Search Results (411)

Background: Vitamin D (25(OH)D) deficiency affects over one billion people globally and is associated with type 2 diabetes (T2D) and cardiometabolic diseases. However, causal relationships remain unclear, as vitamin D supplementation has shown limited benefit in reducing the risk of T2D. Genetic studies have identified variants that influence circulating 25(OH)D levels, but whether genetically determined vitamin D status predicts cardiometabolic outcomes remains uncertain. Methods and Results: Using multi-ethnic populations from the UK Biobank (471,861) and the Asian Indian Diabetic Heart Study (3486), we performed genome-wide univariate and polygenic risk score (PRS)-based bidirectional MR analyses to determine the causal association between vitamin D and T2D. A polygenic score of vitamin D–raising alleles did not affect the risk of T2D or cardiovascular disease. In contrast, a higher T2D PRS was strongly associated with an increased risk for 25(OH)D deficiency. Genetically instrumented per SD increase in T2D PRS was predicted to significantly (p = 9.5 × 10⁻³¹) reduce circulating 25(OH)D (β = −9.1 nmol/L; 95% CI: −8.9 to −9.3). The ancestry-specific univariate MR and sensitivity analyses confirmed that vitamin D levels reduced significantly with increasing T2D risk across all ancestries. Conclusions: Our findings suggest low circulating vitamin D levels are unlikely to causally predict T2D risk but may serve as a marker for secondary prevention in endocrine and cardiovascular health. Instead, genetic susceptibility to T2D appears to contribute to vitamin D insufficiency, which may lead to cardiovascular complications. Further studies are needed to clarify the mechanisms underlying vitamin D deficiency in diabetes. Full article

Severe hypertriglyceridemia (HTG) is genetically heterogeneous, but its genetic architecture remains incompletely characterized. We investigated the genetic determinants of severe HTG in 123 patients with triglyceride (TG) levels > 5.0 mmol/L and available NGS data. We analyzed rare variants in LPL, APOC2, APOA5, GPIHBP1, LMF1, and APOE; the ε2/ε2 APOE genotype; and TG-polygenic risk score (PRS) based on 40 variants. Major genetic determinants were identified in 65.0% of individuals, including rare variants in chylomicronemia genes (24.4%; 28 variants, including 10 novel, 53.6% in LPL), rare APOE variants or the ε2/ε2 genotype (18.7%, overlapping with chylomicronemia variants in 4.1%), and an extreme polygenic burden (35.8%; PRS > 90th percentile), including 26.0% with isolated polygenic HTG. The remaining 35.0% had moderate-to-low PRS. The cohort was categorized into familial chylomicronemia syndrome (FCS, n = 7), multifactorial chylomicronemia syndrome (MCS, n = 21), polygenic HTG (n = 32), familial dysbetalipoproteinemia (FD, n = 20), and moderate-to-low PRS (n = 43) groups based on genetic determinants. FCS had the lowest PRS percentile (median 26) and the most distinct clinical profile, with the highest TG levels (median 30.60 mmol/L) and 6–24-fold higher odds of pancreatitis compared with other groups (p < 0.05), alongside a lower body mass index (median 23.0 kg/m²) than all groups except MCS, whereas FD had the lowest TG levels (10.20 mmol/L, p < 0.05). These results further advance the understanding of the complex genetic architecture of severe HTG and demonstrate that broader genetic analysis, including APOE and TG-PRS, may increase the yield of genetic determinants in severe HTG. Full article

Multiple sclerosis (MS) is a heterogeneous autoimmune disorder of the central nervous system of polygenic nature. Uncovering the genetic predictors of MS phenotype can help to explain the nature of the disease’s clinical heterogeneity, and contribute to the development of novel tools for precise disease prognosis. We conducted a retrospective genetic association study of 35 polymorphic variants in immune-related genes with MS severity assessed using the Multiple Sclerosis Severity Score (MSSS) in a sample of 548 Russian relapsing-onset MS patients who have not previously received immunomodulatory therapy. Variants in the CXCR5, EOMES, TNFRSF1A, IRF8, PVT1, CCR5, HLA-DRB1, IL6, TCF7, and CD40 genes were identified as MSSS-associated in at least two of the three models analyzed (MSSS > 3.5 versus ≤3.5; MSSS > 5.0 versus <2.5; MSSS as a continuous variable). Among them, variants in CCR5, HLA-DRB1 and IL6 genes were associated with MSSS only in women, while variants in the TCF7 and CD40 genes only in men. The variant in CXCR5 was MSSS-associated both in the total sample and in subgroups of female and male MS patients. Thus, we demonstrate that several GWAS-identified MS risk genes, along with other immunological loci, act as modifiers of the MS phenotype. Full article

