Genome-wide Association Studies Data Research Articles

Systematic genome-wide Mendelian randomization reveals the causal links between miRNAs and Parkinson’s disease

BackgroundMicroRNAs (miRNAs) have pivotal roles in gene regulation. Circulating miRNAs have been developed as novel candidate non-invasive biomarkers for diagnosis, prognosis, and treatment response for diseases. However, miRNAs that have causal effects on Parkinson’s Disease (PD) remain largely unknown. To investigate the causal relationships between miRNAs and PD, here we conduct a Mendelian randomization (MR) study.MethodsThis study utilized the summary-level data of respective genome-wide association studies (GWAS) for 2083 miRNAs and seven PD-related outcomes to comprehensively reveal the causal associations between the circulating miRNAs and PD. Two-sample MR design was deployed and the causal effects were estimated with inverse variance weighted, MR-Egger, and weighted median. Comprehensively sensitive analyses were followed, including Cochran’s Q test, MR-Egger intercept test, MR-PRESSO, and leave-one-out analysis, to validate the robustness of our results. Finally, we investigated the potential role of the MR significant miRNAs by predicting their target genes and functional enrichment analysis.ResultsInverse variance weighted estimates suggested that two miRNAs, miR-205-5p (β = −0.46, 95%CI: −0.690 to −0.229, p = 9.3 × 10−5) and miR-6800-5p (β = −0.389, 95%CI: −0.575 to −0.202, p = 4.32 × 10−5), significantly decreased the rate of cognitive decline among PD patients. In addition, eight miRNAs were nominally associated with more than three PD-related outcomes each. No significant heterogeneity of instrumental variables or horizontal pleiotropy was found. Gene Ontology (GO) analysis showed that the targets of these causal miRNAs were significantly enriched in cell cycle, apoptotic, and aging pathways.ConclusionThis MR study identified two miRNAs whose genetically regulated expression might have a causal role in the development of PD dementia. Our findings provided potential miRNA biomarkers to make better and early diagnoses and risk assessments of PD.

Exploring the casual association between gut microbiome, circulating inflammatory cytokines and chronic pancreatitis: A Mendelian randomization analysis.

It has been established that gut dysbiosis contributed to the pathogenesis of digestive disorders. We aimed to explore the causal relationships between intestinal microbiota, circulating inflammatory cytokines and chronic pancreatitis (CP). Summary statistics of genome-wide association studies (GWAS) of intestinal microbiome was retrieved from the MiBioGen study and the GWAS data of 91 circulating inflammatory cytokines and CP were obtained from the GWAS catalog. The 2-sample bidirectional Mendelian randomization (MR) analysis was performed between gut microbiota, circulating inflammatory cytokines and CP, in which the inverse variance weighted (IVW) method was regarded as the primary analysis approach. To prove the reliability of the causal estimations, multiple sensitivity analyses were utilized. IVW results revealed that genetically predicted 2 genera, including Sellimonas and Eubacteriumventriosumgroup, and plasm C-C motif chemokine 23 (CCL23) level were positively associated with CP risk, while genus Escherichia Shigella, Eubacteriumruminantiumgroup and Prevotella9, and plasma Caspase 8, Adenosine Deaminase (ADA), and SIR2-like protein 2 (SIRT2) level, demonstrated an ameliorative effect on CP. Leave-one-out analysis confirmed the robustness of the aforementioned causal effects and no significant horizontal pleiotropy or heterogeneity of the instrumental variables was detected. However, no association was found from the identified genera to the CP-related circulating inflammatory cytokines. Besides, the reverse MR analysis demonstrated no causal relationship from CP to the identified genera and circulating inflammatory cytokines. Taken together, our comprehensive analyses offer evidence in favor of the estimated causal connections from the 5 genus-level microbial taxa and 4 circulating inflammatory cytokines to CP risk, which may help to reveal the underlying pathogenesis of CP.

Causal relationship between hypertension and risk of constipation: A 2-way 2-sample Mendelian randomization study.

