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PubMed2025

Streamlining large-scale genomic data management: Insights from the UK Biobank whole-genome sequencing data.

Biobank-scale whole-genome sequencing (WGS) studies are increasingly pivotal in unraveling the genetic bases of diverse health outcomes. However, managing and analyzing these datasets' sheer volume and complexity presents significant challenges. We highlight the annotated genomic data structure (aGDS) format, substantially reducing the WGS data file size while enabling seamless integration of genomic and functional information for comprehensive WGS analyses. The aGDS format yielded 23 chromosome-specific files for the UK Biobank 500k WGS dataset, occupying only 1.10 tebibytes of storage. We develop the vcf2agds toolkit that streamlines the conversion of WGS data from VCF to aGDS format. Additionally, the STAARpipeline equipped with the aGDS files enabled scalable, comprehensive, and functionally informed WGS analysis, facilitating the detection of common and rare coding and noncoding phenotype-genotype associations. Overall, the vcf2agds toolkit and STAARpipeline provide a streamlined solution that facilitates efficient data management and analysis of biobank-scale WGS data across hundreds of thousands of samples.

Humans↗

PubMed2026

Scalable, generalizable and uncertainty-aware integration of spatial multiomics across diverse modalities and platforms with SCIGMA.

Recent advances in spatial omics technologies have enabled simultaneous profiling of transcriptomic, proteomic, epigenomic, metabolomic and imaging data at high spatial resolution, offering unprecedented opportunities to dissect tissue complexity. However, integrating these diverse and large-scale spatial multimodal datasets remains a major computational challenge. We present SCIGMA, a scalable and generalizable deep learning framework for spatial multiomics integration. SCIGMA introduces an uncertainty-aware contrastive learning objective and multiview graph neural networks to preserve modality-specific signals while learning biologically meaningful joint representations. Unlike previous methods, SCIGMA provides spatially resolved uncertainty estimates, interpretably identifying regions of biological or technical heterogeneity. SCIGMA supports integration of up to five modalities, and its modular framework is extensible to future technologies with even more modalities. It also scales to more than 1 million spatial locations, enabling analysis of high-resolution datasets such as Visium HD and Xenium Prime. We evaluated SCIGMA across 19 datasets spanning 8 modalities, 10 tissues and 9 platforms. On benchmarkable datasets, SCIGMA outperformed other methods in spatial domain detection, modality preservation, feature reconstruction and reproducibility. SCIGMA identifies biologically meaningful structures, refined spatial domains and modality-specific regulatory programs, providing a robust, flexible and future-ready solution for scalable spatial multimodal integration.

Multiomics↗

PubMed2026

HyLnc: a hybrid deep learning and feature-based approach for long non-coding RNA prediction.

Long non-coding RNAs (lncRNAs) play important roles in gene regulation, development and disease, yet accurate identification of lncRNAs from transcriptomic data remains a major computational challenge. Existing methods often rely either on handcrafted sequence features or deep learning approaches, each with their inherent limitations in capturing the full complexity of RNA sequences. In this study, we proposed HyLnc, a computational framework that integrates transformer-based contextual embeddings with biologically meaningful sequence features for improved lncRNA prediction. A custom BERT-based model was first pre-trained on a large corpus of metazoan RNA sequences using a masked language modelling strategy to learn contextual nucleotide dependencies. The model was subsequently fine-tuned on curated datasets of lncRNAs and protein-coding transcripts and 256-dimensional deep sequence embeddings were extracted. Parallelly, 348 handcrafted features, including ORF characteristics, untranslated region (UTR) properties, nucleotide composition and Fickett scores, were computed. A multi-stage feature selection strategy was applied to identify the most informative features, resulting in optimized hybrid feature sets. Multiple machine learning classifiers were evaluated, with the RF model achieving the best performance. The proposed framework attained an accuracy of 91.30%, F1-score of 91.23% and MCC of 82.60 on an independent validation dataset, outperforming several existing lncRNA prediction tools. Thus, HyLnc demonstrates that integrating deep contextual representations with biologically interpretable features enhances lncRNA prediction. This approach provides a robust and scalable solution for large-scale transcriptome annotation and can be extended to other sequence-based prediction.

