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PubMed2026

Genome-Wide Association Analyses Identify Distinct Genetic Architectures for Extreme Early-Onset and Late-Onset T2D.

AIMS: Type 2 diabetes (T2D) is a heterogeneous disorder with substantial variation in age at onset (AAO). This study aimed to characterize the distinct genetic architectures and biological mechanisms underlying extreme AAO-defined T2D subtypes. MATERIALS AND METHODS: Using 74 795 European-ancestry participants from the UK Biobank, we performed genome-wide association studies (GWAS) of relatively early-onset T2D (eoT2D; AAO < 55 years) and late-onset T2D (loT2D; AAO ≥ 70 years). We investigated subtype-specific genetic loci, SNP-based heritability, genetic correlations, Mendelian randomization (MR)-based relationships, polygenic risk scores (PRS) and phenome-wide association studies (PheWAS). Single-cell transcriptomic data from human pancreatic tissues were further used to evaluate cell-type-specific expression patterns of candidate genes. RESULTS: SNP-based heritability was substantially higher for eoT2D than loT2D (11.2% vs. 6.4%), with eoT2D displaying distinct genetic loci related to β-cell function and insulin regulation, including SLC30A8 and IRS1. By contrast, loT2D showed a comparatively lipid-related genetic profile, featuring APOE-associated signals and expression patterns in immune-related cell populations. Linkage disequilibrium score regression (LDSC) and MR analyses further underscored this divergence: eoT2D exhibited broader genetic overlap with cardiometabolic traits, whereas loT2D showed stronger relationships with traditional metabolic risk factors. Finally, subtype-specific PRSs improved risk discrimination beyond conventional covariates, although their clinical utility warrants further evaluation. CONCLUSIONS: Extreme AAO-defined T2D subtypes exhibit partially distinct genetic architectures, highlighting AAO as an important dimension of T2D heterogeneity and providing a framework for future age-stratified genetic risk assessment.

Type 2 diabetes↗

PubMed2025

DiscoDivas: Leveraging genetic ancestry continuum information to interpolate PRS for admixed populations.

The relatively low representation of admixed populations in both discovery and fine-tuning individual-level datasets limits polygenic risk score (PRS) development and equitable clinical translation for admixed populations. Under the assumption that the most informative PRS model for a genetically homogeneous sample varies linearly in an ancestry continuum space, we introduce a Genetic Distance-assisted PRS Combination Pipeline for Diverse Genetic Ancestries (DiscoDivas) to interpolate a harmonized PRS for diverse, especially admixed, genetic ancestries, leveraging multiple PRS models fine-tuned within existing samples, which are mostly of single ancestry, and genetic distance. DiscoDivas treats genetic ancestry as a continuous variable and does not require shifting between different models when calculating PRS for different ancestries. We generated PRS with DiscoDivas and the current conventional method, i.e. fine-tuning multiple GWAS PRS using the matched or similar genetic ancestry samples. DiscoDivas generated a harmonized PRS of the accuracy comparable to or higher than the conventional approach, with the greatest advantage exhibited in admixed individuals.

PRS harmonization↗

PubMed2025

Genetic Differences in Reactivity to the Environment Impact Psychotic-Like and Affective Reactivity in Daily Life.

BACKGROUND AND HYPOTHESIS: Consistent with diathesis-stress models, psychosis research has focused on genetic moderation of adverse environmental exposures. In contrast, the Differential Susceptibility (DS) model suggests that the same genetic variants that increase risk-inducing effects of adverse experiences also enhance beneficial effects from positive experiences. This study examined whether individuals with high genetic susceptibility to the environment showed differential psychotic-like and affective reactivity in response to positive and negative events in daily life. STUDY DESIGN: Experience sampling methodology assessed context (positive and stressful) and momentary levels of paranoia, psychotic-like experiences (PLE), and positive (PA) and negative affect (NA) in 217 non-clinical adults oversampled for schizotypy. Linear mixed models examined whether Polygenic Risk Scores of Environmental Sensitivity (PRS-ES) moderated the impact of current context on subsequent experiences. STUDY RESULTS: PRS-ES moderated positive, but not stressful, context on subsequent levels of momentary paranoia, NA, and PA, but not PLE. Genetic and environmental (G × E) interactions indicated diathesis-stress at lower thresholds of PRS-ES, but a DS model at the highest threshold of the PRS-ES. Participants with elevated PRS-ES showed increased paranoia and NA and decreased PA in subsequent assessments when reporting low levels of positive situations, but also decreased paranoia and NA and increased PA when rating contexts as positive. CONCLUSIONS: Findings support the influence of genetic sensitivity to the environment on psychotic-like and affective reactivity in daily life, particularly in response to positive contexts. This highlights the transdiagnostic protective role of positive experiences and informs ecological momentary interventions.

