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PubMed2025

Robust pleiotropy-decomposed polygenic scores identify distinct contributions to elevated coronary artery disease polygenic risk.

BACKGROUND: Polygenic risk score (PRS) have proved to offer robust risk prediction for coronary artery disease (CAD). However, the global CAD PRS summarizes the joint effects of all the markers in the genome, masking potential genetic heterogeneity that may be important for disease interpretation and targeted interventions. METHODS: Using summary-level data, we identified 43 significant CAD-related traits based on genetic correlations, and further classified them into eight pleiotropy clusters based on their biological functions. We then partitioned the genome into 2,353 near-independent regions. Variants in each region were assigned to the trait most genetically similar to CAD, and then were labeled with the corresponding pleiotropy cluster. We grouped variants without labels into a ninth, non-specific cluster. The Pleiotropy Decomposed (PD) PRSs for each of the nine clusters were calculated using variants assigned to each cluster for 407,903 samples of European ancestry from the UK Biobank (UKBB). RESULTS: We decomposed the CAD PRS into nine PD-PRSs and further stratified individuals with high CAD-PRS into nine subgroups. Each PD-PRS accounted for a higher proportion of the global CAD-PRS within its corresponding subgroup than in the remaining subjects with high CAD-PRS (e.g., 25.2% (0.07) vs. 10.06% (0.07) for lipids-PD-PRS). Additionally, these subgroups showed distinct clinical features. For example, in the lipids-related subgroup, lipoprotein(a) and LDL-cholesterol levels were 67.5% and 18.3% higher, respectively, compared to the remaining high-risk individuals. Furthermore, significant interactions were observed between blood pressure and BP PD-PRS, and between current smoking and respiratory system PD-PRS. CONCLUSION: Our findings suggest that PD-PRSs may reveal substantial genetic and phenotypic heterogeneity among individuals with high CAD-PRS. The unique PD-PRS compositions of each individual can highlight the relative importance of different pleiotropic regions.

Humans↗

PubMed2026

Methods for modeling gene-environment interplay using polygenic risk scores.

Polygenic risk scores (PRS) are increasingly recognized as pivotal tools for quantifying disease risk through the aggregation of multiple genetic variants. As sample sizes in genome-wide association studies (GWAS) continue to expand and PRS become more powerful, they are set to play a key role in translational research and personalized medicine. Understanding the interplay of PRS with environmental factors is critical for interpreting and applying PRS in diverse contexts. This interplay manifests in two forms: PRS-by-environment interaction (PRS × E) and gene-environment correlation (rGE). However, despite the growing application and importance of PRS, there are limited guidelines for performing PRS × E interaction analyses while controlling for rGE, which can lead to inconsistencies across studies and misinterpretation of results. Here we provide a review of different methods for performing PRSxE interaction in various epidemiological study designs, propose recommendations for best-practice, and discuss future challenges.

Gene-Environment Interaction↗

PubMed2025

Stratifying Lung Adenocarcinoma Risk with Multi-ancestry Polygenic Risk Scores in East Asian Never-Smokers.

BACKGROUND: Lung adenocarcinoma (LUAD) in never-smokers is a major public health burden, especially among East Asian women. Polygenic risk scores (PRSs) are promising for risk stratification but are primarily developed in European-ancestry populations. We aimed to develop and validate single- and multi-ancestry PRSs for East Asian never-smokers to improve LUAD risk prediction. METHODS: PRSs were developed using genome-wide association study summary statistics from East Asian (8,002 cases; 20,782 controls) and European (2,058 cases; 5,575 controls) populations. Single-ancestry models included PRS-25, PRS-CT, and LDpred2; multi-ancestry models included LDpred2+PRS-EUR128, PRS-CSx, and CT-SLEB. Performance was evaluated in independent East Asian data from the Female Lung Cancer Consortium (FLCCA) and externally validated in the Nanjing Lung Cancer Cohort (NJLCC). We assessed predictive accuracy via AUC, with 10-year and (age 30-80) absolute risks estimates. RESULTS: The best multi-ancestry PRS, using East Asian and European data via CT-SLEB (clumping and thresholding, super learning, empirical Bayes), outperformed the best East Asian-only PRS (LDpred2; AUC=0.629, 95% CI:0.618,0.641), achieving an AUC of 0.640 (95% CI:0.629,0.653) and odds ratio of 1.71 (95% CI:1.61,1.82) per SD increase. NJLCC Validation confirmed robust performance (AUC =0.649, 95% CI: 0.623, 0.676). The top 20% PRS group had a 3.92-fold higher LUAD risk than the bottom 20%. Further, the top 5% PRS group reached a 6.69% lifetime absolute risk. Notably, this group reached the average population 10-year LUAD risk at age 50 (0.42%) by age 41, nine years earlier. CONCLUSIONS: Multi-ancestry PRS approaches enhance LUAD risk stratification in East Asian never-smokers, with consistent external validation, suggesting future clinical utility.