Precision medicine in prostate cancer (PCa) is increasingly best understood as a continuum linking risk-adapted detection, multimodal diagnosis and phenotyping, and implementation-ready decision pathways. Contemporary clinical guidelines emphasize structured diagnostic strategies, appropriate use of advanced imaging, and selective deployment of biomarkers when results can alter management. Upstream risk enrichment using polygenic risk scores and multivariable prediction models may improve the yield of clinically significant disease while mitigating harms related to overdiagnosis. At the point of suspicion, magnetic resonance imaging-first pathways and reflex biomarker testing provide practical tools to reduce unnecessary biopsy while maintaining safeguards for the detection of clinically important disease. Beyond diagnosis, prostate-specific membrane antigen positron emission tomography refines disease-state phenotyping in initial staging, biochemical recurrence, and limited-burden presentations, while standardized acquisition and reporting improve reproducibility and multidisciplinary communication. Germline and tumor-based molecular profiling should be operationalized as a longitudinal care process with clear consent, turnaround targets, and test-to-action rules that define what each result enables at specific decision nodes. Finally, longitudinal monitoring approaches, including liquid biopsy and artificial intelligence-enabled pathology, are evolving rapidly and require transparent reporting and rigorous risk-of-bias appraisal before broad clinical adoption. This narrative review synthesizes key evidence across the precision continuum and outlines a decision-node-based, test-to-action framework for maximizing clinical benefit, maintaining quality, and ensuring equitable access. Full article

Background/Objectives: Chronic obstructive pulmonary disease (COPD) is increasingly viewed as a disorder of impaired cellular adaptation to chronic stress, involving oxidative injury, mitochondrial dysfunction, and accelerated cellular senescence. We investigated whether genetic variation in these pathways contributes to disease susceptibility, lung function impairment, and polygenic risk prediction. Methods: Thirty-three single-nucleotide variants were analysed in 747 patients with COPD and 703 controls. Associations with disease susceptibility and lung function parameters were assessed using regression models with correction for multiple testing. Weighted and unweighted polygenic scores were constructed from associated variants and evaluated using receiver operating characteristic and net reclassification improvement analyses. Results: Significant associations were identified in genes involved in antioxidant defence (NFE2L2, HMOX1, GSR), PI3K/AKT/mTOR signalling (PIK3R1, PTEN), mitochondrial function (TOMM40), cellular stress responses (FOXO3A), and long non-coding RNA regulation (MEG3, CDKN2B-AS1). The strongest association was observed for PIK3R1 rs831125 (OR = 2.31, p = 2.53 × 10⁻¹⁰). Variants in NFE2L2, PIK3R1, MEG3, MALAT1, and SIRT3 were additionally associated with pulmonary function parameters. The weighted polygenic score demonstrated good discriminative ability (AUC 68.8%, 95% CI 65.9–71.7%) and substantially improved prediction when combined with age, sex, and smoking exposure (AUC 88.1%, 95% CI 86.3–89.8%; NRI = 0.62, p = 2.21 × 10⁻²⁸). Conclusions: The identified loci converge on interconnected pathways involved in cellular stress adaptation, mitochondrial homeostasis, and senescence, supporting their contribution to chronic obstructive pulmonary disease susceptibility and functional decline. Full article

Common forms of migraine are complex disorders characterized by significant clinical diversity. Their genetic basis has been extensively studied but remains unclear. This study represents the first pilot genome-wide association study (GWAS) integrating a polygenic risk score (PRS) in the Portuguese population, designed to identify migraine susceptibility loci through a case–control study and unravel population-specific variants. Genotyping data was acquired with Applied Biosystems Axiom™ PMDA array, producing 12,035,248 single-nucleotide polymorphisms (SNPs) post-imputation, providing a comprehensive scope for GWAS analysis. PRS models were created and tested using a k-folds cross-validation framework and the optimal significance threshold was assessed. We detected 12 potential risk loci corresponding to 12 lead SNPs (RP11-204N11.2, CTA-481E9.4/CTA-481E9.3, RAP1A, TIGD4, CADPS2, RP11-46E17.6, RP4-569D19.5, RP11-398K14.1, PCBP1-AS1, TCF15, IL6R and UNC13A). The top three variants (RP11-204N11.2, CTA-481E9.4/CTA-481E9.3 and RAP1A) were also supported by the PRS model. We highlight that several variants present putative biological relevance to migraine pathophysiology, reinforcing the importance of neurotransmitter release, synaptic transmission and the involvement of vascular components in migraine pathophysiology. Full article

