Individual Single Nucleotide Polymorphisms Research Articles

Causal association between air pollution and allergic rhinitis, asthma: a Mendelian randomization study

BackgroundsObservational studies suggest that air pollutants, including particulate matter and nitrogen compounds, could elevate asthma and allergic rhinitis health risks. Nevertheless, the exact nature of the causal relationship between air pollution and asthma and allergic rhinitis remains unknown. This study utilizes the Mendelian randomization (MR) technique to explore the potential causal links between air pollution components (PM2.5, PM2.5–10, PM10, NO₂, and nitrogen dioxide) and the incidence of allergic rhinitis and asthma.MethodsA MR study utilized summary statistics from GWAS that are publicly accessible. The inverse variance weighting (IVW) approach served as the foundational analysis technique. To ensure robustness, supplementary methodologies such as the weighted median, MR-Egger regression, simple mode, and weighted model were also applied. Heterogeneity was evaluated using Cochran’s Q test, and the presence of pleiotropy was determined through MR-Egger regression. The MR-PRESSO test was employed for outlier detection, and the analysis’s sensitivity was scrutinized via a leave-one-out strategy.ResultsThe IVW technique showed a strong correlation between PM10 and asthma (OR = 0.625, 95% CI = 0.396–0.988, p = 0.044). No significant associations were found between asthma and other air pollutants such as PM2.5, PM2.5–10, NO₂, or nitrogen dioxide. Similarly, allergic rhinitis showed no causal relationships with any studied air pollution metrics. Pleiotropy was absent in the findings. Sensitivity analyses, employing the leave-one-out method, confirmed the stability of these results, unaffected by individual single nucleotide polymorphisms (SNPs).ConclusionThis Mendelian randomization study establishes a causal link between PM10 exposure and asthma, suggesting that interventions to reduce air pollution may decelerate the adverse progression of asthma.

Predicting regulatory mutations and their target genes by new computational integrative analysis: A study of follicular lymphoma

Mutations in DNA regulatory regions are increasingly being recognized as important drivers of cancer and other complex diseases. These mutations can regulate gene expression by affecting DNA-protein binding and epigenetic profiles, such as DNA methylation in genome regulatory elements. However, identifying mutation hotspots associated with expression regulation and disease progression in non-coding DNA remains a challenge. Unlike most existing approaches that assign a mutation score to individual single nucleotide polymorphisms (SNP), a mutation block (MB)-based approach was introduced in this study to assess the collective impact of a cluster of SNPs on transcription factor-DNA binding affinity, differential gene expression (DEG), and nearby DNA methylation. Moreover, the long-distance target genes of functional MBs were identified using a new permutation-based algorithm that assessed the significance of correlations between DNA methylation at regulatory regions and target gene expression. Two new Python packages were developed. The Differential Methylation Region (DMR-analysis) analysis tool was used to detect DMR and map them to regulatory elements. The second tool, an integrated DMR, DEG, and SNP analysis tool (DDS-analysis), was used to combine the omics data to identify functional MBs and long-distance target genes. Both tools were validated in follicular lymphoma (FL) cohorts, where not only known functional MBs and their target genes (BCL2 and BCL6) were recovered, but also novel genes were found, including CDCA4 and JAG2, which may be associated with FL development. These genes are linked to target gene expression and are significantly correlated with the methylation of nearby DNA sequences in FL. The proposed computational integrative analysis of multiomics data holds promise for identifying regulatory mutations in cancer and other complex diseases.

Causal relationship between sex hormones and cutaneous melanoma: a two-sample Mendelian randomized study.

This study aimed to elucidate the genetic aspects of the relationship between sex hormones and cutaneous melanoma risk, providing valuable insights into this complex association. In this study, we used estradiol, bioavailable testosterone, sex hormone-binding globulin, and total testosterone as the exposure and melanoma as the outcome for two-sample Mendelian randomization analysis. In this study, a random-effects inverse-variance weighting (IVW) model was used as the main analysis model, and the corresponding weighted median, simple mode, weighted mode, and Mendelian randomization‒Egger methods were used as supplementary methods. We assessed both heterogeneity and horizontal pleiotropy in our study, scrutinizing whether the analysis results were affected by any individual single nucleotide polymorphism. The random-effects IVW method indicated that estradiol [odds ratio (OR), 1.000; 95% confidence interval (CI), 0.998-1.003; P = 0.658], bioavailable testosterone (OR = 1.001, 95% CI, 0.999-1.003; P = 0.294), sex hormone-binding globulin (IVW: OR, 1.000; 95% CI, 0.998-1.003; P = 0.658), and total testosterone (IVW: OR, 1.002; 95% CI, 0.999-1.005; P = 0.135) were not genetically linked to cutaneous melanoma. No analyses exhibited heterogeneity, horizontal pleiotropy, or deviations. We were unable to find genetic evidence for a causal relationship between sex hormones and the occurrence of cutaneous melanoma in this study. These results are limited by sample size and population, so the causal relationship between sex hormones and cutaneous melanoma needs to be further studied.

