Genome-wide Association Studies Statistics Research Articles

Genetic evidence of bidirectional mendelian randomization study on the causality between gut microbiome and respiratory diseases contributes to gut-lung axis

Observational studies and clinical trials have suggested the relationship between the gut microbiome and respiratory diseases, but the causality between them remains unclear. Firstly, we selected eight respiratory diseases Genome-wide association study (GWAS) datasets mainly from the FinnGen collaboration as outcomes. The exposure was based on GWAS statistics about the gut microbiome, sourced from the MiBioGen consortium, including gut microbial taxa. The causal link between the gut microbiome and respiratory illnesses was then estimated using a Two-sample Mendelian randomization (MR) analysis, including the inverse-variance weighted (IVW), weighted median, MR-Egger, simple mode, and weighted mode. To ensure reliability, F-statistics and sensitivity tests were conducted. Furthermore, we performed a reverse MR analysis of the pre-Mendelian positive findings to possible reverse causality. For the 196 gut microbe taxa, the IVW analysis suggested 88 potential associations with eight clinically prevalent respiratory diseases. Among them, 30 causal associations were found in more than one MR method. Multiple statistical corrections have confirmed three causal associations: genus Holdemanella was a risk factor for chronic obstructive pulmonary disease (COPD) (P = 1.3 × 10−4, OR = 1.18), family FamilyXIII was a protective factor for COPD (P = 1.3 × 10−3, OR = 0.75), and genus Oxalobacter was a risk factor for asthma (P = 2.1 × 10−4, OR = 1.09). Our MR analysis results indicate that there would be a causal relationship between the gut microbiome and respiratory diseases, contributing to the gut-lung axis. This finding offers new insights into the gut microbiome’s roles in respiratory diseases’ clinical prevention, pathogenesis, and improvement of clinical symptoms. Further randomized controlled trials are necessary to clarify the protective effect of probiotics and fecal microbial transplantation on respiratory health.

Integrating transcriptomics, eQTL, and Mendelian randomization to dissect monocyte roles in severe COVID-19 and gout flare.

There are considerable similarities between the pathophysiology of gout flare and the dysregulated inflammatory response in severe COVID-19 infection. Monocytes are the key immune cells involved in the pathogenesis of both diseases. Therefore, it is critical to elucidate the molecular basis of the function of monocytes in gout and COVID-19 in order to develop more effective therapeutic approaches. The single-cell RNA sequencing (scRNA-seq), large-scale genome-wide association studies (GWAS), and expression quantitative trait loci (eQTL) data of gout and severe COVID-19 were comprehensively analyzed. Cellular heterogeneity and intercellular communication were identified using the scRNA-seq datasets, and the monocyte-specific differentially expressed genes (DEGs) between COVID-19, gout and normal subjects were screened. In addition, the correlation of the DEGs with severe COVID-19 and gout flare was analyzed through GWAS statistics and eQTL data. The scRNA-seq analysis exhibited that the proportion of classical monocytes was increased in both severe COVID-19 and gout patient groups compared to healthy controls. Differential expression analysis and MR analysis showed that NLRP3 was positively associated with the risk of severe COVID-19 and involved 11 SNPs, of which rs4925547 was not significantly co-localized. In contrast, IER3 was positively associated with the risk of gout and involved 9 SNPs, of which rs1264372 was significantly co-localized. Monocytes have a complex role in gout flare and severe COVID-19, which underscores the potential mechanisms and clinical significance of the interaction between the two diseases.

Genetically defined causal effects of natural killer cells related traits in risk of infection: a Mendelian randomization study