Patients with hypertension have a higher risk of having constipation and vice versa. The causal association between these 2 variables is not proven. We performed a retrospective Mendelian randomization analysis to determine the causal association between constipation and hypertension. Two-sample 2-way Mendelian randomization analysis was used. Genetic variants for constipation were derived from genome-wide association study data of European origin (15,902 cases and 395,721 controls). Corresponding genetic associations for hypertension were derived from European ancestry GWAS data (54,358 cases and 408,652 controls). Genetic susceptibility to hypertension was associated with an increased risk of constipation (OR: 3.459, 95% CI: 1.820-6.573, P < .001). In an inverse Mendelian randomization analysis, no causal effect of constipation on hypertension was found (OR: 0.999, 95% CI: 0.987-1.011, P = .834). In sensitivity analyses, these associations persisted and no multiple effects were found. This study suggests that there is a causal relationship between hypertension and constipation and that hypertension may increase the risk of developing constipation.

The possible causal relationship between COVID-19 and imaging markers of cerebral small vessel disease: a Mendelian randomization study

ABSTRACT Objectives Observational studies have suggested that SARS-CoV-2 infection may increase the burden of cerebral small vessel disease (CSVD). This study aims to explore the causal correlation between COVID-19 and the imaging markers of CSVD using Mendelian randomization (MR) methods. Methods Summary-level genome-wide association study (GWAS) statistics for COVID-19 susceptibility, hospitalization, and severity were utilized as proxies for exposure. Large-scale meta-analysis GWAS data on three neuroimaging markers of white matter hyperintensity, lacunar stroke, and brain microbleeds, were employed as outcomes. Our primary MR analysis employed the inverse variance weighted (IVW) approach, supplemented by MR-Egger, weighted median, and MR-PRESSO methods. We also conducted multivariable MR analysis to address confounding bias and validate the robustness of the established causal estimates. Comprehensive sensitivity analyses included Cochran’s Q test, Egger-intercept analysis, MR-PRESSO, and leave-one-out analysis. Results The MR analysis revealed a significant causal correlation between the severity of COVID-19 and an increased risk of lacunar stroke, as demonstrated by the IVW method (ORivw = 1.08, 95% CI: 1.03–1.16, pivw = 0.005, FDR = 0.047). Nevertheless, no causal correlations were observed between COVID-19 susceptibility or hospitalization and any CSVD imaging markers. The robustness and stability of these findings were further confirmed by multivariable MR analysis and comprehensive sensitivity analyses. Discussion This study provides compelling evidence of a potential causal effect of severe COVID-19 on the incidence of lacunar stroke, which may bring fresh insights into the understanding of the comorbidity between COVID-19 and CSVD.

Relationships between orofacial pain and sleep: Analysis of UK biobank and genome-wide association studies data

Relationships between orofacial pain and sleep: Analysis of UK biobank and genome-wide association studies data

Association between adding salt in food and dementia in European descent: A mendelian randomization study.

High salt intake has been proposed as a risk factor for dementia. However, causal relationship between salt intake and dementia remains uncertain. The aim of this study was to employ a mendelian randomization (MR) design to investigate the causal impact of salt intake on the risk of dementia. Genome-wide association study (GWAS) data of exposures and outcomes (any dementia, cognitive performance, different types of dementia, Alzheimer's disease [AD], and Parkinson's disease) were obtained from the IEU database. MR estimates were generated though inverse-variance weighted model. MR-Egger, weighted median, and MR-Pleiotropy Residual Sum and Outlier (MR-PRESSO) method also used in our study. Sensitivity analyses included Cochran's Q test, MR-Egger intercept, MR-PRESSO global test and outlier test, leave-one-out analysis, and funnel plot assessment. Our MR analysis provided evidence of a causal association between high salt added to food and dementia (odds ratio [OR]=1.73, 95% confidence interval [CI]: 1.21-2.49, and p=.003), dementia in AD (OR=2.10, 95% CI: 1.15-3.83, and p=.015), and undefined dementia (OR=2.61, 95% CI: 1.26-5.39, and p=.009). Higher salt added was also associated with increased risk of AD (OR=1.80, 95% CI: 1.12-2.87, and p=.014) and lower cognitive performance (β=-.133, 95% CI: -.229 to -.038, and p=.006). This study provides evidence suggesting that high salt intake is causally associated with an increased risk of developing dementia, including AD and undefined dementia, highlighting the potential importance of reducing salt consumption as a preventive measure.