Humans↗

PubMed2025

Leveraging local ancestry and cross-ancestry genetic architecture to improve genetic prediction of complex traits in admixed populations.

The broader application of polygenic risk score (PRS) is hindered by the limited transferability of PRS developed in Europeans to non-European populations. While many statistical methods have been developed to improve the performance of PRS in non-European populations, most of them focused on discrete genetic ancestry clusters and did not consider admixed individuals. Admixed individuals pose a unique challenge for PRS calculation due to the complexity of local ancestry and cross-ancestry effect sizes. Here, we present a statistical method called SDPR_admix for calculating PRS in admixed individuals. SDPR_admix characterizes the joint distribution of the effect sizes of a genetic variant with two ancestries to be both zero, ancestry enriched, or shared with correlation. SDPR_admix outperformed other methods in simulations and improved the prediction of real traits in European-African admixed individuals in UK Biobank when trained on the Population Architecture using Genomics and Epidemiology (PAGE) dataset (N = 13,000). Deployment of SDPR_admix on All of Us (N = 52,000) further increased the prediction accuracy by approximately 5-fold on average compared with training on PAGE. This enhancement was achieved with manageable computational time and cost, demonstrating the feasibility of training PRS models on large-scale All of Us data. We provided several examples demonstrating that both ancestral-enriched and shared effects, as included in the SDPR_admix prediction model, are helpful for improving polygenic prediction in admixed populations. We also applied SDPR_admix to construct PRS for admixed Americans with mixture of European and Amerindigenous ancestries and showed that SDPR_admix overall outperformed other methods.

Humans↗

PubMed2025

Non-HLA Genetics and Application to Living Donor Candidates.

PURPOSE OF REVIEW: This review examines the expanding role of non-human leukocyte antigen (non-HLA) genetic factors in kidney transplantation, with a particular focus on their implications for living donor evaluation and outcomes. It emphasizes the potential of genetic testing to improve risk stratification beyond conventional HLA matching, especially for donor candidates with a family history of hereditary kidney disease. RECENT FINDINGS: Non-HLA genetic mismatches, including single nucleotide variants affecting minor histocompatibility antigens, can drive alloimmune responses, leading to graft rejection and failure even in HLA-matched transplants. The presence of non-HLA antibodies further contributes to adverse immunological outcomes. Genetic testing in related living donors can uncover monogenic kidney diseases, enabling early identification of at-risk individuals and enhancing donor safety. While polygenic risk scores and gene panels show promise in predicting complications and guiding post-transplant care, most genome-wide association studies have focused on recipients. There remains a significant gap in understanding how donor-specific genetic factors influence post-donation kidney function and long-term health outcomes. SUMMARY: The integration of non-HLA genetic testing into living donor evaluation supports a precision medicine approach to kidney transplantation, offering improved risk assessment and donor-recipient matching. As the field advances, longitudinal studies and robust data collection, particularly around donor genetics, are essential to optimize transplant outcomes, inform clinical decision-making, and uphold ethical standards in donor care.

genetics↗

PubMed2026

Integrating biological pathway polygenic scores and trauma in psychosis: findings from the EU-GEI study.

