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PubMed2025

Worldwide Innovative Network (WIN) Consortium in Personalized Cancer Medicine: Bringing next-generation precision oncology to patients.

The human genome project ushered in a genomic medicine era that was largely unimaginable three decades ago. Discoveries of druggable cancer drivers enabled biomarker-driven gene- and immune-targeted therapy and transformed cancer treatment. Minimizing treatment not expected to benefit, and toxicity-including financial and time-are important goals of modern oncology. The Worldwide Innovative Network (WIN) Consortium in Personalized Cancer Medicine founded by Drs. John Mendelsohn and Thomas Tursz provided a vision for innovation, collaboration and global impact in precision oncology. Through pursuit of transcriptomic signatures, artificial intelligence (AI) algorithms, global precision cancer medicine clinical trials and input from an international Molecular Tumor Board (MTB), WIN has led the way in demonstrating patient benefit from precision-therapeutics through N-of-1 molecularly-driven studies. WIN Next-Generation Precision Oncology (WINGPO) trials are being developed in the neoadjuvant, adjuvant or metastatic settings, incorporate real-world data, digital pathology, and advanced algorithms to guide MTB prioritization of therapy combinations for a diverse global population. WIN has pursued combinations that target multiple drivers/hallmarks of cancer in individual patients. WIN continues to be impactful through collaboration with industry, government, sponsors, funders, academic and community centers, patient advocates, and other stakeholders to tackle challenges including drug access, costs, regulatory barriers, and patient support. WIN's collaborative next generation of precision oncology trials will guide treatment selection for patients with advanced cancers through MTB and AI algorithms based on serial liquid and tissue biopsies and exploratory omics including transcriptomics, proteomics, metabolomics and functional precision medicine. Our vision is to accelerate the future of precision oncology care.

Humans↗

PubMed2026

Integrative Multiomics and Drug Sensitivity Profiling Reveal Potential Biomarkers and Therapeutic Strategies in Pediatric Solid Tumors.

UNLABELLED: Cure rates for childhood malignancies using established therapy protocols have increased to an average of 80% but have reached a plateau. Moreover, survival rates are particularly low for some pediatric tumors-such as high-risk group 3 medulloblastomas, osteosarcomas, Ewing sarcomas, high-risk neuroblastomas, and high-grade gliomas-and dismal for patients with relapsed malignancies. A functional drug response profiling platform for pediatric solid and brain tumors has been established within the INFORM program to identify patient-specific vulnerabilities and biomarkers and to unravel molecular mechanisms associated with drug response profiles for clinical translation. In this study, we performed a multiomics analysis using drug sensitivity profiles, as well as genomic and transcriptomic data, of 81 pediatric solid tumor samples. The integrative analysis suggested two multiomics signatures associated with drug sensitivity. One signature distinguished neuroblastoma samples with sensitivity to navitoclax, a BCL2 family inhibitor. A second signature was specific to a subset of Wilms tumors harboring the SIX1 (Q177R) hotspot mutation that displayed high expression of MGAM, PTPN14, STAT4, and KDM2B and high sensitivity to MEK inhibitors. A patient-specific causal interaction network analysis suggested possible molecular interactions between MEK inhibitors and the SIX1 mutation in Wilms tumor samples. In conclusion, the integration of drug sensitivity profiling and multiomics data revealed potential biomarkers that may be associated with drug sensitivity in pediatric solid tumors. Patient-specific causal interaction network analysis further elucidated the interaction between inhibitors and signature biomarkers, providing insights that may inform clinical translation. SIGNIFICANCE: The combination of multiomics analysis and drug sensitivity profiling identified two signatures related to drug sensitivity in pediatric solid tumors, contributing to the advancement of functional precision medicine and personalized treatment strategies. This article is part of a special series: Driving Cancer Discoveries with Computational Research, Data Science, and Machine Learning/AI .

Humans↗

PubMed2026

Integrating ex vivo platforms with AI to guide glioblastoma treatment.