Impact of gene-by-trauma interaction in MDD-related multimorbidity clusters

BackgroundMajor depressive disorder (MDD) is considerably heterogeneous in terms of comorbidities, which may hamper the disentanglement of its biological mechanism. In a previous study, we classified the lifetime trajectories of MDD-related multimorbidities into seven distinct clusters, each characterized by unique genetic and environmental risk-factor profiles. The current objective was to investigate genome-wide gene-by-environment (G × E) interactions with childhood trauma burden, within the context of these clusters. MethodsWe analyzed 77,519 participants and 6,266,189 single-nucleotide polymorphisms (SNPs) of the UK Biobank database. Childhood trauma burden was assessed using the Childhood Trauma Screener (CTS). For each cluster, Plink 2.0 was used to calculate SNP × CTS interaction effects on the participants' cluster membership probabilities. We especially focused on the effects of 31 candidate genes and associated SNPs selected from previous G × E studies for childhood maltreatment's association with depression. ResultsAt SNP-level, only the high-multimorbidity Cluster 6 revealed a genome-wide significant SNP rs145772219. At gene-level, MPST and PRH2 were genome-wide significant for the low-multimorbidity Clusters 1 and 3, respectively. Regarding candidate SNPs for G × E interactions, individual SNP results could be replicated for specific clusters. The candidate genes CREB1, DBH, and MTHFR (Cluster 5) as well as TPH1 (Cluster 6) survived multiple testing correction. LimitationsCTS is a short retrospective self-reported measurement. Clusters could be influenced by genetics of individual disorders. ConclusionsThe first G × E GWAS for MDD-related multimorbidity trajectories successfully replicated findings from previous G × E studies related to depression, and revealed risk clusters for the contribution of childhood trauma.

Genetic polymorphisms of TLR1, TLR2, TLR3 and TLR4 in patients with recurrent or severe infections.

Toll-like receptors (TLRs) play an important role in innate immunity. Previous studies have shown that single nucleotide polymorphisms (SNPs) in the genes coding for these innate immune molecules can affect susceptibility to and the outcome of certain diseases. The aim of the present study was to examine the clinical relevance of well-studied TLR1-4 SNPs in individuals who are prone to infections. Four functional SNPs, TLR1 rs5743618 (1805C>A, Ser602Ile), TLR2 rs5743708 (2258G>A, Arg753Gln), TLR3 rs3775291 (1234C>T, Leu412Phe) and TLR4 rs4986790 (896A>G, Asp299Gly), were analysed in 155 patients with recurrent respiratory infections (n=84), severe infections (n=15) or common variable immunodeficiency (n=56), and in 262 healthy controls, using the High Resolution Melting Analysis method. Polymorphisms of TLR2 rs5743708 (odds ratio [OR] 3.16; 95% confidence interval [CI] 1.45-6.83, p=.004, ap=.016) and TLR4 rs4986790 (OR 1.8; 95% CI 1.05-3.12, p=.028, ap=.112) were more frequent in patients with recurrent or severe infections than in controls. Interestingly, seven patients were found to carry both variant genotypes of TLR2 and TLR4, whereas none of the control group carried such genotypes (p≤.0001). Moreover, TLR2 polymorphism was associated with increased risk for acute otitis media episodes (OR, 3.02; 95% CI 1.41-6.47; p=.012). This study indicates that children and adults who are more prone to recurrent or severe respiratory infections carry one or both variant types of TLR2 and TLR4 more often than control subjects. Genetic variations of TLRs help explain why some children are more susceptible to respiratory infections.

Unraveling genetic causality between metformin and myocardial infarction on the basis of Mendelian randomization.