RNA, Long Noncoding↗

PubMed2025

Sparse polygenic risk score inference with the spike-and-slab LASSO.

MOTIVATION: Large-scale biobanks, with rich phenotypic and genomic data across hundreds of thousands of samples, provide ample opportunities to elucidate the genetics of complex traits and diseases. Consequently, there is growing demand for robust and scalable methods for disease risk prediction from genotype data. Inference in this setting is challenging due to the high-dimensionality of genomic data, especially when coupled with smaller sample sizes. Popular Polygenic Risk Score (PRS) inference methods address this challenge by adopting sparse Bayesian priors or penalized regression techniques, such as the Least Absolute Shrinkage and Selection Operator (LASSO). However, the former class of methods are not as scalable and do not produce exact sparsity, while the latter tends to over-shrink large coefficients. RESULTS: In this study, we present SSLPRS, a novel PRS method based on the Spike-and-Slab LASSO (SSL) prior, which offers a theoretical bridge between the two frameworks. We extend previous work to derive a coordinate-ascent inference algorithm that operates on GWAS summary statistics, which is orders-of-magnitude more efficient than corresponding individual-level-based implementations. To illustrate the statistical properties of the proposed model, we conducted experiments involving nine simulation configurations and nine quantitative phenotypes from the UK Biobank. Our results demonstrate that SSLPRS is competitive with state-of-the-art methods in terms of prediction accuracy and exhibits superior variable selection performance, especially in sparse genetic architectures. In simulations, this translates to upwards of 50% improvement in positive predictive value. In analysis of real phenotypes, we show that selected variants are highly enriched for meaningful genomic annotations and have better replication rates in larger meta-analyses. AVAILABILITY AND IMPLEMENTATION: SSLPRS is available in the open-source package https://github.com/li-lab-mcgill/penprs.

Multifactorial Inheritance↗

PubMed2025

PLAID: ultrafast single-sample gene set enrichment scoring.

SUMMARY: In recent years, computational methods have emerged that calculate enrichment of gene signatures within individual samples. These signatures offer critical insights into the coordinated activity of functionally related genes, proteins or metabolites, enabling the identification of unique molecular profiles in individual cells and patients. This strategy is pivotal for patient stratification and advancement of personalized medicine. However, the rise of large-scale datasets, including single-cell profiles and population biobanks, has exposed significant computational inefficiencies in existing methods. Current methods often demand excessive runtime and memory resources, becoming impractical for large datasets. Overcoming these limitations is a focus of current efforts by bioinformatics teams in academia and the pharmaceutical industry, as essential to support basic and clinical biomedical research. To address this critical need, we developed PLAID (Pathway Level Average Intensity Detection), an ultrafast and memory optimized single sample gene set enrichment algorithm that utilizes sparse matrix computation. PLAID delivers highly accurate gene set scoring and surpasses the performance of current methods in single-cell and bulk transcriptomics, and proteomics data. PLAID uniquely integrates the most widely used gene set scoring algorithms, enabling researchers to apply multiple methods for cross-validation with outstanding runtime efficiency and minimal memory requirement. AVAILABILITY AND IMPLEMENTATION: PLAID is implemented in the R language for statistical computing. PLAID source code and installation instructions are available with no restrictions at https://github.com/bigomics/plaid.

Algorithms↗

PubMed2026

Secure bioinformatics: privacy-preserving federated analytics using homomorphic encryption.

MOTIVATION: Large-scale bioinformatics analyses increasingly require collaboration across multiple cohorts and institutions, yet existing workflows often rely on data co-localization, which is slow, difficult to scale, and raises privacy concerns. We present a privacy-preserving federated analytics framework that enables secure statistical analysis across distributed datasets without transferring raw data, by performing all computations on encrypted data via cryptographic methods. RESULTS: We evaluate the framework by validating polygenic risk scores and conducting meta-analyses on two real-world cohorts. The proposed solution achieves over 99.9% accuracy relative to plaintext analyses, while maintaining scalable runtime performance with increasing data size and number of participating sites. These results demonstrate the feasibility of secure federated analytics for practical bioinformatics applications involving sensitive data.

Computational Biology↗

PubMed2026

ADAMIXTURE: adaptive first-order optimization for biobank-scale genetic clustering.