East Asian never smokers↗

PubMed2026

Development and pilot testing of a prostate cancer polygenic risk report.

BACKGROUND: Polygenic risk scores (PRS) are increasingly being incorporated into clinical care, yet optimal strategies for communicating PRS results to patients and clinicians remain undefined. Effective report design is critical to ensure comprehension and appropriate use, particularly for complex conditions such as prostate cancer where screening decisions are nuanced. We developed and pilot tested patient-facing materials to communicate integrated polygenic and monogenic risk for prostate cancer in the context of a randomized clinical trial. METHODS: We designed a summary report and accompanying Frequently Asked Questions (FAQ) page to communicate prostate cancer genetic risk within the Prostate Cancer, Genetic Risk, and Equitable Screening Study (ProGRESS). Materials were developed through an iterative, multidisciplinary process informed by existing literature on genomic risk communication. We conducted semi-structured interviews with a national sample of eight men eligible for prostate cancer screening to evaluate comprehension, interpretation of visual elements, perceived usefulness, and preferences for improvement. Interviews were transcribed and analyzed using reflexive thematic analysis. RESULTS: Participants generally found the summary report and FAQ page understandable and visually engaging. Graphical displays of absolute risk, particularly pictograph arrays, facilitated comprehension and helped contextualize risk. Visual cues such as color and bold formatting effectively directed attention to key information, with red coloring perceived as particularly salient for high-risk results. In contrast, more complex visualizations, including bell curves and incidence curves, were frequently misunderstood or not interpreted as intended. Participants expressed a desire for clearer guidance regarding next steps and additional accessible information, suggesting supplementary resources such as hyperlinks or QR codes. Concerns about readability included small font size and high text density. CONCLUSIONS: In this qualitative pilot study, patient-facing materials for communicating prostate cancer PRS were generally well received, with specific design features such as simple visualizations and clear formatting enhancing understanding. Findings highlight the importance of intuitive risk displays and actionable guidance in PRS reporting. These results provide practical insights to inform the design of genomic risk reports as PRS-based prostate cancer screening approaches move toward clinical implementation. TRIAL REGISTRATION: ClinicalTrials.gov NCT05926102; date of registry: July 3, 2023.

Aged↗

PubMed2025

Polygenic Risk Scores for Preeclampsia Prediction Beyond Gold-Standard Clinical Models in Multiethnic Populations.

BACKGROUND: Preeclampsia is a major cause of maternal and fetal mortality and morbidity. Early risk stratification enables timely preventative therapy in high-risk women. Polygenic risk scores (PGS) improve prediction in complex diseases, but their added value for preeclampsia remains unclear, particularly in comparison to gold-standard first-trimester prediction models and across non-European ancestries. METHODS: We evaluated the performance of both a preeclampsia and systolic blood pressure PGS in 2 prospective pregnancy cohorts with detailed phenotyping: the Fetal Medicine Foundation study (n=5207; 2127 cases) and the Pregnancy Outcome Prediction study (n=3659; 228 cases). Risk models included (1) clinical factors; (2) clinical factors plus PGS; (3) advanced model including first-trimester mean arterial pressure, PAPP-A (pregnancy-associated plasma protein-A), and uterine artery pulsatility index; and (4) advanced model plus PGS. Discriminative performance, measured by the area under the receiver operating characteristic curve, was assessed overall and by ancestry. RESULTS: The preeclampsia PGS was independently associated with preeclampsia (odds ratio per SD, 1.24 [95% CI, 1.17-1.31]; P<0.001). It modestly improved prediction over clinical models (area under the receiver operating characteristic curve 0.746 versus 0.750; P=0.017) but not over the advanced model (area under the receiver operating characteristic curve 0.817 versus 0.818; P=0.326). The systolic blood pressure PGS showed stronger performance, improving prediction over both models in women of European ancestry. No improvement was observed with either score in women of African ancestry. CONCLUSIONS: PGSs for preeclampsia and SBP provide modest added predictive value beyond clinical risk factors in European ancestry women. Limited utility in African ancestry women reflects underrepresentation in the genome-wide association studies used to develop current scores. As cohort sizes grow and models are refined, PGSs may become important tools for equitable risk stratification in maternal health.