In recent years, several studies have explored the effect of metformin on myocardial infarction (MI), but whether metformin has an improvement effect in patients with MI is controversial. This study was aimed to investigate the causal relationship between metformin and MI using Mendelian randomization (MR) analysis. The genome-wide significant (P<5×10-8) single-nucleotide polymorphisms (SNPs) in patients with metformin and patients with MI were screened from the Open genome-wide association study (GWAS) project as instrumental variables (IVs). The study outcomes mainly included MI, old MI, acute MI, acute transmural MI of inferior wall, and acute transmural MI of anterior wall. The inverse variance weighted (IVW) method was applied to assess the main causal effect, and weighted median, simple mode, weighted mode methods, and MR-Egger regression were auxiliary applied for supplementary proof. The causal relationship between metformin and MI was assessed using odds ratios (OR) and 95% confidence intervals (95% CI). A leave-one-out method was used to explore the effect of individual SNPs on the results of IVW analyses, and a funnel plot was used to analyze the potential bias of the study results, thus ensuring the robustness of the results. In total, 16, 84, 39, 26, and 34 SNPs were selected as IVs to assess the genetic association between metformin and outcomes of MI, old MI, acute MI, acute transmural MI of inferior wall, and acute transmural MI of anterior wall, respectively. Treatment with metformin does not affect the risk of acute transmural MI of anterior wall at the genetic level (P>0.05; OR for inverse variance weighted was 1.010). In the cases of MI, old MI, acute MI, and acute transmural MI of inferior wall, metformin may even be a risk factor for patients (P<0.05; ORs for inverse variance weighted were 1.078, 1.026, 1.022 and 1.018 respectively). There was no horizontal pleiotropy or heterogeneity among IVs. The results were stable when removing the SNPs one by one. Metformin is not protective against the risk of myocardial infarction in patients and may even be a risk factor for MI, old MI, acute MI, and acute transmural MI of inferior wall.

The impact of lipidome on breast cancer: a Mendelian randomization study.

This study aims to investigate the association between specific lipidomes and the risk of breast cancer (BC) using the Two-Sample Mendelian Randomization (TSMR) approach and Bayesian Model Averaging Mendelian Randomization (BMA-MR) method. The study analyzed data from large-scale GWAS datasets of 179 lipidomes to assess the relationship between lipidomes and BC risk across different molecular subtypes. TSMR was employed to explore causal relationships, while the BMA-MR method was carried out to validate the results. The study assessed heterogeneity and horizontal pleiotropy through Cochran's Q, MR-Egger intercept tests, and MR-PRESSO. Moreover, a leave-one-out sensitivity analysis was performed to evaluate the impact of individual single nucleotide polymorphisms on the MR study. By examining 179 lipidome traits as exposures and BC as the outcome, the study revealed significant causal effects of glycerophospholipids, sphingolipids, and glycerolipids on BC risk. Specifically, for estrogen receptor-positive BC (ER+ BC), phosphatidylcholine (P < 0.05) and phosphatidylinositol (OR: 0.916-0.966, P < 0.05) within glycerophospholipids play significant roles, along with the importance of glycerolipids (diacylglycerol (OR = 0.923, P < 0.001) and triacylglycerol, OR: 0.894-0.960, P < 0.05)). However, the study did not observe a noteworthy impact of sphingolipids on ER+BC. In the case of estrogen receptor-negative BC (ER- BC), not only glycerophospholipids, sphingolipids (OR = 1.085, P = 0.008), and glycerolipids (OR = 0.909, P = 0.002) exerted an influence, but the protective effect of sterols (OR: 1.034-1.056, P < 0.05) was also discovered. The prominence of glycerolipids was minimal in ER-BC. Phosphatidylethanolamine (OR: 1.091-1.119, P < 0.05) was an important causal effect in ER-BC. The findings reveal thatphosphatidylinositol and triglycerides levels decreased the risk of BC, indicating a potential protective role of these lipid molecules. Moreover, the study elucidates BC's intricate lipid metabolic pathways, highlighting diverse lipidome structural variations that may have varying effects in different molecular subtypes.

Causal relationship between the immune cells and ankylosing spondylitis: univariable, bidirectional, and multivariable Mendelian randomization.

Ankylosing spondylitis (AS) is an autoimmune disease that affects millions of individuals. Immune cells have been recognized as having a crucial role in the pathogenesis of AS. However, their relationship has not been fully explored. We chose to employ Mendelian randomization (MR) to investigate the potential correlation between immune cells and AS. We sourced the data on immune cells from the latest genome-wide association studies (GWASs). We obtained data on AS from the FinnGen consortium. Our comprehensive univariable MR analysis covered 731 immune cells to explore its potential causal relationship with AS. The primary analysis method was inverse-variance weighted (IVW). Additionally, we used Cochran's Q test and the MR-Egger intercept test to assess the presence of pleiotropy and heterogeneity. We examined whether our results could be influenced by individual single-nucleotide polymorphisms (SNPs) using the leave-one-out test. We conducted a bidirectional MR to investigate the reverse relationship. We also applied multivariable MR to decrease the potential influence between the immune cells. Overall, our univariable MR analysis revealed eight immune cells associated with AS. Among these, four immune cells contributed to an increased risk of AS, while four immune cells were identified as protective factors for AS. However, the Bonferroni test confirmed only one risk factor and one protective factor with a significance level of p < 6.84E-05. CD8 on effector memory CD8+ T cell could increase the risk of AS (p: 1.2302E-05, OR: 2.9871, 95%CI: 1.8289-4.8786). HLA DR on CD33dim HLA DR+ CD11b+ could decrease the risk of AS (p: 1.2301E-06, OR: 0.5446, 95%CI: 0.4260-0.6962). We also identified a bidirectional relationship between CD4 on CD39+ activated CD4 regulatory T cells and AS utilizing the bidirectional MR. To address potential confounding among immune cells, we employed multivariable MR analysis, which revealed that only one immune cell had an independent effect on AS. HLA DR on CD33dim HLA DR+ CD11b+ could decrease the risk of AS (p: 2.113E-06, OR: 0.0.5423, 95%CI: 0.4210-0.6983). Our findings were consistently stable and reliable. Our findings indicated a potential link between immune cells and AS, which could provide a new idea for future research. Nevertheless, the specific underlying mechanisms require further exploration.