PURPOSE: Ex vivo platforms can rapidly and cost-effectively screen patient-derived tumor cells or tissue. Artificial intelligence (AI) algorithms can search and identify patterns in large datasets and provide predictions. This review focuses on integrating microphysiological platforms with AI to inform physician and patient decision-making. METHODS AND RESULTS: Combining efficacy, safety, and pharmacology results from drug screens with the output of extensive AI searches can yield insights to guide physician and patient decision-making and potentially improve a patient's prognosis. We detail ex vivo platforms at different stages of development that represent the diversity of approaches: a microphysiological system and a high-throughput screen that assesses drug cytotoxicity in both bulk and drug-tolerant tumor cells. We review AI approaches that can enhance the utility of microphysiological platforms. CONCLUSION: Integrating emerging microphysiological platforms with AI is expected to significantly impact physician and patient choice of treatment.

Humans↗

PubMed2026

Metabolic reprogramming and taxonomic drivers in bacterial vaginosis: A large-scale metagenomic meta-analysis.

OBJECTIVE: Bacterial vaginosis (BV) represents a profound ecological shift from a Lactobacillus-dominated microbiota to a diverse polymicrobial biofilm associated with adverse outcomes. While taxonomic signatures are well-documented, the functional mechanisms driving this transition remain obscured. This study elucidates the genomic potential for metabolic reprogramming and the putative "functional handover" underpinning the stability of the dysbiotic state. METHODS: A computational meta-analysis of 3557 vaginal microbiomes from diverse global cohorts was performed using the standardized MGnify pipeline. A high-resolution subset of 187 whole-genome shotgun (WGS) metagenomes was stratified to compare functional potential across demographic groups. Taxon-function interaction networks were constructed, utilizing a dual-filter statistical approach (p < 0.05 and effect size ranking), to map the shift from homeostatic maintenance to dysbiotic metabolic potential. RESULTS: BV was characterized by a fundamental shift from "maintenance" pathways to high-turnover "growth-oriented" genomic repertoires. While ABC transporter-like domains were present in healthy communities, dysbiosis was marked by a quantitative expansion and diversification of these systems alongside P-loop NTPases. Network analysis revealed a putative "functional handover": while Gardnerella serves as the adherent structural scaffold, the metabolic burden appears to be associated with secondary anaerobes, specifically BVAB1 and Sneathia, which exhibit strong genomic correlations with nutrient transport and stress response pathways. Crucially, microbiomes from women of African ancestry (Black cohort) exhibited a distinct functional profile with genomic signatures consistent with functions previously associated with resistome expansion (e.g., tetracycline/macrolide resistance), contrasting with Asian cohorts. CONCLUSION: BV is a state of metabolic reprogramming where genomic functional dominance is transferred from Lactobacillus to a cooperative network of anaerobic opportunists. Identifying BVAB1 and Sneathia as candidate metabolic engines, supported by a Gardnerella scaffold, challenges current therapeutic paradigms and highlights the potential for precision medicine targeting specific functional drivers and resistome profiles across diverse populations.

Humans↗

PubMed2025

Computational modeling of human genetic variants in mice.

Mouse models represent a powerful platform to study genes and variants associated with human diseases. While genome editing technologies have increased the rate and precision of model development, predicting and installing specific types of mutations in mice that mimic the native human genetic context is complicated. Computational tools can identify and align orthologous wild-type genetic sequences from different species; however, predictive modeling and engineering of equivalent mouse variants that mirror the nucleotide and/or polypeptide change effects of human variants remains challenging. Here, we present H2M (human-to-mouse), a computational pipeline to analyze human genetic variation data to systematically model and predict the functional consequences of equivalent mouse variants. We show that H2M can integrate mouse-to-human and paralog-to-paralog variant mapping analyses with precision genome editing pipelines to devise strategies tailored to model specific variants in mice. We leveraged these analyses to establish a database containing > 3 million human-mouse equivalent mutation pairs, as well as in silico-designed base and prime editing libraries to engineer 4,944 recurrent variant pairs. Using H2M, we also found that predicted pathogenicity and immunogenicity scores were highly correlated between human-mouse variant pairs, suggesting that variants with similar sequence change effects may also exhibit broad interspecies functional conservation. Overall, H2M fills a gap in the field by establishing a robust and versatile computational framework to identify and model homologous variants across species while providing key experimental resources to augment functional genetics and precision medicine applications. The H2M database (including software package and documentation) can be accessed at https://human2mouse.com.

