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PubMed2024

No causal relationship between glucose and inflammatory bowel disease: a bidirectional two-sample mendelian randomization study.

BACKGROUND: Association between glucose and inflammatory bowel disease (IBD) was found in previous observational studies and in cohort studies. However, it is not clear whether these associations reflect causality. Thus, this study investigated whether there is such a causal relation between elevated glucose and IBD, Crohn's disease (CD) and ulcerative colitis (UC). METHODS: We performed a two-sample Mendelian Randomization (MR) with the independent genetic instruments identified from the largest available genome-wide association study (GWAS) for IBD (5,673 cases; 213,119 controls) and its main subtypes, CD and UC. Summarized data for glucose which included 200,622 cases and glycemic traits including HbA1c and type 2 diabetes(T2DM) were obtained from different GWAS studies. Primary and secondary analyses were conducted by preferentially using the radial inverse-variance weighted (IVW) approach. A number of other meta-analysis approach and sensitivity analyses were carried out to assess the robustness of the results. RESULTS: We did not find a causal effect of genetically predicted glucose on IBD as a whole (OR 0.858; 95% CI 0.649-1.135; P = 0.286). In subtype analyses glucose was also suggestively not associated with Crohn's disease (OR 0.22; 95% CI 0.04-1.00; P = 0.05) and ulcerative colitis (OR 0.940; 95% CI 0.628-1.407; P = 0.762). In the other direction, IBD and its subtypes were not related to glucose and glycemic traits. CONCLUSIONS: This MR study is not providing any evidence for a causal relationship between genetically predicted elevated glucose and IBD as well as it's subtypes UC and CD. Regarding the other direction, no causal associations could be found. Future studies with robust genetic instruments are needed to confirm this conclusion.

Humans↗

PubMed2025

Reproducibility of genetic risk factors identified for long COVID using combinatorial analysis across US and UK patient cohorts with diverse ancestries.

BACKGROUND: Long COVID is a major public health burden causing a diverse array of debilitating symptoms in tens of millions of patients globally. In spite of this overwhelming disease prevalence, staggering cost, severe impact on patients' lives and intense global research efforts, study of the disease has proved challenging due to its complexity. Genome-wide association studies (GWAS) have identified only four loci potentially associated with the disease, although these results did not statistically replicate between studies. A previous combinatorial analysis study identified a total of 73 genes that were highly associated with two long COVID cohorts in the predominantly (> 91%) white European ancestry Sano GOLD population, and we sought to reproduce these findings in the independent and ancestrally more diverse All of Us (AoU) population. METHODS: We assessed the reproducibility of the 5343 long COVID disease signatures from the original study in the AoU population. Because the very small population sizes provide very limited power to replicate findings, we initially tested whether we observed a statistically significant enrichment of the Sano GOLD disease signatures that are also positively correlated with long COVID in the AoU cohort after controlling for population substructure. RESULTS: For the Sano GOLD disease signatures that have a case frequency greater than 5% in AoU, we consistently observed a significant enrichment (77-83%, p < 0.01) of signatures that are also positively associated with long COVID in the AoU cohort. These encompassed 92% of the genes identified in the original study. At least five of the disease signatures found in Sano GOLD were also shown to be individually significantly associated with increased long COVID prevalence in the AoU population. Rates of signature reproducibility are strongest among self-identified white patients, but we also observe significant enrichment of reproducing disease associations in self-identified black/African-American and Hispanic/Latino cohorts. Signatures associated with 11 out of the 13 drug repurposing candidates identified in the original Sano GOLD study were reproduced in this study. CONCLUSION: These results demonstrate the reproducibility of long COVID disease signal found by combinatorial analysis, broadly validating the results of the original analysis. They provide compelling evidence for a much broader array of genetic associations with long COVID than previously identified through traditional GWAS studies. This strongly supports the hypothesis that genetic factors play a critical role in determining an individual's susceptibility to long COVID following recovery from acute SARS-CoV-2 infection. It also lends weight to the drug repurposing candidates identified in the original analysis. Together these results may help to stimulate much needed new precision medicine approaches to more effectively diagnose and treat the disease. This is also the first reproduction of long COVID genetic associations across multiple populations with substantially different ancestry distributions. Given the high reproducibility rate across diverse populations, these findings may have broader clinical application and promote better health equity. We hope that this will provide confidence to explore some of these mechanisms and drug targets and help advance research into novel ways to diagnose the disease and accelerate the discovery and selection of better therapeutic options, both in the form of newly discovered drugs and/or the immediate prioritization of coordinated investigations into the efficacy of repurposed drug candidates.