No genetic causality between obesity and benign paroxysmal vertigo: A two-sample Mendelian randomization study.

We applied Mendelian randomization to explore the causal relationship between obesity and benign paroxysmal vertigo (BPV). We chose two types of obesity diseases. Obesity due to excessive calories and other or unspecified obesity from the FinnGen database. We used genomic significance (p < 5 × 10-8) to obtain independent single nucleotide polymorphisms (SNPs) as instrumental variables. Similarly, genome-wide association study data for the disease BPV were selected from the FinnGen database. R was then used to test the data for multiplicity and heterogeneity, as well as to detect the effect of individual SNPs on the results. Random effects inverse variance weighting was used as the main statistical analysis. First, by analyzing, we found an outlier in obesity due to excessive calories (rs12956821). Outliers were then removed, and the statistical results were analyzed without heterogeneity (p > 0.05) and horizontal pleiotropy (p > 0.05), as well as individual SNPs having no effect on the results. Meanwhile, random-effects IVW results showed obesity due to excessive calories (p = 0.481; OR = 0.941), and other or unspecified obesity (p = 0.640; OR = 0.964). The present study did not find a causal relationship between the above two obesity types and BPV at the genetic level.

Causal Relationship Between Endometriosis and Pelvic Inflammatory Diseases: Mendelian Randomization Study.

This study explores the causal relationship between endometriosis and pelvic inflammatory diseases (PID). The study utilized genome-wide association study (GWAS) datasets for endometriosis ("finn-b-N14_ENDOMETRIOSIS") and PID ("finn-b-N14_OTHFEMPELINF"). Subsequently, two-sample Mendelian randomization (MR) analyses were conducted using inverse variance weighting (IVW), Egger regression (MR-Egger), and weighted median (WM) methods. Heterogeneity was evaluated using Cochran's Q test, and in case of detected outliers, they were removed for re-evaluation of MR causality. From the endometriosis GWAS dataset, 33 single nucleotide polymorphisms (SNPs) were selected as instrumental variables. All three methods, IVW (OR = 1.39, P < 1×10-8), MR-Egger (OR = 1.41, P = 0.003), and WM (OR = 1.37, P = 1.16×10-5) confirmed a causal relationship between endometriosis and PID. The association between endometriosis and pelvic inflammation remained unaffected by the exclusion of individual SNPs. Lastly, Cochran's Q test and funnel plots showed no evidence of SNP asymmetry. The results of the MR analysis support a potential causal relationship between endometriosis and an increased risk of PID.

Use of polygenic and clinical risk scores in wide spectrum congenital heart malformations: a diagnostic and therapeutic perspective