BackgroundThe intricate interplay between genetics and immunology often dictates the host’s susceptibility to various diseases. This study explored the genetic causal relationship between natural killer (NK) cell-related traits and the risk of infection.MethodsSingle-nucleotide polymorphisms (SNPs) significantly associated with NK cell-related traits were selected as instrumental variables to estimate their genetic causal effects on infection. SNPs from a genome-wide association study (GWAS) on NK cell-related traits, including absolute cell counts, median fluorescence intensities reflecting surface antigen levels, and relative cell counts, were used as exposure instruments. Summary-level GWAS statistics of four phenotypes of infection were used as the outcome data. The exposure and outcome data were analyzed via the two-sample Mendelian randomization method.ResultsEach one standard deviation increase in the expression level of human leukocyte antigen (HLA)-DR on HLA-DR+ NK cells was associated with a lower risk of pneumonia (P < 0.05). An increased HLA-DR+ NK/CD3− lymphocyte ratio was related to a lower of risk of pneumonia (P < 0.05). Each one standard deviation increase in the absolute count of HLA-DR+ NK cells was associated with a lower risk of both bacterial pneumonia and pneumonia (P < 0.05). An increased HLA-DR+ NK/NK ratio was associated with a decreased risk of both pneumonia and bacterial pneumonia (P < 0.05). The results were robust under all sensitivity analyses. No evidence for heterogeneity, pleiotropy, or potential reverse causality was detected. Notably, our analysis did not reveal any significant associations between NK cell-related traits and other phenotypes of infection, including cellulitis, cystitis, and intestinal infection.ConclusionsHLA-DR+ NK cells could be a novel immune cell trait associated with a lower risk of bacterial pneumonia or pneumonia.

Causal influence of immune factors on the risk of diabetic retinopathy: a mendelian randomization study

ObjectivesDiabetic retinopathy (DR) is a prevalent microvascular complication in diabetic patients. Various mechanisms have been implicated in the pathogenesis of DR. Previous studies have observed the relationship between immune factors and DR, but the causal relationship has not been determined.MethodsWe conducted a two-sample Mendelian randomization (MR) analysis of 731 immune cells and DR, using publicly available genome-wide association study (GWAS) summary statistics, to evaluate potential causal relationships between them. Four types of immune traits were included in the analysis through flow cytometry. GWAS statistics for DR were obtained from the Finngen database, which performed GWAS on 190,594 European individuals (Ncase = 14,584, Ncontrol = 176,010) to assess genetically predicted DR. The primary method used to perform causality analysis was inverse variance weighting (IVW).ResultsFollowing false discovery rate (FDR) correction, 11MFI-DR, 5AC-DR, 5RC-DR, and 1MP-DR reached a significant causal association level (PFDR < 0.05). Notably, all AC traits exhibited potential associations with a decreased risk of DR(OR < 1), while a majority of MFI traits, along with the singular MP trait, exhibited potential associations with an increased risk of DR (OR > 1). The highest proportion of T-cell subsets in the final results.ConclusionThis study elucidates that the progression of DR is intricately influenced by immune responses, thereby confirming the immunological susceptibility of DR. Our findings may offer new targets for diagnosing and treating DR, as well as aid in developing therapeutic strategies from an immunological standpoint.

Privacy-Preserving Fingerprinting Against Collusion and Correlation Threats in Genomic Data

Sharing genomic databases is critical to the collaborative research in computational biology. A shared database is more informative than specific genome-wide association studies (GWAS) statistics as it enables do-it-yourself calculations. Genomic databases involve intellectual efforts from the curator and sensitive information of participants, thus in the course of data sharing, the curator (database owner) should be able to prevent unauthorized redistributions and protect genomic data privacy. As it becomes increasingly common for a single database be shared with multiple recipients, the shared genomic database should also be robust against collusion attack, where multiple malicious recipients combine their individual copies to forge a pirated one with the hope that none of them can be traced back. The strong correlation among genomic entries also make the shared database vulnerable to attacks that leverage the public correlation models. In this paper, we assess the robustness of shared genomic database under both collusion and correlation threats. To this end, we first develop a novel genomic database fingerprinting scheme, called Gen-Scope. It achieves both copyright protection (by enabling traceability) and privacy preservation (via local differential privacy) for the shared genomic databases. To defend against collusion attacks, we augment Gen-Scope with a powerful traitor tracing technique, i.e., the Tardos codes. Via experiments using a real-world genomic database, we show that Gen-Scope achieves strong fingerprint robustness, e.g., the fingerprint cannot be compromised even if the attacker changes 45% of the entries in its received fingerprinted copy and colluders will be detected with high probability. Additionally, Gen-Scope outperforms the considered baseline methods. Under the same privacy and copyright guarantees, the accuracy of the fingerprinted genomic database obtained by Gen-Scope is around 10% higher than that achieved by the baseline, and in terms of preservations of GWAS statistics, the consistency of variant-phenotype associations can be about 20% higher. Notably, we also empirically show that Gen-Scope can identify at least one of the colluders even if malicious receipts collude after independent correlation attacks.

Evaluation of the causal effects of immune cells on ischemic stroke: a Mendelian randomization study.