Journal Article↗

PubMed2026

Genetic and epigenetic determinants of cytochrome P450 activity in psychopharmacology: from pharmacogenetics to functional pharmacogenomics.

Classical pharmacogenetics has explained interindividual variability in psychotropic drug response primarily through inherited polymorphisms in cytochrome P450 enzymes. This framework successfully identified extreme metabolizer phenotypes and informed genotype-guided dosing recommendations. However, genotype-based predictions frequently correlate more strongly with pharmacokinetic parameters than with clinical outcomes. Patients sharing similar CYP genotypes often exhibit divergent therapeutic trajectories, while metabolic phenotypes may change during treatment without corresponding alterations in DNA sequence. These observations suggest the existence of a genotype-phenotype gap mediated by regulatory processes not captured by genotyping alone. Evidence from epigenetic regulation, environmental modulation of pharmacogene expression, and phenoconversion indicates that metabolic capacity is better understood as a dynamic functional state rather than a fixed inherited trait. This review examines the role of these mechanisms in psychiatric pharmacotherapy and explores the implications of shifting the predictive focus of precision medicine from static genotype to functional state.

Humans↗

PubMed2025

Insulin receptor variants: Extending the traditional Mendelian spectrum.

PURPOSE: INSR encodes the insulin receptor, the essential entrainer of growth and metabolism to nutritional cues. INSR variants cause a spectrum of monogenic insulin resistance (IR) syndromes, namely, type A insulin resistance, Rabson-Mendenhall, and Donohue syndromes. However, to our knowledge, no large cohort studies focused on variant classification and its diagnostic value have been described. METHODS: This multicentric cohort study included 73 patients carrying INSR variants, referred for IR by 52 centers from 6 countries. Variants were classified using new bioinformatic tools relying on different prediction mechanisms and the American College of Medical Genetics and Genomics guidelines. RESULTS: Besides expanding the INSR mutational spectrum, this study suggested a semidominant inheritance in several Donohue/Rabson-Mendenhall syndrome families. Questioning strictly Mendelian inheritance, heterozygous loss-of-function (LoF) variants were mostly found in overweight patients, with a higher LoF frequency in IR patients than in the general population (odds ratio 5.77). Diagnostic challenges arose when trying to refine classification criteria for variants of uncertain significance. Among the variant effect predictors assessed, MISTIC and AlphaMissense outperformed REVEL. CONCLUSION: The spectrum of INSR-related disorders extends beyond traditional entities. Heterozygous INSR LoF variants may increase IR susceptibility. International collaboration and functional assays are needed to drive precision medicine forward.

Humans↗

PubMed2026

Add-on treatment with vinpocetine reduces seizure frequency and improves comorbidities in patients with loss-of-function γ-aminobutyric acid type A receptor variants.