Worldwide, congenital heart defects (CHDs) are a leading cause of mortality and morbidity. Globally, the annual prevalence rate of CHDs is about 1.5 million. It is one of the most common life-threatening congenital malformations, with a visible difference between high-income countries (HICs) and developing countries. This difference may be attributed to the diagnostic facilities and interventions.1 Moreover, the use of genetic and clinical risk scores is less studied in this field compared to other diagnostic and therapeutic approaches. Thus, we comprehensively review the available literature on adult and pediatric syndromic or non-syndromic CHDs and propose limitations and future directions in this field. Risk factors for this fatal disease include genetic and environmental factors.2,3 Risk stratification based on prenatal diagnosis with genetic counseling has shown better clinical outcomes. The modified World Health Organization (mWHO) guidelines are among the first proposed for risk stratification based on clinical scores.4 The combination of polygenic risk scores (PRS) and clinical risk evaluation has been adopted in various populations as better diagnostic and therapeutic approaches for various diseases, including coronary artery disease,5 diabetes,6 obesity,7 and cancers.8,9 However, unlike these diseases, polygenic risk scores are rarely used for the genetic diagnosis of CHD patients. The clinical utility of polygenic risk scores for CHDs is still in the early stages and requires validation studies in different cohorts. Incorporating PRS into the clinical setting may improve CHD diagnosis and therapeutic strategies.10 Recently, an initial attempt was made to determine the clinical utility of polygenic risk scores in a large dataset of the European cohort. Data from this study suggested a high potential for PRS in distinguishing between clinical phenotypes (mild, moderate, and severe) of congenital heart malformations.11 Furthermore, this hypothesis was tested in patients with transposition of the great arteries (TGA) to assess the utility of risk stratification after arterial switch operation. The cumulative risk score was computed for single nucleotide polymorphisms (SNPs) that reached the threshold statistical significance of p&lt;1×10−5 in the initial analysis. Combined clinical and polygenic risk scores showed improved risk stratification in mild, moderate, and severe TGA phenotypes.12 Similarly, in another European cohort, the risk allele frequency was higher in the diseased group compared to the control group. However, statistical analysis suggested a non-significant association with p&gt;0.05. Interestingly, combined risk scores constructed for selected SNPs showed a significant association with the disease with a p-value of 0.002. This evidence further supports the hypothesis that the cumulative effect of polymorphisms has better diagnostic and therapeutic efficacy than individual SNPs.13 There are certain limitations to its clinical use. Firstly, social stigma and lack of awareness regarding this disease are major hurdles. Secondly, there is a need to recruit a large number of samples from different populations to validate the clinical utility of PRS. Additionally, a lack of resources in developing countries also contributes to fatal outcomes. We recommend conducting large cohort studies in different ethnic groups to predict better risk associations, develop better therapeutic strategies, and formulate management guidelines.

BayesKAT: bayesian optimal kernel-based test for genetic association studies reveals joint genetic effects in complex diseases.

Genome-wide Association Studies (GWAS) methods have identified individual single-nucleotide polymorphisms (SNPs) significantly associated with specific phenotypes. Nonetheless, many complex diseases are polygenic and are controlled by multiple genetic variants that are usually non-linearly dependent. These genetic variants are marginally less effective and remain undetected in GWAS analysis. Kernel-based tests (KBT), which evaluate the joint effect of a group of genetic variants, are therefore critical for complex disease analysis. However, choosing different kernel functions in KBT can significantly influence the type I error control and power, and selecting the optimal kernel remains a statistically challenging task. A few existing methods suffer from inflated type 1 errors, limited scalability, inferior power or issues of ambiguous conclusions. Here, we present a new Bayesian framework, BayesKAT (https://github.com/wangjr03/BayesKAT), which overcomes these kernel specification issues by selecting the optimal composite kernel adaptively from the data while testing genetic associations simultaneously. Furthermore, BayesKAT implements a scalable computational strategy to boost its applicability, especially for high-dimensional cases where other methods become less effective. Based on a series of performance comparisons using both simulated and real large-scale genetics data, BayesKAT outperforms the available methods in detecting complex group-level associations and controlling type I errors simultaneously. Applied on a variety of groups of functionally related genetic variants based on biological pathways, co-expression gene modules and protein complexes, BayesKAT deciphers the complex genetic basis and provides mechanistic insights into human diseases.

Study on the causal relationship between educational attainment and delirium: A two-sample Mendelian randomization study

This study aimed to investigate whether there is a causal relationship between educational attainment and delirium at the genetic level using the Mendelian randomization method, and provide new evidence for studies in this field. We found a causal relationship between educational attainment and delirium at the genetic level after excluding confounders using Mendelian randomization. The inverse variance weighting method of random effects was the main analysis method. The weighted median and Mendelian Randomization-Egger methods, as well as simple, and weighted modes were used as supplementary analysis methods. Additionally, horizontal pleiotropy tests were conducted, including the Mendelian Randomization-Egger intercept test and Mendelian Randomization Pleiotropy RESidual Sum and Outlier. Cochran's Q statistic was used to assess the size of heterogeneity. We retrieved all second single nucleotide polymorphism features and performed multivariate Mendelian randomization to adjust for the effect of potential confounders on our results. The inverse variance weighting suggested a negative correlation between genetically predicted educational attainment and delirium (0.67[0.49–0.92], p = 0.013); Mendelian Randomization Pleiotropy RESidual Sum and Outlier (0.67[0.49–0.92], p = 0.013) and multivariate Mendelian randomization (0.52[0.33–0.82], p = 0.005) results were generally consistent with the inverse variance weighting method. The Mendelian Randomization-Egger, simple, and weighted mode results were consistent with the inverse variance weighting results. Our results were not affected by pleiotropy or heterogeneity (p > 0.05, for both pleiotropy and heterogeneity). In addition, the “leave-one-out” analysis showed that the results of our Mendelian randomization analysis were not influenced by individual single nucleotide polymorphisms. Studies have found a causal relationship between educational attainment and delirium at the genetic level; higher educational attainment may be a protective factor against delirium. Clinically, more attention should be paid to patients at a high risk of delirium with low educational attainment.

