medRxiv : the preprint server for health sciences最新文献

The contribution of common tandem repeats (TR) variants to common, complex disease remains unknown, especially in populations historically underrepresented in genetic research. We identified common TR variants associated with risk of primary open-angle glaucoma (POAG) in individuals of African ancestry. The POAG-associated TR variants were predominantly found at Alu poly(A) tail elements, regions, retinal development enhancers, and harbor binding sites of a POAG-associated transcription factor, LMX1B, suggesting a convergent mechanism of how common TR variation arises and contributes to POAG pathophysiology.

Most genetic variants associated with complex traits and diseases occur in non-coding genomic regions and are hypothesized to regulate gene expression. To understand the genetics underlying gene expression variability, we characterize 14,324 ancestrally diverse RNA-sequencing samples from the NHLBI Trans-Omics for Precision Medicine (TOPMed) program and integrate whole genome sequencing data to perform cis and trans expression and splicing quantitative trait locus ( cis -/trans-e/sQTL) analyses in six tissues and cell types, most notably whole blood (N=6,454) and lung (N=1,291). We show this dataset enables greater detection of secondary cis-e/sQTL signals than was achieved in previous studies, and that secondary cis-eQTL and primary trans-eQTL signal discovery is not saturated even though eGene discovery is. Most TOPMed trans-eQTL signals colocalize with cis-e/sQTL signals, suggesting many trans signals are mediated by cis signals. We fine-map European UK BioBank GWAS signals from 164 traits and colocalize the resulting 34,107 fine-mapped GWAS signals with TOPMed e/sQTL signals, finding that of 10,611 GWAS signals with a colocalization, 7,096 GWAS signals colocalize with at least one secondary e/sQTL signal. These results demonstrate that larger e/sQTL analyses will continue to uncover secondary e/sQTL signals, and that these new signals will benefit GWAS interpretation.

The genomic landscape of the Indian population, particularly for age-related disorders like Parkinson's disease (PD) remains underrepresented in global research. Genetic variability in PD has been studied predominantly in European populations, offering limited insights into its role within the Indian population. To address this gap, we conducted the first pan-India genomic survey of PD involving 4,806 cases and 6,364 controls, complemented by a meta-analysis integrating summary statistics from a multi-ancestry PD meta-analysis (N=611,485). We further leveraged RNA-sequencing data from lymphoblastoid cell lines of 731 individuals from the 1000 Genomes project to evaluate the expression of key loci across global populations. Our findings reveal a higher genetic burden of PD in the Indian population compared to Europeans, accounting for ∼30% of the previously unexplained heritability. Thirteen genome-wide significant loci were identified, including two novel loci, with an additional three loci uncovered through meta-analysis. Polygenic risk score analysis showed moderate transferability from European populations. Our results highlight the importance of genetic loci in immune function, lipid metabolism and SNCA aggregation in PD pathogenesis, with gene expression variability emphasizing population-specific differences. We also established South Asia's largest PD biobank, providing a foundation for patient-centric approaches to PD research and treatment in India.

Background: Obesity is a major risk factor for endometrial cancer, but it is unknown whether it impacts the association between genetic risk and endometrial cancer. We incorporated polygenic risk score and epidemiological risk factors in the prediction of and investigated associations of BMI and polygenic risk score with endometrial cancer risk.

Methods: We generated polygenic risk score for endometrial cancer in 129,829 unrelated female participants of European ancestry (including 956 incident cases with endometrial cancer) in the UK Biobank and predicted endometrial cancer using endometrial cancer polygenic risk score and established epidemiological risk factors, including BMI. We evaluated the performance of endometrial cancer prediction models by odds ratios and area under the receiver operating characteristic curves (AUCs) to using logistic regression. Individual and joint associations of BMI and polygenic risk score with endometrial cancer were assessed using Cox proportional hazards models.

Results: An integrated model incorporating both polygenic risk score and epidemiological risk factors achieved a modest, but statistically significant, improvement in predicting endometrial cancer status compared with the model that included epidemiologic risk factors alone (AUC = 0.74 versus 0.73; P = 3.98 × 10 ^-5 ). Obese participants (BMI ≥ 30 kg/m ² ) in the top polygenic risk tertile had the highest endometrial cancer risk. We observed independent effects of genetic risk and BMI on endometrial cancer risk.

Conclusion: Integrating polygenic risk score with epidemiological risk factors may offer insights into population stratification for endometrial cancer susceptibility. Higher endometrial cancer polygenic risk is associated with endometrial cancer, irrespective of BMI.

Objective: Identify microbial and microbiota-associated metabolites in monozygotic (MZ) and dizygotic (DZ) twins discordant for type 1 diabetes (T1D) to gain insight into potential environmental factors that may influence T1D.

Research design and methods: Serum samples from 39 twins discordant for T1D were analyzed using a semi-targeted metabolomics approach via liquid chromatography-high-resolution tandem mass spectrometry (LC-HRMS/MS). Statistical analyses identified significant metabolites (p < 0.1) within three groups: All twins (combined group), MZ twins, and DZ twins.