Genetic causal relationship between multiple immune cell phenotypes and Parkinson's disease: a two sample bidirectional Mendelian randomization study.

The exact etiology of Parkinson's disease (PD), a degenerative disease of the central nervous system, is unclear. It is currently believed that its main pathological basis is a decrease in dopamine concentration in the striatum of the brain. Although many researchers have previously focused on the critical role of the immune response in PD, there has been a lack of valid genetic evidence for a causal association between specific immune cell traits and phenotypes and PD. We employed Mendelian randomisation (MR) as an analytical method to effectively assess genetic associations between exposure and outcome. Based on the largest genome-wide association study (GWAS) data to date, causal associations between multiple immune cell phenotypes and PD were validly assessed by using single nucleotide polymorphisms (SNPs), which are randomly assigned and not subject to any causality. By testing 731 immune cell phenotypes and their association with PD, the results of IVW analysis suggested that some phenotypes were considered to have a causal effect on PD(P＜0.05) . In addition, PD could have an effect on certain immunophenotypes located on Myeloid cell panel, Monocyte panel, the specific immunophenotypic results and statistical analysis values are shown in the text. The results of sensitivity analyses suggested that none of them observed the presence of horizontal pleiotropy. Our study identified a close link between immune cells and PD, and the results of this study provide ideas for the study of the immune mechanism of PD and the exploration of effective therapeutic means.

Obesity and Hypertension Mediate the Effect of Education on Deep Intracerebral Hemorrhage: A Mendelian Randomization Study.

Educational attainment (EA) as a stable indicator of socioeconomic status has been confirmed to affect intracerebral hemorrhage (ICH), but the mechanism relating EA and ICH is still unknown. To explore the causal relationship between EA and ICH through a bidirectional and two-step Mendelian randomization (MR) study. Using summary-level Genome-wide Association Study (GWAS) data based from European ancestry, we performed bidirectional and two-step MR analyses to explore the causal relationship between educational attainment and ICH to understand the mediating influence of risk factors in this process. We also carried out subgroup analysis according to the different sites (deep and lobar) of ICH. A set of sensitivity analyses were performed to test valid MR assumptions. Bidirectional MR analysis consistently demonstrated a unidirectional causal effect, revealing that higher EA had a protective influence on ICH. Each additional 1-standard deviation (SD) increase in genetically predicted years of schooling was associated with a reduced risk of all ICH (inverse variance weighted (IVW) OR: 0.381 [95%CI: 0.264-0.549]), deep ICH (OR: 0.334 [95%CI: 0.216-0.517]), and lobar ICH (OR: 0.422 [95%CI: 0.261-0.682]). The mediating effect of EA on all ICH was mediated via systolic blood pressure (SBP) (6.93% [1.20-13.45%]) and body mass index (BMI) (17.87% [3.92-34.64%]), and the mediating effect of EA on deep ICH was also mediated via SBP (7.85% [1.55-15.07%]) and BMI (18.63% [4.02-36.26%]). This study provides robust genetic evidence for supporting the protective effect of EA on ICH risk, with further evidence that the effect of EA on deep ICH is partially mediated through hypertension and obesity. Further validation is needed to ascertain whether these findings are applicable to other racial or general population groups.

Roles of blood metabolites in mediating the relationship between vitiligo and autoimmune diseases: Evidence from a Mendelian randomization study

ObjectiveThis study employed Mendelian Randomization (MR) to investigate the causal relationship between genetic susceptibility to vitiligo and the risk of various autoimmune diseases, along with the mediating role of blood metabolites. MethodsWe performed two-sample MR analyses using aggregated genome-wide association studies (GWAS) data on 486 blood metabolites, vitiligo, and nine autoimmune diseases to investigate blood metabolites' causal effects on the susceptibility of vitiligo and the associations of vitiligo with nine autoimmune comorbidities. We also applied multivariable MR to unravel metabolites by which vitiligo influences the pathogenesis of autoimmune diseases. ResultsOur findings indicate that vitiligo amplified the risk of several autoimmune diseases, including rheumatoid arthritis (OR 1.17; 95 % CI 1.08–1.27), psoriasis (OR 1.10; 95 % CI 1.04–1.17), type 1 diabetes (OR 1.41; 95 % CI 1.23–1.63), pernicious anemia (OR 1.23; 95 % CI 1.12–1.36), autoimmune hypothyroidism (OR 1.19; 95 % CI 1.11–1.26), alopecia areata (OR 1.22; 95 % CI 1.10–1.35), and autoimmune Addison's disease (OR 1.22; 95 % CI 1.12–1.33). Additionally, our analysis identified correlations with vitiligo for 14 known (nine risk, five protective) and seven uncharacterized serum metabolites. After adjusting for genetically predicted levels of histidine and pyruvate, the associations between vitiligo and these diseases were attenuated. ConclusionsWe substantiated vitiligo's influence on susceptibility to seven autoimmune diseases and conducted a thorough investigation of serum metabolites correlated with vitiligo. Histidine and pyruvate are potential mediators of vitiligo associated with autoimmune diseases.By combining metabolomics with genomics, we provide new perspectives on the etiology of vitiligo and its immune comorbidities.

