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PubMed2026

Exploring the shared genetic architecture of sarcopenia using genomic structural equation modeling.

Sarcopenia is a common age-associated condition characterized by the progressive loss of skeletal muscle mass, strength, and physical functionality. While large-scale genome-wide association studies (GWAS) have previously addressed isolated traits of sarcopenia, the multifactorial genetic architecture underlying this condition remains largely undefined. To characterize the common genetic basis of sarcopenia-related traits, genomic structural equation modeling (Genomic-SEM) was implemented. Multiple post-GWAS analytic approaches were integrated to pinpoint susceptibility loci. These analyses encompassed identifying enriched genetic pathways and relevant genomic elements, as well as cell-type-specific enrichment in skeletal muscle satellite stem cells, mesenchymal stem cells, and skeletal muscle satellite cells in limb muscle. Furthermore, based on the integrated GWAS data of sarcopenia-related traits, polygenic risk score (PRS) analysis was conducted to evaluate risk associations at the chromosomal level. A well-fitted Genomic-SEM successfully integrated the GWAS data, revealing the shared genetic architecture of sarcopenia-related traits. We identified 110 single nucleotide polymorphisms (SNPs) reaching genome-wide significance (p < 5 × 10-8), of which 9 represent novel discoveries. Subsequent fine-mapping procedures and gene-set analyses identified 15 causal variants alongside 77 candidate susceptibility genes. This study provides a comprehensive genetic characterization of sarcopenia via Genomic-SEM, offering new insights into the etiological pathways underlying sarcopenia.

Sarcopenia↗

PubMed2026

A polygenic risk score for peripheral artery disease and major adverse limb events.

BACKGROUND AND AIMS: Large-scale genome-wide association studies have identified common genetic variants that predict the risk of peripheral artery disease (PAD). This study assessed whether a polygenic risk score (PRS) is associated with PAD and the incidence of major adverse limb events (MALE) independent of clinical risk factors in patients with established cardiometabolic disease. METHODS: A genetic analysis was performed, pooling individual patient-level data from six TIMI trials. The association of a recently validated PAD PRS with prevalent PAD and the incidence of MALE (acute limb ischaemia, chronic limb-threatening ischaemia, major amputation, or peripheral revascularization) was assessed. RESULTS: A total of 68 816 patients were included in this analysis, with a median follow-up of 2.6 years. Of these, 5986 (8.7%) had known PAD at baseline. After adjusting for clinical risk factors, a higher PAD PRS was independently associated with a 15% greater odds of prevalent PAD (adjusted odds ratio per 1-SD: 1.15 [95% confidence interval 1.12-1.18], P < .0001), a magnitude of risk as strong as established clinical risk factors. A total of 577 patients experienced MALE during follow-up. A higher PAD PRS was associated with a 30% increased risk of MALE (adjusted hazard ratio per 1-SD: 1.30 [1.19-1.42], P < .0001). Adding the PAD PRS to clinical risk factors resulted in a statistically significant but modest improvement in discrimination (area under the curve went from 0.651 to 0.662 P < .0001). CONCLUSIONS: In a broad spectrum of patients with cardiometabolic disease, the PAD PRS is associated with an increased risk of PAD and the incidence of MALE beyond clinical risk factors; however, the improvement in discrimination was statistically significant but clinically modest.

Humans↗

PubMed2026

Genetic Risk Factors for Kidney Function in Individuals with Type 1 Diabetes.