Causal associations between atrial fibrillation and breast cancer: A bidirectional Mendelian randomization analysis.

Previous observational studies indicated that atrial fibrillation may increase the risk of breast cancer. Following a breast cancer diagnosis, the chance of developing atrial fibrillation may increase as well. However, it is uncertain whether the link is causal or just due to confounding factors. Using bidirectional Mendelian randomization (MR) analysis, we sought to assess the bidirectional causal relationship between atrial fibrillation and breast cancer from a genetic level. Large genome-wide association studies yielded summary-level data for atrial fibrillation and breast cancer. The preliminary estimate was inverse variance weighted (IVW) under a random model. MR-Egger, weighted median, simple mode, weighted mode, and multivariable MR (adjusting body mass index, smoking, and alcohol drinking) were performed as sensitivity analyses. Genetically predicted atrial fibrillation presented no statistically significant association with overall breast cancer (odds ratio [OR] = 1.00; 95% confidence interval [CI]: 0.97-1.04; p = 0.79), estrogen receptor (ER) + (OR = 1.00; 95% CI: 0.96-1.03; p = 0.89) or ER- subtypes (OR = 1.00; 95% CI: 0.97-1.04; p = 0.89). Similarly, genetically predicted overall breast cancer (OR = 1.01; 95% CI: 0.98-1.04; p = 0.37), ER+ (OR = 1.02; 95% CI: 0.99-1.05; p = 0.16) or ER- (OR = 0.98; 95% CI: 0.93-1.02; p = 0.32) subtypes had no causal effect on atrial fibrillation. Sensitivity analyses yielded similar results. Individual single nucleotide polymorphism had little effect on the total estimate. We did not observe any evidence of horizontal pleiotropy. Our bidirectional MR studies revealed that there may be no causal links between atrial fibrillation and breast cancer.

GWASTool: A web pipeline for detecting SNP-phenotype associations

The genome-wide association study (GWAS) aims to detect associations between individual single nucleotide polymorphisms (SNPs) or SNP interactions and phenotypes to decipher the genetic mechanism. Existing GWAS analysis tools have different focuses and advantages, but suffer a series of tedious and heterogeneous configurations for computation. It is inconvenient for researchers to simply choose and apply these tools, statistically and biologically analyze their results for different usages. To address these issues, we develop a user friendly web pipeline GWASTool for detecting associations, which includes simulation data generation, associated loci detection, result visualization, analysis and comparison. GWASTool provides a unified and plugin-able framework to encapsulate the heterogeneity of GWAS algorithms, simplifies the analysis steps and energizes GWAS tasks. GWASTool is implemented in Java and is freely available for public use at http://www.sdu-idea.cn/GWASTool. The website hosts a comprehensive collection of resources, including a user manual, description of integrated algorithms, data examples and standalone version for download.

Interactions between TNFAIP3, PTPN22, and TRAF1-C5 gene polymorphisms in patients with primary Sjögren's syndrome.

The aim of our study was to investigate whether TNFAIP3, PTPN22, and TRAF1-5 single nucleotide polymorphisms (SNPs) are associated with susceptibility, severity, or serological markers in primary Sjögren's syndrome (pSS). The cases and controls study was conducted between December 2021 and June 2022. TNFAIP3 rs10499194C/T, rs6920220G/A, and rs2230926T/G, PTPN22 rs2476601C/T and rs33996649G/A, and TRAF1-C5 rs10818488G/A polymorphisms were genotyped in 154 female pSS patients (mean age: 45.2±6.8 years) and 313 female control subjects (mean age: 50.3±7.5 years) using the TaqMan® SNP genotyping assay. An association analysis between TNFAIP3, PTPN22, and TRAF1-C5 SNPs and susceptibility, clinical characteristics, and serological markers of pSS was performed. Interactions between TNFAIP3, PTPN22, and TRAF1-C5 SNPs were also evaluated in patients and controls. The genotype and allele frequencies showed no association with susceptibility, severity, or serological markers of pSS. Nevertheless, several interactions between TNFAIP3 and TRAF1-C5 or TNFAIP3, PTPN22, and TRAF1-C5 genotypes were associated with susceptibility to pSS (p<0.01). Individual TNFAIP3, PTPN22, and TRAF1-C5 SNPs are not associated with susceptibility, severity, or serological markers of pSS. However, genetic interactions between TRAF1-C5 and TNFAIP3 or TNFAIP3, PTPN22, and TRAF1-C5 SNPs are risk factors for pSS.