OBJECTIVE: The semisynthetic compound vinpocetine has gained attention as a potential precision medicine for developmental and epileptic encephalopathies caused by loss-of-function (LoF) variants in γ-aminobutyric acid type A (GABAA) receptor genes. As a positive allosteric modulator of GABAA receptors, case reports suggest that vinpocetine can reduce epileptiform activity and seizure frequency, while improving cognitive function in patients with GABAA receptor-related epilepsies. Here, we extend these observations with a retrospective observational study evaluating the response to vinpocetine in an additional seven patients. METHODS: Patients initiated treatment with vinpocetine between 2018 and 2025 at the Danish Epilepsy Centre or abroad. Clinical data were collected from medical records, seizure diaries, and neuropsychological assessments. The modulatory efficacy of vinpocetine was investigated using electrophysiological studies. RESULTS: Nine patients harboring eight GABAA receptor LoF variants were given add-on vinpocetine treatment. Electrophysiological analyses confirmed dose-dependent positive modulation by vinpocetine across tested variants. Six patients with a median age of 15.5 years (range = 6-29) continued treatment for a median of 24 months (range = 12-90), whereas three discontinued due to adverse effects (AEs) or lack of efficacy. The patients' level of function ranged from normal to moderate intellectual disability, psychiatric comorbidities, and behavioral disturbances. Four patients initiated vinpocetine due to uncontrolled seizures. One became seizure-free, and two experienced a 50%-55% reduction. Electroencephalograms demonstrated improved spike-wave indexes in four patients. Six showed improvement in nonseizure factors, and caregivers reported reduced aggressivity and better vocabulary in one. Vinpocetine was well tolerated, with only mild and reversible AEs reported. SIGNIFICANCE: Adjunctive vinpocetine shows promise as a targeted therapy for patients with GABAA receptor LoF variants, decreasing seizure frequency and positively impacting nonseizure factors, with only mild AEs reported. Vinpocetine may be a safe and effective therapy for patients with GABAA receptor-related epilepsies, which should be investigated further in future N-of-1 trials.

Humans↗

PubMed2026

Precision medicine in mental health: applications, challenges, and recommendations.

Mental disorders represent a major and growing public health challenge in Europe and worldwide, characterised by marked clinical, biological, and functional heterogeneity, that limits the effectiveness of current diagnostic and therapeutic approaches. In recent years, advances in precision medicine have initiated a paradigm shift in psychiatry, offering new opportunities to improve prevention, prediction, diagnosis, treatment selection, and long-term management by integrating biological, psychological, social, and environmental information.This EPA Guidance Paper provides an overview of the current state of precision medicine in mental health and outlines its potential clinical, scientific, and policy implications. We review key advances in genomics, epigenetics, neuroimaging, transcriptomics, digital technologies, and artificial intelligence, highlighting their relevance across the full clinical pathway, from risk prediction and early detection to treatment personalisation and monitoring. We also examine major barriers to implementation, including limited biomarker validation, insufficient representativeness of research populations, ethical and regulatory challenges, data protection concerns, and inequalities in access across healthcare systems.Based on the available evidence, we propose strategic recommendations to support the responsible and equitable integration of precision approaches into mental health care in Europe. These include strengthening translational research, promoting multidisciplinary collaboration, updating regulatory and ethical frameworks, enhancing professional training, and prioritising mental health within national and European research and health agendas. By addressing these challenges, precision psychiatry has the potential to contribute to more effective, person-centred, and sustainable mental health care, while supporting innovation, reducing stigma, and improving outcomes for patients and society.

Humans↗

PubMed2025

The new frontier in understanding human and mammalian brain development.

Neurodevelopmental disorders that cause cognitive, behavioural or motor impairments affect around 15% of children and adolescents worldwide1, with diagnoses of profound autism and attention deficit hyperactivity disorder increasing in the USA and contributing to a major economic burden2,3. Yet the origins and mechanisms of these conditions remain poorly understood, limiting progress in therapies. Comprehensive cell atlases of the developing human brain, alongside those of model organisms such as mice and non-human primates, are now providing high-resolution measures of gene expression, cell-type abundance and spatial distribution. In this Perspective, we highlight recent studies that have identified novel developmental cell populations, revealed conserved and divergent patterns of cell genesis, migration and maturation across species, and begun testing hypotheses that link them to processes ranging from transcriptional control of cell fate specification to the emergence of complex behaviours. We present remaining conceptual and technical challenges and provide an outlook on how further studies of human and mammalian brain development can empower a deeper understanding of neurodevelopmental and neuropsychiatric disorders. Future efforts expanding to additional developmental stages, including adolescence, as well as whole-brain, multimodal and cross-species integration, will yield new insights into how development shapes the brain. These atlases promise to serve as essential references for unravelling mechanisms of brain function and disease vulnerability, and for advancing precision medicine.

Humans↗

PubMed2026

Characterization of a Novel BTD Hypomorphic Variant in a Patient with Complex Neurodevelopmental Delay: Resolving Actionable Metabolic Vulnerabilities Beyond Borderline Plasma Biochemistry.