KEY POINTS: Previous research has identified polygenic risk scores that are associated with low eGFR and albuminuria in the general population. We observed that these eGFR and albuminuria polygenic risk scores were associated with eGFR and albuminuria, respectively, in type 1 diabetes. Associations were independent of glycemic control and suggest shared genetic kidney risk factors between type 1 diabetes and the general population. BACKGROUND: Genetic risk factors underlying kidney disease in type 1 diabetes (T1D) remain poorly understood. We examined whether previously established polygenic risk scores (PRS) for eGFR and albuminuria are associated with these measures in adults with T1D in the Diabetes Control and Complications Trial (DCCT)/Epidemiology of Diabetes Interventions and Complications study. METHODS: We applied eGFR and albuminuria PRS derived in general population cohorts to 1304 DCCT/Epidemiology of Diabetes Interventions and Complications participants with genome-wide genotyping. We tested PRS associations with eGFR and urine albumin excretion rate (AER) as well as incident eGFR <60 ml/min per 1.73 m 2 , AER ≥30 mg/24 h, and AER ≥300 mg/24 h. For consistency, PRS values were linearly transformed so higher scores corresponded to higher eGFR and AER. We also examined associations of kidney outcomes with rs55703767 in COL4A3 , which has previously been associated with CKD in T1D. RESULTS: At DCCT baseline, participants had a mean age of 27 years; 53% were male. 49% of participants were randomized to intensive versus conventional glucose-lowering therapy. Participants were followed for median of (first-third quartiles) 35 (33-37) years. The eGFR PRS was significantly associated with continuous eGFR (per one SD higher PRS 2.72 ml/min per 1.73 m 2 higher [95% confidence interval (CI), 2.05 to 3.40]) and incident eGFR <60 ml/min per 1.73 m 2 (hazard ratio [HR]=0.82 [95% CI, 0.73 to 0.92]), but not consistently with albuminuria. There was no association with quantitative AER (2.42 mg/24 h [95% CI, -1.86 to 6.89]) or sustained AER ≥30 mg/24 h (HR=1.03; [95% CI, 0.94 to 1.14]). The albuminuria PRS was significantly associated with incident AER ≥30 mg/24 h (HR=1.12 [95% CI, 1.02 to 1.22]) but not continuous eGFR (0.49 ml/min per 1.73 m 2 higher [95% CI, -0.23 to 1.21]) or incident eGFR <60 ml/min per 1.73 m 2 (HR=0.96 [95% CI, 0.85 to 1.08]). Associations were similar in analyses stratified by DCCT treatment group assignment. rs55703767 was associated with lower incident macroalbuminuria in the overall cohort (HR=0.77 per minor allele [95% CI, 0.59 to 0.99]), and upon stratification by DCCT treatment group assignment, only within the conventional and not intensive glucose-lowering therapy group. CONCLUSIONS: PRS associated with eGFR and albuminuria in the general population were associated with corresponding measures in adults with T1D. The results suggest shared genetic risk factors for kidney disease between T1D and the general population but different genetic risk factors for albuminuria and eGFR in T1D. CLINICAL TRIALS REGISTRATION NUMBERS: NCT00360893 , NCT00360815 .

Adult↗

PubMed2026

Genetic evidence supports the combined targeting of lipoprotein(a) and LDL cholesterol to reduce coronary artery disease risk.

Distinct genetic mechanisms govern how lipoprotein(a) (Lp(a)) and low-density lipoprotein cholesterol (LDL-C) promote atherosclerosis. It remains unclear whether targeting both provides additive cardiovascular benefits. Here we use coding loss-of-function variants in LPA and PCSK9 and genetic scores associated with Lp(a) and LDL-C levels to evaluate the effects of lowering Lp(a) and LDL-C on coronary artery disease (CAD) risk. Among 408,039 individuals from the UK Biobank, LPA or PCSK9 loss-of-function carriers have lower CAD risk than noncarriers (odds ratio (OR) 0.91 and 0.81). Carriers of both variants have even lower CAD risk (OR 0.73). Genetic lowering of Lp(a) and LDL-C showed a stronger reduction of CAD risk (OR 0.70) than either trait individually (OR 0.85 and 0.81) in the two-factor genetic score analysis. Among statin users, Lp(a) reduction was linearly associated with CAD risk. A phenome-wide association study revealed that combined therapy was associated with cardiometabolic benefits without adverse effects. The additive benefits were replicated in 65,171 individuals from the Mass General Brigham Biobank.

Humans↗

PubMed2026

Metabolomic ageing across mental and behavioural disorders.