Humans↗

PubMed2025

Bioavailable testosterone reduces the risk of lung squamous cell carcinoma: a comprehensive data study.

BACKGROUND: The association between testosterone and lung cancer remains unclear. This study investigates the relationship between testosterone levels and lung cancer risk, focusing on bioavailable testosterone levels (BTLs), total testosterone levels (TTLs), and sex hormone-binding globulin (SHBG) in relation to lung cancer subtypes. METHODS: We utilized bidirectional and multivariable Mendelian randomization (MR) analyses based on genome-wide association studies (GWAS) to assess causal links. To validate the findings clinically, immunohistochemical (IHC) staining for androgen receptor (AR) expression and survival analyses were conducted on a cohort of 90 patients with lung squamous cell carcinoma (LUSC). RESULTS: MR analysis demonstrated that higher BTLs were significantly associated with a reduced risk of LUSC (OR = 0.365, P = 0.001), while no significant associations were observed for TTLs or SHBG. Reverse MR analysis found no causal effect of lung cancer on testosterone levels. Multivariable MR confirmed BTLs as an independent protective factor. In the clinical cohort, AR expression was significantly associated with better prognosis, showing improved median progression-free survival (12.3 vs. 9.0 months, P = 0.01) and median overall survival (35.3 vs. 29.4 months, P < 0.01). Cox regression identified AR expression as an independent protective factor for patient outcomes. However, study limitations include potential residual confounding, ethnic heterogeneity between European GWAS data and Asian clinical cohorts, and the lack of direct experimental validation. CONCLUSIONS: Our findings suggest that higher BTLs may play a protective role against LUSC. BTLs and AR expression show potential as valuable biomarkers for the diagnosis and prognostic assessment of LUSC.

Humans↗

PubMed2025

No More Free Lunch: Challenges to Mendelian Randomization Due to Sample Selection and Complex Methods.

Mendelian randomization (MR) is increasingly used in epidemiological studies to investigate causal relationships. MR depends on 3 fundamental instrumental variable assumptions: relevance, independence, and exclusion restriction. Studies often assume that MR mitigates bias from confounding due to the random allocation of genetic variants at conception. In this perspective, using causal directed acyclic graphs, we discuss several scenarios where biases in MR analyses may arise due to the nature of the data or methods being used. These include (1) collider bias due to the nonrandom selection of participants into study populations used for conducting genome-wide association studies (GWAS), (2) indirect genetic effects arising from population-based GWAS rather than within-family studies, and (3) collider bias due to gene-environment interaction effects on the exposure in nonlinear MR analyses. We provide practical considerations for examining and reducing these biases in MR analyses.

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↗

PubMed2025

Tensor decomposition of multi-dimensional splicing events across multiple tissues to identify splicing-mediated risk genes associated with complex traits.

