Data Sharing in the PRIMED Consortium: Design, implementation, and recommendations for future policymaking

BackgroundBreast cancer patients from the indigenous Arab population present much earlier than patients from Western countries and have traditionally been underrepresented in cancer genomics studies. The contribution of polygenic and Mendelian risk toward the earlier onset of breast cancer in the population remains elusive.MethodsWe performed low-pass whole genome sequencing (lpWGS) and whole-exome sequencing (WES) from 220 female breast cancer patients unselected for positive family history from the indigenous Arab population. Using publicly available resources, we imputed population-specific variants and calculated breast cancer burden-sensitive polygenic risk scores (PRS). Variant pathogenicity was also evaluated on exome variants with high coverage.ResultsVariants imputed from lpWGS showed high concordance with paired exome (median dosage correlation: 0.9459, Interquartile range: 0.9410–0.9490). After adjusting the PRS to the Arab population, we found significant associations between PRS performance in risk prediction and first-degree relative breast cancer history prediction (Spearman rho=0.43, p = 0.03), where breast cancer patients in the top PRS decile are 5.53 (95% CI 1.76–17.97, p = 0.003) times more likely also to have a first-degree relative diagnosed with breast cancer compared to those in the middle deciles. In addition, we found evidence for the genetic liability threshold model of breast cancer where among patients with a family history of breast cancer, pathogenic rare variant carriers had significantly lower PRS than non-carriers (p = 0.0205, Mann-Whitney U test) while for non-carriers every standard deviation increase in PRS corresponded to 4.52 years (95% CI 8.88–0.17, p = 0.042) earlier age of presentation.ConclusionsOverall, our study provides a framework to assess polygenic risk in an understudied population using lpWGS and identifies common variant risk as a factor independent of pathogenic variant carrier status for earlier age of onset of breast cancer among indigenous Arab breast cancer patients.

Rare sequence variation in genes underlying cardiac repolarization and common polygenic variation influence QT interval duration. However, current clinical genetic testing of individuals with unexplained QT prolongation is restricted to examination of monogenic rare variants. The recent emergence of large-scale biorepositories with sequence data enables examination of the joint contribution of rare and common variations to the QT interval in the population. We performed a genome-wide association study of the QTc in 84 630 UK Biobank participants and created a polygenic risk score (PRS). Among 26 976 participants with whole-genome sequencing and ECG data in the TOPMed (Trans-Omics for Precision Medicine) program, we identified 160 carriers of putative pathogenic rare variants in 10 genes known to be associated with the QT interval. We examined QTc associations with the PRS and with rare variants in TOPMed. Fifty-four independent loci were identified by genome-wide association study in the UK Biobank. Twenty-one loci were novel, of which 12 were replicated in TOPMed. The PRS composed of 1 110 494 common variants was significantly associated with the QTc in TOPMed (ΔQTc/decile of PRS=1.4 ms [95% CI, 1.3 to 1.5]; P=1.1×10-196). Carriers of putative pathogenic rare variants had longer QTc than noncarriers (ΔQTc=10.9 ms [95% CI, 7.4 to 14.4]). Of individuals with QTc>480 ms, 23.7% carried either a monogenic rare variant or had a PRS in the top decile (3.4% monogenic, 21% top decile of PRS). QTc duration in the population is influenced by both rare variants in genes underlying cardiac repolarization and polygenic risk, with a sizeable contribution from polygenic risk. Comprehensive assessment of the genetic determinants of QTc prolongation includes incorporation of both polygenic and monogenic risk.

