Abstract Polygenic risk scores (PRSs) may enhance risk stratification for pancreatic ductal adenocarcinoma (PDAC), but existing models vary widely in design, predictive performance, and cross-ancestry transferability. We developed genome-wide PRSs using Bayesian methods (LDpred2 and PRS-CS) and p value thresholding (PRSice-2) and systematically evaluated these alongside 13 published PRSs to identify models with robust predictive performance across ancestries. Using GWAS summary statistics from 7531 cases and 10,631 controls, we derived the PRSs and tested associations in an independent sample of 4508 PDAC cases and 46,189 controls, with adjustment for well-established PDAC risk factors. Among all models, the genome-wide LDpred2-based PRS showed the strongest association with PDAC (OR = 1.57 per standard deviation increase; 95% CI: 1.51–1.62) and significantly improved discrimination beyond established risk factors alone (AUC = 0.74–0.76; p < 0.0001). Importantly, the genome-wide LDpred2 PRS demonstrated consistent associations across African, Admixed American, and European ancestry groups, whereas the best-performing published PRS was associated with PDAC risk only in individuals of European ancestry. These findings support genome-wide PRSs as a promising framework for multi-ancestry risk stratification for PDAC and to inform targeted early detection strategies.

The Electronic Medical Records and Genomics (eMERGE) Network developed and implemented a genome-informed risk assessment (GIRA) to communicate genomic (polygenic risk scores [PRSs], integrated risk scores [IRSs], and monogenic results), clinical, and family history-based risk for 11 chronic diseases and provide recommended healthcare recommendations. GIRA reports have now been returned to 23,840 participants and their providers in a large prospective cohort study. We present here the study design and analysis framework for assessing the attributable impact of GIRA return. Pre-specified outcomes include (1) provider/participant adoption of recommended healthcare actions, (2) new diagnosis of disease, (3) treatment initiation/intensification, and (4) clinical outcomes (surrogate markers or clinical events). We assess outcomes in high risk vs. not-high-risk participants, adjusting for covariates. We evaluate the effect of PRS/IRS at pre-established high-risk thresholds using regression discontinuity (RD), a quasi-experimental method that mimics randomization near a cutoff, enabling estimation of causal effects and controlling for unobserved confounders. Monogenic and family history-based risk stratification are analyzed using logistic regression. With 23,840 participants and 12 months of follow-up, the study is powered to detect differences of 2%-11% with 80% power (α = 0.05 in the adoption outcome). Longer follow-up will be required to enable assessment of new disease diagnosis, treatment changes, and clinical outcomes. Through innovative RD analyses and defined outcomes and comparison groups, this study will provide new insights into the real-world clinical impact of genomic risk assessment, address critical evidence gaps, advance understanding of genomic medicine outcomes, and inform future research.

The Personalized Environment and Genes Study (PEGS) is a unique resource comprising genetic and environmental exposure data linked to geospatial data. The PEGS cohort contains 19,445 demographically diverse participants who provided phenotype and exposure data by completing three surveys. Whole-genome sequencing was performed for a subset of 4,737 participants to interrogate common and rare variants and structural variations, including high-resolution human leukocyte antigen (HLA) variants. Geographic coordinates were assigned to participant addresses, enabling the use of distance to contaminant sources and area-level air-pollutant concentrations as surrogates for exposure. Several available tools are available to explore these data and results of exposome-wide association studies (ExWAS) conducted in the data. The i2b2 Query and Analysis Tool enables approved users to build customizable queries for exploring basic statistics from de-identified and aggregated PEGS data. PEGS Explorer allows users to explore published ExWAS results and rigorously calculated exposure correlations. Globe visualizations in this tool reflect the complex mixtures involved in the exposome and allow users to visualize correlations between exposures and common, complex diseases.