Ischemic stroke (IS) is a cerebrovascular disease caused by various factors, and its etiology remains inadequately understood. The role of immune system dysfunction in IS has been increasingly recognized. Our objective was to evaluate whether circulating immune cells causally impact IS risk. We conducted two-sample Mendelian randomization analyses to evaluate the causal effects of 731 immune cell traits on IS, utilizing publicly available genome-wide association studies (GWAS) summary statistics for 731 immune cell traits as exposure data, and two GWAS statistics for IS as outcome data. A set of sensitivity analyses, including Cochran's Q test, I 2 statistics, MR-Egger intercept test, MR-PRESSO global test, and leave-one-out sensitivity analyses, were performed to assess the robustness of the results. Additionally, meta-analyses were conducted to combine the results from the two different IS datasets. Finally, we extracted instrumental variables of immune cell traits with causal effects on IS in both IS datasets for SNP annotation. A total of 41 and 35 immune cell traits were identified to have significant causal effects on IS based on two different IS datasets, respectively. Among them, the immune cell trait CD62L- plasmacytoid Dendritic Cell AC and CD4+ CD8dim T cell%leukocyte respectively served as risk factor and protective element in both IS datasets. The robustness of the causal effects was confirmed through the sensitivity analyses. The results of the meta-analyses further support the causal effects of CD62L- plasmacytoid Dendritic Cell AC (pooled OR=1.030, 95%CI: 1.011-1.049, P=0.002) and CD4+ CD8dim T cell%leukocyte (pooled OR=0.959, 95%CI: 0.935-0.984, P=0.001). Based on these two immune cell traits, 33 genes that may be related to the causal effects were mapped. Our study demonstrated the potential causal effects of circulating immune cells on IS, providing valuable insights for future studies aimed at preventing IS.

Diastolic and systolic blood pressure and gout: a Mendelian randomization study.

Although there is solid epidemiological evidence supporting the connection between hypertension and gout, little has been said about the relationship between diastolic and systolic blood pressure and gout, the causal relationship and direction associated are uncertain, so we aim to research the causal relationship between diastolic and systolic blood pressure and gout. We conducted a two-sample Mendelian randomization (MR) analysis to assess the causal effect between 2 blood pressure phenotypes (including diastolic blood pressure and systolic blood pressure) and 5 gout phenotypes (including gout, drug-induced gout, idiopathic gout, unspecified gout, and strictly defined gout) using genome-wide association study statistics. The inverse variance weighting method was used to generate the main results, while sensitivity analyses using MR-Egger, weighted median, Cochran's Q test, Egger intercept test, and leave-one-out analysis, were performed to assess the stability and reliability of the results. After the screening, we found a causal relationship between diastolic blood pressure and gout, idiopathic gout, unspecified gout, and strictly defined gout, and a causal relationship between systolic blood pressure and gout, idiopathic gout, unspecified gout, and strictly defined gout. From a genetic predisposition, controlling blood pressure may reduce the risk of gout.

Oxidative stress and neurodegenerative diseases: a bidirectional Mendelian randomization study.

Oxidative stress (OS) has been linked to neurodegenerative diseases in numerous epidemiological studies; however, whether it is a pathogenesis or a downstream factor remains controversial. A two-sample bidirectional Mendelian randomization (MR) analysis was implemented to examine evidence of causality of 15 OS injury markers with 3 major neurodegenerative diseases using available genome-wide association studies statistics. As a main approach, inverse-variance weighted (IVW) analysis was performed. The weighted-median (WM) analysis was used to validate the relationship. In order to investigate the existence of horizontal pleiotropy and correct the IVW estimate, the Radial MR approach was applied. To gauge the consistency and robustness of the findings, several sensitivity and pleiotropy analyses were used. For this analysis, p < 0.05 indicates a nominally causal association; according to the Bonferroni correction test, p < 0.0011 indicates a statistically significant causal association. Via IVW and WM, in directional MR, it was genetically predicted that zinc was nominally causally correlated with the risk of Parkinson's disease but not after Bonferroni correction test; alpha-tocopherol was nominally causally correlated with the risk of Amyotrophic lateral sclerosis (ALS) but not after Bonferroni correction test; furthermore, in reverse MR, it was genetically predicted that Alzheimer's disease was causally correlated with uric acid but not after Bonferroni correction test. These above findings were stable across sensitivity and pleiotropy analyses. Based on the current study, there is no authentic genetic causal association between OS biomarkers and neurodegenerative diseases. The complex relationship is required to be confirmed in future experimental research.