Psychotic disorders are complex, multifactorial conditions influenced by both genetic liability and early environmental adversity. Polygenic risk scores (PRSs) derived from genome-wide association studies have shown utility in capturing genetic predisposition, but their biological interpretability remains limited. In this study, we evaluated whether biologically informed pathway-specific polygenic scores (pPGSs) for psychosis, restricted to neurotransmitter-related pathways, could help clarify gene-environment interplay. Using data from 1 192 individuals in the EU-GEI multi-site case-control study, we constructed pPGSs for dopamine, glutamate, GABA, and serotonin systems. We investigated associations between pPGSs and childhood trauma (rGE), their interactions on psychosis risk (GxE), and the influence of the genome-wide psychosis PRS on these relationships. Serotonin, dopamine, and glutamate pPGSs were positively associated with a composite trauma exposure (i.e., abuse and neglect), suggesting shared genetic factors contributing to both psychosis liability and early adversity. Significant negative GxE effects were observed for both dopamine and serotonin pPGSs, indicating that higher trauma exposure diminished the relative influence of genetic liability on psychosis risk. Adjustment for the genome-wide psychosis PRS attenuated most effects, but serotonergic and dopaminergic associations remained robust, supporting pathway-specific contributions beyond general polygenic risk. These findings provide proof-of-concept for the utility of pPGSs in psychiatric research, suggesting both genetic contributions to trauma exposure and GxE effects on psychosis risk. Further research incorporating epigenetic data and longitudinal designs may enhance mechanistic insight and translational potential.

Adult↗

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↗

PubMed2025

Cross-omics risk scores of inflammation markers are associated with all-cause mortality: The Canadian Longitudinal Study on Aging.

Inflammation is a critical component of chronic diseases, aging progression, and lifespan. Omics signatures may characterize inflammation status beyond blood biomarkers. We leveraged genetics (polygenic risk score [PRS]), metabolomics (metabolomic risk score [MRS]), and epigenetics (epigenetic risk score [ERS]) to build multi-omics-multi-marker risk scores for inflammation status represented by the level of circulating C-reactive protein (CRP), interleukin 6 (IL-6), and tumor necrosis factor alpha (TNF-α). We found that multi-omics risk scores generally outperformed single-omics risk scores in predicting all-cause mortality in the Canadian Longitudinal Study on Aging. Compared with circulating inflammation biomarkers, some multi-omics risk scores had a higher hazard ratio (HR) for all-cause mortality when including both score and circulating IL-6 in the same model (1-SD IL-6 MRS-ERS: HR = 2.20 [1.55-3.13] vs. 1-SD circulating IL-6 HR = 0.94 [0.67,1.32]. 1-SD IL-6 PRS-MRS: HR = 1.47 [1.35,1.59] vs. 1-SD circulating IL-6 HR = 1.33 [1.18, 1.51]. 1-SD PRS-MRS-ERS: HR = 1.95 [1.40, 2.70] vs. 1-SD circulating IL-6: HR = 0.99 [0.71, 1.39]). In the Nurses' Health Study (NHS), NHS II, and Health Professional Follow-up Study with available omics, 1 SD of IL-6 PRS and 1-SD IL-6 PRS-MRS had HR = 1.12 [1.00,1.26] and HR = 1.13 [1.01,1.26] among individuals >65 years old without mutual adjustment of the score and circulating IL-6. Our study demonstrates that some multi-omics scores for inflammation markers may characterize important inflammation burden for an individual beyond those represented by blood biomarkers and improve our prediction capability for the aging process and lifespan.

Humans↗

PubMed2026

Diverse mediators of cancer predisposition uncovered by germline whole genome sequencing of unexplained familial cancers.

Cancer frequently clusters in families due to shared environment and genetics. However, many familial cancer cases lack a clinically recognized pathogenic germline variant (PGV). We analyzed germline genomes and family history from 2,726 individuals without a PGV in the All of Us Research Program, including 1,496 cases across 18 cancer types with extensive family history and 1,230 family history-negative, cancer-free controls. We identified allelic series of rare structural variants inactivating MSH2 in individuals with phenotypes consistent with Lynch syndrome and BRCA1 in breast cancer. Cancer polygenic risk scores were enriched in cases and correlated with patterns of cancer diagnoses within families. Exome-wide rare variant analyses nominated six candidate predisposition genes, including TSTD2 and BRAT1 in thyroid and breast cancer, respectively. Overall, polygenic risk and rare variants impacting known genes explained a median of 5% of unexplained familial cancers, increasing to 11% when including newly nominated risk factors.