Understanding germline genetic variation is essential for improving human cancer care. Cancer predisposition genetic testing has become a part of the landscape of healthcare. Clinical guidelines have been established to identify individuals with monogenic risk, support variant classification, and guide enhanced cancer surveillance and prevention strategies. However, genetic mechanisms, cancer syndromes, genetic testing, patient education, and high-risk cancer management are often addressed in separate professional domains leading to limited cross-disciplinary understanding and confusion. A review tailored to a broad spectrum of clinicians is necessary to synthesize information, connect key concepts, and clearly define the principles and reasoning underlying recommended practice. Advanced genetic technology identified numerous genes and countless pathogenic variants contributing to a wide range of cancer predispositions. Rapid and accurate next-generation sequencing has enabled the routine use of multi-gene panel testing to stratify cancer risk. In precision cancer therapies, tumor genomic profiling frequently uncovers not only somatic alterations but also germline mutations, revealing additional cancer risk for the patients and their biological relatives. Beyond monogenic risks, the cumulative effect of numerous common polygenic factors can also significantly influence cancer susceptibility. Despite major advances in integrating germline genetic information into cancer care, substantial challenges remain in variant interpretation, precise risk stratification, and implementing personalized screening and prevention strategies. Using several cancer predisposition syndromes as examples, such as breast and ovarian cancer syndrome, Lynch syndrome, and Li-Fraumeni syndrome, the review provides a high-level overview of key concepts, the evolution of knowledge and technology, and the rationale underlying the current clinical management strategies. Full article

Background/Objectives: Atrial fibrillation (AF) is a complex polygenic disorder; its genetic architecture remains challenging to fully elucidate. Methods: In this study, we leveraged the extensive genetic overlap between AF and a spectrum of cardiometabolic and behavioral factors—collectively defined by Life’s Essential 8 (LE8)—to advance our understanding of its etiology. Results: We first estimated significant genetic correlations between AF and all LE8 components (r_g: −0.11 to 0.19) using LD score regression. We then applied conditional false discovery rate analysis and detected 970 pleiotropic loci associated with AF and at least one LE8 trait. Subsequent colocalization analysis identified 179 loci harboring shared causal variants between AF and one or more LE8 components, which were further refined into 137 distinct colocalized regions. Through region-based annotation and functional predictors, we finally prioritized 164 candidate genes from these colocalized loci, including 40 novel genes. These candidate genes were enriched in pathways related to heart development and regulation of cardiac contraction, and were also enriched among molecular targets of otological agents. Among all LE8 components, blood pressure demonstrated the most extensive shared genetic architecture with AF, supported by the strongest genetic correlation, highest pleiotropic enrichment, and the greatest number of colocalized loci with AF. Polygenic risk scores constructed from these colocalized loci demonstrated significant associations not only for AF but also for arrhythmia and heart failure. Conclusions: Our findings establish a genetic pleiotropy-informed framework that enhances the discovery of novel risk loci of AF and advances our understanding of the shared genetic architecture and potential biological mechanisms between AF and LE8 components. Full article

Introduction/Background: Metformin is the first-line therapy for type 2 diabetes (T2D); however, a considerable inter-individual variability in glycemic response is observed among patients. This heterogeneity suggests that metformin’s effects depend not only on drug exposure but also on the underlying metabolic and genetic factors. Methods: We applied a Gene–Environment interaction Mendelian Randomization (MR-G×E) in a cohort of 2743 individuals to investigate whether genetically influenced metabolite-HbA1c associations differ by metformin use. Metabolites associated with metformin response were used to establish metabolite-specific polygenic risk scores (PRSs) using metabolome-wide association study (mGWAS) variants. Generated PRS were used as genetic instruments within a one-sample, modified two-stage least squares model. An interaction term between PRS and metformin use was included to assess treatment-dependent genetic effects, adjusting for age, sex, body mass index, and genetic ancestry (principal components). Results: Metformin use significantly modified genetically influenced associations between 18 metabolites and HbA1c. Positive and negative PRS-metformin interaction effects indicated attenuation, strengthening or reversal of baseline genetic associations under treatment. Several amino acid metabolites, palmitoyl sphingomyelin (d18:1/16:0), and carbohydrate-related metabolite 1,5-anhydroglucitol showed specific patterns under metformin use. Interestingly, several metabolites (creatinine, gamma glutamylcitrulline, N-acetylthreonine, 3-methyl-2-oxovalerate, glycerol-3-phosphate, 1-(1-enyl-palmitoyl)-GPC (P-16:0), 1-(1-enyl-palmitoyl)-2-linoleoyl-GPC (P-16:0/18:2), sphingomyelin (d18:1/22:1, d18:2/22:0, d16:1/24:1), fructose, and methyl-glucopyranoside (alpha + beta)) showed no basal causal association with HbA1c but exhibited significant interaction effect with metformin use, suggesting metabolic association only in the presence of metformin. Conclusions: These findings indicate that metformin modifies the genetically influenced metabolite-HbA1c relationships, exhibiting treatment-dependent metabolic effects that are not detectable with standard MR approaches. Incorporating pharmacological context into causal inference provides new insights into the metabolic basis for the variable metformin response and helps inform precision strategies for T2D management. Full article