Differential ischemic stroke risk linked to novel subtypes of type 2 diabetes: insights from a Mendelian randomization analysis.

This study employs a two-sample Mendelian randomization (MR) approach to investigate the variation in ischemic stroke risk across novel subtypes of adult-onset type 2 diabetes. Leveraging pooled genome-wide association study (GWAS) data from the Swedish ANDIS cohort, we explored the association of four newly identified type 2 diabetes subtypes-severe insulin-deficient diabetes (SIDD), severe insulin-resistant diabetes (SIRD), mild obesity-related diabetes (MOD), and mild age-related diabetes (MARD)-with ischemic stroke risk. The outcome data for ischemic stroke and its three subtypes (large artery, cardioembolic, and small vessel stroke) were sourced from the MEGASTROKE Consortium. Our analysis applied multiple MR methods, focusing on the inverse-variance weighted (IVW) technique, complemented by thorough sensitivity analyses to examine heterogeneity and potential horizontal pleiotropy. Our findings reveal a significant causal relationship between the SIDD subtype and small vessel stroke (OR = 1.06, 95% CI: 1.01-1.11, p = 0.025), while no causal associations were observed for SIRD with any stroke subtype. MOD was causally linked to small vessel stroke (OR = 1.07, 95% CI: 1.02-1.12, p = 0.004) and large artery stroke (OR = 1.07, 95% CI: 1.01-1.13, p = 0.015). Similarly, MARD showed a causal relationship with small vessel stroke (OR = 1.09, 95% CI: 1.03-1.16, p = 0.006) and overall ischemic stroke risk (OR = 1.04, 95% CI: 1.01-1.08, p = 0.010). Our study highlights distinct causal links between specific type 2 diabetes subtypes and ischemic stroke risks, emphasizing the importance of subtype-specific prevention and treatment strategies.

Anemia and testosterone deficiency risk: insights from NHANES data analysis and a Mendelian randomization analysis

Background Previous research has shown that testosterone deficiency (TD) increases the risk of anemia, but it is unclear whether anemia affects testosterone levels. This study investigated the influence of anemia on testosterone levels. Methods Utilizing data from six NHANES cycles, including demographic, testosterone levels, and hemoglobin concentrations, we employed multivariable-adjusted logistic regression to investigate the relationship between anemia and testosterone levels. Moreover, a two-sample Mendelian randomization (MR) study employing genome-wide association study (GWAS) data examined the causal relationship. Kaplan–Meier survival estimation was used to compared the overall survival (OS) of anemic and nonanemic patients with low testosterone and normal testosterone levels. Results The inclusion of 21,786 participants (2318 with anemia and19,468 without anemia) revealed that nonanemic patients exhibited higher testosterone levels than did anemic patients (β = 22.616, 95% CI: 3.873–41.359, p = 0.01807). MR analysis confirmed anemia as a cause of TD (OR = 1.045, 95% CI: 1.020–1.071, p < 0.001). Anemic males with low testosterone had reduced OS compared to those with normal levels (p < 0.001). Conclusions Anemia emerged as a potential risk factor for TD, highlighting a bidirectional relationship between these conditions. Additional prospective investigations are essential for the validation and reinforcement of our findings.