MOTIVATION: Estimating genetic clusters from sequencing data is a fundamental task in population and medical genetics, enabling demographic inference and adjustment for population structure in association studies. ADMIXTURE, a widely used model-based clustering method, employs an accelerated Expectation-Maximization (EM) algorithm to infer population parameters; however, its computational demands scale poorly, limiting its usefulness for modern biobank-sized datasets. While recent EM acceleration strategies employing second-order quasi-Newton schemes preserve accuracy, they remain computationally intensive. Conversely, EM-free approaches that prioritize speed often compromise solution quality. RESULTS: We introduce ADAMIXTURE, a novel optimization framework that integrates the EM algorithm with Adaptive Moment Estimation (Adam). Unlike traditional acceleration methods, ADAMIXTURE utilizes first-order gradients with adaptive learning rates derived from raw and squared moments to approximate curvature information, bypassing the computational overhead of Hessian approximations. This approach surpasses the convergence efficiency of second-order methods while maintaining the low computational complexity of first-order updates. Across simulated and large-scale empirical datasets, ADAMIXTURE demonstrates substantial reductions in wall-clock runtime and enhanced scalability compared to state-of-the-art methods, while maintaining comparable or improved inference accuracy. Its GPU implementation runs in under 2 h on half a million samples and variants, a two order of magnitude speedup over current state-of-the-art. AVAILABILITY AND IMPLEMENTATION: Source code is available at: https://github.com/AI-sandbox/ADAMIXTURE.

Clustering Algorithms↗

PubMed2026

ECHO: a nanopore sequencing-based workflow for (epi)genetic profiling of the human repeatome.

SUMMARY: The human genome is dominated by repetitive DNA, whose genetic and epigenetic variation plays a key role in gene regulation, genome stability, and disease. Recent advances in long-read sequencing now enable large-scale, haplotype-resolved, and DNA methylation-informative analysis of the human genome, including on previously inaccessible complex and repetitive regions. However, the comprehensive, simultaneous characterisation of the "human repeatome" remains challenging, largely due to the lack of comprehensive tools integrated in a single pipeline that can capture the full spectrum of variation across diverse types of DNA repeats. Here, we present ECHO, a user-friendly, Snakemake-based pipeline for the "(Epi)genomic Characterisation of Human Repetitive Elements using Oxford Nanopore Sequencing." ECHO provides a reproducible and scalable framework for end-to-end analysis of whole-genome nanopore sequencing data, enabling integrative but also tailored (epi)genetic analyses of the human repeatome. AVAILABILITY AND IMPLEMENTATION: ECHO is freely available at Github: https://github.com/leenput/ECHO-pipeline, with the archived version at Zenodo: https://zenodo.org/records/19068468.

Humans↗

PubMed2026

CoSAG-nf: A Scalable Nextflow Pipeline for Co-assembly, Optimization, and Interactive Visualization of High-Throughput Single-Cell Genomes.

MOTIVATION: Single-cell amplified genomes (SAGs) are crucial for resolving intra-population microbial heterogeneity and accurately understanding the metabolic potential of microbial dark matter populations. However, SAGs generated through multiple displacement amplification (MDA) of genomic DNA from single cells with single-copy chromosomes are highly fragmented and prone to contamination, severely hindering high-quality genome reconstruction and functional analysis, which greatly limits their scientific utility. Co-assembly of related SAGs can substantially improve genome quality, but to our knowledge no automated pipeline exists for high-throughput processing, forcing manual implementation of complex workflows that scale poorly to modern dataset sizes. RESULTS: We present CoSAG-nf, an automated high-throughput co-assembly and optimization pipeline for SAGs, implemented following the nf-core framework standards. The pipeline performs alignment-free clustering using sourmash MinHash signatures, then employs iterative tetranucleotide frequency profiling to identify and exclude outlier SAGs from co-assembly groups. CheckM2 quality assessment guides dynamic selection of optimal SAG combinations to optimize genome completeness and minimize contamination. Fully containerized, CoSAG-nf ensures reproducibility and scalability for the high-throughput processing of large-scale SAG datasets across diverse computing environments, including HPC and cloud platforms. The pipeline generates comprehensive HTML reports with quality metrics and taxonomic annotations, providing an end-to-end solution for automated high-throughput single-cell genome reconstruction. AVAILABILITY: CoSAG-nf is freely available under the MIT License at: https://github.com/linfengxu/CoSAG-nf. Archival code repository snapshots are published at zenodo with doi: https://doi.org/10.5281/zenodo.21525244. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Journal Article↗

PubMed2026

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

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

Sarcopenia↗

PubMed2025

ERCnet: Phylogenomic Prediction of Interaction Networks in the Presence of Gene Duplication.