Glioblastoma multiforme (GBM) is the most aggressive primary brain tumor, with well-documented incidence disparities across ethnic populations: highest in Europeans and lowest in East Asians and Africans. Still, the genetic basis of these differences remains poorly understood. This study assessed whether population-level differences in GBM risk allele frequencies correlate with ethnic disparities in prevalence. We analyzed 673 genome-wide significant GBM candidate loci across five ethnic superpopulations and 26 subpopulations using phased genotype data from the 1000 Genomes Project Phase 3. Population genetic structure was characterized using allele frequencies, heterozygosity, Wright’s fixation index, analysis of molecular variance (AMOVA), Nei’s genetic distances, and principal coordinate analysis. Risk allele enrichment was visualized via hypergeometric heatmaps, and polygenic risk scores were compared using Kruskal–Wallis and Dunn’s tests. Significant interpopulation differentiation was detected across all superpopulation pairs (p < 0.001). European populations had the highest polygenic risk scores, followed by South Asian and Admixed American populations, while East Asians had the lowest. Allele frequencies at key loci, including rs634537 (CDKN2B-AS1) and rs55705857 (CCDC26), differed up to tenfold. Finnish populations showed an elevated risk consistent with founder effects. Population genetic structure at GBM risk loci correlates with ethnic incidence disparities, underscoring the need for ancestry-specific approaches in risk modeling and trans-ancestry studies. Full article

Psoriasis is an immune-mediated inflammatory disease with a genetic component, characterized by dysregulation of cytokine signaling and activation of T lymphocytes. This study investigated genetic variants associated with psoriasis, psoriatic arthritis (PsA), and response to tumor necrosis factor alpha (TNF-α) inhibitors (adalimumab, infliximab, and etanercept) in a Russian cohort. A genome-wide association study (GWAS) was conducted in 1026 psoriasis patients and 9212 controls using Infinium Global Screening Array-24 v3.0 microarrays. Exploratory analyses of treatment response (n = 48) and PsA (n = 96) were performed without covariate adjustment or explicit modeling of population structure. Polygenic risk scores (PRS) were derived from internally estimated effect sizes in a split-sample design. The GWAS replicated a robust association in the major histocompatibility complex (MHC) region (rs12189871 near HLA-C, p = 3.2 × 10⁻⁵⁰, OR = 2.99 [2.59–3.45]). Additional loci included variants in ZC3H8 and PLCL2. Nominal signals were observed for IL18R1/IL18RAP in treatment response (including rs17027071) and for RCL1 and FBLIM1 in PsA; these findings remain exploratory. PRS demonstrated moderate predictive performance (AUC = 0.6355) and should be interpreted with caution given the study design. Overall, the results highlight a strong MHC signal in psoriasis, while findings for PsA and treatment response remain hypothesis-generating and require independent validation. Full article

Cardiovascular diseases (CVDs) are the foremost contributor to global mortality, with a significant inherited factor that has long been recognized but only recently become decipherable. Next-generation sequencing (NGS) has transformed the study of cardiovascular genetics, allowing researchers to move beyond single-gene analyses toward comprehensive assessments of both rare and common genetic variations. This review summarizes how NGS informs clinical practice, from the molecular diagnosis of inherited cardiac disorders and risk prediction using polygenic models to emerging applications in precision therapeutics. It also discusses analytical and ethical challenges and highlights new technologies, such as long-read and single-cell sequencing, that are likely to further advance precision cardiology. Full article