Mendelian Randomization Reveals: Triglycerides and Sensorineural Hearing Loss

Background: Sensorineural hearing loss (SNHL) is a multifactorial disorder with potential links to various physiological systems, including the cardiovascular system via blood lipid levels such as triglycerides (TG). This study investigates the causal relationship between TG levels and SNHL using Mendelian randomization (MR), which offers a method to reduce confounding and reverse causality by using genetic variants as instrumental variables. Methods: Utilizing publicly available genome-wide association study (GWAS) data, we performed a two-sample MR analysis. The initial analysis unveiled a causal relationship between TG (GWAS ID: ebi-a-GCST90018975) and SNHL (GWAS ID: finn b-H8_HL_SEN-NAS). Subsequent analysis validated this through MR with a larger sample size for TG (GWAS ID: ieu-b-111) and SNHL. To conduct the MR analysis, we utilized several methods including inverse-variance weighted (IVW), MR Egger, weighted median, and weighted mode. We also employed Cochrane’s Q test to identify any heterogeneity in the MR results. To detect horizontal pleiotropy, we conducted the MR-Egger intercept test and MR pleiotropy residual sum and outliers (MR-PRESSO) test. We performed a leave-one-out analysis to assess the sensitivity of this association. Finally, a meta-analysis of the MR results was undertaken. Results: Our study found a significant positive correlation between TG and SNHL, with OR values of 1.14 (95% CI: 1.07–1.23, p &lt; 0.001) in the IVW analysis and 1.09 (95% CI: 1.03–1.16, p &lt; 0.006) in the replicate analysis. We also found no evidence of horizontal pleiotropy or heterogeneity between the genetic variants (p &gt; 0.05), and a leave-one-out test confirmed the stability and robustness of this association. The meta-analysis combining the initial and replicate analyses showed a significant causal effect with OR values of 1.11 (95% CI: 1.06–1.16, p = 0.01). Conclusion: These findings indicate TG as a risk factor for SNHL, suggesting potential pathways for prevention and intervention in populations at risk. This conclusion underscores the importance of managing TG levels as a strategy to mitigate the risk of developing SNHL.

Fine-mapping analysis including over 254,000 East Asian and European descendants identifies 136 putative colorectal cancer susceptibility genes

Genome-wide association studies (GWAS) have identified more than 200 common genetic variants independently associated with colorectal cancer (CRC) risk, but the causal variants and target genes are mostly unknown. We sought to fine-map all known CRC risk loci using GWAS data from 100,204 cases and 154,587 controls of East Asian and European ancestry. Our stepwise conditional analyses revealed 238 independent association signals of CRC risk, each with a set of credible causal variants (CCVs), of which 28 signals had a single CCV. Our cis-eQTL/mQTL and colocalization analyses using colorectal tissue-specific transcriptome and methylome data separately from 1299 and 321 individuals, along with functional genomic investigation, uncovered 136 putative CRC susceptibility genes, including 56 genes not previously reported. Analyses of single-cell RNA-seq data from colorectal tissues revealed 17 putative CRC susceptibility genes with distinct expression patterns in specific cell types. Analyses of whole exome sequencing data provided additional support for several target genes identified in this study as CRC susceptibility genes. Enrichment analyses of the 136 genes uncover pathways not previously linked to CRC risk. Our study substantially expanded association signals for CRC and provided additional insight into the biological mechanisms underlying CRC development.

Exploring the Causal Effects of Gut Microbiota on Diabetic Nephropathy: A Two-Sample Mendelian Randomization Study.