Assigning gene function from genome sequences is a rate-limiting step in molecular biology research. A protein's position within an interaction network can potentially provide insights into its molecular mechanisms. Phylogenetic analysis of evolutionary rate covariation (ERC) in protein sequence has been shown to be effective for large-scale prediction of functional relationships and interactions. However, gene duplication, gene loss, and other sources of phylogenetic incongruence are barriers for analyzing ERC on a genome-wide basis. Here, we developed ERCnet, a bioinformatic program designed to overcome these challenges, facilitating efficient all-versus-all ERC analyses for large protein sequence datasets. We simulated proteome datasets and found that ERCnet achieves combined false positive and negative error rates well below 10% and that our novel "branch-by-branch" length measurements outperforms "root-to-tip" approaches in most cases, offering a valuable new strategy for performing ERC. We also compiled a sample set of 35 angiosperm genomes to test the performance of ERCnet on empirical data, including its sensitivity to user-defined analysis parameters such as input dataset size and branch-length measurement strategy. We investigated the overlap between ERCnet runs with different species samples to understand how species number and composition affect predicted interactions and to identify the protein sets that consistently exhibit ERC across angiosperms. Our systematic exploration of the performance of ERCnet provides a roadmap for design of future ERC analyses to predict functional interactions in a wide array of genomic datasets. ERCnet code is freely available at https://github.com/EvanForsythe/ERCnet.

Gene Duplication↗

PubMed2025

MetaflowX: a scalable and resource-efficient workflow for multi-strategy metagenomic analysis.

Microbiomes play crucial roles in diverse ecosystems, spanning environmental, agricultural, and human health domains. However, in-depth metagenomic data analysis presents significant technical and resource challenges, particularly at scale. Existing computational pipelines are typically limited to either reference-based or reference-free approaches and exhibit inefficiencies in process large datasets. Here, we introduce MetaflowX (https://github.com/01life/MetaflowX), an open-resource workflow integrating both analytical paradigms for enhanced metagenomic investigations. This modular framework encompasses short-read quality control, rapid microbial profiling, hybrid contig assembly and binning, high-quality metagenome-assembled genome (MAG) identification, as well as bin refinement and reassembly. Benchmarking tests showed that MetaflowX completed full metagenomic analyses up to 14-fold faster and with 38% less disk usage than existing workflows. It also recovered the highest number of high-quality and taxonomically diverse MAGs. A dedicated reassembly module further improved MAG quality, increasing completeness by 5.6% and reducing contamination by 53% on average. Functional annotation modules enable detection of key features, including virulence and antibiotic resistance genes. Designed for extensibility, MetaflowX provides an efficient solution addressing current and emerging demands in large-scale metagenomic research.

Metagenomics↗

PubMed2025

CNV-Finder: Streamlining Copy Number Variation Discovery.