Identifying risk genes associated with complex traits remains challenging. Integrating gene expression data with Genome-Wide Association Study (GWAS) through Transcriptome-Wide Association Study (TWAS) methods has discovered candidate risk genes for various complex traits. Splicing, which explains a comparable heritability of complex traits as gene expression, is under-explored due to its multidimensionality. To leverage multiple splicing events in a gene and shared splicing across tissues, we develop Multi-tissue Splicing Gene (MTSG), which employs tensor decomposition and sparse Canonical Correlation Analysis (sCCA) to extract meaningful information from high-dimensional multiple splicing events across multiple tissues. We build MTSG models using GTEx data and apply them to GWAS summary statistics of Alzheimer's disease (AD) (111,326 cases and 677,663 controls) and schizophrenia (SCZ) (36,989 cases and 113,075 controls). We identify 174 and 497 significant splicing-mediated risk genes for AD and SCZ, respectively, at Bonferroni correction. For AD, our results demonstrate significant enrichment of AD related pathways and identify additional AD risk genes not detected in the single-tissue analysis, while preserving most top genes identified in the brain frontal cortex. Consistently, for SCZ, genes identified by our brain-wide MTSG model, built from a cluster of 13 brain tissues, exhibit stronger enrichment in SCZ-relevant genes and MTSG identifies unique SCZ risk genes compared to single-tissue models. These results showcase that our MTSG models capture distinctive splicing events across tissues, which might be overlooked when using single tissue alone. Our MTSG models can be applied to other complex traits to help identify splicing-mediated disease risk genes.

Humans↗

PubMed2024

Association of different milk fat content with coronary artery disease and myocardial infarction risk: A Mendelian randomization study.

BACKGROUND: Numerous observational studies have investigated on the correlation of whole, semi-skimmed, and skimmed milk with coronary artery disease (CAD) and myocardial infarction (MI) risk; However, no consensus has been reached and evidence on any causal links between these exposures and outcomes remains unclear. This study aimed to conduct univariate and multivariate Mendelian randomization (MR) analyses, using publicly released genome-wide association study summary statistics (GWAS) from the IEU GWAS database, to ascertain the causal association of milk with various fat content with CAD and MI risk. METHODS: For the exposure data, 29, 15, and 30 single-nucleotide polymorphisms for whole milk, semi-skimmed milk, and skimmed milk, respectively, obtained from 360,806 Europeans, were used as instrumental variables. CAD and MI comprised 141,217 and 395,795 samples, respectively. We used inverse variance weighted (IVW), weighted median, MR-Egger regression, and MR Pleiotropy Residual Sum and Outlier analyses to determine whether pleiotropy and heterogeneity could skew the MR results. Sensitivity tests were conducted to verify the robustness of the results. RESULTS: After adjusting for false discovery rates (FDR), we discovered proof that skimmed milk intake is a genetically predicted risk factor for CAD (odds ratio [OR] = 5.302; 95% confidence interval [CI] 2.261-12.432; P < 0.001; FDR-corrected P < 0.001) and MI (OR = 2.287; 95% CI 1.218-4.300; P = 0.010; FDR-corrected P = 0.009). Most sensitivity assessments yielded valid results. Multivariable MR for CAD and MI produced results consistent with those obtained using the IVW method. There was no causal relationship between whole or semi-skimmed milk, and CAD or MI. CONCLUSION: Our findings indicate that the consumption of skimmed milk may increase the risk of CAD and MI. This evidence may help inform dietary recommendations for preventing cardiovascular disease. Further studies are required to elucidate the underlying mechanisms.

Humans↗

PubMed2024

Genomic prediction and genome-wide association studies of morphological traits and distraction index in Korean Sapsaree dogs.