Adult↗

PubMed2025

Psychological and emotional impacts of communicating breast cancer risk using multifactorial assessment with polygenic risk score: Findings from PERSPECTIVE I&I.

PURPOSE: To examine the psychological and emotional outcomes of personalized breast cancer risk communication up to 1 year after disclosure in a risk-stratified breast screening preimplementation study (Personalized Risk Assessment for Prevention and Early Detection of Breast Cancer: Integration and Implementation). METHODS: Among 3753 females aged 40 to 69, unaffected by breast cancer, with a prior mammogram, and who underwent multifactorial risk assessment to estimate their 10-year breast cancer risk, 2734 completed follow-up questionnaires up to 1 year after risk communication: 78.5% were at average risk, 16.5% at higher than average risk, and 5.0% at high risk. The impact of risk communication on breast cancer worry and psychological distress and factors associated with decisional regret were examined. RESULTS: Breast cancer worry and psychological distress scores remained low after risk communication and at 1 year follow-up. Up to 1 year after disclosure, small significant differences in breast cancer worry were observed between risk levels. Decisional regret was very low 1 year after risk communication. Lower levels of decisional regret were significantly associated with some factors, including higher satisfaction with the information received. CONCLUSION: This study suggests that personalized breast cancer risk communication has low negative psychological and emotional effects and highlights the importance of the information received for implementing this approach at population level.

Humans↗

PubMed2025

XPRS: a tool for interpretable and explainable polygenic risk score.

SUMMARY: The polygenic risk score (PRS) is an important method for assessing genetic susceptibility to diseases; however, its clinical utility is limited by a lack of interpretability tools. To address this problem, we introduce eXplainable PRS (XPRS), an interpretation and visualization tool that decomposes PRSs into genes/regions and single nucleotide polymorphism (SNP) contribution scores via Shapley additive explanations (SHAPs), which provide insights into specific genes and SNPs that significantly contribute to the PRS of an individual. This software features a multilevel visualization approach, including Manhattan plots, LocusZoom-like plots, and tables at the population and individual levels, to highlight important genes and SNPs. By implementing with a user-friendly web interface, XPRS allows for straightforward data input and interpretation. By bridging the gap between complex genetic data and actionable clinical insights, XPRS can improve communication between clinicians and patients. AVAILABILITY AND IMPLEMENTATION: The XPRS software is publicly available on GitHub at https://github.com/nayeonkim93/XPRS and can see the demo through our cloud-based web service at https://xprs.leelabsg.org/.

Software↗

PubMed2026

Polygenic Risk Based Detection and Treatment of Subclinical Coronary Atherosclerosis in the PROACT Clinical Trials.