Genomic testing is now embedded in contemporary prostate cancer care, yet the clinical meaning of different genomic platforms varies substantially by disease state and clinical context. In localized disease, tissue-based genomic classifiers primarily serve prognostic functions by refining risk estimates beyond clinicopathologic variables, whereas in advanced disease, germline and somatic testing identify predictive biomarkers linked to therapy selection. This distinction is clinically consequential because the supporting evidence, endpoints, and implementation challenges differ across assays and across points on the disease continuum. In this review, we position tissue-based assays, germline testing, somatic sequencing, circulating tumor DNA (ctDNA), and artificial intelligence-enabled biomarkers within a unified clinical framework spanning localized disease, biochemical recurrence, and metastatic progression. We critically compare commercially available genomic assays with respect to methodology, specimen type, intended use, validation cohorts, and clinically relevant outcomes. We distinguish prognostic classifiers from predictive biomarkers such as homologous recombination repair deficiency and mismatch repair deficiency, and we evaluate emerging approaches, including liquid biopsy, multimodal integration with imaging, and digital pathology-based algorithms. We further address implementation barriers that may limit real-world impact, including reimbursement uncertainty, disparities in access to next-generation sequencing, limited provider familiarity with genomic interpretation, and the need for patient-centered communication and navigation in genomics-informed care. A clinically useful framework for prostate cancer genomics must therefore move beyond cataloging tests and instead clarify when genomic results change management, where evidence remains immature, and how implementation strategies can improve equity and actionability.

BACKGROUND:Performance and transferability of contemporary polygenic risk scores (PRS) for atherosclerotic cardiovascular disease phenotypes may vary across PRS methods, training data, and trait ascertainment. METHODS:We aimed to investigate the performance and transferability of contemporary PRS for atherosclerotic cardiovascular disease subtypes: coronary heart disease (CHD), abdominal aortic aneurysm (AAA), ischemic stroke (IS), and peripheral artery disease (PAD), using the All of Us Workbench, which consists of a large, diverse cohort with whole-genome sequence data. We also developed and evaluated a multi-trait PRS for each subtype. Performance of PRS for 4 atherosclerotic cardiovascular disease subtypes was compared across genetic similarity groups in 245 388 All of Us participants. Groups genetically similar to European, African, admixed American, and remaining groups (combined as other) were used to assess PRS for CHD, IS, AAA, PAD, and multi-trait. RESULTS:PRS for CHD and AAA performed better than IS and PAD. For CHD, CHDPGS003725 performed the best (hazard ratio per SD increase [95% CI]), across genetic ancestry groups, European, and African (1.72 [1.67-1.78], 1.23 [1.17-1.29]), with CHDPGS004696 being best for admixed American (1.91 [1.70-2.15]), and CHDPGS003356 for other (1.75 [1.58-1.95]). The best performing PRS for AAA was AAAMulti for European, other, and admixed American (1.71 [1.52-1.92], 1.59 [1.07-2.37], 1.50 [0.90-2.52]) and AAAPGS003972 for African (1.39 [1.19-1.63]). For IS, ISMulti performed best for other and European (1.49 [1.17-1.89], 1.33 [1.25-1.42]), and ISPGS000039 performed best in admixed American and African (1.17 [1.07-1.27], 1.09 [1.04-1.15]). For PAD, PADMulti performed best for all groups (other, 1.51 [1.19-1.92]; European, 1.32 [1.24-1.41]; admixed American, 1.23 [1.05-1.45]; and African, 1.18 [1.04-1.34]). CONCLUSIONS:Multi-trait and multi-ancestry PRS performed better than individual trait and/or single ancestry PRS for each atherosclerotic cardiovascular disease phenotype across ancestrally diverse and admixed individuals, with minimal change including adjustment for conventional risk factors.

Many factors, including environmental and genetic variables, contribute to Colorectal Cancer (CRC) risk. The genetic components of risk can be divided into monogenic and polygenic factors. Just as monogenic factors can increase risk for more than one condition, polygenic factors may also underlie multiple phenotypes, including behavioral traits. In order to understand the biology of CRC risk better, it is important to understand the shared polygenic genetic architecture contributing to CRC risk and other phenotypes, including CRC associated risk factors. We investigated potential shared genetics by performing a Phenome-wide association study (PheWAS) with a multi-ancestry CRC polygenic risk score (PRS). The discovery cohort (N = 426,464) consisted of ancestrally diverse participants from the United Kingdom Biobank. The replication cohort (N = 87,271) consisted of ancestrally diverse participants from the electronic Medical Records and Genomics Network phase 3. We used a mixed-effects model to adjust for the presence of related individuals. To preserve power, we limited the number of tests by restricting analysis to ancestor phecodes derived from the electronic health record (EHR) that were not likely to be a result of CRC or its treatment. We discovered and replicated associations between the CRC PRS and breast cancer, prostate cancer, obesity, smoking and alcohol use (discovery p < 1.1e-4; replication p < 0.0019). The association between CRC risk and prostate cancer may be a novel finding, whereas the association with breast cancer has been previously observed using orthogonal methods. The association between CRC risk and behavioral risk factors corroborate previous studies, also using orthogonal methods, and may reveal potential prevention or treatment strategies. As these results corroborate findings from other studies using orthogonal methods, we demonstrate that a CRC PRS can be used as a proxy for genetic risk for CRC when investigating shared genetics between CRC and other phenotypes. Further study of the relationship between PRS from multiple traits with EHR data may reveal additional shared genetic factors. Ultimately, understanding these underlying genetic correlations may identify prevention and treatment strategies for CRC.