The Korean Sapsaree dog is a native breed known for its distinctive appearance and historical significance in Korean culture. The accurate estimation of breeding values is essential for the genetic improvement and conservation of such indigenous breeds. This study aimed to evaluate the accuracy of breeding values for body height, body length, chest width, hair length, and distraction index (DI) traits in Korean Sapsaree dogs. Additionally, a genome-wide association study (GWAS) was conducted to identify the genomic regions and nearby candidate genes influencing these traits. Phenotypic data were collected from 378 Korean Sapsaree dogs, and of these, 234 individuals were genotyped using the 170k Illumina CanineHD BeadChip. The accuracy of genomic predictions was evaluated using the traditional BLUP method with phenotypes only on genotyped animals (PBLUP-G), another traditional BLUP method using a pedigree-based relationship matrix (PBLUP) for all individuals, a GBLUP method based on a genomic relationship matrix, and a single-step GBLUP (ssGBLUP) method. Heritability estimates for body height, body length, chest width, hair length, and DI were 0.45, 0.39, 0.32, 0.55, and 0.50, respectively. Accuracy values varied across methods, with ranges of 0.22 to 0.31 for PBLUP-G, 0.30 to 0.57 for PBLUP, 0.31 to 0.54 for GBLUP, and 0.39 to 0.67 for ssGBLUP. Through GWAS, 194 genome-wide significant SNPs associated with studied Sapsaree traits were identified. The selection of the most promising candidate genes was based on gene ontology (GO) terms and functions previously identified to influence traits. Notable genes included CCKAR and DCAF16 for body height, PDZRN3 and CNTN1 for body length, TRIM63, KDELR2, and SUPT3H for chest width, RSPO2, EIF3E, PKHD1L1, TRPS1, and EXT1 for hair length, and DDHD1, BMP4, SEMA3C, and FOXP1 for the DI. These findings suggest that significant QTL, combined with functional candidate genes, can be leveraged to improve the genetic quality of the Sapsaree population. This study provides a foundation for more effective breeding strategies aimed at preserving and enhancing the unique traits of this Korean dog breed.

Animals↗

PubMed2026

Shared genetic basis and spatial cellular atlas of psoriasis and metabolic syndrome.

BACKGROUND: Psoriasis (PS) and metabolic syndrome (MetS) frequently co-occur. Characterizing their shared genetic architecture and spatially enriched cellular populations may clarify the context of their co-occurrence and generate hypotheses for functional validation. METHODS: We integrated genome-wide association study (GWAS) summary statistics for PS, MetS, and five related components with spatially resolved single-cell transcriptomic data. Global and local genetic correlations were assessed using linkage disequilibrium score regression, genetic covariance analysis, high-definition likelihood, and local analysis of variant association. A bivariate causal mixture model quantified polygenic overlap. Conditional/conjunctional false discovery rate and composite-null pleiotropy analyses identified shared susceptibility loci. Finally, gsMap evaluated trait-associated enrichment across annotated embryonic tissues at single-cell resolution. RESULTS: Genetic approaches identified significant genome-wide correlations and polygenic sharing between PS, MetS, and its components. Local and cross-trait analyses identified region-specific signals and cross-validated shared loci. gsMap revealed trait-specific tissue enrichment. PS showed the strongest enrichment in the epidermis (pCauchy = 1.0573 × 10  - ⁴), adipose tissue (pCauchy = 1.5366 × 10 - ⁴), and liver (pCauchy = 1.0167 × 10 - ³). Across MetS, FBG, HDL-C, hypertension, and TG, enriched regions mainly involved the liver, adipose tissue, and epidermis. WC enrichment was predominantly observed in adipose tissue (pCauchy = 1.7823 × 10 - ⁴), with no significant liver or epidermal enrichment. CONCLUSION: Integrating GWAS with single-cell transcriptomic and spatial information characterized shared genetic architecture between PS and MetS-related phenotypes and their spatial enrichment patterns. These findings provide a framework for generating testable hypotheses about comorbidity biology and guiding future functional and clinical validation.

Psoriasis↗

PubMed2026

Does Toxoplasma gondii infection have a causal impact on human psychopathology? A Mendelian randomization analysis.

Toxoplasma gondii (T. gondii) is a prevalent zoonotic parasite that has been implicated in influencing human psychiatric disorders and risk-taking behaviors. Using genome-wide association study (GWAS) data, we selected 25 and 76 single-nucleotide polymorphisms as instrumental variables for anti-T. gondii IgG seropositivity and anti-T. gondii IgG levels, respectively, and conducted two-sample Mendelian randomization (MR) analyses across 18 GWAS datasets to investigate potential causal effects on addiction, bipolar disorder, obsessive-compulsive disorder, schizophrenia, and risk-taking behavior in European populations. Contrary to previous epidemiological evidence, our MR analyses do not support a significant causal association between T. gondii infection and any of the studied psychiatric disorders or risk-taking behavior. Collectively, these results establish that any true causal effect of genetic liability to T. gondii infection is likely to be small (OR < 1.18 for schizophrenia, < 1.29 for bipolar disorder) and below the effect sizes typically reported in observational seroepidemiological studies, although small or infection-phase-specific effects cannot be excluded.