Causal associations between autoimmune diseases and sarcopenia-related traits: a bi-directional Mendelian randomization study.

Sarcopenia is common in patients with autoimmune diseases (ADs); however, the causal associations between ADs and sarcopenia remain unclear. Therefore, this study investigated the causal associations using bi-directional Mendelian randomization analysis. Exposure-related single-nucleotide polymorphisms (SNPs) were extracted from genome-wide association studies (GWASs). GWAS statistics for common ADs [Crohn's disease (CD), ulcerative colitis (UC), rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), psoriasis (PSO), and multiple sclerosis (MS)] and sarcopenia-related traits [hand grip strength (HGS), appendicular fat-free mass (FFM), and walking pace] were obtained from public datasets. Inverse-variance weighting as the main method was used to evaluate the causal effect. Genetically predicted CD had causal effects on whole-body FFM (β = -0.005, p = 0.001), leg FFM (βleft = -0.006, p = 1.8E-4; βright = -0.007, p = 2.0E-4), and arm FFM (βleft = -0.005, p = 0.005; βright = -0.005, p = 0.001), while RA had causal effects on 8 sarcopenia-related traits, namely, HGS (βleft = -2.06, p = 2.8E-38; βright = -2.311, p = 2E-20), whole-body FFM (β = -0.842, p = 4.7E-10), leg FFM (βleft = -0.666, p = 2.6E-6; βright = -0.073, p = 2.1E-3), arm FFM (βleft = -0.63, p = 4.4E-6; βright = -0.736, p = 4.4E-8), and walking pace (β = -1.019, p = 6.2E-14). In the reverse direction, HGS (odds ratio [OR]left = 10.257, p = 3.6E-5; ORright = 16.445, p = 3.7E-7) had causal effects on CD, while HGS (ORleft = 0.994, p = 0.004; ORright = 0.993, p = 1.4E-4), leg FFM (ORleft = 1.003, p = 0.005; ORright = 1.005, p = 1.9E-4), and walking pace (OR = 0.985, p = 5.7E-5) were causally associated with RA. No evidence showed causal associations of UC, SLE, PSO, or MS with sarcopenia-related traits. Our study demonstrated that the genetic susceptibility to CD and RA was associated with high risk of sarcopenia, and some sarcopenia-related traits had causal effects on CD or RA.

Associations between gut microbiota and gynecological cancers: A bi-directional two-sample Mendelian randomization study.

Growing evidence has suggested that gut microbiota is associated with gynecologic cancers. However, whether there is a causal relationship between these associations remains to be determined. A two-sample Mendelian randomization (MR) evaluation was carried out to investigate the mechanism associating gut microbiota and 3 prevalent gynecological cancers, ovarian cancer (OC), endometrial cancer, and cervical cancer as well as their subtypes in individuals of European ancestry. The Genome-wide association studies statistics, which are publically accessible, were used. Eligible instrumental single nucleotide polymorphisms that were significantly related to the gut microbiota were selected. Multiple MR analysis approaches were carried out, including inverse variance weighted, MR-Egger, Weighted Median methods, and a range of sensitivity analyses. Lastly, we undertook a reverse MR analysis to evaluate the potential of reverse causality. We sifted through 196 bacterial taxa and identified 33 suggestive causal relationships between genetic liability in the gut microbiota and gynecological cancers. We found that 11 of these genera could be pathogenic risk factors for gynecological cancers, while 19 could lessen the risk of cancer. In the other direction, gynecological cancers altered gut microbiota composition. Our MR analysis revealed that the gut microbiota was causally associated with OC, endometrial cancer, and cervical cancer. This may assist in providing new insights for further mechanistic and clinical studies of microbiota-mediated gynecological cancer.

Association of autoimmune and allergic diseases with senile cataract: a bidirectional two-sample Mendelian randomization study.