In recent years, an increasing number of studies have indicated a bidirectional relationship between gut microbiota and the kidneys (the gut-kidney axis). Currently, the potential causal relationship between gut microbiota and diabetic nephropathy remains unclear. This study explores the causal effects of gut microbiota on diabetic nephropathy through Mendelian randomization. We carried out a comprehensive Mendelian Randomization (MR) analysis, drawing on the Genome-wide Association Study (GWAS) data for 196 varieties of gut microbiota and diabetic nephropathy. The primary analytical approach employed was the inverse-variance weighted, supplemented by the MR-Egger, weighted median, simple mode, and weighted mode. We rigorously assessed heterogeneity with Cochran's Q test and examined pleiotropy via MREgger intercept and MR-PRESSO tests. To ensure the reliability of our findings, we conducted funnel plots and leave-one-out analysis. Our study indicates a causal relationship between the increased risk of diabetic nephropathy and specific gut microbiota, including the Bacteroidia (P=0.01892; OR=1.593; 95%CI, 1.080-2.350), Bacteroidales (P=0.01892; OR=1.593; 95%CI, 1.080-2.350), and LachnospiraceaeUCG008 (P=0.01350; OR=1.452; 95%CI, 1.080-1.953). Conversely, potential protective factors include the Proteobacteria (P=0.00397; OR=0.528; 95%CI, 0.342-0.815), Gammaproteobacteria (P=0.00965; OR=0.474; 95%CI, 0.270-0.834), Lentisphaeria (P=0.04417; OR=0.756; 95%CI, 0.576-0.993), Victivallales (P=0.04417; OR=0.756; 95%CI, 0.576-0.993), and Dialister (P=0.00118; OR=0.513; 95%CI, 0.343-0.768). This study confirms the causal effects of gut microbiota on diabetic nephropathy. Identifying the risk and protective factors within the gut microbiota for diabetic nephropathy offers fresh insights and novel approaches for preventing and treating this condition.

Application of Deep Learning for the Detection of Genetic Variations: Its Implementation in Classifying Alzheimer's Disease

Deep learning emerges as a promising technique, utilizing nonlinear transformations for feature extraction from high-dimensional datasets. However, its application encounters challenges in genome-wide association studies (GWAS) dealing with high-dimensional genomic data. This study introduces an innovative three-step method termed SWAT-CNN for the identification of genetic variants. This approach employs deep learning to pinpoint phenotype-related single nucleotide polymorphisms (SNPs), facilitating the development of precise disease classification models. In the first step, the entire genome undergoes division into non overlapping fragments of an optimal size. Subsequently, convolutional neural network (CNN) analysis is conducted on each fragment to identify phenotype-associated segments. The second step, employs a Sliding Window Association Test (SWAT), where CNN is utilized on the selected fragments to compute phenotype influence scores (PIS) and detect phenotype-associated SNPs based on these scores. The third step involves running CNN on all identified SNPs to construct a comprehensive classification model. Validation of the proposed approach utilized GWAS data from the Alzheimer’s disease Neuroimaging Initiative (ADNI), encompassing 981 subjects, including cognitively normal older adults (CN) and individuals with Alzheimer's disease (AD). Notably, the method successfully identified the widely recognized APOE region as the most significant genetic locus for AD. The resulting classification model exhibited an area under the curve (AUC) of 0.82, demonstrating compatibility with traditional machine learning approaches such as random forest and XGBoost. SWAT-CNN, as a groundbreaking deep learning-based genome-wide methodology, not only identified AD-associated SNPs but also presented a robust classification model for Alzheimer's disease, suggesting potential applications across diverse biomedical domains.

XWAS analysis in neuropsychiatric disorders by integrating multi-molecular phenotype quantitative trait loci and GWAS summary data

BackgroundIntegrating quantitative trait loci (QTL) data related to molecular phenotypes with genome-wide association study (GWAS) data is an important post-GWAS strategic approach employed to identify disease-associated molecular features. Various types of molecular phenotypes have been investigated in neuropsychiatric disorders. However, these findings pertaining to distinct molecular features are often independent of each other, posing challenges for having an overview of the mapped genes.MethodsIn this study, we comprehensively summarized published analyses focusing on four types of risk-related molecular features (gene expression, splicing transcriptome, protein abundance, and DNA methylation) across five common neuropsychiatric disorders. Subsequently, we conducted supplementary analyses with the latest GWAS dataset and corresponding deficient molecular phenotypes using Functional Summary-based Imputation (FUSION) and summary data-based Mendelian randomization (SMR). Based on the curated and supplemented results, novel reliable genes and their functions were explored.ResultsOur findings revealed that eQTL exhibited superior ability in prioritizing risk genes compared to the other QTL, followed by sQTL. Approximately half of the genes associated with splicing transcriptome, protein abundance, and DNA methylation were successfully replicated by eQTL-associated genes across all five disorders. Furthermore, we identified 436 novel reliable genes, which enriched in pathways related with neurotransmitter transportation such as synaptic, dendrite, vesicles, axon along with correlations with other neuropsychiatric disorders. Finally, we identified ten multiple molecular involved regulation patterns (MMRP), which may provide valuable insights into understanding the contribution of molecular regulation network targeting these disease-associated genes.ConclusionsThe analyses prioritized novel and reliable gene sets related with five molecular features based on published and supplementary results for five common neuropsychiatric disorders, which were missed in the original GWAS analysis. Besides, the involved MMRP behind these genes could be given priority for further investigation to elucidate the pathogenic molecular mechanisms underlying neuropsychiatric disorders in future studies.