BACKGROUND: Coronary artery disease (CAD) polygenic risk scores (PRS) may identify individuals at elevated genetic risk "flying under the radar" in contemporary practice. The aims of the PROACT (Polygenic Risk Based Detection and Treatment of Subclinical Coronary Atherosclerosis) trials are to prospectively identify these individuals, quantify subclinical coronary plaque, and slow its progression with pharmacologic interventions. OBJECTIVES: The aim of this study is to report interim feasibility and implementation findings from PROACT, a genotype-first, biobank-enabled trial, characterizing eligibility yield, callback engagement, and subclinical coronary atherosclerosis on coronary computed tomographic angiography among individuals with high CAD PRS. METHODS: Within a hospital-based biobank, adults 40 to 75 years of age with high CAD PRS, without cardiovascular disease, and not on lipid-lowering therapy were invited. The authors characterize 2,495 eligible individuals with high CAD PRS, report on the feasibility and early operational outcomes of a genotype-first callback strategy for a clinical trial in the first 1,314 invited, and describe plaque prevalence by age and sex in the first 204 participants using coronary computed tomographic angiography. RESULTS: Among 64,092 genotyped participants, 2,495 (3.9%) were eligible and had high CAD PRS despite low clinical risk (median 10-year pooled cohort equations risk for atherosclerotic cardiovascular disease 3%; Q1-Q3: 1%-8%). Recruitment showed high engagement: among 1,314 invited individuals, 283 (21.5%) opted in, and 204 (15.5%) completed baseline imaging. Compared with participants who did not opt in, those who opted in had higher specialty care engagement and lived closer to the study site. Analysis of the first 204 participants enrolled by January 31, 2025 (mean age 56.3 ± 8.5 years, 69% women), showed that despite the low clinical risk and favorable cardiovascular health (mean Life's Essential 8 score 73.3 ± 11.5 vs the U.S. average of ∼65), one-half the participants (102 of 204) had subclinical plaque. Subclinical plaque prevalence was 76.2% in men and 38.3% in women and was high across age groups. CONCLUSIONS: These exploratory findings highlight the feasibility of implementing genotype-first recruitment for prevention trials and reveal a large proportion of "silent" high-genetic risk individuals with subclinical plaque for whom pharmacotherapy could be beneficial but who remain undetected by standard clinical assessments. (Polygenic Risk Based Detection of Subclinical Coronary Atherosclerosis and Change in Cardiovascular Health [PROACT 1], NCT05819814; Polygenic Risk Based Detection of Subclinical Coronary Atherosclerosis and Intervention With Statin and Colchicine [PROACT 2], NCT05850091).

Adult↗

PubMed2026

Additive value of polygenic risk and family history for coronary heart disease risk stratification in two diverse US cohorts.

Whether polygenic risk, monogenic familial hypercholesterolemia (FH), and family history (FamHx) are additively informative for coronary heart disease (CHD) risk prediction across self-identified race/ethnicity (SIRE) groups has not been established. In two diverse cohorts-Electronic Medical Records and Genomics (eMERGE) phase IV (eIV; n = 19,348) and All of Us (AoU; n = 239,645)-we quantified the associations of a polygenic risk score (PRSCHD), pathogenic/likely pathogenic variants in genes associated with FH, and FamHx with CHD and evaluated their incremental value when added to the pooled cohort equations (PCEs). CHD was defined as myocardial infarction, unstable angina, or coronary revascularization. We modeled associations with multivariable logistic regression (prevalent CHD in eIV) and Cox proportional hazards (incident CHD in AoU) and characterized predictive performance with the c-statistic and reclassification and decision-curve net benefits across actionable 10-year risk thresholds. The effects of PRSCHD and FamHx were independent and additive in both cohorts and consistent across White, Black, and Latino SIRE groups. In eIV, adding PRSCHD and FamHx to the PCE increased the c-statistic for prevalent CHD from 0.719 to 0.753 (p-diff = 9.1 × 10-3) and reclassified 18.8% of participants at the 7.5% 10-year threshold, yielding approximately 4 additional true-positive CHD identifications per 1,000 screened. Net benefit gains were observed between the 7.5% and 10% thresholds across all three SIRE groups. In conclusion, PRSCHD and FamHx were independently and additively associated with CHD across major SIRE groups in two diverse cohorts in the United States (US), motivating the addition of these factors to clinical risk algorithms.

Humans↗

PubMed2026

Genome-wide association, polygenic risk scores, and machine learning for chronic post-surgical pain risk stratification: A UK biobank study.