Prostate cancer (PCa) screening has long relied on PSA testing, a strategy that has shaped diagnostic pathways for decades but remains limited by substantial overdiagnosis and downstream overtreatment. As screening practices evolve, the relevance of historical evidence must be reconsidered in the context of contemporary diagnostic workflows that now incorporate imaging, refined biopsy techniques, and risk-adapted management. This narrative review examines the transition from PSA-only screening toward modern, risk-adapted early detection strategies. We synthesize evidence supporting the integration of mpMRI, refined biopsy techniques, and active surveillance (AS) as key components of contemporary screening pathways aimed at improving the detection of clinically significant disease while minimizing unnecessary interventions. Particular emphasis is placed on second-line MRI-based approaches, which consistently reduce the number of avoidable biopsies and enhance the diagnostic precision. In addition, we review the role of blood-based and genomic biomarkers in pre-biopsy risk stratification, discussing established tools within a unified framework of individualized screening. The review also contextualizes very recent regulatory developments, including the FDA approval of a novel structure-based PSA assay, as part of the ongoing evolution of biomarker-supported screening rather than a paradigm shift. Overall, this article provides a timely synthesis of mature randomized evidence and emerging diagnostic innovations, offering a clinically grounded perspective on how PCa screening is being reshaped toward more personalized and harm-aware strategies. Full article

Background/Objectives: Risk-based breast cancer (BC) screening can provide tailored recommendations based on individual risk. We aimed to identify key predictors for BC risk stratification to inform implementation in screening programs. Methods: We estimated 10-year BC risks using BOADICEA v.6 (CanRisk) in 3753 women aged 40–70 with no cancer history from the PERSPECTIVE I&I cohort. The primary endpoint was risk reclassification, assessed as the proportion of women whose assigned 10-year risk category changed when using different risk factor combinations against a full multifactorial model including questionnaire-based risk factors (QRFs), polygenic score (PGS), mammographic density (MD), and pedigree-structured first- and second-degree family history (FH) of breast, ovarian, pancreatic and prostate cancer, including both affected and unaffected relatives. Relative risk thresholds were set as <1.5 (average), 1.5–2.7 (higher-than-average), and ≥2.7 (high), equivalent to the remaining lifetime risk categories of <15%, 15–25% and ≥25% for women aged 30 (the anchor) to age 80. We quantified individual-level reclassification flows by direction and magnitude. Results: Excluding PGS from risk calculations led to the highest overall reclassification. Using only the BC status in first- and second-degree relatives produced comparable risk classification to that of the full FH data that included breast, ovarian, prostate and pancreatic cancer (reclassification = 0.5%). However, collecting only affected relatives led to overestimation of risk. Excluding either PGS, MD or FH resulted in a greater proportion of reclassification among younger women. Adding the PGS to risk factors already collected in provincial screening programs reduced reclassification from 23% to ~13%. Conclusions: PGS, MD, QRFs and FH of BC in affected and unaffected first- and second-degree relatives are key for refining risk stratification. These findings provide real-world evidence on how incorporating different sets of risk factors, both those routinely collected in screening programs and those requiring additional data collection, affect individual-level risk classification amongst a population-based cohort, and how the impacts differ across age groups. While risk classification reflects model-based changes in estimated risk categories rather than direct evidence of mis-screening or clinical outcomes, comparison with the current eligibility criteria used to identify women at higher-than-average risk highlights the potential clinical value of a multifactorial risk assessment approach in ensuring more appropriate screening strategies. Full article

Molecular diagnosis has advanced rapidly in neurogenetic disorders, yet translating genotype into patient-specific predictions of brain network dysfunction and progression remains limited. Virtual brain models provide a structured solution by embedding individual anatomy and connectomics into biophysical whole-brain simulations. The critical step is to position genetics not as a diagnostic label, but as a constructive input to model design. This review outlines a genetics-centered framework for virtual brain modeling. First, atlas-derived transcriptomic and cell-type maps can define region-specific molecular priors, constraining vulnerability or excitability parameters and reducing model degeneracy. Second, when reproducible genotype-linked network phenotypes exist, mutation groups can inform stratified initialization and progression regimes. Third, at the patient level, exome and CNV data—summarized as pathway burdens and, where appropriate, calibrated polygenic modifiers—can be translated into individualized priors or regularizers, provided that mapping rules are explicit and externally validated. By integrating genetics at multiple levels of evidence, virtual brain models gain mechanistic plausibility, improved calibration, and explicit uncertainty quantification. The most realistic impact over the next few years is likely to be improved stratification, progression-aware forecasting, and scenario-based decision support in rare neurogenetic diseases, especially where longitudinal cohort infrastructure and validated biomarker inputs are already available, rather than deterministic individual prediction. Full article