Humans↗

PubMed2026

Genomic dimensions deconstruct the clinical heterogeneity of bipolar disorder.

Bipolar disorder's (BD) clinical heterogeneity has an unresolved genetic basis. We meta-analyzed genome-wide association studies (GWAS) of 16 BD subphenotypes in 226,032 individuals from 57 cohorts (38,022 cases); 10 advanced to multivariate and multi-trait analyses. Four factors (compulsive, psychotic, dysregulated, internalizing) explained 82.8% of shared genetic variance. BD1 and BD2 loaded on distinct factors despite a high genetic correlation; 87.0% of common-factor loci were significant in neither subtype. Unipolar mania aligned with psychosis over internalizing, and was distinguishable from BD1, and rapid cycling showed heritable cross-domain liability. We identified 356 risk loci, 158 novel, including the first univariate-GWAS associations for psychosis, unipolar mania, rapid cycling and schizoaffective disorder-and 249 credible genes (89 high-confidence), 12 with approved-drug or clinical-phase annotations. Cell-type association showed a midbrain dopaminergic-GABAergic gradient along the psychotic factor. BD's genetic architecture appears hierarchical-a general liability resolving into dimensions of course and comorbidity, beyond subtypes.

Journal Article↗

PubMed2026

Genetically Predicted Gene Expression and Circulating Metabolites Associated with Cervical High-Grade Squamous Intraepithelial Lesion: A Mendelian Randomization Study.

BACKGROUND: High-grade squamous intraepithelial lesion (HSIL) is a precancerous condition of the cervix. Identifying risk factors associated with HSIL and understanding their potential mechanisms may inform prevention strategies. This study aimed to investigate the associations of genetically predicted gene expression and circulating metabolites with HSIL risk using Mendelian randomization (MR). METHODS: We performed two-sample MR analysis to evaluate the associations of genetically predicted gene expression (eQTLGen consortium, N=31,684) and circulating metabolites (genome-wide association study [GWAS], N=8,299) with HSIL risk (FinnGen R12, N=293,218; 8,291 cases). Mediation analysis was conducted to explore whether metabolites might mediate the associations between genes and HSIL. Sensitivity analyses, including Mendelian randomization pleiotropy residual sum and outlier (MR-PRESSO), leave-one-out, and colocalization, were performed to assess the robustness of the findings. All GWAS data used in this study were derived from European-ancestry populations. RESULTS: Eleven genes showed significant associations with HSIL after false discovery rate (FDR) correction (q<0.05), including VWA7, PAX8, GUSBP1, IKZF3, PAX8-AS1, NFKBIL1 (interpret with caution due to an influential single nucleotide polymorphism [SNP]), ERBB2, COL11A2, SKIV2L, TCF19, and PGAP3. Eleven circulating metabolites were also significantly associated with HSIL. Mediation analysis suggested that two phospholipid metabolites (GCST90200685 and GCST90200692) might mediate a small proportion of the total protective association of COL11A2 with HSIL (1.46% and 1.45%, respectively), indicating that the protective association of COL11A2 is largely independent of these circulating metabolites. Colocalization analysis showed strong evidence of shared causal variants for eight genes (PP.H4>0.98), while COL11A2 showed weak evidence of colocalization (PP.H4=1.58×10-15). Functional enrichment analysis indicated that COL11A2-related genes were enriched in extracellular matrix (ECM)-receptor interaction and PI3K-Akt signaling pathways. CONCLUSION: This MR study identified 11 genes and 11 circulating metabolites associated with HSIL risk. Among these, COL11A2 showed a protective association that appeared to be largely independent of circulating phospholipid metabolites, suggesting potential local mechanisms. These findings provide genetic and metabolic clues for future studies on HSIL etiology.