In this report, we provide a follow-up analysis of a previously published genome-wide association study (GWAS) evaluating the effect of genetic polymorphisms on inter-individual variations in cell-mediated immune responses to mumps vaccine. Here we report the results of a polygenic score (PGS) analysis showing how common variants can predict mumps vaccine response. We found higher PGS for IFNγ, IL-2, and TNFα were predictive of higher post-vaccine IFNγ (p value = 2e-6), IL-2 (p = 2e-7), and TNFα (p = 0.004) levels, respectively. Control of immune responses after vaccination is complex and polygenic in nature. Our results suggest that the PGS-based approach enables better capture of the combined genetic effects that contribute to mumps vaccine-induced immunity, potentially offering a more comprehensive understanding than traditional single-variant GWAS. This approach will likely have broad utility in studying genetic control of immune responses to other vaccines and to infectious diseases.

BACKGROUND:Admixture mapping can be leveraged to identify genomic loci associated with disease traits in admixed individuals such as Hispanic Americans. METHODS AND RESULTS:We characterized fine-scale population structure of participants (n=105 108) in the multiethnic eMERGE-III (electronic Medical Records and Genomics) cohort, using reference panels from the 1000 Genomes Project and a Native American cohort. We defined "genetically admixed Hispanic Americans" as those having ≥10% Native American genetic ancestry (n=5025), in addition to European (specifically, Iberian) and African genetic ancestry. This population descriptor was subsequently applied to 3 other cohorts: the MESA (Multi-Ethnic Study of Atherosclerosis; n=1176), WHI (Women's Health Initiative; n=2873), and the HCHS/SOL (Hispanic Community Health Study/Study of Latinos; n=7804). We inferred local ancestry along the genome in genetically admixed Hispanic Americans in each cohort using RFMix2 with phased haplotypes from SHAPEIT2. Local ancestry proportions were used for admixture mapping of lipid traits (low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, total cholesterol, and triglycerides) using extended linear mixed models that incorporated multiple or single ancestries. After accounting for fixed-effect covariates and random effects, ancestry proportions at the following loci were associated with lipid traits in the HCHS/SOL cohort: 11q12.2 (P=6.8×10-7) with low-density lipoprotein cholesterol, 2p23.3 (P=5.2×10-7) with total cholesterol, and 11q23.3 (P=7.3×10-12) with triglycerides. The LDL cholesterol and triglyceride loci replicated in at least one independent cohort, whereas the total cholesterol locus showed suggestive evidence for replication in the eMERGE cohort (P=0.073). Native American (NAMR) ancestry appeared to drive the LDL-C association, whereas both NAMR and Iberian population in Spain (IBS) ancestries contributed to the triglyceride signal. CONCLUSIONS:Admixture mapping in genetically admixed Hispanic Americans identified genomic loci influencing lipid traits and the different ancestral origins driving these associations.

The use of polygenic scores (PGS) for personalized medicine has gained momentum, along with caution to avoid accentuating health disparities. Greater ancestral diversity in genetic studies is needed, as well as close attention to the social determinants of health (SDoH).We measured the correlations between 3,030 PGS from the PGS Catalog and SDoH among participants in the Personalized Environment and Genes Study (PEGS). Correlations mainly ranged from -0.05 to 0.05, yet there was a heterogeneity of correlations across SDoH themes, with the largest amount of heterogeneity for PGS predicting body measures and smoking, as well as some common diseases. We also quantify the expected bias of PGS effect size on disease risk when strong predictors, such as SDoH, are omitted from models, emphasizing the importance of including SDoH with PGS to avoid biased estimates of PGS risk and to achieve equitable precision medicine.