Journal Article↗

PubMed2026

Inherited Predisposition to Increased Systemic Inflammation Predicts a Broad Class of Disease Phenotypes.

Chronic, low-grade systemic inflammation is a polygenic trait captured with the INFLA-score, a composite of C-reactive protein, platelet count, leukocyte count, and granulocyte-to-lymphocyte ratio. We derived a polygenic risk score from the INFLA-score (iPRS) in a multi-ancestry population from the UK Biobank (n=421,368), then evaluated and used it in a phenome-wide association study among participants in the All of Us Research Program (AoU). The multi-ancestry iPRS was tested for association with the INFLA-score in AoU (N=4,833 with biomarker data) via linear regression, adjusting for age, sex, and genetically-determined principal components (PCs) and with 2,821 phecodeX-defined phenotypes in AoU (N=265,068) via logistic regression, adjusting for sex, age, EHR length, race, ethnicity and PCs. The iPRS predicted the INFLA-score (R-squared=0.026, beta=0.980, p<2x10-16) and was associated with 47 phenotypes (Bonferroni-corrected p<0.05). The strongest associations were with blood-related phenotypes: elevated white blood cell count (OR=1.19, p=3.85x10-66), thrombocytopenia (OR=0.86, p=5.70x10-44), platelet defects (OR=0.86, p=2.47x10-43), neutropenia (OR= 0.86, p=5.52x10-18), myeloproliferative disorder (OR= 1.2, p=2.77x10-15). Others included celiac disease (OR=0.713, p=2.98x10-46), ankylosing spondylitis (OR=1.4, p=1.33 x 10-17), hypertension (OR=1.04, p=4.56x10-15), rheumatoid arthritis (OR=1.09, p=1.02x10-13), hematuria (OR=1.05, p=1.96x10-10). Removing major-histocompatibility-complex SNPs abolished associations with known autoimmune diseases, while all other associations remained. We replicated 17 (42.5%) of 40 significant phenotypes available in the Vanderbilt University Medical Center's BioVU. Our findings demonstrate that systemic inflammation can be predicted using the iPRS across multiple ancestries, and the iPRS is associated with numerous clinical endpoints. This multi-ancestry iPRS may have future utility in stratifying risk for inflammation-driven conditions across diverse populations.

Journal Article↗

PubMed2025

Prevalence and predictors of low bone mineral density in pediatric inflammatory bowel disease.

OBJECTIVES: Bone health is at risk in children with inflammatory bowel disease (IBD). This study examined the prevalence and predictors of low bone mineral density (BMD) in a cohort of children and young adults with IBD. METHODS: This single-center retrospective study included patients with IBD, ages 3.5-22 years, with completed dual x-ray absorptiometry (DXA) scans from 2006 to 2019. Demographic, clinical, and laboratory data were collected. Logistic regression analysis identified predictors associated with low BMD (Z-scores ≤ -2 standard deviations [SDs]) for three outcomes. In an overlapping IBD cohort with available genetic data between 2002 and 2019 (n = 378), genetic risk for diminished bone health was calculated using published polygenic risk scores generated from genome-wide association studies based on DXA or heel ultrasound speed of sound (SOS). Linear regression analysis examined associations of low BMD and genetic risk. RESULTS: Low BMD prevalence was 7% in our cohort (n = 600) based on spine bone mineral apparent density (BMAD), which best accounts for growth delays. Median (interquartile range [IQR]) spine BMAD Z-score was -0.37 SD (-1.11 to 0.35). Predictors of low BMAD included lower BMI Z-score (odds ratio [OR]: 0.67, p value: 0.02) and decreased height Z-score (OR: 0.6, p value: 0.005). Of those with longitudinal data (n = 118), low BMI (OR: 0.44, p value: <0.001) and steroid use (OR: 3.42, p value: 0.01) were associated with suboptimal bone health (Z-scores ≤ -1SD). In the cohort with genetic data, heel genomic SOS (β [standard error] = 0.17 [0.35], p ≤ 0.01) was associated with BMD. CONCLUSIONS: Lower BMI should prompt DXA monitoring in pediatric IBD. Genetic predisposition may identify an at-risk subpopulation.