COL11A2↗

PubMed2026

Causal Relationships Between Modifiable Risk Factors and Gastroesophageal Reflux Disease: A Two-Sample Mendelian Randomization Study.

INTRODUCTION: Gastroesophageal reflux disease (GERD) is a prevalent digestive disorder, yet the causal roles of modifiable risk factors remain unclear. This study aims to investigate the causal relationships between 28 modifiable risk factors (including obesity traits, mental health disorders, sleep traits, metabolic comorbidities, and serum parameters) and GERD using two-sample Mendelian randomization (MR). Gastroesophageal reflux disease (GERD). Our findings aim to inform targeted prevention and treatment strategies for GERD. METHODS: This study obtained data from extensive genome-wide association studies (GWAS). Pooled data associated with gastroesophageal reflux associations were obtained from the 23andMe Research team's research, which included a total of 129,080 cases of gastroesophageal reflux and 473,524 controls of European ancestry. We conducted a univariable Mendelian randomization (MR) analysis to ascertain whether genetic evidence of exposure demonstrated a statistically significant association with the risk of GERD. Subsequently, a multivariable MR analysis was carried out to estimate the independent effects of the exposures on GERD. RESULTS: Univariable MR analysis utilizing extensive GWAS data suggested that genetic factors such as BMI, Waist circumference, Arm fat mass (left and right), Leg fat mass (left and right), Attention Deficit and Hyperactivity Disorder (ADHD), Major Depressive Disorder (MDD), Schizophrenia, Negative emotions (including nervousness, anxiety, tension, or depression), Insomnia, Sleep apnea syndrome, Sleep duration, and Snoring, as well as Total cholesterol levels and Apolipoprotein B levels, are associated with the development of GERD. Multivariate Mendelian randomization of BMI and Negative emotion as correction factors showed that Waist circumference, Arm fat mass (left and right), Leg fat mass (left and right), ADHD, Insomnia, Sleep apnea syndrome, and Snoring were associated with an increased risk of GERD (p< 0.05). Conversely, longer sleep duration was associated with a reduced risk of GERD (p< 0.05). DISCUSSION: This MR study reveals novel causal mechanisms in GERD pathogenesis: (1) Peripheral adiposity (arm/leg fat mass) exerts independent effects beyond central obesity, indicating site-specific fat distribution significance; (2) ADHD emerges as a distinct psychiatric risk factor independent of mental disorders; (3) Sleep apnea operates through BMI-independent pathways. Collectively, these findings redefine GERD pathophysiology, highlighting fat depot specificity and brain-gut interactions as critical mechanistic drivers. CONCLUSION: Overall, our findings suggest that multiple risk factors are associated with the risk of GERD. These results provide a theoretical basis for controlling body weight and plasticity, improving sleep habits, and preventing and timely seeking medical attention to reduce the occurrence of psychiatric disorders, which will be important strategies to prevent and alleviate GERD.

Humans↗

PubMed2026

Genetic Association Between Sleep Traits and Vertigo Risk: A Two-sample Bidirectional Mendelian Randomization Study.

BACKGROUND: Observational studies suggest the potential association between sleep traits and vertigo; however, causal evidence remains limited. OBJECTIVE: This study aimed to explore the relationship between genetically predicted sleep traits and vertigo with the Mendelian randomization (MR) method. METHODS: Instrumental variables for sleep traits (snoring, sleep duration, insomnia, daytime sleepiness, daytime napping, and chronotype) were adopted from genomewide association studies (GWAS) data of European ancestry from UK Biobank. The summary-level datasets of vertigo were retrieved from the GWAS of FinnGen. Inversevariance weighted (IVW) method was adopted as the main analysis. RESULTS: IVW analysis revealed a significant association between genetically predicted daytime napping (OR = 1.51, 95% CI =1.08-2.12, P = 0.016) and chronotype (OR = 1.13, 95% CI =1.01-1.26, P = 0.033), both of which were associated with an increased risk of vertigo. However, we did not find evidence for a causal effect of snoring, overall sleep duration, long sleep duration, short sleep duration, insomnia, and excessive daytime sleepiness on vertigo. No reverse causality was detected. CONCLUSION: Our findings suggest that abnormal sleep patterns may serve as risk factors for vertigo disorders and offer opportunities for the prevention and management of vertigo disorders.