Plasma biochemistry often presents significant limitations in diagnosing borderline metabolic disorders, particularly within complex neurodevelopmental phenotypes. Here, we present the clinical genomic evaluation of a six-year patient presenting with early-onset hypotonia and severe gastrointestinal complications whose newborn screening panel did not evaluate biotinidase (BTD) activity. While initial baseline plasma biochemistry yielded borderline residual BTD function (46% of the population mean), targeted sequencing identified a novel, compound heterozygous hypomorphic variant (p.Thr459Met) in trans with the common p.Asp424His allele. In vitro functional validation confirmed that p.Thr459Met induces severe protein misfolding and intracellular retention, impairing enzyme secretion. Biotin supplementation triggered a documented and favorable therapeutic improvement, establishing this borderline enzymatic background as an actionable metabolic vulnerability unmasked by chronic gastrointestinal stressors. This study underscores the critical value of functional genomic characterization over static enzymatic biomarkers to identify highly treatable metabolic components within heterogeneous clinical landscapes.

Humans↗

PubMed2026

Automated patch clamp data improve variant classification and penetrance stratification for SCN5A-Brugada syndrome.

BACKGROUND AND AIMS: Brugada Syndrome (BrS) is an inherited arrhythmia disorder that causes an elevated risk of sudden cardiac death. Approximately 20% of patients with BrS have rare variants in SCN5A, which encodes the cardiac sodium channel NaV1.5. Genetic workup of BrS is often complicated by SCN5A variants of uncertain significance (VUS) and/or incomplete penetrance. This study deployed an SCN5A-BrS functional assay at cohort scale to facilitate the implementation of genetic and precision medicine. METHODS: All 252 missense and in-frame insertion/deletion SCN5A variants from a previously published large cohort of BrS cases (n = 3335 patients) were analysed using a calibrated high-throughput automated patch-clamp (APC) assay. Variant functional Z-scores were assigned evidence levels ranging from BS3_moderate (normal function) to PS3_strong (loss-of-function), as defined by American College of Medical Genetics and Genomics criteria. Functional evidence was combined with population frequency, hotspot, case counts, protein-length changes, and in silico predictions. Odds ratios of BrS case-control enrichment and penetrance for BrS were calculated from variant frequencies in the BrS cohort and in gnomAD. RESULTS: Most variants (146/252) were functionally abnormal (Z ≤ -2), with 100 having severe loss-of-function (Z ≤ -4). Functional evidence enabled the reclassification of 110 of 225 VUS; 104 to likely pathogenic and 6 to likely benign. SCN5A variants with loss-of-function were mainly localized to the transmembrane domains, especially the regions comprising the central pore. SCN5A variant penetrance was proportional to the severity of loss-of-function; variants with Z ≤ -6 had penetrance of 24.5% (15.9%-37.7% CI) and an odds ratio of 501 for BrS. CONCLUSIONS: This cohort-scale APC dataset stratifies SCN5A variants found in BrS patients into normal function 'bystander' variants that have a low risk of BrS and loss-of-function variants that have a high risk for BrS. Functional data can be integrated with other criteria to reclassify a substantial fraction of VUS. The dataset helps clarify the SCN5A-BrS relationship and will improve the diagnosis and clinical management of BrS probands and their families.

Humans↗

PubMed2026

GraphyloVar: predicting the impact of non-coding variants using a multi-species sequence model.