10544 Background: Incorporation of Polygenic Risk Scores (PRS) can refine traditional breast cancer risk assessment models to provide precise estimates of breast cancer risk. However, the impact of such an integrated model on clinical decision-making related to breast cancer surveillance and preventive strategies is not fully understood. Methods: The GENRE-2 is a prospective single-arm multisite clinical trial (NCT04474834) incorporating PRS into standard breast cancer risk assessment models to determine the impact of PRS on clinical decisions on breast cancer prevention and surveillance. Women at high risk of breast cancer due to NCI-BCRAT 5-year risk of ≥ 3%, or IBIS (Tyrer-Cuzik) 10-year breast cancer risk of ≥5%, biopsy-proven high-risk breast lesion, or a pathogenic variant (PV) in ATM , BRCA1, BRCA2, CHEK2 or PALB2 , were enrolled from five sites in the United States. All women were invited to complete surveys on their breast surveillance and cancer prevention decisions based on pre-PRS standard risk models and post-PRS risk estimation, and further annual surveys are planned for 10 years. Results: Among 902 women enrolled in the study, 605 (median age: 52 years) received PRS results and completed a survey to date. Of those who received PRS results,195 (32.2%) were PV carriers. Among non-carriers, the median 10-year and lifetime pre-PRS IBIS-based risk was 10.0% and 28.7%, respectively. Among PV carriers, the CanRisk-based 10-year and lifetime pre-PRS risk estimates were 6.3% and 25.2% for ATM , 22.3% and 77.6% for BRCA1 , 18.5% and 77.7% for BRCA2 , 7.1% and 25.7% for CHEK2 , and 16.8% and 38.0% for PALB2 PV carriers, respectively. After the incorporation of PRS, the lifetime risk of breast cancer increased by at least 10% in 31% of non-carriers and 7.7% of PV carriers and decreased by at least 10% in 10.7% of non-carriers and 7.7% of PV carriers. The proportion of non-carriers with lifetime risk < 20% or > 40% changed from 17.3% and 22.0%, respectively, in the pre-PRS evaluation to 24.4% and 32.9%, in the post-PRS evaluation. A higher lifetime post-PRS score was associated with intent to take preventive action (surgery or endocrine agents). In non-carriers, the proportion of women with a lifetime risk < 20% with intent to take preventive action was 11%, compared to 36.8% of those with a lifetime risk > 40% (p < 0.001). Similarly, in PV carriers, the proportion of women with lifetime risk < 20% with intent to take preventive action was 20.8% compared to 41.1% of those with lifetime risk > 40% (p = 0.015). Conclusions: The GENRE-2 trial demonstrates that the incorporation of PRS into breast cancer risk assessment models in high-risk women is feasible and leads to clinically meaningful changes in breast cancer risk estimates and decision-making regarding preventive strategies. Evaluation of the implementation of breast cancer risk management strategies in study participants is ongoing and will be reported. Clinical trial information: NCT04474834 .

Uterine leiomyomata, or fibroids, are common gynecological tumors causing pelvic and menstrual symptoms that can negatively affect quality of life and child-bearing desires. As fibroids grow, symptoms can intensify and lead to invasive treatments that are less likely to preserve fertility. Identifying individuals at highest risk for fibroids can aid in access to earlier diagnoses. Polygenic risk scores (PRS) quantify genetic risk to identify those at highest risk for disease. Utilizing the PRS software PRS-CSx and publicly available genome-wide association study (GWAS) summary statistics from FinnGen and Biobank Japan, we constructed a multi-ancestry (META) PRS for fibroids. We validated the META PRS in two cross-ancestry cohorts. In the cross-ancestry Electronic Medical Record and Genomics (eMERGE) Network cohort, the META PRS was significantly associated with fibroid status and exhibited 1.11 greater odds for fibroids per standard deviation increase in PRS (95% confidence interval [CI]: 1.05 - 1.17, p = 5.21x10-5). The META PRS was validated in two BioVU cohorts: one using ICD9/ICD10 codes and one requiring imaging confirmation of fibroid status. In the ICD cohort, a standard deviation increase in the META PRS increased the odds of fibroids by 1.23 (95% CI: 1.15 - 1.32, p = 9.68x10-9), while in the imaging cohort, the odds increased by 1.26 (95% CI: 1.18 - 1.35, p = 2.40x10-11). We subsequently constructed single ancestry PRS for FinnGen (European ancestry [EUR]) and Biobank Japan (East Asian ancestry [EAS]) using PRS-CS and discovered a nominally significant association in the eMERGE cohort within fibroids and EAS PRS but not EUR PRS (95% CI: 1.09 - 1.20, p = 1.64x10-7). These findings highlight the strong predictive power of multi-ancestry PRS over single ancestry PRS. This study underscores the necessity of diverse population inclusion in genetic research to ensure precision medicine benefits all individuals equitably.