Challenges With Colorectal Cancer Family History Assessment—Motivation to Translate Polygenic Risk Scores Into Practice

Introduction : Genome-wide association studies reveal genetic correlations between venous and arterial cardiovascular phenotypes, such as venous thromboembolism (VTE), coronary artery disease (CAD), and peripheral arterial disease (PAD). The extent to which these diseases share metabolomic signatures has not been established. Methods : Polygenic risk scores (PRS) for CAD, PAD, and VTE were constructed using PRS-CS with external summary statistics and tested on ~120,000 UK Biobank participants with metabolomic profiling. Linear regression adjusted for age, sex, genotyping array, the first five principal components of ancestry, and statin prescription estimated the effect of polygenic disease risk on 249 metabolites among participants free of prevalent cardiovascular disease. For all metabolomic associations common to at least two diseases, bidirectional two-sample Mendelian randomization tested the causal relationship between metabolite and phenotype. Results : We found 206, 201, and 116 metabolites were associated with genetic risk for CAD, PAD, and VTE, respectively, at an adjusted p-value of 0.001 ( Figure ). Metabolomic profiles were similar between CAD and PAD (R = 0.75, P = 1.25E-46) and CAD and VTE (R = 0.76, P = 8.27E-49), but not between VTE and PAD (R = 0.24, P = 1.21E-4). Mendelian randomization revealed 28 metabolites that increased risk for both CAD and PAD and 2 metabolites that increased risk for CAD but decreased risk for VTE. There were no metabolites with shared causal relationship between PAD and VTE. Conclusions : Polygenic risks for arterial and venous disease are associated with unique metabolomic profiles. Mendelian randomization reveals overlap in causal metabolites between CAD and PAD but little overlap between VTE and either CAD or PAD despite moderate genetic correlation.

Identification of individuals at highest risk of coronary artery disease (CAD)—ideally before onset—remains an important public health need. Prior studies have developed genome-wide polygenic scores to enable risk stratification, reflecting the substantial inherited component to CAD risk. Here we develop a new and significantly improved polygenic score for CAD, termed GPSMult, that incorporates genome-wide association data across five ancestries for CAD (>269,000 cases and >1,178,000 controls) and ten CAD risk factors. GPSMult strongly associated with prevalent CAD (odds ratio per standard deviation 2.14, 95% confidence interval 2.10–2.19, P < 0.001) in UK Biobank participants of European ancestry, identifying 20.0% of the population with 3-fold increased risk and conversely 13.9% with 3-fold decreased risk as compared with those in the middle quintile. GPSMult was also associated with incident CAD events (hazard ratio per standard deviation 1.73, 95% confidence interval 1.70–1.76, P < 0.001), identifying 3% of healthy individuals with risk of future CAD events equivalent to those with existing disease and significantly improving risk discrimination and reclassification. Across multiethnic, external validation datasets inclusive of 33,096, 124,467, 16,433 and 16,874 participants of African, European, Hispanic and South Asian ancestry, respectively, GPSMult demonstrated increased strength of associations across all ancestries and outperformed all available previously published CAD polygenic scores. These data contribute a new GPSMult for CAD to the field and provide a generalizable framework for how large-scale integration of genetic association data for CAD and related traits from diverse populations can meaningfully improve polygenic risk prediction.

To construct a polygenic risk score (PRS) for stroke and evaluate its utility in risk stratification and primary prevention for stroke. Using a meta-analytic approach and large genome-wide association results for stroke and stroke-related traits in East Asians, we generated a combined PRS (metaPRS) by incorporating 534 genetic variants in a training set of 2,872 patients with stroke and 2,494 controls. We then validated its association with incident stroke using Cox regression models in large Chinese population-based prospective cohorts comprising 41,006 individuals. During a total of 367,750 person-years (mean follow-up 9.0 years), 1,227 participants developed stroke before age 80 years. Individuals with high polygenic risk had an about 2-fold higher risk of incident stroke compared with those with low polygenic risk (hazard ratio [HR] 1.99, 95% confidence interval [CI] 1.66-2.38), with the lifetime risk of stroke being 25.2% (95% CI 22.5%-27.7%) and 13.6% (95% CI 11.6%-15.5%), respectively. Individuals with both high polygenic risk and family history displayed lifetime risk as high as 41.1% (95% CI 31.4%-49.5%). Individuals with high polygenic risk achieved greater benefits in terms of absolute risk reductions from adherence to ideal fasting blood glucose and total cholesterol than those with low polygenic risk. Maintaining favorable cardiovascular health (CVH) profile could substantially mitigate the increased risk conferred by high polygenic risk to the level of low polygenic risk (from 34.6% to 13.2%). Our metaPRS has great potential for risk stratification of stroke and identification of individuals who may benefit more from maintaining ideal CVH. This study provides Class I evidence that metaPRS is predictive of stroke risk.