Many observational studies have been reported that patients with autoimmune or allergic diseases seem to have a higher risk of developing senile cataract, but the views are not consistent. In order to minimize the influence of reverse causality and potential confounding factors, we performed Mendelian Randomization (MR) analysis to investigate the genetic causal associations between autoimmune, allergic diseases and senile cataract. Single nucleotide polymorphisms associated with ten common autoimmune and allergic diseases were obtained from the IEU Open genome-wide association studies (GWAS) database. Summary-level GWAS statistics for clinically diagnosed senile cataract were obtained from the FinnGen research project GWAS, which consisted of 59,522 individuals with senile cataracts and 312,864 control individuals. MR analysis was conducted using mainly inverse variance weighted (IVW) method and further sensitivity analysis was performed to test robustness. As for ten diseases, IVW results confirmed that type 1 diabetes (OR = 1.06; 95% CI = 1.05-1.08; p = 2.24×10-12), rheumatoid arthritis (OR = 1.05; 95% CI = 1.02-1.08; p = 1.83×10-4), hypothyroidism (OR = 2.4; 95% CI = 1.42-4.06; p = 1.12×10-3), systemic lupus erythematosus (OR = 1.02; 95% CI = 1.01-1.03; p = 2.27×10-3), asthma (OR = 1.02; 95% CI = 1.01-1.03; p = 1.2×10-3) and allergic rhinitis (OR = 1.07; 95% CI = 1.02-1.11; p = 2.15×10-3) were correlated with the risk of senile cataract. Celiac disease (OR = 1.04; 95% CI = 1.01-1.08; P = 0.0437) and atopic dermatitis (OR = 1.05; 95% CI = 1.01-1.10; P = 0.0426) exhibited a suggestive connection with senile cataract after Bonferroni correction. These associations are consistent across weighted median and MR Egger methods, with similar causal estimates in direction and magnitude. Sensitivity analysis further proved that these associations were reliable. The results of the MR analysis showed that there were causal relationships between type 1 diabetes, rheumatoid arthritis, hypothyroidism, systemic lupus erythematosus, asthma, allergic rhinitis and senile cataract. To clarify the possible role of autoimmune and allergy in the pathophysiology of senile cataract, further studies are needed.

Principal and independent genomic components of brain structure and function.

The highly polygenic and pleiotropic nature of behavioural traits, psychiatric disorders and structural and functional brain phenotypes complicate mechanistic interpretation of related genome-wide association study (GWAS) signals, thereby obscuring underlying causal biological processes. We propose genomic principal and independent component analysis (PCA, ICA) to decompose a large set of univariate GWAS statistics of multimodal brain traits into more interpretable latent genomic components. Here we introduce and evaluate this novel methods various analytic parameters and reproducibility across independent samples. Two UK Biobank GWAS summary statistic releases of 2240 imaging-derived phenotypes (IDPs) were retrieved. Genome-wide beta-values and their corresponding standard-error scaled z-values were decomposed using genomic PCA/ICA. We evaluated variance explained at multiple dimensions up to 200. We tested the inter-sample reproducibility of output of dimensions 5, 10, 25 and 50. Reproducibility statistics of the respective univariate GWAS served as benchmarks. Reproducibility of 10-dimensional PCs and ICs showed the best trade-off between model complexity and robustness and variance explained (PCs: |rz - max| = 0.33, |rraw - max| = 0.30; ICs: |rz - max| = 0.23, |rraw - max| = 0.19). Genomic PC and IC reproducibility improved substantially relative to mean univariate GWAS reproducibility up to dimension 10. Genomic components clustered along neuroimaging modalities. Our results indicate that genomic PCA and ICA decompose genetic effects on IDPs from GWAS statistics with high reproducibility by taking advantage of the inherent pleiotropic patterns. These findings encourage further applications of genomic PCA and ICA as fully data-driven methods to effectively reduce the dimensionality, enhance the signal to noise ratio and improve interpretability of high-dimensional multitrait genome-wide analyses.

The Association between the Gut Microbiota and Erectile Dysfunction.