Results: Thirteen metabolites were identified as significant. 3-indoxyl sulfate and 5-hydroxyindole were significantly reduced in T1D individuals across all groups. Carnitine was reduced, and threonine, muramic acid, and 2-oxobutyric acid were significantly elevated in both All and MZ groups. Allantoin was significantly reduced and 3-methylhistidine was significantly elevated in All and DZ groups.

Conclusions: Metabolite dysregulation associated with gut dysbiosis was observed. However, further validation of our findings in a larger cohort is needed.

Article highlights: Why did we undertake this study? We believed this cohort of twins discordant for type 1 diabetes (T1D) would allow for control over genetic variability to examine environmental factors.What is the specific question(s) we wanted to answer? We aimed to identify differences in microbial and microbiota-associated metabolites in twins discordant for T1D to examine the effect of the gut microbiome on T1D.What did we find? Thirteen metabolites were identified as significantly different.What are the implications of our findings? Our results show the dysregulation of several microbial metabolites in twin pairs, suggesting that the gut microbiome plays a role in the pathogenesis of T1D.

Identification of drug-repurposing targets with genetic and biological support is an economically and temporally efficient strategy for improving treatment of diseases. We employed a cross-disciplinary approach to identify potential treatments for metabolic dysfunction associated steatotic liver disease (MASLD) using humans as a model organism. We identified 212 putative causal genes associated with MASLD using data from a large multi-ancestry genetic association study, of which 158 (74.5%) are novel. From this set we identified 57 genes that encode for druggable protein targets, and where the effects of increasing genetically predicted gene expression on MASLD risk align with the function of that drug on the protein target. These potential targets were then evaluated for evidence of efficacy using Mendelian randomization, pathway analysis, and protein structural modeling. Using these approaches, we present compelling evidence to suggest activation of FADS1 by icosopent ethyl as well as S1PR2 by fingolimod could be promising therapeutic strategies for MASLD.

Classified as a critical public health threat by the World Health Organization, Cryptococcus neo-formans infections with significant morbidity and mortality. Reports of cryptococcosis persistence, relapse, and reinfection date back to the 1950s, yet the factors driving chronic infections remain poorly understood. A major challenge is the scarcity of serial patient specimens and detailed medical records to study the simultaneous evolution of the pathogen and host health status. This study provides the first genomic and phenotypic analysis of in-host evolution of C. neoformans during chronic infections lasting over a year in six immunocompromised patients. We find fungal genome evolution during persistent infection is characterized by large-scale genome restructuring and increasing genomic heterogeneity. Phenotypic changes show diversification in virulence traits and antifungal susceptibility. Genotypically and phenotypically distinct sub-lineages arise and co-persist within the same tissues, consistent with a model of diversifying selection and niche partitioning in the complex environment of human hosts.

Background: In Alzheimer's disease (AD) clinical trials, efficacy inference is traditionally based on the last visit (e.g., 18 months). However, recent studies suggest that disease-modifying treatment effects may emerge as early as 3 months post-baseline.

Objective: To explore this further, our study aimed to assess the increased statistical power achieved by incorporating all or multiple post-baseline visits to estimate treatment effect, compared to relying solely on the last visit.

Methods: We developed explicit formulas for the base functions of the natural cubic spline model, ensuring compatibility with standard SAS procedures. Through simulations using disease progression trajectories from ClarityAD and TRAILBLAZER-ALZ2 trials, we comprehensively evaluated various models in terms of power and type I error. Additionally, we offer SAS codes that to facilitate seamless implementation of different modeling approaches.

Results: Simulations based on ClarityAD and TRAILBLAZER-ALZ2 disease trajectories demonstrated that models incorporating multiple or all post-baseline visits yield greater power than those using only the last visit, while maintaining Type I error control. Furthermore, when three post-baseline visits were included, adding more visits resulted in minimal power gains.

Conclusions: Our findings support prioritizing statistical models that incorporate multiple or all post-baseline visits for treatment efficacy inference, as they offer greater efficiency than models relying solely on the last visit.

Understanding the disease trajectories of specific diseases can provide important clinical insights. In this paper, we aimed to discover signature disease trajectories of 3 rare cancer types: pancreatic cancer, soft tissue sarcoma (STS) of the trunk and extremity (STS-TE), and STS of the abdomen and retroperitoneum (STS-AR), leveraging IQVIA Oncology Electronic Medical Record. We identified significant diagnosis pairs in patients with these cancers through matched cohort sampling, statistical computation, right-tailed binomial hypothesis test, and visualized trajectories up to 3 progressions. Results included 266 significant diagnosis pairs for pancreatic cancer, 130 for STS-TE, and 118 for STS-AR. We further found 44 2-hop (i.e., 2- progression) and 136 3-hop trajectories before pancreatic cancer, 36 2-hop and 37 3-hop trajectories before STS-TE, and 17 2-hop and 5 3-hop trajectories before STS-AR. Meanwhile, we found 54 2-hop and 129 3-hop trajectories following pancreatic cancer, 11 2-hop and 17 3- hop trajectories following STS-TE, 5 2-hop and 0 3-hop trajectories following STS-AR. Systematic validation of discovered trajectories with the UTHealth Electronic Health Records confirmed the feasibility and reliability of our method. Our result suggested that some key clinical features can potentially serve as early markers of rare cancers. This approach is generalizable to other disease types and real-world longitudinal patient records.