Genetic evidence strengthens the bidirectional connection between gut microbiota and Shigella infection: insights from a two-sample Mendelian randomization study.

In recent investigations, substantial strides have been made in the precise modulation of the gut microbiota to prevent and treat a myriad of diseases. Simultaneously, the pressing issue of widespread antibiotic resistance and multidrug resistance resulting from Shigella infections demands urgent attention. Several studies suggest that the antagonistic influence of the gut microbiota could serve as a novel avenue for impeding the colonization of pathogenic microorganisms or treating Shigella infections. However, conventional research methodologies encounter inherent challenges in identifying antagonistic microbial agents against Shigella, necessitating a comprehensive and in-depth analysis of the causal relationship between Shigella infections and the gut microbiota. Utilizing the aggregated summary statistics from Genome-Wide Association Studies (GWAS), we conducted Mendelian Randomization (MR) analyses encompassing 18,340 participants to explore the interplay between the gut microbiota and Shigella infections. This investigation also involved 83 cases of Shigella infection patients and 336,396 control subjects. In the positive strand of our findings, we initially performed a preliminary analysis using the Inverse Variance Weighting (IVW) method. Subsequently, we undertook sensitivity analyses to assess the robustness of the results, addressing confounding factors' influence. This involved employing the Leave-One-Out method and scrutinizing funnel plots to ensure the reliability of the MR analysis outcomes. Conclusively, a reverse MR analysis was carried out, employing the Wald ratio method due to the exposure of individual Single Nucleotide Polymorphisms (SNPs). This was undertaken to explore the plausible associations between Shigella infections and genetically predicted compositions of the gut microbiota. In this study, we employed 2,818 SNPs associated with 211 species of gut microbiota as instrumental variables (IVs). Through IVW analysis, our positive MR findings revealed a significant negative correlation between the occurrence of Shigella infections and the phylum Tenericutes (OR: 0.18, 95% CI: 0.04-0.74, p = 0.02), class Mollicutes (OR: 0.18, 95% CI: 0.04-0.74, p = 0.02), genus Intestinimonas (OR: 0.16, 95% CI: 0.04-0.63, p = 0.01), genus Gordonibacter (OR: 0.39, 95% CI: 0.16-0.93, p = 0.03), and genus Butyrivibrio (OR: 0.44, 95% CI: 0.23-0.87, p = 0.02). Conversely, a positive correlation was observed between the occurrence of Shigella infections and genus Sutterella (OR: 10.16, 95% CI: 1.87-55.13, p = 0.01) and genus Alistipes (OR: 12.24, 95% CI: 1.71-87.34, p = 0.01). In sensitivity analyses, utilizing MR-Egger regression analysis and MR Pleiotropy Residual Sum and Outlier (MR-PRESSO) detection, all outcomes demonstrated robust stability. Simultaneously, in the reverse MR analysis, Shigella infections resulted in an upregulation of four bacterial genera and a downregulation of three bacterial genera. In summation, the MR analysis outcomes corroborate the presence of bidirectional causal relationships between the gut microbiota and Shigella infections. This study not only unveils novel perspectives for the prevention and treatment of Shigella infections but also furnishes fresh insights into the mechanistic underpinnings of how the gut microbiota contributes to the pathogenesis of Shigella infections. Consequently, the established dual causal association holds promise for advancing our understanding and addressing the complexities inherent in the interplay between the gut microbiota and Shigella infections, thereby paving the way for innovative therapeutic interventions and preventive strategies in the realm of Shigella-related diseases.

Expected values for the accuracy of predicted breeding values accounting for genetic differences between reference and target populations

BackgroundGenetic merit, or breeding values as referred to in livestock and crop breeding programs, is one of the keys to the successful selection of animals in commercial farming systems. The developments in statistical methods during the twentieth century and single nucleotide polymorphism (SNP) chip technologies in the twenty-first century have revolutionized agricultural production, by allowing highly accurate predictions of breeding values for selection candidates at a very early age. Nonetheless, for many breeding populations, realized accuracies of predicted breeding values (PBV) remain below the theoretical maximum, even when the reference population is sufficiently large, and SNPs included in the model are in sufficient linkage disequilibrium (LD) with the quantitative trait locus (QTL). This is particularly noticeable over generations, as we observe the so-called erosion of the effects of SNPs due to recombinations, accompanied by the erosion of the accuracy of prediction. While accurately quantifying the erosion at the individual SNP level is a difficult and unresolved task, quantifying the erosion of the accuracy of prediction is a more tractable problem. In this paper, we describe a method that uses the relationship between reference and target populations to calculate expected values for the accuracies of predicted breeding values for non-phenotyped individuals accounting for erosion. The accuracy of the expected values was evaluated through simulations, and a further evaluation was performed on real data.ResultsUsing simulations, we empirically confirmed that our expected values for the accuracy of PBV accounting for erosion were able to correctly determine the prediction accuracy of breeding values for non-phenotyped individuals. When comparing the expected to the realized accuracies of PBV with real data, only one out of the four traits evaluated presented accuracies that were significantly higher than the expected, approaching h2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\sqrt{{{\ ext{h}}}^{2}}$$\\end{document}.ConclusionsWe defined an index of genetic correlation between reference and target populations, which summarizes the expected overall erosion due to differences in allele frequencies and LD patterns between populations. We used this correlation along with a trait’s heritability to derive expected values for the accuracy (R\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\ ext{R}}$$\\end{document}) of PBV accounting for the erosion, and demonstrated that our derived ER|erosion\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\ ext{E}}\\left[{\ ext{R}}|{\ ext{erosion}}\\right]$$\\end{document} is a reliable metric.