Many factors, including environmental and genetic variables, contribute to Colorectal Cancer (CRC) risk. Some of these risk factors may share underlying genetics with CRC. We investigated potential shared genetics by performing a Phenome-wide association study (PheWAS) with a multi-ancestry CRC polygenic risk score (PRS). The discovery cohort (N=426,464) consisted of ancestrally diverse participants from the United Kingdom Biobank. The replication cohort (N=87,271) consisted of ancestrally diverse participants from the electronic Medical Records and Genomics Network. We used a mixed-effects model to adjust for the presence of related individuals in both datasets. To preserve power, we limited testing to ancestor phecodes derived from the electronic health record (EHR), which were not likely to be a result of CRC or its treatment. We discovered and replicated associations between the CRC PRS and breast cancer, prostate cancer, obesity, smoking and alcohol use (discovery p< 1.1e-4; replication p<0.0019). As these results corroborate findings from other studies using orthogonal methods, we demonstrate that a CRC PRS can be used as a proxy for genetic risk for CRC when investigating shared genetics between CRC and other phenotypes. Further study of the relationship between PRS from multiple traits with EHR data may reveal additional shared genetic factors.

Functional genomic annotations can improve polygenic scores (PGS) within and between genetic ancestry groups. While general annotations are commonly used in PGS development, tissue- and cell-type-specific annotations derived from open chromatin and gene expression experiments may further enhance PGS for cardiometabolic traits. We developed PGS for 14 cardiometabolic traits in the UK Biobank using SBayesRC. We integrated GWAS summary statistics from FinnGen and GLGC with three annotation sources: (1) Baseline-LD model version 2.2 (general annotations), (2) cell-type-specific snATAC-seq peaks, and (3) tissue-specific eQTLs/sQTLs. We created PGS using two EUR LD reference panels (1.2 million [1.2M] HapMap3 variants and 7M imputed variants). Tissue- and cell-type-specific annotations showed stronger heritability enrichment than Baseline-LD annotations on average, particularly coronary snATAC-seq peaks and fine-mapped eQTLs. Without annotations, HapMap3 and 7M variant PGS performed similarly. However, with all annotations, 7M variant PGS outperformed HapMap3 variant PGS (8% average increase in relative performance in EUR). Compared to using no annotations, modeling Baseline-LD annotations improved performance by 5% for HapMap3 and 11% for 7M variant PGS, while modeling all annotations yielded improvements of 5% and 13%, respectively. Although annotations provided greater relative improvement for cross-ancestry prediction, they did not decrease the disparity in PGS performance between genetic ancestry groups. In conclusion, functional annotations improved PGS for cardiometabolic traits. Despite strong heritability enrichment, tissue- and cell-type-specific snATAC-seq and eQTL annotations provided marginal performance gains beyond general genomic annotations.