Humans↗

PubMed2023

Low and differential polygenic score generalizability among African populations due largely to genetic diversity.

African populations are vastly underrepresented in genetic studies but have the most genetic variation and face wide-ranging environmental exposures globally. Because systematic evaluations of genetic prediction had not yet been conducted in ancestries that span African diversity, we calculated polygenic risk scores (PRSs) in simulations across Africa and in empirical data from South Africa, Uganda, and the United Kingdom to better understand the generalizability of genetic studies. PRS accuracy improves with ancestry-matched discovery cohorts more than from ancestry-mismatched studies. Within ancestrally and ethnically diverse South African individuals, we find that PRS accuracy is low for all traits but varies across groups. Differences in African ancestries contribute more to variability in PRS accuracy than other large cohort differences considered between individuals in the United Kingdom versus Uganda. We computed PRS in African ancestry populations using existing European-only versus ancestrally diverse genetic studies; the increased diversity produced the largest accuracy gains for hemoglobin concentration and white blood cell count, reflecting large-effect ancestry-enriched variants in genes known to influence sickle cell anemia and the allergic response, respectively. Differences in PRS accuracy across African ancestries originating from diverse regions are as large as across out-of-Africa continental ancestries, requiring commensurate nuance.

Humans↗

PubMed2026

Genomic and integrative based progression biomarker discovery in adult sepsis: toward clinical stratification and precision medicine.

Sepsis is a life-threatening syndrome characterized by a heterogeneous host response to infection that remains a major cause of mortality worldwide. Current clinical scoring systems capture organ dysfunction but fail to reflect the underlying biological diversity, limiting their utility for patient stratification and targeted therapy. This review provides a comprehensive overview of molecular biomarker approaches used to predict sepsis course and prognosis in adult patients, covering genetic, transcriptomic, proteomic, and integrative strategies up to May 2026. Here, we summarize findings from genetic association studies, along with analyses based on polygenic risk scores to aggregate genetic effects, Mendelian randomization, and rare-variant sequencing approaches. We also review transcriptomic and proteomic strategies for endotyping, and diagnostic and prognostic discrimination. Lastly, we discuss how multi-omics integration is emerging as a promising framework to assist in distinguishing causal therapeutic targets from non-causal biomarkers. We also address the challenges that still constrain clinical translation towards precision medicine.

Biomarker↗

PubMed2026

A doubly robust framework for addressing outcome-dependent selection bias in multi-cohort EHR studies.

Selection bias can hinder accurate estimation of association parameters in binary disease risk models using non-probability samples like electronic health records (EHRs). The issue is compounded when participants are recruited from multiple clinics/centers with varying selection mechanisms that may depend on the disease/outcome of interest. Traditional inverse-probability-weighted (IPW) methods, based on constructed parametric selection models, often struggle with misspecifications when selection mechanisms vary across cohorts. This paper introduces a new Joint Augmented Inverse Probability Weighted (JAIPW) method, which integrates individual-level data from multiple cohorts collected under potentially outcome-dependent selection mechanisms, with data from an external probability sample. JAIPW offers double robustness by incorporating a flexible auxiliary score model to address potential misspecifications in the selection models. We outline the asymptotic properties of the JAIPW estimator, and our simulations reveal that JAIPW achieves up to 6 times lower relative bias and 5 times lower root mean square error (RMSE) compared to the best performing joint IPW methods under scenarios with misspecified selection models. Applying JAIPW to the Michigan Genomics Initiative (MGI), a multi-clinic EHR-linked biobank, combined with external national probability samples, resulted in cancer-sex association estimates closely aligned with national benchmark estimates. We also analyzed the association between cancer and polygenic risk scores (PRS) in MGI to illustrate a situation where the exposure variable is not measured in the external probability sample.

Selection Bias↗

PubMed2026

Refining the Genetic Contribution to Type 2 Diabetes Subtypes.