Copy Number Variations (CNVs) play pivotal roles in the etiology of complex diseases and are variable across diverse populations. Understanding the association between CNVs and disease susceptibility is significant in disease genetics research and often requires analysis of large sample sizes. One of the most cost-effective and scalable methods for detecting CNVs is based on normalized signal intensity values, such as Log R Ratio (LRR) and B Allele Frequency (BAF), from Illumina genotyping arrays. In this study, we present CNV-Finder, a novel pipeline integrating deep learning techniques on array data, specifically a Long Short-Term Memory (LSTM) network, to expedite the large-scale identification of CNVs within predefined genomic regions. This facilitates efficient prioritization of samples for time-consuming or costly subsequent analyses such as Multiplex Ligation-dependent Probe Amplification (MLPA), short-read, and long-read whole genome sequencing. We incorporate four genes to establish our methods-Parkin (PRKN), Leucine Rich Repeat And Ig Domain Containing 2 (LINGO2), Microtubule Associated Protein Tau (MAPT), and alpha-Synuclein (SNCA)-which may be relevant to neurological diseases such as Alzheimer's disease (AD), Parkinson's disease (PD), Progressive Supranuclear Palsy (PSP), or related disorders such as essential tremor (ET). By training our models on expert-annotated samples and validating them across diverse cohorts, including those from the Global Parkinson's Genetics Program (GP2) and additional dementia-specific databases, we demonstrate the efficacy of CNV-Finder in accurately detecting deletions and duplications. Our pipeline outputs app-compatible files for visualization within CNV-Finder's interactive web application. This interface enables researchers to review predictions and filter displayed samples by model prediction values, LRR range, and variant count in order to explore or confirm results. Our pipeline integrates this human feedback to enhance model performance and reduce false positive rates. Through a series of comprehensive analyses and validations using visual inspection, MLPA, short-read, and long-read sequencing data, we demonstrate the robustness and adaptability of CNV-Finder in identifying CNVs with regions of varied size, probe density, and noise. Our findings highlight the significance of contextual understanding and human expertise in enhancing the precision of CNV identification, particularly in complex genomic regions like 17q21.31. The CNV-Finder pipeline is a scalable, publicly available resource for the scientific community, available on GitHub (https://github.com/GP2code/CNV-Finder; DOI 10.5281/zenodo.14182563). CNV-Finder not only expedites accurate candidate identification but also significantly reduces the manual workload for researchers, enabling future targeted validation and downstream analyses in regions or phenotypes of interest.

Copy Number Variation (CNV)↗

PubMed2025

SPC: a SPectral Component approach leveraging Identity-by-Descent graphs to address recent population structure in genomic analysis.

Population structure is a well-known confounder in statistical genetics, particularly in genome-wide association studies (GWAS), where it can lead to inflated test statistics and spurious associations. Traditional methods, such as principal components (PCs), commonly used to adjust for population structure, are limited in capturing fine-scale, non-linear patterns that arise from recent demographic events - patterns that are crucial for understanding rare variant effects. To address this challenge, we propose a novel method called SPectral Components (SPCs), which leverages identity-by-descent (IBD) graphs to capture and transform local, non-linear fine-scale population structure into continuous representations that can be seamlessly integrated into genetic analysis pipelines. Using both simulated datasets and empirical data from the UK Biobank (N ≈ 420,000), we demonstrate that SPCs outperform PCs in adjusting for fine-scale population structure. In simulations, SPCs explained over 90% of the fine-scale population structure with fewer components, while PCs captured less than 5%. In the UK Biobank, SPCs reduced the inflation of p-values in the GWAS of an environmental-driven phenotype by 12% compared to PCs, while maintaining a similar performance to PCs in height, a highly heritable phenotype. Additionally, SPCs improved rare variant association analyses, reducing genomic inflation (e.g., from 7.6 to 1.2 in one analysis), and provided more accurate heritability estimates. Spatial autocorrelation analysis further confirmed the ability of SPCs to account for environmental effects, reducing Moran's I for both environmental and heritable phenotypes more effectively than PCs. Overall, our findings demonstrate that SPCs provide a robust, scalable adjustment for recent population structure, offering a powerful alternative or complement to PCs in large-scale biobank studies.

GWAS↗

PubMed2026

Genetically Predicted Muscle Mass and Function in Relation to Deep Vein Thrombosis: A Two-step Mendelian Randomization Study Highlighting the Mediating Role of BMI.