BACKGROUND: Individuals with mental disorders face excess morbidity and premature mortality. Accelerated ageing has been proposed as a contributing mechanism but population-scale evidence across diverse diagnoses is limited. OBJECTIVE: To examine whether metabolomic ageing differs across mental disorders and whether associations vary by sex, age group and genetic liability. METHODS: Using plasma metabolomic profiles from UK Biobank participants, we applied a metabolomic ageing clock (MileAge) to estimate disorder-specific differences between metabolite-predicted and chronological age. Mental disorders were ascertained from health records and self-reported physician diagnoses. We analysed nine diagnostic groups and 45 individual disorders and assessed sex and age group differences and associations with polygenic scores. FINDINGS: Among 225 212 participants (54% female; mean age 56.97), 38 524 had a diagnosis preceding baseline. Substance use, psychotic, affective and neurotic disorders were associated with a metabolite-predicted age older than chronological age, largest for psychosis (β=0.556, 95% CI 0.250 to 0.861, p<0.001). Obsessive-compulsive and eating disorders were associated with a metabolite-predicted age younger than chronological age. Several associations were stronger in males and in individuals aged <65 years. Higher genetic liability to depression, autism and attention-deficit/hyperactivity disorder predicted an older metabolomic age (β range=0.020 to 0.047), whereas polygenic scores for psychosis and tobacco use disorder predicted a younger metabolomic age (β range=-0.023 to -0.040). For obsessive-compulsive disorder and anorexia nervosa, clinical and genetic associations indicated younger metabolomic ageing. CONCLUSIONS: Metabolomic ageing in mental disorders is heterogeneous. While many disorders are associated with an older biological age, some are linked to a younger biological age. Divergence between genetic liability and clinical phenotypes suggests that non-genetic factors shape biological ageing differences. CLINICAL IMPLICATIONS: Biological age should not be assumed to uniformly exceed chronological age across mental disorders. Sex and age-specific approaches could improve understanding of biological ageing processes in psychiatry.

Humans↗

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

Linking cortical structure and delirium in the elderly: insights from cohort study and shared genetic risk analysis.

BACKGROUND: This study aimed to assess the association between regional cortical changes measured via baseline magnetic resonance imaging (MRI) and the incidence of delirium. METHODS: Observational associations were assessed using a prospective cohort from the UK Biobank and an independent clinical cohort. The population-based study included participants aged 60 years or older who had undergone structural brain MRI since 2014. Regional cortical volume, mean thickness, and surface area were extracted based on the Desikan-Killiany cortical atlas. Delirium was defined using ICD-10 diagnostic codes. Additionally, preoperative brain MRI images from participants in another cohort were collected and automatically segmented using deep learning algorithms to obtain cortical measurements. Logistic analysis was performed to investigate the associations between cerebral cortical structure and delirium risk. Lastly, genome-wide association study data derived from the ENIGMA Consortium and FinnGen Biobank were utilized to conduct conditional/conjunctional false discovery rate (cond/conjFDR) analyses to identify shared genetic loci associated with cortical structures and delirium. RESULTS: This observational analysis included 31,890 participants from the UK Biobank and 152 participants from an independent cohort. In the UK Biobank cohort, decreased cortical thickness in the 17 regions was associated with a significantly increased risk of delirium. Similarly, a preoperative reduction in cortical volume in 7 regions was associated with an increased risk of delirium in the independent cohort. Besides, 100 single-nucleotide polymorphisms (SNPs) were identified as significantly associated with cortical structures when conditioned on delirium. Finally, colocalization analysis demonstrated that these pleiotropic risk loci modulated the expression of NT5C2, RGP1, CCDC25, TPM2, EEF1AKMT2, IQANK1 and LHPP in blood and brain tissues. CONCLUSION: Regional cortical atrophy is associated with an increased risk of delirium in the elderly. Brain MRI examinations may be beneficial for preoperative delirium risk assessment in elderly individuals undergoing elective surgery.

Humans↗

PubMed2026

Social disconnection integrates genetic and proteomic risks in suicidal ideation and depression.

Suicidal ideation (SI) and major depressive disorder (MDD) are complex psychiatric conditions arising from the interplay of genetic liability, molecular processes, and psychosocial factors. While these dimensions have been extensively studied in isolation, their joint contribution to SI and MDD remains unclear. This study integrates multi-modal data to elucidate these synergistic effects and develop robust models for individual-level risk stratification. Leveraging longitudinal multi-modal data from 13,085 UK Biobank participants, we integrated genomic, proteomic, and social connection profiles. We developed interpretable risk scores using a rigorous supervised machine learning framework encompassing diverse linear and ensemble classifiers. Permutation importance was employed to quantify feature contributions and derive transparent, weighted risk metrics across diverse classifiers. These scores were validated through association, interaction, and mediation analyses. Social connection-based risk scores significantly differentiated cases and controls across the two suicidal ideation phenotypes at 2017 and 2023 with cross-sectional analyses (AUCs: 0.70 - 0.73), outperforming proteomic-only models. Functional dimensions of social connection emerged as the most informative predictors. Longitudinal analyses revealed that social risk scores at baseline predicted suicidal ideation onset six years later, independent of demographic covariates. Interaction analyses demonstrated that polygenic risk for suicide attempt significantly interacted with both social and proteomic risk features in relation to depression. Structural equation models further confirmed that social disconnection acts as a key mediator linking genetic predisposition to MDD and SI. Social disconnection is a critical risk factor mediating the impact of genetic vulnerability on psychiatric outcomes. Integrating social, genetic, and molecular data supports a multilevel framework for risk stratification and highlights the potential of socially oriented interventions to mitigate biological risk.