BACKGROUND: Type 2 diabetes (T2D) is a complex and highly heterogeneous disease driven in part by genetic predisposition and can be stratified into clinical subgroups to aid disease management. We recently grouped T2D subjects in the Qatar Biobank (QBB) cohort into Severe Insulin-Deficient Diabetes (SIDD), Severe Insulin-Resistant Diabetes (SIRD), Mild Obesity-Related Diabetes (MOD) and Mild Age-Related Diabetes (MARD) subtypes. Herein, we focused on the genetic makeup of these subtypes. METHODS: We used the QBB cohort (n = 13,808), of whom 2687 were with T2D, and comprehensively assessed polygenic risk scores (PGS) across T2D subtypes, investigated genetic loci associated with each subtype by leveraging the most recent and largest GWAS for T2D, evaluated SNP associations across T2D genetic clusters, and identified protein interaction pathways associated with these distinct T2D subtypes. RESULTS: MOD showed consistently lower PGS compared with other T2D subtypes across all tested scores. SIDD showed more associations with SNPs mapping to residual glycemic cluster compared with other T2D subtypes. The incremental analysis of PGS004838 demonstrated a high ΔAUC of 0.101 for SIDD and a moderate ΔAUC of 0.068 for SIRD, but not for MOD and MARD. Protein interaction analyses identified candidate subtype-associated gene networks linked to pathways related to glucose homeostasis in SIDD, insulin signalling and hepatic metabolism in SIRD, body fat distribution in MOD and vascular-related processes in MARD. CONCLUSION: We found heterogeneous genetic architectures across clinically defined T2D subtypes in a Middle Eastern population. Our findings provide evidence supporting differential polygenic burden, subtype genetic associations and subtype-associated biological pathways across T2D subtypes. These observations support the utility of subtype-based genetic analyses for improving biological understanding of T2D heterogeneity.

Humans↗

PubMed2026

All of Us diversity and scale yield context-dependent improvements in polygenic prediction.

Polygenic risk scores (PRSs) trained on multiancestry data can improve prediction in under-represented groups, but large linked genetic and health datasets capturing broad human diversity remain limited. Using 245,388 whole-genome sequences from the All of Us research program (AoU) together with UK Biobank data, we developed multiancestry PRSs for 32 traits and diseases. We evaluated how ancestry, methodology and genetic architecture influenced PRS performance across ancestrally diverse AoU participants. Increased diversity in the AoU improved PRS accuracy for several traits, especially in under-represented populations. However, maximizing sample size by meta-analyzing AoU and UK Biobank was not universally optimal: for less polygenic traits, AoU-only training performed best in African ancestry participants, consistent with ancestry-enriched effects. Individual PRS accuracy declined linearly with increasing ancestry divergence from the discovery GWAS, but this decay was attenuated using multiancestry training data. These findings underscore the value of more representative biobanks for equitable PRS performance.

Journal Article↗

PubMed2025

Genetic Predisposition to Low-Density Lipoprotein Cholesterol and Incident Type 2 Diabetes.