An exploration of causal relationships between nine neurological diseases and the risk of breast cancer: a Mendelian randomization study.

Some preceding researches have observed that certain neurological disorders, such as Alzheimer's disease and multiple sclerosis, may affect breast cancer risk. However, whether there are causal relationships between these neurological conditions and breast cancer is inconclusive. This study was designed to explore whether neurological disorders affected the risks of breast cancer overall and of the two subtypes (ER+ and ER-). In the course of this study, genome-wide association study (GWAS) data for nine neurological diseases (Alzheimer's disease, multiple sclerosis, Parkinson's disease, myasthenia gravis, generalized epilepsy, intracerebral haemorrhage, cerebral atherosclerosis, brain glioblastoma, and benign meningeal tumour) were collected from the Complex Trait Genetics lab and the MRC Integrative Epidemiology Unit, and single-nucleotide polymorphisms (SNPs) extensively associated with these neurological ailments had been recognized as instrumental variables (IVs). GWAS data on breast cancer were collected from the Breast Cancer Association Consortium (BCAC). Two-sample Mendelian randomization (MR) analyses as well as multivariable MR analyses were performed to determine whether these SNPs contributed to breast cancer risk. Additionally, the accuracy of the results was evaluated using the false discovery rate (FDR) multiple correction method. Both heterogeneity and pleiotropy were evaluated by analyzing sensitivities. According to the results of two-sample MR analyses, Alzheimer's disease significantly reduced the risks of overall (OR 0.925, 95% CI [0.871-0.982], P = 0.011) and ER+ (OR 0.912, 95% CI [0.853-0.975], P = 0.007) breast cancer, but there was a negative result in ER- breast cancer. However, after multiple FDR corrections, the effect of Alzheimer's disease on overall breast cancer was not statistically significant. In contrast, multiple sclerosis significantly increased ER+ breast cancer risk (OR 1.007, 95% CI [1.003-1.011], P = 0.001). In addition, the multivariable MR analyses showed that Alzheimer's disease significantly reduced the risk of ER+ breast cancer (IVW: OR 0.929, 95% CI [0.864-0.999], P=0.047; MR-Egger: OR 0.916, 95% CI [0.846-0.992], P=0.031); however, multiple sclerosis significantly increased the risk of ER+ breast cancer (IVW: OR 1.008, 95% CI [1.003-1.012], P=4.35×10-4; MR-Egger: OR 1.008, 95% CI [1.003-1.012], P=5.96×10-4). There were no significant associations between the remainder of the neurological diseases and breast cancer. This study found the trends towards a decreased risk of ER+ breast cancer in patients with Alzheimer's disease and an increased risk in patients with multiple sclerosis. However, due to the limitations of Mendelian randomization, we cannot determine whether there are definite causal relationships between neurological diseases and breast cancer risk. For conclusive evidences, more prospective randomized controlled trials will be needed in the future.

Metabolome-wide Mendelian randomization reveals causal effects of betaine and N-acetylornithine on impairment of renal function