Chronic post-surgical pain is a prevalent and debilitating complication following surgery, representing a clinical challenge. Despite the established heritability of pain phenotypes, large-scale genetic studies remain limited. This study aimed to identify genetic variants associated with chronic post-surgical pain, develop polygenic risk scores, and integrate these with clinical features for risk prediction. UK Biobank data from 47,836 participants (2490 cases and 45,346 controls) were split into training (80%; n = 38,268) and validation (20%; n = 9568) sets prior to analysis. A genome-wide association study was conducted on the training set only, across 19 million variants, and polygenic risk scores were constructed and integrated with clinical features in a logistic regression framework. Two close, rare, imputed signals crossed the genome-wide significance threshold but lacked local linkage-disequilibrium support, while 220 variants crossed the suggestive threshold. In the held-out validation set, cases had higher mean polygenic risk scores than controls (0.138 vs. -0.021; Cohen's d = 0.16, p < 0.001). A logistic regression model integrating clinical features and polygenic risk scores achieved an area under the curve of 0.639 (95% CI: 0.583-0.693), higher than models using either feature set alone. The polygenic risk score for chronic post-surgical pain was among the most important predictors. Risk stratification revealed the top quartile had 3.84-fold higher odds of chronic post-surgical pain than the bottom quartile (95% CI: 2.00-7.37). These findings suggest a possible modest genetic contribution to chronic post-surgical pain. Polygenic risk scores may complement clinical factors in surgical risk stratification. PERSPECTIVE: Chronic post-surgical pain may have a modest genetic contribution. This UK Biobank study identified over 220 variants at suggestive significance and constructed a polygenic risk score that was significantly elevated in cases. A combined clinical-genomic model achieved a 3.84-fold difference in odds across predicted-risk quartiles.

Chronic post-surgical pain↗

PubMed2025

Development and evaluation of patient-centred polygenic risk score reports for glaucoma screening.

BACKGROUND: Polygenic risk scores (PRS), which provide an individual probabilistic estimate of genetic susceptibility to develop a disease, have shown effective risk stratification for glaucoma onset. However, there is limited best practice evidence for reporting PRS and patient-friendly reports for communicating PRS effectively are lacking. Here we developed patient-centred PRS reports for glaucoma screening based on the literature, and evaluated them with participants using a qualitative research approach. METHODS: We first reviewed existing PRS reports and literature on probabilistic risk communication. This informed the development of a draft glaucoma screening PRS report for a hypothetical high risk individual from the general population. We designed three versions of the report to illustrate risk using a pictograph, a pie chart and a bell curve. We then conducted semi-structured interviews to assess preference of visual risk communication aids, understanding of risk, content, format and structure of the reports. Participants were invited from an existing study, which aims to evaluate the clinical validity of glaucoma PRS among individuals > 50 years from the general population. Numeracy and literacy levels were assessed. RESULTS: We interviewed 12 individuals. The cohort was highly educated (42% university education), all were European and 50% were female. Numeracy (mean 2.1 ± 0.9, range 0 to 3), graph literacy (mean 2.8 ± 0.8, range 0 to 4) and genetic literacy (mean 24.2 ± 6.2, range - 20 to + 46) showed a range of levels. We analysed the reports under three main themes: visual preferences, understanding risk and reports formatting. The visual component was deemed important to understanding risk, with the pictograph being the preferred visual risk representation, followed by the pie chart and the bell curve. Participants expressed preference for absolute risk in understanding risk, along with the written content explaining the results. The importance of follow-up recommendations and time to glaucoma onset were deemed important. Participants expressed varied opinions in the level of information and the colours used, which informed revisions of the report. CONCLUSIONS: Our study revealed preferences for reporting PRS information in the context of glaucoma screening, to support the development of clinical PRS reporting. Further research is needed to assess PRS communication in other groups representative of target populations and with other target audiences (e.g. referring clinicians), and its potential psychosocial impact in the wider community.

Humans↗

PubMed2026

Polygenic Risk Identifies Older Adults Who May Benefit From Aspirin for the Primary Prevention of Ischemic Stroke.