IMPORTANCE: Treatment to lower high levels of low-density lipoprotein cholesterol (LDL-C) reduces incident coronary artery disease (CAD) risk but modestly increases the risk for incident type 2 diabetes (T2D). The extent to which genetic factors across the cholesterol spectrum are associated with incident T2D is not well understood. OBJECTIVE: To investigate the association of genetic predisposition to increased LDL-C levels with incident T2D risk. DESIGN, SETTING, AND PARTICIPANTS: In this large prospective, population-based cohort study, UK Biobank participants who underwent whole-exome sequencing and genome-wide genotyping were included. Participants were separated into 7 groups with familial hypercholesterolemia (FH), predicted loss of function (pLOF) in APOB or PCSK9 variants, and LDL-C polygenic risk score (PRS) quintiles. Data were collected between 2006 and 2010, with a median follow-up of 13.7 (IQR, 12.9-14.5) years. Data were analyzed from March 1 to November 1, 2024. EXPOSURES: LDL-C level, LDL-C PRS, FH, or pLOF variant status. MAIN OUTCOMES AND MEASURES: Cox proportional hazards regression models adjusted for age, sex, genotyping array, lipid-lowering medication use, and the first 10 genetic principal components were fitted to assess the association between LDL-C genetic factors and incident T2D and CAD risks. RESULTS: Among the 361 082 participants, mean (SD) age was 56.8 (8.0) years, 194 751 (53.9%) were female, and mean (SD) baseline LDL-C level was 138.0 (33.6) mg/dL. During the follow-up period, 22 619 (6.3%) participants developed incident T2D and 17 966 (5.0%) developed incident CAD. The hazard ratio for incident T2D was lowest in the FH group (0.65; 95% CI, 0.54-0.77), while the highest risk was in the pLOF group (1.48; 95% CI, 1.18-1.86). The association between LDL-C PRS and incident T2D was 0.72 (95% CI, 0.66-0.79) for very high LDL-C PRS, 0.87 (95% CI, 0.84-0.90) for high LDL-C PRS, 1.13 (95% CI, 1.09-1.17) for low LDL-C PRS, and 1.26 (95% CI, 1.15-1.38) for very low LDL-C PRS. CAD risk increased directly with the LDL-C PRS. CONCLUSIONS AND RELEVANCE: In this cohort study, LDL-C and T2D risks were inversely associated across genetic mechanisms for LDL-C variation. Further elucidation of the mechanisms associating low LDL-C risk with increased risk of T2D is warranted.

Humans↗

PubMed2025

Contributions of Common, Rare, and Somatic Genetic Variants to Incidence of Atrial Fibrillation.

IMPORTANCE: Atrial fibrillation (AF) has a complex genetic architecture involving common, rare, and somatic variants. The association between these components requires further investigation. OBJECTIVE: To examine the individual and combined contributions of polygenic, monogenic, and somatic genetic variants to AF incidence, and develop an integrated genomic model (IGM-AF) for improved risk prediction. DESIGN, SETTING, AND PARTICIPANTS: This cohort study used whole-genome sequence data from participants of the UK Biobank, with follow-up for AF events through hospital records, death registries, and self-report. The UK Biobank recruited participants aged 40 to 69 years in the UK between 2006 and 2010. Study data were analyzed from August 2022 to November 2024. EXPOSURES: IGM-AF comprising an AF polygenic risk score (PRS), a composite rare variant gene set (AFgeneset), and somatic variants associated with clonal hematopoiesis of indeterminate potential (CHIP). Clinical AF risk was estimated using the Cohorts for Heart and Aging Research in Genomic Epidemiology AF (CHARGE-AF) score. MAIN OUTCOMES AND MEASURES: The primary outcome was hazard ratios (HRs) for 5-year incident AF attributable to PRS, AFgeneset, CHIP, and their interactions. The predictive performance of IGM-AF and its components was quantified using HRs, C statistics, and reclassification indices. RESULTS: A total of 416 085 individuals (mean [SD] age, 56.6 [8.0] years; 224 642 female [54.0%]) with 30 797 AF cases were included. The PRS (HR per 1 SD, 1.65; 95% CI, 1.63-1.67; P < 1 × 10-8), AFgeneset (HR, 1.63; 95% CI, 1.52-1.75; P = 1.46 × 10-42), and CHIP (HR, 1.26; 95% CI, 1.15-1.38; P = 1.41 × 10-6) were associated with incident AF. The 5-year cumulative incidence of AF was at least 2-fold among individuals having all 3 genetic drivers (common, rare, and somatic drivers) compared with those with only 1 driver. Integration of IGM-AF with a clinical risk model (CHARGE-AF) showed higher predictive performance (C statistic, 0.80; 95% CI, 0.80-0.80) compared with IGM-AF and CHARGE-AF alone. The classification of the at-risk population for AF was improved when IGM-AF was added to CHARGE-AF (net reclassification index, 0.08; 95% CI, 0.07-0.09). CONCLUSIONS AND RELEVANCE: Results of this cohort study demonstrated the complementary value of common, rare, and somatic variants in shaping genomic AF risk. Leveraging comprehensive genetic information may enhance screening and preventive interventions for AF.

Humans↗