Metabolic-dysfunction associated steatotic liver disease-related diseases, cognition and dementia: A two-sample mendelian randomization study.

The results of current studies on metabolic-dysfunction associated steatotic liver disease (MASLD)-related diseases, cognition and dementia are inconsistent. This study aimed to elucidate the effects of MASLD-related diseases on cognition and dementia. By using single-nucleotide polymorphisms (SNPs) associated with different traits of NAFLD (chronically elevated serum alanine aminotransferase levels [cALT], imaging-accessed and biopsy-proven NAFLD), metabolic dysfunction-associated steatohepatitis, and liver fibrosis and cirrhosis, we employed three methods of mendelian randomization (MR) analysis (inverse-variance weighted [IVW], weighted median, and MR-Egger) to determine the causal relationships between MASLD-related diseases and cognition and dementia. We used Cochran's Q test to examine the heterogeneity, and MR-PRESSO was used to identify outliers (NbDistribution = 10000). The horizontal pleiotropy was evaluated using the MR-Egger intercept test. A leave-one-out analysis was used to assess the impact of individual SNP on the overall MR results. We also repeated the MR analysis after excluding SNPs associated with confounding factors. The results of MR analysis suggested positive causal associations between MASLD confirmed by liver biopsy (p of IVW = 0.020, OR = 1.660, 95%CI = 1.082-2.546) and liver fibrosis and cirrhosis (p of IVW = 0.009, OR = 1.849, 95%CI = 1.169-2.922) with vascular dementia (VD). However, there was no evidence of a causal link between MASLD-related diseases and cognitive performance and other types of dementia (any dementia, Alzheimer's disease, dementia with lewy bodies, and frontotemporal dementia). Sensitivity tests supported the robustness of the results. This two-sample MR analysis suggests that genetically predicted MASLD and liver fibrosis and cirrhosis may increase the VD risk. Nonetheless, the causal effects of NAFLD-related diseases on VD need more in-depth research.

Identifying causal relationships between gastroesophageal reflux and extraesophageal diseases: A Mendelian randomization study.

Traditional observational and in vivo studies have suggested an etiological link between gastroesophageal reflux disease (GERD) and the development of extraesophageal diseases (EEDs), such as noncardiac chest pain. However, evidence demonstrating potential causal relationships is lacking. This study evaluated the potential causal relationship between GERD and EEDs, including throat and chest pain, asthma, bronchitis, chronic rhinitis, nasopharyngitis and pharyngitis, gingivitis and periodontal disease, cough, using multiple Mendelian randomization (MR) methods, and sensitivity analysis was performed. The Mendelian randomization Pleiotropy RESidual Sum and Outlier and PhenoScanner tools were used to further check for heterogeneous results and remove outliers. MR with inverse-variance weighted (IVW) showed a significant causal relationship between GERD and EEDs after Bonferroni correction. IVW results indicated that GERD increased the risk of chronic rhinitis, nasopharyngitis and pharyngitis (odds ratio [OR] = 1.482, 95% confidence interval [CI] = 1.267-1.734, P < .001], gingivitis and periodontal disease (OR = 1.166, 95% CI = 1.046-1.190, P = .001), throat and chest pain (OR = 1.585, 95% CI = 1.455-1.726, P < .001), asthma (OR = 1.539, 95% CI = 1.379-1.717, P < .001), and bronchitis (OR = 1.249, 95% CI = 1.168-1.335, P < .001). Sensitivity analysis did not detect pleiotropy. Leave-one-out analysis shows that MR results were not affected by individual single nucleotide polymorphisms. The funnel plot considers the genetic instrumental variables to be almost symmetrically distributed. This MR supports a causal relationship among GERD and EEDs. Precise moderation based on causality and active promotion of collaboration among multidisciplinary physicians ensure high-quality diagnostic and treatment recommendations and maximize patient benefit.