Humans↗

PubMed2026

Assessing the genetic potential of a milk mid-infrared prediction of heat stress response in dairy cows using a temperature-humidity index-independent approach.

Selection for heat tolerance remains challenging due to the difficulty of accessing reliable phenotypes at large scale. An alternative could be established using mid-infrared spectra, which are collected routinely through milk recording, and have already shown their value as proxies for a variety of phenotypes that are costly or difficult to measure. Recently, a first prediction of heat stress response in dairy cows based solely on milk mid-infrared spectra was developed. This prediction was obtained using models calibrated on surface body temperature and milk composition variations. Its potential as a detection tool was explored, but no genetic analyses has been performed. On this basis, the objectives of this study were to estimate the heritability of the predicted heat stress response, assess its genetic correlations with traits from the Walloon official genetic evaluation, and identify genomic regions associated with heat tolerance through a GWAS, all without using temperature-humidity index (THI) information. The estimated heritability (0.10) was low but sufficient to enable genetic selection and consistent with expectations for a heat stress-related trait. Also as expected, an antagonistic relationship between heat tolerance and milk production was observed, but its extent was notably reduced compared with traditional approaches. In addition, genetic correlations with other traits were neutral (fat yield) or favorable (protein yield, SCS, fertility, longevity). Concerning the GWAS, genomic regions and candidate genes previously associated with the response to heat stress were highlighted, as well as others related to energy balance maintenance. Overall, these results support the relevance of the prediction for the heat stress response as a new phenotype for heat tolerance selection that does not require any THI information. They also reinforce the importance of energy balance for dairy cows to cope with heat stress.

Journal Article↗

PubMed2026

Evaluation of population-specific polygenic risk scores for blood lipids: insights from Taiwanese cohorts and multiancestry meta-analysis.

BACKGROUND: Blood lipids are heritable risk factors for cardiovascular disease (CVD), a leading cause of mortality worldwide. However, the genetic architecture of lipid traits and the performance of polygenic risk scores (PRSs) remain underexplored in East Asian (EAS) populations, including Taiwanese Han individuals. METHODS: We conducted genome-wide association studies and PRS analyses for five lipid traits: total cholesterol, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, triglycerides, and the ratio of low-density lipoprotein cholesterol to total cholesterol. Lipid profile data were obtained from the China Medical University Hospital cohort. PRSs were evaluated on the basis of their correlations with measured lipid levels. To evaluate trans-ancestry PRS transferability, localized models were systematically compared against models derived from discovery-stage GWAS meta-analyses incorporating five ancestry groups from the Global Lipids Genetics Consortium. The performance of the PRS models in predicting lipid-related diseases was evaluated through receiver operating characteristic curve analyses. RESULTS: The population-specific PRS models explained 11%-40% of the variance in lipid levels within the target cohort. Models leveraging global multiancestry GWAS meta-analysis weights revealed limited predictive performance (r 2 = 0.04-0.19), whereas analyses incorporating EAS-specific data yielded higher correlations (r 2 = 0.13-0.30), although these correlations did not exceed those derived from the hospital-based cohort alone. When combined with age and sex, the PRS models demonstrated strong predictive performance for coronary artery disease, atherosclerosis, and ischemic stroke, with area under the curve values of 0.910, 0.926, and 0.854, respectively. CONCLUSION: Population-specific PRS models derived from a Taiwanese population outperformed meta-analysis-derived frameworks in predicting lipid levels and demonstrated substantial potential for predicting CVD risk, indicating the importance of ancestry-matched genetic studies in precision medicine.

LDL-C/TC ratio↗

PubMed2026

Freely available genomic datasets for atrial fibrillation research: current resources and analytical pipeline.

Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia, characterized by clinical and genetic heterogeneity. Increasing use of genomics and other omics approaches has driven reliance on publicly available AF datasets to advance biological discovery. Thus, this systematic review aimed to identify freely available genomic AF datasets through Mendeley Data and its interconnected repositories, and to characterize the most common analyses performed on these data. The search was conducted in adherence to the PRISMA 2020 guideline. Nineteen freely available genomic AF datasets were identified: Summary statistics for 'Biobank-driven genomic discovery yields new insight into atrial fibrillation biology', hum0014.v8.58qt.v1, AF GWAS in UK Biobank, UK Biobank (Publication 9659), GWAS summary statistics from a 2025 multi-ancestry AF meta-analysis, GSE115574, GSE128188, GSE14975, GSE2240, GSE238242, GSE254133, GSE261170, GSE271748, GSE271839, GSE293813, GSE294456, GSE31821, GSE41177, and GSE79768. The GEO datasets were further examined using differential gene expression, functional enrichment, protein-protein interaction networks, hub gene analysis, microRNA target prediction, and gene clustering, as well as, for the more recently deposited datasets, eQTL colocalization, single-cell/single-nucleus clustering, cell-cell communication analysis, and gene-dosage-dependent transcriptional and electrophysiological profiling. These analyses show some consistency but also considerable heterogeneity in initial conditions, data normalization, and analytical methodological settings. In conclusion, only a limited number of datasets are freely available, so additional, well-characterized and standardized datasets are needed to provide a complete picture of the AF pathology.

Mendeley Data↗

PubMed2026

Integrative cross-tissue transcriptome-wide association and metabolomic analysis reveals novel genetic risk loci for aortic aneurysm.

BACKGROUND: Aortic aneurysm (AA) is a life-threatening cardiovascular condition with a strong genetic component, however, its molecular mechanisms remain poorly understood. Although genome-wide association studies (GWAS) have identified numerous risk loci, most prior studies have investigated genetic and metabolic factors separately, leaving the causal pathways from genetic variants to disease largely unexplored. METHODS: We established an integrative framework combining cross-tissue transcriptome-wide association studies (TWAS) with metabolomic mediation analysis. First, we integrated GWAS data from FinnGen R12 with multi-tissue expression quantitative trait loci (eQTL) data from Genotype-Tissue Expression Project (GTEx) V8, then performed cross-tissue TWAS using the Unified Test for MOlecular SignaTures (UTMOST) and single-tissue validation with the Functional Summary-based Imputation (FUSION) to prioritize susceptibility genes. Second, we applied Mendelian randomization (MR), colocalization, and Fine-mapping Of CaUsal gene Sets (FOCUS) to assess causality and identify high-confidence genes. Third, we performed metabolite mediation analysis to uncover metabolic pathways linking genetic variants to disease risk. Finally, we validated key findings in mouse models of thoracic aortic aneurysm (TAA) and abdominal aortic aneurysm (AAA) using Quantitative Real-Time Reverse Transcription Polymerase Chain Reaction (RT-qPCR) and Western blotting. RESULTS: We identified multiple novel susceptibility genes for AA and its subtypes. Key genes included ADH family members (ADH1A, ADH1B, ADH4, ADH6) and ZNF827, which showed cross-subtype associations with strong colocalization evidence in vascular tissues. Metabolite mediation analysis revealed significant pathways involving N-acetylphenylalanine and methionine sulfoxide. Functional enrichment revealed distinct biological mechanisms: AA and AAA were primarily associated with metabolic pathways, whereas TAA-related genes were enriched in developmental and contractile processes. PheWAS indicated no significant off-target associations. Critically, experimental validation in mouse models confirmed significant upregulation of ZNF827 in TAA and ADH6 in AAA at both mRNA and protein levels, corroborating the genetic predictions. CONCLUSION: This integrated cross-omics analysis identifies novel genetic loci and, crucially, uncovers specific nutrient-related metabolic pathways that mediate genetic risk. These findings provide a mechanistic basis for future nutritional and metabolic intervention studies in AA and its subtypes.

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