BACKGROUND:Clinical risk calculators for coronary heart disease (CHD) do not include genetic, social, and lifestyle-psychological risk factors. OBJECTIVE:To improve CHD risk prediction by developing and evaluating a prediction model that incorporated a polygenic risk score (PRS) and a polysocial score (PSS), the latter including social determinants of health and lifestyle-psychological factors. DESIGN:Cohort study. SETTING:United Kingdom. PARTICIPANTS:UK Biobank participants recruited between 2006 and 2010. MEASUREMENTS:Incident CHD (myocardial infarction and/or coronary revascularization); 10-year clinical risk based on pooled cohort equations (PCE), Predicting Risk of cardiovascular disease EVENTs (PREVENT), and QRISK3; PRS (Polygenic Score Catalog identification: PGS000018) for CHD (PRSCHD); and PSSCHD from 100 related covariates. Machine-learning and time-to-event analyses and model performance indices. RESULTS:In 388 224 participants (age, 55.5 [SD, 8.1] years; 42.5% men; 94.9% White), the hazard ratio for 1 SD increase in PSSCHD for incident CHD was 1.43 (95% CI, 1.38 to 1.49; P < 0.001) and for 1 SD increase in PRSCHD was 1.59 (CI, 1.53 to 1.66, P < 0.001). Non-White persons had higher PSSCHD than White persons. The effects of PSSCHD and PRSCHD on CHD were independent and additive. At a 10-year CHD risk threshold of 7.5%, adding PSSCHD and PRSCHD to PCE reclassified 12% of participants, with 1.86 times higher CHD risk in the up- versus down-reclassified persons and showed superior performance compared with PCE as reflected by improved net benefit while maintaining good calibration relative to the clinical risk calculators. Similar results were seen when incorporating PSSCHD and PRSCHD into PREVENT and QRISK3. LIMITATION:A predominantly White cohort; possible healthy participant effect and ecological fallacy. CONCLUSION:A PSSCHD was associated with incident CHD and its joint modeling with PRSCHD improved the performance of clinical risk calculators. PRIMARY FUNDING SOURCE:National Human Genome Research Institute.

BACKGROUND:Predictive performance of polygenic risk scores (PRS) varies across populations. To facilitate equitable clinical use, we developed PRS for coronary heart disease (CHD; PRSCHD) for 5 genetic ancestry groups. METHODS:We derived ancestry-specific and multi-ancestry PRSCHD based on pruning and thresholding (PRSPT) and ancestry-based continuous shrinkage priors (PRSCSx) applied to summary statistics from the largest multi-ancestry genome-wide association study meta-analysis for CHD to date, including 1.1 million participants from 5 major genetic ancestry groups. Following training and optimization in the Million Veteran Program, we evaluated the best-performing PRSCHD in 176,988 individuals across 9 diverse cohorts. RESULTS:Multi-ancestry PRSPT and PRSCSx outperformed ancestry-specific PRSPT and PRSCSx across a range of tuning values. Two best-performing multi-ancestry PRSCHD (ie, PRSPTmult and PRSCSxmult) and 1 ancestry-specific (PRSCSxEUR) were taken forward for validation. PRSPTmult demonstrated the strongest association with CHD in individuals of South Asian ancestry and European ancestry (odds ratio per 1 SD [95% CI, 2.75 [2.41-3.14], 1.65 [1.59-1.72]), followed by East Asian ancestry (1.56 [1.50-1.61]), Hispanic/Latino ancestry (1.38 [1.24-1.54]), and African ancestry (1.16 [1.11-1.21]). PRSCSxmult showed the strongest associations in South Asian ancestry (2.67 [2.38-3.00]) and European ancestry (1.65 [1.59-1.71]), lower in East Asian ancestry (1.59 [1.54-1.64]), Hispanic/Latino ancestry (1.51 [1.35-1.69]), and the lowest in African ancestry (1.20 [1.15-1.26]). CONCLUSIONS:The use of summary statistics from a large multi-ancestry genome-wide meta-analysis improved the performance of PRSCHD in most ancestry groups compared with single-ancestry methods. Despite the use of one of the largest and most diverse sets of training and validation cohorts to date, improvement of predictive performance was limited in African ancestry. This highlights the need for larger genome-wide association study datasets of underrepresented populations to enhance the performance of PRSCHD.