Explore the causal relationship between the gut microbiota and erectile dysfunction (ED) at phylum, class, order, family, and genus levels, and identify specific pathogenic bacteria that may be associated with the onset and progression of ED. The genetic variation data of 196 human gut microbiota incorporated in our study came from the human gut microbiome Genome Wide Association Studies (GWAS) dataset released by the MiBioGen Consortium. The GWAS statistics for ED were extracted from one study by Bovijn et al., which included 223,805 participants of European ancestry, of whom 6,175 were diagnosed with ED. Subsequently, Mendelian randomization (MR) analysis was carried out to explore whether a causal relationship exists between the gut microbiota and ED. Additionally, bidirectional MR analysis was performed to examine the directionality of the causal relationship. Through MR analysis, we found that family Lachnospiraceae (odds ratio [OR]: 1.27, 95% confidence interval [CI]: 1.05-1.52, p=0.01) and its subclass genus LachnospiraceaeNC2004 group (OR: 1.17, 95% CI: 1.01-1.37, p=0.04) are associated with a higher risk of ED. In addition, genus Oscillibacter (OR: 1.17, 95% CI: 1.02-1.35, p=0.03), genus Senegalimassilia (OR: 1.32, 95% CI: 1.06-1.64, p=0.01) and genus Tyzzerella3 (OR: 1.14, 95% CI: 1.02-1.27, p=0.02) also increase the risk of ED. In contrast, the inverse variance weighted estimate of genus RuminococcaceaeUCG013 (OR: 0.77, 95% CI: 0.61-0.96, p=0.02) suggests that it has a protective effect against the occurrence of ED. This study preliminarily identified 6 bacterial taxa that may have a causal relationship with ED, including family Lachnospiraceae, genus Lachnospiraceae NC2004 group, Oscillibacter, Senegalimassilia, Tyzzerella 3 and Ruminococcaceae UCG013. These identified important bacterial taxa may serve as candidates for microbiome intervention in future ED clinical trials.

A Mendelian randomization study on the causal association of circulating cytokines with colorectal cancer

Background Circulating cytokines have been associated with colorectal cancer (CRC). However, their causal correlation remains undetermined. This investigation uses genetic data to evaluate the mechanism that links circulating cytokines and CRC via Mendelian Randomization (MR). Methods A two-sample MR evaluation was carried out to investigate the mechanism associating circulating cytokines and CRC in individuals of European ancestry. The Genome-wide association studies statistics, which are publically accessible, were used. Eligible instrumental SNPs that were significantly related to the circulating cytokines were selected. Multiple MR analysis approaches were carried out, including Simple Mode, inverse variance weighted (IVW), MR-Egger, Weighted Mode, Weighted Median, and MR pleiotropy residual sum and outlier (MR-PRESSO) methods. Results The evidence supporting the association of genetically predicted circulating levels with the increased risk of CRC was revealed; these included vascular endothelial growth factor (OR = 1.352, 95% CI: 1.019–1.315, P = 0.024), interleukin-12p70 (OR = 1.273, 95% CI: 1.133–1.430, P = 4.68×10−5), interleukin-13 (OR = 1.149, 95% CI: 1.012–1.299, P = 0.028), interleukin-10 (OR = 1.230, 95% CI: 1.013–1.493, P = 0.037), and interleukin-7 (OR = 1.191, 95% CI: 1.023–1.386 P = 0.024). Additionally, MR analysis negative causal association between macrophage colony stimulating factor and CRC (OR = 0.854, 95% CI: 0.764–0.955, P = 0.005). The data from Simple Mode, Weighted Median, MR-Egger, and Weighted Mode analyses were consistent with the IVW estimates. Furthermore, the sensitivity analysis indicated that the presence of no horizontal pleiotropy to bias the causal estimates. Conclusion This investigation identified a causal association between circulating cytokines levels risk of CRC and may provide a deeper understanding of the pathogenesis of CRC, as well as offer promising leads for the development of novel therapeutic targets for CRC.

Causal associations between inflammatory bowel disease and anxiety: A bidirectional Mendelian randomization study.

Anxiety is common in patients with inflammatory bowel disease (IBD), including those with ulcerative colitis (UC) and Crohn's disease (CD); however, the causal relationship between IBD and anxiety remains unknown. To investigate the causal relationship between IBD and anxiety by using bidirectional Mendelian randomization analysis. Single nucleotide polymorphisms retrieved from genome-wide association studies (GWAS) of the European population were identified as genetic instrument variants. GWAS statistics for individuals with UC (6968 patients and 20464 controls; adults) and CD (5956 patients and 14927 controls; adults) were obtained from the International IBD Genetics Consortium. GWAS statistics for individuals with anxiety were obtained from the Psychiatric Genomics Consortium (2565 patients and 14745 controls; adults) and FinnGen project (20992 patients and 197800 controls; adults), respectively. Inverse-variance weighted was applied to assess the causal relationship, and the results were strengthened by heterogeneity, pleiotropy and leave-one-out analyses. Genetic susceptibility to UC was associated with an increased risk of anxiety [odds ratio: 1.071 (95% confidence interval: 1.009-1.135), P = 0.023], while genetic susceptibility to CD was not associated with anxiety. Genetic susceptibility to anxiety was not associated with UC or CD. No heterogeneity or pleiotropy was observed, and the leave-one-out analysis excluded the potential influence of a particular variant. This study revealed that genetic susceptibility to UC was significantly associated with anxiety and highlighted the importance of early screening for anxiety in patients with UC.