BACKGROUND: Low-dose aspirin is no longer recommended for routine primary prevention in older adults due to bleeding risks outweighing vascular benefits. We hypothesized that an integrative polygenic score (iPGS) could identify a subgroup of older individuals who derive net benefit from aspirin for the primary prevention of ischemic stroke. METHODS: We performed post hoc analysis of the ASPREE randomized, placebo-controlled trial (Aspirin in Reducing Events in the Elderly) of daily 100-mg aspirin, in 12 031 genotyped participants of European ancestry aged >70 years without prior cardiovascular disease. The iPGS was derived from >1.2 million variants and evaluated both continuously and by quintiles. Cox models assessed associations between polygenic risk, ischemic stroke, and major bleeding events, and tested the interaction between the iPGS and treatment allocation, with adjustment for baseline lifestyle and clinical covariates. RESULTS: The mean age of participants was 75.1 years, and 54.9% were women. Over a median of 4.6 years, 187 ischemic strokes and 373 major bleeds occurred, including 101 intracranial bleeds (46 hemorrhagic strokes). Each 1-SD increase in the iPGS was associated with higher incident ischemic stroke risk (hazard ratio, 1.39 [95% CI, 1.20-1.62]). An interaction between the continuous iPGS and aspirin allocation was observed for ischemic stroke (P=0.04) but not major bleeding. In the highest iPGS quintile, aspirin reduced ischemic stroke by 51% (hazard ratio, 0.49 [95% CI, 0.28-0.85]) without significantly increasing major bleeding (hazard ratio, 1.15 [95% CI, 0.71-1.88]). No benefit was observed in the overall cohort or in lower-risk quintiles. CONCLUSIONS: Among older adults, high polygenic risk identifies individuals who may experience substantial stroke reduction with aspirin, with no excess bleeding. These findings raise the possibility that genomic risk stratification may enable targeted aspirin use for the primary prevention of ischemic stroke. REGISTRATION: URL: https://www.clinicaltrials.gov; Unique identifier: NCT01038583.

Humans↗

PubMed2025

Integrating Imaging-Derived Clinical Endotypes with Plasma Proteomics and External Polygenic Risk Scores Enhances Coronary Microvascular Disease Risk Prediction.

Coronary microvascular disease (CMVD) is an underdiagnosed but significant contributor to the burden of ischemic heart disease, characterized by angina and myocardial infarction. The development of risk prediction models such as polygenic risk scores (PRS) for CMVD has been limited by a lack of large-scale genome-wide association studies (GWAS). However, there is significant overlap between CMVD and enrollment criteria for coronary artery disease (CAD) GWAS. In this study, we developed CMVD PRS models by selecting variants identified in a CMVD GWAS and applying weights from an external CAD GWAS, using CMVD-associated loci as proxies for the genetic risk. We integrated plasma proteomics, clinical measures from perfusion PET imaging, and PRS to evaluate their contributions to CMVD risk prediction in comprehensive machine and deep learning models. We then developed a novel unsupervised endotyping framework for CMVD from perfusion PET-derived myocardial blood flow data, revealing distinct patient subgroups beyond traditional case-control definitions. This imaging-based stratification substantially improved classification performance alongside plasma proteomics and PRS, achieving AUROCs between 0.65 and 0.73 per class, significantly outperforming binary classifiers and existing clinical models, highlighting the potential of this stratification approach to enable more precise and personalized diagnosis by capturing the underlying heterogeneity of CMVD. This work represents the first application of imaging-based endotyping and the integration of genetic and proteomic data for CMVD risk prediction, establishing a framework for multimodal modeling in complex diseases.

Cardiovascular Disease↗

PubMed2026

A multi-ancestry polygenic risk score for body mass index predicts longitudinal weight change.