BackgroundChronic kidney disease (CKD) is a common public health problem, which is characterized as impairment of renal function. The associations between blood metabolites and renal function remained unclear. This study aimed to assess the causal effect of various circulation metabolites on renal function based on metabolomics.MethodsWe performed a two-sample Mendelian randomization (MR) analysis to estimate the causality of genetically determined metabolites on renal function. A genome-wide association study (GWAS) of 486 metabolites was used as the exposure, while summary-level data for creatinine-based estimated glomerular filtration rate (eGFR) or CKD occurrence were set the outcomes. Inverse variance weighted (IVW) was used for primary causality analysis and other methods including weight median, MR-egger, and MR-PRESSO were applied as complementary analysis. Cochran Q test, MR-Egger intercept test, MR-PRESSO global test and leave-one-out analysis were used for sensitivity analysis. For the identified metabolites, reverse MR analysis, linkage disequilibrium score (LDSC) regression and multivariable MR (MVMR) analysis were performed for further evaluation. The causality of the identified metabolites on renal function was further validated using GWAS data for cystatin-C-based eGFR. All statistical analyses were performed in R software.ResultsIn this MR analysis, a total of 44 suggestive associations corresponding to 34 known metabolites were observed. After complementary analysis and sensitivity analysis, robust causative associations between two metabolites (betaine and N-acetylornithine) and renal function were identified. Reverse MR analysis showed no causal effects of renal function on betaine and N-acetylornithine. MVMR analysis revealed that genetically predicted betaine and N-acetylornithine could directly influence independently of each other. The causal effects of betaine and N-acetylornithine were also found on cystatin-C-based eGFR.ConclusionOur study provided evidence to support the causal effects of betaine and N-acetylornithine on renal function. These findings required further investigations to conduct mechanism exploration and drug target selection of these identified metabolites.

Polygenic Risk Scores and Genetically Complex Eye Disease.

The success of genome-wide association studies (GWASs) in uncovering genetic variants associated with complex eye diseases has paved the way for the development of risk prediction approaches based on disease genetics. Derived from GWAS data, polygenic risk scores (PRSs) have been emerging as a promising indicator of an individual's genetic liability to disease. In this review, we recap the current progress of PRS development and utility across a range of common eye diseases. While illustrating the prediction accuracy of PRSs and their valuable role in risk stratification for certain eye diseases, we also address PRSs' uncertain implementation in clinical settings at this stage, particularly in circumstances where limited treatment options are available. Finally, we discuss obstacles in translating PRSs into practice, including barriers to clinical impact, issues when working with different ancestry groups, and communicating risk scores, as well as projections for future improvements.

Investigating potential biomarkers of acute pancreatitis in patients with a BMI>30 using Mendelian randomization and transcriptomic analysis

BackgroundAcute pancreatitis (AP) has become a significant global health concern, and a high body mass index (BMI) has been identified as a key risk factor exacerbating this condition. Within this context, lipid metabolism assumes a critical role. The complex relationship between elevated BMI and AP, mediated by lipid metabolism, markedly increases the risk of complications and mortality. This study aimed to accurately define the correlation between BMI and AP, incorporating a comprehensive analysis of the interactions between individuals with high BMI and AP.MethodsMendelian randomization (MR) analysis was first applied to determine the causal relationship between BMI and the risk of AP. Subsequently, three microarray datasets were obtained from the GEO database. This was followed by an analysis of differentially expressed genes and the application of weighted gene coexpression network analysis (WGCNA) to identify key modular genes associated with AP and elevated BMI. Functional enrichment analysis was then performed to shed light on disease pathogenesis. To identify the most informative genes, machine learning algorithms, including Random Forest (RF), Support Vector Machine-Recursive Feature Elimination (SVM-RFE), and Least Absolute Shrinkage and Selection Operator (LASSO), were employed. Subsequent analysis focused on the colocalization of the Quantitative Trait Loci (eQTL) data associated with the selected genes and Genome-Wide Association Studies (GWAS) data related to the disease. Preliminary verification of gene expression trends was conducted using external GEO datasets. Ultimately, the diagnostic potential of these genes was further confirmed through the development of an AP model in mice with a high BMI.ResultsA total of 21 intersecting genes related to BMI>30, AP, and lipid metabolism were identified from the datasets. These genes were primarily enriched in pathways related to cytosolic DNA sensing, cytokine‒cytokine receptor interactions, and various immune and inflammatory responses. Next, three machine learning techniques were utilized to identify HADH as the most prevalent diagnostic gene. Colocalization analysis revealed that HADH significantly influenced the risk factors associated with BMI and AP. Furthermore, the trend in HADH expression within the external validation dataset aligned with the trend in the experimental data, thus providing a preliminary validation of the experimental findings.The changes in its expression were further validated using external datasets and quantitative real-time polymerase chain reaction (qPCR).ConclusionThis study systematically identified HADH as a potential lipid metabolism-grounded biomarker for AP in patients with a BMI>30.