Genetic effects on alcohol use can vary over time but are often examined using longitudinal models that predict a distal outcome at a single time point. The vast majority of these studies predominately examine effects using White, European American (EA) samples or examine the etiology of genetic variants identified from EA samples in other racial/ethnic populations, leading to inconclusive findings about genetic effects on alcohol use. The current study examined how genetic influences on alcohol use varied by age across a 15 year period within a diverse ethnic/racial sample of adolescents. Using a multi-ethnic approach, polygenic risk scores were created for African American (AA, n = 192) and EA samples (n = 271) based on racially/ethnically aligned genome wide association studies. Age-varying associations between polygenic scores and alcohol use were examined from age 16 to 30 using time-varying effect models separately for AA and EA samples. Polygenic risk for alcohol use was found to be associated with alcohol use from age 22–27 in the AA sample and from age 24.50 to 29 in the EA sample. Results are discussed relative to the intersection of alcohol use and developmental genetic effects in diverse populations.

Polygenic risk for five psychiatric disorders and cross-disorder and disorder-specific neural connectivity in two independent populations.

Breast cancer has been associated with monogenic, polygenic, and epidemiologic (clinical, reproductive, and lifestyle) risk factors, but studies evaluating the combined effects of these factors have been limited. We extended previous work in breast cancer risk modeling, incorporating pathogenic variants (PV) in six breast cancer predisposition genes and a 105-SNP polygenic risk score (PRS), to include an epidemiologic risk score (ERS) in a sample of non-Hispanic White women drawn from prospective cohorts and population-based case-control studies, with 23,518 cases and 22,832 controls, from the Cancer Risk Estimates Related to Susceptibility (CARRIERS) Consortium. The model predicts 4.4-fold higher risk of breast cancer for postmenopausal women with no predisposition PV and median PRS, but with the highest versus lowest ERS. Overall, women with CHEK2 PVs had >20% lifetime risk of breast cancer. However, 15.6% of women with CHEK2 PVs and a family history of breast cancer, and 45.1% of women with CHEK2 PVs but without a family history of breast cancer, had low (<20%) predicted lifetime risk and thus were below the threshold for MRI screening. CHEK2 PV carriers at the 10th percentile of the joint distribution of ERS and PRS, without a family history of breast cancer, had a predicted lifetime risk similar to the general population. These results illustrate that an ERS, alone and combined with the PRS, can contribute to clinically relevant risk stratification. Integrating monogenic, polygenic, and epidemiologic risk factors in breast cancer risk prediction models may inform personalized screening and prevention efforts.

Working memory (WM) deficit in schizophrenia is thought to arise from a widespread neural inefficiency. However, we do not know if this deficit results from the illness-related genetic risk and influence the symptom burden in various domains, especially in patients who have an early onset illness. We used graph theory to examine the topology of the functional connectome in 99 subjects (27 early-onset schizophrenia (EOS), 24 asymptomatic siblings, and 48 healthy subjects) during an n-back task, and calculated their polygenic risk score (PRS) for susceptibility to schizophrenia. Linear regression analysis was used to test associations of the PRS, clinical symptoms, altered connectomic properties, and WM accuracy in EOS. Indices of small-worldness and segregation were elevated in EOS during the WM task compared with the other two groups; these connectomic aberrations correlated with increased PRS and negative symptoms. In patients with higher polygenic risk, WM performance was lower only when both the connectomic aberrations and the burden of negative symptoms were higher. Negative symptoms had a stronger moderating role in this relationship. Our findings suggest that the aberrant connectomic topology is a feature of WM task performance in schizophrenia; this relates to higher polygenic risk score as well as higher burden of negative symptoms. The deleterious effects of polygenic risk on cognition are played out via its effects on the functional connectome, as well as negative symptoms.