BACKGROUND: Identifying individuals at risk for future weight gain is challenging, partly because associations with traditional clinical risk factors may be biased by confounding and reverse causation. Polygenic risk scores (PRS) provide a stable, lifelong measure of genetic predisposition to obesity. However, existing PRS have not been evaluated for their association with longitudinal weight change in adulthood and often lack generalizability across diverse genetic ancestry groups. METHODS: We conducted ancestry-specific genome-wide association study meta-analyses of body mass index (BMI) in populations of European, African or African American, Admixed American, East Asian, and South Asian ancestries and developed ancestry-specific PRS. A multi-ancestry polygenic risk score (MAPRS) was trained using ancestry-specific PRS in a model selection dataset (N = 39,685) from the All of Us Research Program (AoU). We evaluated the MAPRS in an independent AoU model evaluation dataset (N = 158,743) for BMI prediction and in a separate AoU test dataset (N = 78,219) with repeated measurements over 1.5-2.5 years for weight change prediction. The outcomes included change in BMI and ≥ 10% or ≥ 5% total body weight (TBW) gain. We further examined the relationship between MAPRS and 12 clinical risk factors commonly comorbid with obesity in relation to weight change. RESULTS: The MAPRS captured 7.05% of the variance in measured BMI in the AoU model evaluation dataset and demonstrated improved generalizability across all non-European genetic ancestry groups. In the AoU test dataset, conditioned on baseline BMI at the second-to-last measurement, a one SD increase in MAPRS was associated with a 0.16 kg/m2 increase in future BMI (standard error = 0.012 kg/m2; p-value = 2.2 × 10-39), 1.27-fold increased odds of experiencing ≥ 10% TBW gain (95% CI: 1.24-1.31; p-value = 1.4 × 10-55), and 1.15-fold increased odds of experiencing ≥ 5% TBW gain (95% CI: 1.13-1.18; p-value = 2.8 × 10-39). These associations were observed across all genetic ancestry groups and remained highly consistent after adjustment for any clinical risk factor. In contrast, most clinical risk factors demonstrated inconsistent or weaker associations with weight change outcomes. CONCLUSIONS: We developed an MAPRS for BMI that represents a robust and generalizable risk factor for longitudinal weight gain in adulthood, providing a foundation for genetically informed risk stratification and earlier, more targeted obesity prevention strategies.

Humans↗

PubMed2022

Patient and provider perspectives on polygenic risk scores: implications for clinical reporting and utilization.

BACKGROUND: Polygenic risk scores (PRS), which offer information about genomic risk for common diseases, have been proposed for clinical implementation. The ways in which PRS information may influence a patient's health trajectory depend on how both the patient and their primary care provider (PCP) interpret and act on PRS information. We aimed to probe patient and PCP responses to PRS clinical reporting choices METHODS: Qualitative semi-structured interviews of both patients (N=25) and PCPs (N=21) exploring responses to mock PRS clinical reports of two different designs: binary and continuous representations of PRS. RESULTS: Many patients did not understand the numbers representing risk, with high numeracy patients being the exception. However, all the patients still understood a key takeaway that they should ask their PCP about actions to lower their disease risk. PCPs described a diverse range of heuristics they would use to interpret and act on PRS information. Three separate use cases for PRS emerged: to aid in gray-area clinical decision-making, to encourage patients to do what PCPs think patients should be doing anyway (such as exercising regularly), and to identify previously unrecognized high-risk patients. PCPs indicated that receiving "below average risk" information could be both beneficial and potentially harmful, depending on the use case. For "increased risk" patients, PCPs were favorable towards integrating PRS information into their practice, though some would only act in the presence of evidence-based guidelines. PCPs describe the report as more than a way to convey information, viewing it as something to structure the whole interaction with the patient. Both patients and PCPs preferred the continuous over the binary representation of PRS (23/25 and 17/21, respectively). We offer recommendations for the developers of PRS to consider for PRS clinical report design in the light of these patient and PCP viewpoints. CONCLUSIONS: PCPs saw PRS information as a natural extension of their current practice. The most pressing gap for PRS implementation is evidence for clinical utility. Careful clinical report design can help ensure that benefits are realized and harms are minimized.

Clinical Decision-Making↗

PubMed2026

Improving the reliability of polygenic risk score-based prediction for cardiovascular and renal complications across ancestries in type 2 diabetes using Mondrian Cross-Conformal Prediction.