Humans↗

PubMed2026

Genetic and Lifestyle Factors Influence High 1-Hour Plasma Glucose, a Predictor of Type 2 Diabetes Mellitus.

BACKGRUOUND: High 1-hour plasma glucose (1-h PG) level has been proposed by the International Diabetes Federation to identify high-risk individuals and diagnose type 2 diabetes mellitus (T2DM). In a longitudinal cohort, we examined T2DM risk, β-cell function, and the effects of genetic and lifestyle factors on high 1-h PG. METHODS: We analyzed 7,464 participants without T2D at baseline from a community-based prospective cohort in Korea, who underwent biennial 2-h 75-g oral glucose tolerance tests over 14 years. Incident T2D risk were assessed across 1-h PG groups: < 155, 155-208, and ≥ 209 mg/dL. In 6,588 participants with at least two 1-h PG measurements, we analyzed 1-h PG trajectories by T2D polygenic risk score (PRS; low, 1st quintile; intermediate, 2nd-4th quintiles; high, 5th quintile) and lifestyle, assessed using Life's Essential 8. RESULTS: Compared to the <155 mg/dL group, hazard ratios for T2DM were 3.34 (95% confidence interval [CI], 2.99 to 3.74; P<0.001) for 155-208 mg/dL, and 6.81 (95% CI, 5.81 to 7.98; P<0.001) for ≥209 mg/dL. Both groups had lower baseline disposition index compared to the <155 mg/dL group (57.3% and 72.7%, respectively; both P<0.001). Higher T2DM PRS was associated with elevated baseline 1-h PG (low: 131 mg/dL, intermediate: 141 mg/dL, high: 151 mg/dL) and faster increase in 1-h PG (1.36 vs. 1.85 vs. 2.21 mg/dL/year; all P<0.001). Importantly, healthy lifestyle attenuated the rate of increase across all PRS groups. CONCLUSION: High 1-h PG predicts T2DM risk and is associated with β-cell dysfunction. The 1-h PG level is influenced by genetic risk and can be modified with a healthy lifestyle.

Humans↗

PubMed2024

Cost-effectiveness of population-wide genomic screening for Lynch Syndrome and polygenic risk scores to inform colorectal cancer screening.

PURPOSE: Genomic screening to identify individuals with Lynch Syndrome (LS) and those with a high polygenic risk score (PRS) promises to personalize colorectal cancer (CRC) screening. Understanding its clinical and economic impact is needed to inform screening guidelines and reimbursement policies. METHODS: We developed a Markov model to simulate individuals over a lifetime. We compared LS+PRS genomic screening with standard of care (SOC) for a cohort of US adults at age 30. The Markov model included health states of no CRC, CRC stages (A-D), and death. We estimated incidence, mortality, and discounted economic outcomes of the population under different interventions. RESULTS: Screening 1000 individuals for LS+PRS resulted in 1.36 fewer CRC cases and 0.65 fewer deaths compared with SOC. The incremental cost-effectiveness ratio was $124,415 per quality-adjusted life year; screening had a 69% probability of being cost-effective using a willingness-to-pay threshold of $150,000/quality-adjusted life year . Setting the PRS threshold at the 90th percentile of the LS+PRS screening program to define individuals at high risk was most likely to be cost-effective compared with 95th, 85th, and 80th percentiles. CONCLUSION: Population-level LS+PRS screening is marginally cost-effective, and a threshold of 90th percentile is more likely to be cost-effective than other thresholds.

Humans↗

PubMed2025

Diabetes mellitus polygenic risk scores: heterogeneity and clinical translation.