Primary open-angle glaucoma (POAG) remains the leading cause of irreversible blindness worldwide, with early detection crucial for preventing vision loss. However, current risk assessment methods have limited predictive power. Here, we present a multi-trait polygenic probability risk score (PPRS) approach that integrates multiple glaucoma-related traits and leverages functional genomic annotations to enhance POAG prediction across diverse ancestries. We constructed PRSs for POAG, intraocular pressure (IOP), vertical cup-to-disc ratio (VCDR), and retinal nerve fiber layer (RNFL) thickness using extensive genomic coverage (>7 million variants) and 96 functional annotations through the SBayesRC method. Validation in the UK Biobank (n=324,713, European ancestry) and Mexican American Glaucoma Genetic Study (MAGGS, n=4,549, Latino ancestry) demonstrated significant improvements in predictive accuracy over conventional approaches. Our multi-trait PPRS achieved area under the curve (AUC) values of 0.814 in Europeans and 0.801 in Latinos, compared to AUC ≤0.79 for single-trait models. We identified ancestry-specific differences in genetic contributions, with IOP demonstrating the strongest association in Europeans (OR=1.63, P = 5.37 × 10-89), while VCDR was predominant in Latinos (OR=1.64, P = 2.04 × 10-11). The model achieved remarkable risk stratification, with the highest PPRS decile showing 80.2-fold and 51.1-fold increased POAG risk in Europeans and Latinos, respectively, compared to the lowest decile. Importantly, the top PPRS quintile captured 65.9% and 62.2% of POAG cases in Europeans and Latinos, substantially improving upon previous approaches. Our findings demonstrate that integrating multiple disease-relevant traits and functional annotations significantly enhances polygenic prediction of POAG across diverse populations, with significant implications for targeted screening, early intervention, and reduction of disease burden.

Advances in gene–environment (GE) research underscore the need for novel statistical methods to clarify how social stress and genetic liability jointly influence Alcohol Use Disorder (AUD). This study examined the effect of Stressful Life Events (SLEs) on past-year DSM-5 AUD severity using propensity score matching and tested whether Polygenic Risk Scores (PRS) moderated the association between stress exposure and AUD severity. A secondary analysis was performed using the data from National Epidemiologic Survey on Alcohol and Related Conditions III (NESARC III; N = 36,309) and the newly available NESARC III genomic dataset (N = 22,848). Of 10,775 self-reported White subjects, individuals in the high-stress group (i.e., more than two stressors) (N = 4436) were matched to those in the low-stress group (N = 5048) using propensity score matching. Further, a moderation analysis was conducted to evaluate the interaction between PRS and SLE. Exposure to two or more SLEs in the past year significantly increased the odds of AUD severity after covariate adjustment (estimate: 0.37; 95%CI: 0.27–0.48; t-value: 6.96; p < 0.001). PRS was independently associated with higher AUD criteria counts after adjustment (estimate: 0.23; 95%CI: 0.07–0.39; t-value: 2.87; p = 0.01). PRS significantly moderated the relationship between the stress exposure and the AUD severity (interaction effect = 0.30; 95% CI: 0.06–0.54; t = 2.47; p = 0.01), indicating stronger stress-related effects among individuals with higher genetic risk. Additional sensitivity analyses suggested that PRS moderation by stress exposure is small and sensitive to model specification. This novel GE interaction model provides evidence that polygenic risk can potentiate the effects of high-stress exposure on the severity of AUD, thereby advancing post-GWAS modeling efforts and support precise identification of high-risk subpopulations for targeted prevention and intervention. Not applicable.