MOTIVATION: Understanding the functional impact of genetic variants is a key problem for precision medicine. Tools like CADD, PhyloP, and PhastCons are useful, but they often look at each position in the genome in isolation. This means they can miss important information from the evolutionary history that connects different species. In this paper, we extend our previous model, Graphylo, to predict the effects of variants. Our new model, GraphyloVar, is built to directly utilize the phylogenetic tree that relates the species. RESULTS: GraphyloVar is a deep learning model that considers both DNA sequence and evolutionary patterns from many species. It uses two main components: Graph Convolutional Networks (GCNs) to process the phylogenetic tree, and Transformer encoders to extract features from the DNA sequences. Pre-trained to predict population-level allele frequencies on the TOPMed whole-genome sequencing cohort, GraphyloVar achieves an AUROC of 0.6246 zero-shot on ∼149M held-out variants, and an ensemble with CADD reaches 0.6442 (+0.020, P<10-15). Fine-tuned GraphyloVar achieves the highest AUROC across all 13 MPRA benchmark datasets. By integrating deep learning with explicit phylogenetic input, GraphyloVar offers a powerful and complementary approach to variant effect prediction that utilizes the full evolutionary history from many species to better identify and prioritize important non-coding variants. AVAILABILITY AND IMPLEMENTATION: Code and datasets are available at https://github.com/DongjoonLim/GraphyloVar under DOI: 10.5281/zenodo.20616818.

Phylogeny↗

PubMed2026

Population proteomics for equitable precision medicine.

Population proteomics is emerging as a new framework for equitable precision medicine. By studying protein variation across populations, this field bridges population genomics and conventional proteomics to capture the functional molecular states through which genetic ancestry, environmental exposures and other contextual factors shape human health. Here, we discuss how recent advances are moving population proteomics beyond biomarker discovery toward equitable clinical translation through cross-population validation, mechanistic multiomics and global research infrastructures. Its ultimate promise is not to classify populations as fixed biological categories, but to make human diversity measurable, interpretable and clinically actionable for equitable precision medicine.

Journal Article↗

PubMed2026

The mighty microproteins: from versatile cellular regulators to precision medicine therapeutics.

Microproteins, are tiny proteins encoded by small open reading frame (sORF), translation of these non-canonical open reading frames (ncORFs) has been implicated in diverse biological processes and diseases. This review summarizes recent developments in the discovery, biogenesis, and functional characterization of microproteins, and their involvement in various disease, with special focus on their roles in cancer, cardiovascular, metabolic, neurodegenerative and immune-related disorders. We emphasize the regulation of key cellular pathways by microproteins, including mitochondrial homeostasis, apoptosis, metabolic reprogramming, and immune signaling, all of which affect disease initiation and progression. Emerging evidence also supports their potential as disease biomarkers and therapeutic candidates for precision medicine. Finally, the review critically discusses the current challenges including discrepancies in microprotein annotation, the limitations of ribosome profiling and proteogenomic approaches, the gap between computationally predicted and experimentally validated microproteins, and the need for rigorous orthogonal validation by means of CRISPR-based genome editing, ribosome release assays, mutational analysis, high-resolution mass spectrometry, and functional studies. Finally, we review recent development of AI-assisted ORF prediction, single-cell translatomics, spatial proteomics, and integrated multi-omics as emerging technologies reshaping. Microprotein discovery and functional annotation. Finally, we discuss the translational potential of microproteins and highlight the remaining challenges to clinical application, including peptide stability, pharmacokinetics, tissue-specific delivery, immunogenicity, and the need for rigorous preclinical and clinical validation. Together, this review provides an updated and critical overview of the rapidly evolving microprotein field and highlights future research priorities for translating these molecules into clinically useful biomarkers and precision therapeutics.

Microproteins↗

PubMed2026

Personalized medicine strategy for MPNSTs: using precision oncology on PDOX models to inform tumor boards.