BackgroundSarcopenia is observationally linked to venous thromboembolism, but the causal architecture and underlying biological pathways remain largely unclear. This study investigated the causal effects of sarcopenia-related traits on lower extremity deep vein thrombosis (DVT) and quantified potential mediating mechanisms.MethodsWe performed two-sample bidirectional Mendelian randomization (MR) and two-step mediation MR using large-scale GWAS data from UK Biobank, EMBL-EBI, and FinnGen. Exposures included appendicular lean mass (ALM), leg fat-free mass (LFM), hand grip strength, and walking pace. Eighteen candidate mediators were screened for indirect pathways.ResultsGenetically predicted higher ALM was significantly associated with increased DVT risk (FinnGen: OR = 1.288, 95% CI: 1.215-1.365, P < 0.001). Similar positive associations were observed for LFM (OR = 1.920-1.954, P < 0.001). By contrast, muscle functional traits - grip strength and walking pace - demonstrated no consistent causal effects. Reverse MR confirmed a unidirectional relationship. Body mass index (BMI) emerged as a pivotal mediator, accounting for 7.58% - 10.50% of the ALM-DVT effect and 52.74% - 62.73% of the LFM-DVT effect. Notably, the independent effect of ALM was largely attenuated after adjusting for metabolic confounders in multivariable MR.ConclusionGenetic predisposition to high muscle mass, rather than functional strength, increases DVT risk. This relationship appears to be significantly driven by metabolic adiposity, suggesting that the "muscle-vascular-coagulation" interaction is partly explained by body-size-related metabolic burden. Risk stratification should integrate muscle mass evaluation with comprehensive metabolic health assessments.

Humans↗

PubMed2026

Assessing the impact of maternal blood pressure during pregnancy on perinatal health: a wide-angled Mendelian randomization study.

BACKGROUND: Observational studies link high blood pressure in pregnancy to numerous adverse pregnancy and perinatal outcomes; however, findings may be affected by residual confounding or reverse causation. This study aimed to assess the causal effect of blood pressure during pregnancy on a range of pregnancy and perinatal outcomes. METHODS: We performed two-sample Mendelian randomization (MR) to assess the effect of systolic and diastolic blood pressure (SBP/DBP) during pregnancy on 16 primary and eight secondary adverse pregnancy and perinatal outcomes. We obtained genetic association data from large-scale meta-analyses of genome-wide association studies involving predominantly European ancestry individuals for SBP/DBP (N = 1,028,980), and pregnancy and perinatal outcomes (N = 74,368-714,899). We used inverse-variance weighted (IVW) MR for main analyses and MR-Egger, weighted median, weighted mode, multivariable MR, and IVW adjusted for fetal genetic effects for sensitivity analyses. RESULTS: A 10 mmHg higher genetically predicted maternal SBP increased the odds of gestational diabetes, induction of labour, low birth weight (LBW), small-for-gestational age (SGA), preterm birth (PTB), and neonatal intensive care unit (NICU) admission (OR ranging from 1.11 [95% CI 1.02 to 1.20] for NICU admission to 1.33 [1.26 to 1.41] for LBW); while decreasing the odds of high birth weight (HBW), large-for-gestational age (LGA), and post-term birth [OR ranging from 0.76 (0.69 to 0.83) for HBW to 0.94 (0.90 to 0.99) for post-term birth]. We did not find evidence that genetically predicted higher maternal SBP was related to miscarriage or stillbirth. The results for maternal DBP were similar to the results for SBP. Overall, the main results were consistent across sensitivity analyses accounting for pleiotropic instruments and fetal genetic effects. CONCLUSIONS: Higher maternal blood pressure reduces gestation duration and fetal growth and increases the risks of induction of labour, gestational diabetes, and neonatal intensive care unit admission. This and other emerging evidence highlight the value of interventions aimed at controlling blood pressure in the population to reduce the burden of adverse pregnancy outcomes.

Humans↗

PubMed2025

A study of the link between food-water insecurities and self-reported psychotic experiences among young adults in Gaza Strip: What role does social support play under conditions of war and extreme suffering?