Genome-wide association studies have identified common genomic variants associated with increased susceptibility to breast cancer. In the general population, the risk associated with these known variants seems insufficient to inform clinical management. Their contribution to the development of familial breast cancer is less clear. We studied 1,143 women with breast cancer who had completed BRCA1 and BRCA2 mutation screening as a result of a high risk for hereditary breast cancer. Genotyping of 22 breast cancer-associated genomic variants was performed. A polygenic risk score (PRS), calculated as the sum of the log odds ratios for each allele, was compared with the same metric in 892 controls from the Australian Ovarian Cancer Study. The clinical features associated with the high and low ends of the polygenic risk distribution were compared. Women affected by familial breast cancer had a highly significant excess of risk alleles compared with controls (P = 1.0 × 10(-16)). Polygenic risk (measured by the PRS) was greater in women who tested negative for a BRCA1 or BRCA2 mutation compared with mutation carriers (P = 2.3 × 10(-6)). Non-BRCA1/2 women in the top quartile of the polygenic risk distribution were more likely to have had early-onset breast cancer (< 30 years of age, odds ratio [OR]= 3.37, P = .03) and had a higher rate of second breast cancer (OR 1.96, P = .02) compared with women with low polygenic risk. Genetic testing for common risk variants in women undergoing assessment for familial breast cancer may identify a distinct group of high-risk women in whom the role of risk-reducing interventions should be explored.

While respiratory diseases such as chronic obstructive pulmonary disease (COPD) and asthma share many risk factors, most studies investigate them in isolation and in predominantly European-ancestry populations. Here, we conducted the most powerful multi-trait and multi-ancestry genetic analysis of respiratory diseases and auxiliary traits to date, identifying 25 new loci associated with lung function in individuals of East Asian ancestry. Using these results, we developed PRSxtra (cross-trait and cross-ancestry), a multi-trait and multi-ancestry polygenic risk score (PRS) approach that leverages shared components of heritable risk via pleiotropic effects. PRSxtra significantly improved the prediction of asthma, COPD and lung cancer compared to trait- and ancestry-matched PRSs in a multi-ancestry cohort from the All of Us Research Program, especially in diverse populations. Our results present a new framework for multi-trait and multi-ancestry studies of respiratory diseases to improve genetic discovery and polygenic prediction.

Red blood cell (RBC) concentration impacts cerebrovascular disease, yet it is unclear whether RBC concentrations relate to dementia risk, particularly in racially/ethnically diverse cohorts. We investigated whether RBC concentrations associate with incident dementia risk in a diverse population of stroke-free individuals and explored whether cerebral small vessel disease (CSVD) mediates this relationship. A longitudinal observational analysis was performed using a population-based cohort of stroke-free, older adult participants (>50 years) from the Northern Manhattan Study (NOMAS) enrolled between 2003 and 2008. Participants received baseline hematocrit testing, MRI neuroimaging, and cognitive assessments at baseline and long-term follow-up. Associations of baseline hematocrit as a categorical variable (low, normal [reference], and high based on laboratory reference levels) with incident dementia were assessed using Cox models adjusting for relevant covariates. Separate analyses investigated whether MRI CSVD mediated these relationships. We studied 1,207 NOMAS participants (mean age 71 ± 9 years, 60% female, 66% Hispanic). Mean hematocrit was 41.2% (±3.8) with 16% of participants developing incident dementia. Lower hematocrit associated with increased dementia risk (adjusted hazard ratio 1.81 [1.01-3.23]) after adjusting for age, sex, race/ethnicity, education, APOE status, and comorbidities. High hematocrit was not associated with dementia risk. No interactions by sex or race/ethnicity were seen and baseline CSVD did not mediate relationships between hematocrit and dementia. Low hematocrit associated with dementia risk in our diverse population cohort. However, our study limitations in laboratory and neuroimaging timing in addition to clarifying mechanistic underpinnings for our observations necessitates further work to clarify whether anemia can serve as a trackable, preventable/treatable risk factor for dementia.