BACKGROUND: Malignant peripheral nerve sheath tumors (MPNSTs) are a heterogeneous group of aggressive soft tissue sarcomas with poor prognosis. Currently there is a lack of effective treatments for MPNSTs. Here, we propose a personalized medicine approach that integrates a precision oncology strategy guided by MPNST genomic analysis, with a functional validation of treatment response in an orthotopic xenograft model (PDOX) derived from the same MPNST. METHODS: Comprehensive whole genome sequencing analysis was performed in primary MPNSTs, relapses and (in one case) metastases, following disease progression in two independent individuals. Matched MPNST PDOX models were generated by orthotopically implanting tumor fragments near the sciatic nerve of immunodeficient mice. Candidate targeted combination therapies were prioritized based on genomic alterations and tested in vivo in the PDOX models. RESULTS: The feasibility of the developed strategy is illustrated for two MPNST patients, one Neurofibromatosis type 1 (NF1) individual that developed two independent MPNSTs and another sporadic MPNST case with multiple metastatic relapses. Genomic analysis revealed a remarkable degree of genomic stability across primary MPNSTs and their successive relapses in each patient, and even metastases in one individual. While based on a small number of cases requiring additional analyses, this finding aligns with previous evidence suggesting a fair genomic conservation throughout tumor evolution. This stability supports the identification of consistent therapeutic vulnerabilities throughout disease progression. Among the therapies tested, co-treatment of MEK inhibitor (MEKi) plus bromodomain inhibitor (BETi) elicited the highest antitumor activity, resulting in approximately 60% tumor volume reduction in the sporadic MPNST PDX model, whose patient has been receiving this therapy for eight months with sustained remission. CONCLUSIONS: This study demonstrates the feasibility and clinical utility of integrating genomic-driven precision oncology with PDOX-based functional testing for MPNSTs. This strategy may support molecular tumor boards (MTBs) in their treatment decisions. The observed genomic stability supports the use of longitudinal tumor profiling to guide treatment, and the success of MEKi+BETi highlights its potential as a combination therapy for MPNSTs.

Precision Medicine↗

PubMed2026

SNP-derived CpG variation and DNA methylation linking genetic susceptibility to metabolic disease.

DNA methylation at CpG dinucleotides represents a key epigenetic mechanism linking genetic variation to gene regulation in complex human diseases. Single-nucleotide polymorphisms (SNPs) that create or disrupt CpG sites can alter local DNA methylation and transcriptional activity, thereby influencing disease susceptibility. These CpG-modifying variants provide a functional interface between inherited genetic variation and epigenetic regulation in complex metabolic disorders. This review summarizes current evidence on SNP-derived CpG variation and its role in allele-specific DNA methylation and gene regulation in metabolically relevant tissues. By integrating findings from genome-wide association studies, epigenome-wide association studies, and multi-omics research, this review provides a mechanistic framework explaining how CpG-modifying polymorphisms influence adipogenesis, pancreatic β-cell function, inflammation, and glucose metabolism. Special emphasis is placed on South Asian populations, who exhibit early β-cell dysfunction and increased visceral adiposity. Many CpG-modifying variants act as methylation quantitative trait loci (meQTLs), influencing allele-specific methylation and gene expression. Understanding SNP-CpG-methylation interactions may improve functional interpretation of disease-associated genetic variants, enhance biomarker discovery, and support precision medicine strategies for metabolic disease.

Humans↗

PubMed2026

Fairness-aware supervised hierarchical contrastive semantic learning for sexual dimorphism analysis.

MOTIVATION: Sexual dimorphism is a fundamental biological determinant driving systematic differences in disease susceptibility, progression, and clinical outcomes. However, current sex-combined AI-based genomic models often exhibit algorithmic bias and fail to capture these sex-specific mechanisms, creating a critical barrier to unbiased precision medicine. Ensuring fairness in the context of sexual dimorphism requires understanding and addressing the distinct biological mechanisms functioning in each sex, rather than focusing solely on equalizing predictive performance. RESULTS: We propose a fairness-aware supervised hierarchical contrastive learning approach, called FairHICON, to discover unbiased sex-common and sex-specific predictive features. Evaluations on cancer and asthma transcriptomic datasets demonstrate that FairHICON significantly outperforms state-of-the-art benchmarks, improving predictive performance by up to 9% while effectively reducing the performance gap between male and female sexes. Furthermore, prognostic validation confirms that the identified sex-specific pathways stratify patient survival significantly better within their corresponding sex groups. This validates FairHICON to elucidate the molecular heterogeneity of sexual dimorphism, advancing inclusive precision medicine. AVAILABILITY AND IMPLEMENTATION: The source code and data is available at https://github.com/datax-lab/FairHICON.

Sex Characteristics↗