BACKGROUND: The war in Gaza, coupled with severe restrictions on humanitarian access and assistance, have led to a rapid and large-scale exacerbation of food insecurity (FI) and water insecurity (WI) in Gaza, which can cause a myriad of deleterious effects on both physical and mental health. Previous evidence suggests that environmental stressors (such as FI and WI), coupled with lack of support, can compromise coping ability and trigger psychosis. Elucidation of these pathways can inform efforts to reduce the incidence of psychosis in war settings in particular, and in global communities more generally. This study aimed to assess the mediating role of social support on the association between food-water insecurities and psychotic experiences (PEs). METHODS: This study was carried-out over a period of one month (September 2024) by using a free online form builder. Data were collected at a single time point using snowball sampling. A total of 476 adults aged 18–35 years and living in Gaza at the time of the survey took part in this study. The Prodromal Questionnaire- Brief, the Food Insecurity Experience Scale, the Four-Item Household Water Insecurity Experiences Scale, and the Single Item Measure of Social Supports have been administered to participants. RESULTS: A total of 110 participants (23.1%) reported low tangible social support (i.e., having no or 1 person in the social support network), 27.3% were classified as moderately-to-severely food insecure and 41% were categorized as water-insecure. The mediation analysis was adjusted for the following covariates: marital status, living arrangement and age. Higher food-water insecurity experiences were significantly associated with lower social support. Higher food-water insecurity experiences were also significantly associated with higher PEs. Higher social support was significantly associated with lower PEs. The results of the mediation analysis showed that social support mediated the link of FI (indirect effect: Beta = 0.06; Boot SE = 0.03; Boot CI 0.01; 0.12) and WI (indirect effect: Beta = 0.04; Boot SE = 0.02; Boot CI 0.01; 0.08) to PEs. CONCLUSION: Our findings support the case for bolstering social support networks in war-affected communities facing survival challenges such as FI and WI. This study points to the urgent need for increased food aid and improved water supplies in the short term, and sustainable food and water systems in the long term, to support mental health. In times of war, mental health prevention and interventions strategies might do well to increase social support and resources in order to help buffer such intense environmental stressors and prevent the emergence of psychosis. CLINICAL TRIAL NUMBER: Not applicable.

Humans↗

PubMed2022

Association of Vitamin D Polygenic Risk Scores and Disease Outcome in People With Multiple Sclerosis.

BACKGROUND AND OBJECTIVES: Observational studies suggest low levels of 25-hydroxyvitamin D (25[OH]D) may be associated with increased disease activity in people with multiple sclerosis (PwMS). Large-scale genome-wide association studies (GWAS) suggest 25(OH)D levels are partly genetically determined. The resultant polygenic scores (PGSs) could serve as a proxy for 25(OH)D levels, minimizing potential confounding and reverse causation in analyses with outcomes. Herein, we assess the association of genetically determined 25(OH)D and disease outcomes in MS. METHODS: We generated 25(OH)D PGS for 1,924 PwMS with available genotyping data pooled from 3 studies: the CombiRx trial (n = 575), Johns Hopkins MS Center (n = 1,152), and Immune-Mediated Inflammatory Diseases study (n = 197). 25(OH)D-PGS were derived using summary statistics (p < 5 × 10-8) from a large GWAS including 485,762 individuals with circulating 25(OH)D levels measured. We included clinical and imaging outcomes: Expanded disability status scale (EDSS), timed 25-foot walk (T25FW), nine-hole peg test (9HPT), radiologic activity, and optical coherence tomography-derived ganglion cell inner plexiform layer (GCIPL) thickness. A subset (n = 935) had measured circulating 25(OH)D levels. We fitted multivariable models based on the outcome of interest and pooled results across studies using random effects meta-analysis. Sensitivity analyses included a modified p value threshold for inclusion in the PGS (5 × 10-5) and applying Mendelian randomization (MR) rather than using PGS. RESULTS: Initial analyses demonstrated a positive association between generated 25(OH)D-PGS and circulating 25(OH)D levels (per 1SD increase in 25[OH]D PGS: 3.08%, 95% CI: 1.77%, 4.42%; p = 4.33e-06; R2 = 2.24%). In analyses with outcomes, we did not observe an association between 25(OH)D-PGS and relapse rate (per 1SD increase in 25[OH]D-PGS: 0.98; 95% CI: 0.87-1.10), EDSS worsening (per 1SD: 1.05; 95% CI: 0.87-1.28), change in T25FW (per 1SD: 0.07%; 95% CI: -0.34 to 0.49), or change in 9HPT (per 1SD: 0.09%; 95% CI: -0.15 to 0.33). 25(OH)D-PGS was not associated with new lesion accrual, lesion volume or other imaging-based outcomes (whole brain, gray, white matter volume loss or GCIPL thinning). The results were similarly null in analyses using other p value thresholds or those applying MR. DISCUSSION: Genetically determined lower 25(OH)D levels were not associated with worse disease outcomes in PwMS and raises questions about the plausibility of a treatment effect of vitamin D in established MS.

Humans↗