at the highest level of polygenic risk distribution having a least a twofold increased risk of the disease. Polygenic testing promises to revolutionize health services by providing personalized risk assessments to women at high-risk of breast cancer and within population breast screening programs. However, implementation of polygenic testing needs to be considered in light of its current limitations, such as limited risk prediction for women of non-European ancestry. This article aims to provide a comprehensive review of the evidence for polygenic breast cancer risk, including the discovery of variants associated with breast cancer at the genome-wide level of significance and the use of polygenic risk scores to estimate breast cancer risk. We also review the different applications of this technology including testing of women from high-risk breast cancer families with uninformative genetic testing results, as a moderator of monogenic risk, and for population screening programs. Finally, a potential framework for introducing testing for polygenic risk in familial cancer clinics and the potential challenges with implementing this technology in clinical practice are discussed.

Genomic estimated breeding values (GEBVs) in livestock and polygenic risk scores (PRS) in humans are conceptually similar; however, the between-species differences in linkage disequilibrium (LD) provide a fundamental point of distinction that impacts approaches to data analyses... In this Review, we focus on the similarity of the concepts underlying prediction of estimated breeding values (EBVs) in livestock and polygenic risk scores (PRS) in humans. Our research spans both fields and so we recognize factors that are very obvious for those in one field, but less so for those in the other. Differences in family size between species is the wedge that drives the different viewpoints and approaches. Large family size achievable in nonhuman species accompanied by selection generates a smaller effective population size, increased linkage disequilibrium and a higher average genetic relationship between individuals within a population. In human genetic analyses, we select individuals unrelated in the classical sense (coefficient of relationship <0.05) to estimate heritability captured by common SNPs. In livestock data, all animals within a breed are to some extent “related,” and so it is not possible to select unrelated individuals and retain a data set of sufficient size to analyze. These differences directly or indirectly impact the way data analyses are undertaken. In livestock, genetic segregation variance exposed through samplings of parental genomes within families is directly observable and taken for granted. In humans, this genomic variation is under-recognized for its contribution to variation in polygenic risk of common disease, in both those with and without family history of disease. We explore the equation that predicts the expected proportion of variance explained using PRS, and quantify how GWAS sample size is the key factor for maximizing accuracy of prediction in both humans and livestock. Last, we bring together the concepts discussed to address some frequently asked questions.

The joint associations across genetic risk, modifiable lifestyle factors, and inflammatory bowel disease (IBD) remains unclear. Genetic susceptibility to Crohn's disease (CD) and ulcerative colitis (UC) was estimated by polygenic risk scores and further categorized into high, intermediate, and low genetic risk categories. Weighted healthy lifestyle scores were constructed based on 5 common lifestyle factors and categorized into favorable (4 or 5 healthy lifestyle factors), intermediate (3 healthy lifestyle factors), and unfavorable (0-2 healthy lifestyle factors) groups. Cox proportional hazards regression model was used to estimate the hazard ratios (HR) and 95% confidence interval (CI) for their associations. During the 12-year follow-up, 707 cases with CD and 1576 cases with UC were diagnosed in the UK Biobank cohort. Genetic risk and unhealthy lifestyle categories were monotonically associated with CD and UC risk with no multiplicative interaction between them. The HR of CD and UC were 2.24 (95% CI 1.75-2.86) and 2.15 (95% CI 1.82-2.53) for those with a high genetic risk, respectively. The HR of CD and UC for individuals with an unfavorable lifestyle were 1.94 (95% CI 1.61-2.33) and 1.98 (95% CI 1.73-2.27), respectively. The HR of individuals with a high genetic risk but a favorable lifestyle (2.33, 95% CI 1.58-3.44 for CD, and 2.05, 95% CI 1.58-2.66 for UC) were reduced nearly by half, compared with those with a high genetic risk but an unfavorable lifestyle (4.40, 95% CI 2.91-6.66 for CD and 4.44, 95% CI 3.34-5.91 for UC). Genetic and lifestyle factors were independently associated with susceptibility to incident CD and UC. Adherence to a favorable lifestyle was associated with a nearly 50% lower risk of CD and UC among participants at a high genetic risk.