Diabetes mellitus encompasses several disorders, each with differing clinical presentation, prognoses and pathophysiology. Distinct polygenic architectures underlie type 1 diabetes mellitus and type 2 diabetes mellitus, and govern numerous pathophysiological pathways that converge on dysglycaemia. Over the previous decade, polygenic risk scores (PRS) derived from large genome-wide association studies have become broadly recognized for their potential in precision medicine. PRS, and now partitioned polygenic scores generated by clustering of risk variants, can quantify individual genetic predisposition to diabetes mellitus and reveal molecular heterogeneity responsible for variation in clinical presentation and prognoses. In this Review, we examine and contrast progress in the development of type 1 diabetes mellitus PRS and type 2 diabetes mellitus PRS, and discuss paths to further methodological advances. We examine how studies in the past 10 years have harnessed PRS and novel partitioned polygenic scores to reveal insights into diabetes mellitus aetiology and characterize changes in cellular and tissue-specific disease-modifying molecular pathways. Additionally, we discuss advances and opportunities in areas of clinical translation, including improved classification of diabetes mellitus type, screening of those at risk and personalized interventions informed by PRS. Finally, we emphasize the urgent need to overcome ancestry-related challenges and highlight current progress and gaps in ensuring the equitable translation of PRS for diabetes mellitus precision medicine.

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

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↗

PubMed2002

Family score as an indicator of genetic risk of primary open-angle glaucoma.

OBJECTIVES: To assess the genetic risk of open-angle glaucoma (OAG) in individuals by calculating a family score (FS), which summarizes the information of all relatives including their disease status, age, sex, and degree of kinship and to examine the genetic contribution to OAG with and without an increased intraocular pressure. METHODS: Case and control probands, derived from the Rotterdam Study, underwent the same ophthalmologic examination as their relatives. The FS of each proband was the sum of the differences between observed and expected values of OAG for all relatives. The FSs were compared between case and control probands using logistic regression analysis, adjusted for intraocular pressure. RESULTS: Of 37 case probands, 44 (half-) siblings and 86 children were available, and of 83 control probands there were 97 (half-) siblings and 155 children. Family scores ranged from -0.44 to 7.08 in case probands and from -0.98 to 2.46 in control probands. One unit increase in FS was significantly associated with a higher risk of OAG (odds ratio, 1.59; 95% confidence interval, 1.14-2.23). Adjustments for intraocular pressure did not change the odds ratio. CONCLUSIONS: These data show that the FS strongly predicts OAG, independent of the intraocular pressure. Therefore, the FS is useful to identify individuals with a high genetic risk.

Aged↗

PubMed2026

Large-scale multi-omics enhance risk prediction for type 2 diabetes.

BACKGROUND: Polygenic risk scores (PRS), metabolomics, and proteomics have each shown promise in improving type 2 diabetes risk prediction, but their combined utility beyond established clinical models remains unclear. We aimed to evaluate whether integrating multi-omics biomarkers enhances 10-year type 2 diabetes risk prediction beyond single-omics extensions and the clinical Cambridge Diabetes Risk Score (CDRS), which includes HbA1c measurements. METHODS: We analysed data from 42,840 UK Biobank participants without diagnosed diabetes at baseline. The study population was split into a derivation set (Phase 1 metabolomics release, N = 23,108) to fit models and an independent validation set (Phase 2 release, N = 19,732) to evaluate performance. Data for a PRS for type 2 diabetes, 11 metabolites, and 15 proteins were added to the CDRS to develop multi-omics prediction models. Model performance was evaluated using Harrell's C-index and the net reclassification index (NRI). RESULTS: During 10 years of follow-up, 1090 participants developed incident type 2 diabetes. Among individual omics layers, proteomics contributed the greatest improvement in predictive performance, increasing the C-index from 0.862 (clinical CDRS) to 0.884 (ΔC-index; + 0.022; P < 0.001), with a continuous NRI of 42.0%. The full multi-omics model further significantly increased the C-index compared to a model combining the clinical CDRS with proteomics data (C-index, 0.891; ΔC-index; + 0.007; P < 0.001). CONCLUSION: Integrating proteomics, metabolomics, and a diabetes-PRS into a clinical model substantially improves type 2 diabetes risk prediction beyond single-omics extensions. Several of the selected proteins and metabolites are on cardiovascular disease pathways, highlighting the link between diabetes and cardiovascular risk. However, the C-index difference between the proteomics extended and full multi-omics extended models is small, and the clinical models extended with proteomics data would be easier to translate into routine care because it needs only the measurement of 15 proteins. External validation and cost-effectiveness analyses are needed to support clinical adoption.