Polygenic risk scores (PRS) developed in European populations often show reduced predictive performance in non-European populations, limiting their clinical utility. This lack of transferability across ancestries remains a major challenge in genomic medicine and raises concerns about health equity. We aimed to evaluate whether uncertainty-aware prediction, implemented through Mondrian Cross-Conformal Prediction, improves the performance and reliability of polygenic risk score-based predictions across ancestries for nephropathy, stroke, and myocardial infarction in individuals with type 2 diabetes in a multi-ethnic cohort. We leveraged Mondrian Cross-Conformal Prediction (MCCP), an uncertainty quantification framework, combined with logistic regression applied to a multi-polygenic risk score (multiPRS) to predict the risk of nephropathy, stroke, and myocardial infarction in individuals with type 2 diabetes. Two training frameworks were evaluated: one using 4,098 individuals with type 2 diabetes of European ancestry from the ADVANCE trial for training and 17,574 White British, 1,145 South Asian, and 749 African UK Biobank participants for testing; and another using the 17,574 White British UK Biobank participants for training and the South Asian and African participants for testing. Logistic regression provided robust baseline performance across populations. On top of this baseline, MCCP did not improve performance but added capabilities absent from probability-based stratification: for each individual, it issued a prediction together with an explicit confidence and credibility level; it allowed a tolerated error level to be set in advance and delivered prediction sets respecting it in the majority of settings; and it flagged individuals for whom no reliable prediction could be made. Applying MCCP to PRS-based prediction thus enables uncertainty-aware risk stratification and improves the reliability of risk prediction across ancestries, providing a more equitable framework for clinical use.

Female↗

PubMed2026

Implementing a Multi-Ancestry Polygenic Risk Score for Coronary Heart Disease in a Diverse Cohort.

PURPOSE: We describe a prospective cohort study (NCT05277116) conducted in phase IV of the electronic MEdical Records and GEnomics (eMERGE) Network to implement a multi-ancestry polygenic risk score for coronary heart disease (PRSCHD: PGS004696) and assess outcomes after return of results (RoR). METHODS: PRSCHD was considered alongside family history (FamHxCHD), monogenic risk from familial hypercholesterolemia (FH), and clinical risk factors, to return CHD risk as part of a Genome Informed Risk Assessment (GIRA) report. Participants with high PRSCHD (top 5th percentile) or FH received their results from study personnel, while participants with FamHxCHD were informed by mail/email. Results were placed in the electronic health record and communicated to the primary care provider. The primary outcome of initiation/intensification of lipid lowering therapy within 12 months after RoR is compared between participants with PRSCHD ≥95th percentile and those with PRSCHD 90th-94th percentile, using a regression discontinuity design. Secondary outcomes include ordering of screening tests, a new CHD diagnosis, and lifestyle changes. RESULTS: By April 2025, 20,421 adults were enrolled: mean age 50±15 years (range 18-75 years), 68% female, 50% belonging to health disparity groups, and 40% non-White by self-report. Prevalence of CHD, FamHxCHD, high PRSCHD and FH was 4.0%, 10.2%, 4.3% and 0.7%, respectively; 14.3% had at least one of the three CHD genetic risk factors and CHD risk estimates were highest in those who self-reported as Black. CONCLUSION: The prevalence of increased genetic risk for CHD was high and at least one of the three genetic risk factors for CHD was present in 14.2% of the cohort. Analyses are underway to assess outcomes after PRSCHD implementation in the context of FamHxCHD, FH, and clinical risk, across the age spectrum in a diverse cohort.

PRS↗

PubMed2024

The expected polygenic risk score (ePRS) framework: an equitable metric for quantifying polygenetic risk via modeling of ancestral makeup.

Polygenic risk scores (PRSs) depend on genetic ancestry due to differences in allele frequencies between ancestral populations. This leads to implementation challenges in diverse populations. We propose a framework to calibrate PRS based on ancestral makeup. We define a metric called "expected PRS" (ePRS), the expected value of a PRS based on one's global or local admixture patterns. We further define the "residual PRS" (rPRS), measuring the deviation of the PRS from the ePRS. Simulation studies confirm that it suffices to adjust for ePRS to obtain nearly unbiased estimates of the PRS-outcome association without further adjusting for PCs. Using the TOPMed dataset, the estimated effect size of the rPRS adjusting for the ePRS is similar to the estimated effect of the PRS adjusting for genetic PCs. Similarly, we applied the ePRS framework to six cardiovascular-related traits in the All of Us dataset, and the results are consistent with those from the TOPMed analysis. The ePRS framework can protect from population stratification in association analysis and provide an equitable strategy to quantify genetic risk across diverse populations.

Journal Article↗