Assessing the causal association between human blood metabolites and the risk of gout.

The occurrence of gout is closely related to metabolism, but there is still a lack of evidence on the causal role of metabolites in promoting or preventing gout. We applied a two-sample Mendelian randomization (MR) analysis to assess the association between 486 serum metabolites and gout using genome-wide association study statistics. The inverse variance weighting method was used to generate the main results, while sensitivity analyses using MR-Egger, weighted median, Cochran's Q test, Egger intercept test, and leave-one-out analysis, were performed to assess the stability and reliability of the results. We also performed a metabolic pathway analysis to identify potential metabolic pathways. After screening, 486 metabolites were retained for MR analysis. After screening by IVW and sensitivity analysis, 14 metabolites were identified with causal effect on gout (P < 0.05), among which hexadecanedioate was the most significant candidate metabolite associated with a lower risk of gout (IVW OR = 0.50; 95% CI = 0.38-0.67; P = 1.65 × 10-6 ). Metabolic pathway analysis identified one pathway that may be associated with the disease. This MR study combining genomics with metabolomics provides a novel insight into the causal role of blood metabolites in the risk of gout, which implies that examination of certain blood metabolites would be a feasible strategy for screening populations with a higher risk of gout.

The association between constipation and stroke based on the NHANES and Mendelian randomization study.

This study aimed to investigate putative causal effects between constipation and stroke using bidirectional Mendelian randomization (MR) analysis. Based on the cross-sectional study, logistic regression models were developed to assess the association between constipation and stroke prevalence. Subsequently, genome-wide association studies statistics were employed to perform MR analysis between constipation and stroke, as well as its subtypes. The inverse variance weighting (IVW) method was the primary method, complemented by four additional methods, namely weighted median, weighted mode, simple mode, and MR-Egger regression. Cochran's Q test, MR-Egger intercept test, MR Pleiotropy RESidual Sum and Outlier, and MR Steiger test were performed to assess heterogeneity and pleiotropy effects. Constipation was associated with a greater risk of stroke even after adjusting for all covariates in logistic regression [odds ratio (OR) = 1.46, 95% confidence interval (CI) = 1.01-2.09, p = 0.042]. IVW MR analysis revealed that constipation affected large artery atherosclerosis (LAS; IVW OR = 1.5, 95% CI = 1.07-2.104, p = 0.019). No significant or suggestive association was observed with the risk of stroke or its various subtypes in MR analysis. Meanwhile, reverse MR analysis revealed no significant causal relationship between stroke or other stroke subtypes and constipation. The results of sensitivity analyses revealed no significant horizontal pleiotropy affecting causal estimates. While cross-sectional studies have established that constipation increases the risk of stroke, this two-sample bidirectional MR analysis revealed a positive correlation between constipation and LAS. However, no such correlation was observed between constipation and stroke, including its various subtypes.

Systematic analyses of GWAS summary statistics from UK Biobank identified novel susceptibility loci and genes for upper gastrointestinal diseases.

In recent decades, upper gastrointestinal (GI) diseases have been highly prevalent worldwide. Although genome-wide association studies (GWASs) have identified thousands of susceptibility loci, only a few of them were conducted for chronic upper GI disorders, and most of them were underpowered and with small sample sizes. Additionally, for the known loci, only a tiny fraction of heritability can be explained and the underlying mechanisms and related genes remain unclear. In this study, we conducted a multi-trait analysis by the MTAG software and a two-stage transcriptome-wide association study (TWAS) with UTMOST and FUSION for seven upper GI diseases (oesophagitis, gastro-oesophageal reflux disease, other diseases of oesophagus, gastric ulcer, duodenal ulcer, gastritis and duodenitis and other diseases of stomach and duodenum) based on summary GWAS statistics from UK Biobank. In the MTAG analysis, we identified 7 loci associated with these upper GI diseases, including 3 novel ones at 4p12 (rs10029980), 12q13.13 (rs4759317) and 18p11.32 (rs4797954). In the TWAS analysis, we revealed 5 susceptibility genes in known loci and identified 12 novel potential susceptibility genes, including HOXC9 at 12q13.13. Further functional annotations and colocalization analysis indicated that rs4759317 (A>G) driven the association for GWAS signals and expression quantitative trait loci (eQTL) simultaneously at 12q13.13. The identified variant acted by decreasing the expression of HOXC9 to affect the risk of gastro-oesophageal reflux disease. This study provided insights into the genetic nature of upper GI diseases.