Humans↗

PubMed2026

Variance Polygenic Scores (vPGS) as a Tool for Studying Gene-Environment Interactions Associated With Refractive Error.

PURPOSE: Conventional polygenic scores predict an individual's phenotype based on their genetics. By contrast, variance polygenic scores (vPGS) quantify genetic predisposition to phenotypic variance. We tested the hypothesis that a vPGS for refractive error can identify individuals with increased susceptibility to environmental risk factors for myopia. METHODS: Six vPGS construction strategies were evaluated in UK Biobank participants: three variance heterogeneity genome-wide association study (vGWAS) methods and two reweighting schemes. vPGS performance was assessed using two metrics: (i) "Diff"-difference in phenotypic variance in vPGS decile ten versus one; (ii) Spearman correlation of phenotypic variance versus vPGS decile. The optimal vPGS was used to test for vPGS × time spent reading or vPGS × time spent outdoors interactions in children aged 15 years (ALSPAC cohort; n = 3471). RESULTS: Of the vGWAS methods, conditional quantile regression outperformed SCAMPI and Levene's Test. Of the re-weighting schemes, LDpred2 outperformed pruning and thresholding. In an independent sample of UK Biobank participants (n = 19,470), the top-performing vPGS successfully stratified individuals into groups with increasing variance in refractive error, even after adjusting for a conventional PGS (Diff: 2.55, 95% confidence interval [CI], 1.64-3.47; Spearman correlation = 0.87; 95% CI, 0.43-0.93). However, in ALSPAC participants, there was minimal support for vPGS interactions with time reading (P = 0.80) or time outdoors (P = 0.89). CONCLUSIONS: A novel vPGS successfully stratified individuals into groups with relatively high or low genetic susceptibility to refractive error variance. However, the vPGS could not identify individuals at enhanced risk from lifestyle risk factors for myopia.

Humans↗

PubMed2026

Multi-trait polygenic scores for COPD and COPD exacerbations implicate druggable proteins.

BACKGROUNDWe constructed multi-trait polygenic risk scores (PRSs) predicting chronic obstructive pulmonary disease (COPD) and exacerbations, validated their performance in diverse cohorts, and identified PRS-related proteins for potential therapeutic targeting.METHODSPRSmix+, a multi-trait PRS framework, is used to train a composite PRS (PRSmulti) in COPDGene non-Hispanic White participants (n = 6,647). Associations of PRSmulti with COPD status (GOLD 2-4 vs. GOLD 0 or ICD) and exacerbation frequency were tested in COPDGene African American (n = 2,466), ECLIPSE (n = 1,858), Mass General Brigham Biobank (n = 15,152), and All of Us (n = 118,566). Protein prediction models were applied to GWAS summary statistics from traits contributing to PRSmulti and were validated with proteomic data in COPDGene (n = 5,173) and UK Biobank (n = 5,012).RESULTSPRSmix+ selected 7 traits for PRSmulti. In multivariable models, PRSmulti was associated with COPD status (meta-analysis random effects [RE] OR 1.58 [95% CI: 1.28-1.94]) and exacerbation frequency (meta-analysis RE β 0.21 [95% CI: 0.11-0.31]), with higher effect sizes observed in smoking-enriched cohorts. PRSmulti outperformed traditional single-trait PRS in all tested cohorts. Using protein prediction models, we identified 73 proteins associated with the PRSs that were also validated with measured protein levels in COPDGene and UK Biobank. Of these proteins, 25 were linked to approved or investigational drugs. Notable targets include RAGE/sRAGE, IL1RL1, and SCARF2, all implicated in COPD pathogenesis and exacerbations.CONCLUSIONSMulti-trait PRS improves prediction of COPD and exacerbation risk. Integration with proteomic data identifies druggable protein targets, offering a promising avenue for precision medicine in COPD management.TRIAL REGISTRATIONCOPDGene: ClinicalTrials.gov NCT00608764; ECLIPSE: ClinicalTrials.gov NCT00292552.

Humans↗

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↗