Background Social determinants of health (SDOH) influence the risk of common diseases such as coronary heart disease (CHD). Objectives This study sought to test the associations of self-reported race/ethnicity, SDOH, and a polygenic risk score (PRS), with CHD in a large and diverse U.S. cohort. Methods In 67,256 All of Us (AoU) participants with available SDOH and whole-genome sequencing data, we ascertained self-reported race/ethnicity and 22 SDOH measures across 5 SDOH domains, and we calculated a PRS for CHD (PRSCHD, PGS004696). We developed an SDOH score for CHD (SDOHCHD). We tested the associations of SDOH and PRSCHD with CHD in regression models that included clinical risk factors. Results SDOH across 5 domains, including food insecurity, income, educational attainment, health literacy, neighborhood disorder, and loneliness, were associated with CHD. SDOHCHD was highest in self-reported Black and Hispanic people. Self-reporting as Blacks had higher odds of having CHD than Whites but not after adjustment for SDOHCHD. SDOHCHD and PRSCHD were weakly correlated. In the test set (n = 33,628), 1-SD increases in SDOHCHD and PRSCHD were associated with CHD in models that adjusted for clinical risk factors (OR: 1.32; 95% CI: 1.23-1.41 and OR: 1.36; 95% CI: 1.28-1.44, respectively). SDOHCHD and PRSCHD were associated with incident CHD events (n = 52) over a median follow-up of 214 days (Q1-Q3: 88 days). Conclusions Increased odds of CHD in people who self-report as Black are likely due to a higher SDOH burden. SDOH and PRS were independently associated with CHD. Our findings suggest that including both PRS and SDOH in CHD risk models could improve their accuracy.

Polygenic risk scores (PRSs) summarize the genetic predisposition of a complex human trait or disease and may become a valuable tool for advancing precision medicine. However, PRSs that are developed in populations of predominantly European genetic ancestries can increase health disparities due to poor predictive performance in individuals of diverse and complex genetic ancestries. We describe genetic and modifiable risk factors that limit the transferability of PRSs across populations and review the strengths and weaknesses of existing PRS construction methods for diverse ancestries. Developing PRSs that benefit global populations in research and clinical settings provides an opportunity for innovation and is essential for health equity.

Background:The joint effects of polygenic risk and social determinants of health (SDOH) on coronary heart disease (CHD) in the United States are unknown. Methods:In 67,256 All of Us (AoU) participants with available SDOH data, we ascertained self-reported race/ethnicity and calculated a polygenic risk score for CHD (PRS CHD ). We used 90 SDOH survey questions to develop an SDOH score for CHD (SDOH CHD ). We assessed the distribution of SDOH CHD across self-reported races and US states. We tested the joint association of SDOH CHD and PRS CHD with CHD in regression models that included clinical risk factors. Results:SDOH CHD was highest in self-reported black and Hispanic people. Self-reporting as black was associated with higher odds of CHD but not after adjustment for SDOH CHD . Median SDOH CHD values varied by US state and were associated with heart disease mortality. A 1-SD increase in SDOH CHD was associated with CHD (OR=1.36; 95% CI, 1.29 to 1.46) and incident CHD (HR=1.73; 95% CI, 1.27 to 2.35) in models that included PRS CHD and clinical risk factors. Among people in the top 20% of PRS CHD , CHD prevalence was 4.8% and 7.8% in the bottom and top 20% of SDOH CHD , respectively. Conclusions:Increased odds of CHD in self-reported black people are likely due to higher SDOH burden. SDOH and PRS were independently associated with CHD in the US. Our findings emphasize the need to consider both PRS and SDOH for equitable disease risk assessment.

The Supplemental Materials and Methods file depicts a representative graphical report provided to participants at Visits 1 and 2 that illustrates the participant’s estimated 5-year, 10-year, and remaining lifetime breast cancer risk (determined by NCI-BCRAT and IBIS with and without PRS).

Experiences of childhood adversity can double the risk for depression. Although the mechanisms underlying this relationship remain unclear, DNA methylation (DNAm) has emerged as a potential pathway to explain the link between adversity and depression. We thus investigated whether epigenome-wide DNAm statistically mediates the association between childhood adversity and adolescent depressive symptoms. Specifically, we performed epigenome-wide mediation analyses to investigate the role of blood-based DNAm (age 7 years) in linking seven types of adversity (ages 0-7 years) to depressive symptoms (age 10.6 years). Primary analyses were conducted in the Avon Longitudinal Study of Parents and Children and replicated in the Future of Families and Child Wellbeing Study and Generation R Study. We identified 70 cytosine-guanine dinucleotides (CpGs) that mediated 10-73% of the correlation between adversity and depressive symptoms, with DNAm differences at 39 of these CpGs showing protective effects. Our findings suggest DNAm reflects a biological pathway linking childhood adversity to depression and a potential mechanism towards resilience.