Natural killer cells-related immune traits and amyotrophic lateral sclerosis: A Mendelian randomization study.

BackgroundObservational studies have suggested that peripheral immune disorders are associated with amyotrophic lateral sclerosis (ALS). Previous studies predominantly focused on changes in adaptive immunity. However, emerging evidence showed natural killer (NK) cells, an essential component of innate immunity, were involved in the degeneration of motor neurons. However, the causal relationship between dysregulated NK cells-related immune traits and ALS remains unclear.ObjectiveThis study aimed to explore the causal relationship between NK cells-related immune traits and the risk of ALS.Materials and methodsSingle nucleotide polymorphisms (SNPs) significantly associated with NK cells-related immune traits were selected as instrumental variables to estimate their causal effects on ALS. SNPs from a genome-wide association study (GWAS) on NK cells-related immune traits were used as exposure instruments, including an absolute NK-cells count, absolute HLA-DR+ NK-cells count, NK cells/lymphocytes, NK cells/CD3– lymphocytes, HLA DR+ NK cells/NK cells, HLA DR+ NK cells/CD3– lymphocytes, and the median fluorescence intensities of CD16–CD56+ on NK cells and HLA-DR+ NK cells. Summary-level GWAS statistics of ALS were used as the outcome data. Exposure and outcome data were analyzed using the two-sample Mendelian randomization (MR) method.ResultsEach one standard deviation increase in the expression levels of CD16–CD56+ on NK cells and HLA-DR+ NK cells were associated with a lower risk of ALS in both the MR-Egger and inverse variance weighted methods (P < 0.05). The results proved robust under all sensitivity analyses. Neither instrumental outliers nor heterogeneity were detected.ConclusionOur results suggest that higher expression levels of CD16–CD56+ on NK cells and HLA-DR+ NK cells are associated with a lower risk of ALS.

Sharing of either phenotypes or genetic variants can increase the accuracy of genomic prediction of feed efficiency

BackgroundSharing individual phenotype and genotype data between countries is complex and fraught with potential errors, while sharing summary statistics of genome-wide association studies (GWAS) is relatively straightforward, and thus would be especially useful for traits that are expensive or difficult-to-measure, such as feed efficiency. Here we examined: (1) the sharing of individual cow data from international partners; and (2) the use of sequence variants selected from GWAS of international cow data to evaluate the accuracy of genomic estimated breeding values (GEBV) for residual feed intake (RFI) in Australian cows.ResultsGEBV for RFI were estimated using genomic best linear unbiased prediction (GBLUP) with 50k or high-density single nucleotide polymorphisms (SNPs), from a training population of 3797 individuals in univariate to trivariate analyses where the three traits were RFI phenotypes calculated using 584 Australian lactating cows (AUSc), 824 growing heifers (AUSh), and 2526 international lactating cows (OVE). Accuracies of GEBV in AUSc were evaluated by either cohort-by-birth-year or fourfold random cross-validations. GEBV of AUSc were also predicted using only the AUS training population with a weighted genomic relationship matrix constructed with SNPs from the 50k array and sequence variants selected from a meta-GWAS that included only international datasets. The genomic heritabilities estimated using the AUSc, OVE and AUSh datasets were moderate, ranging from 0.20 to 0.36. The genetic correlations (rg) of traits between heifers and cows ranged from 0.30 to 0.95 but were associated with large standard errors. The mean accuracies of GEBV in Australian cows were up to 0.32 and almost doubled when either overseas cows, or both overseas cows and AUS heifers were included in the training population. They also increased when selected sequence variants were combined with 50k SNPs, but with a smaller relative increase.ConclusionsThe accuracy of RFI GEBV increased when international data were used or when selected sequence variants were combined with 50k SNP array data. This suggests that if direct sharing of data is not feasible, a meta-analysis of summary GWAS statistics could provide selected SNPs for custom panels to use in genomic selection programs. However, since this finding is based on a small cross-validation study, confirmation through a larger study is recommended.