Dataset Information

Two-sample Mendelian randomization study does not reveal a significant relationship between cytomegalovirus (CMV) infection and autism spectrum disorder.

ABSTRACT:

Background

Autism spectrum disorder (ASD) is a neurodevelopmental disorder affecting ~ 2% of children worldwide and is characterized by repetitive, stereotypical behaviours and impaired expressive communication. Cytomegalovirus (CMV) is considered a risk factor for ASD; however, published studies are usually limited by covering too few events and have different conclusions, indicating that the relationship between CMV infection and ASD remains elusive.

Methods

To investigate the association between CMV infection and ASD, we conducted this 2-sample Mendelian randomization (MR) study using genome-wide association studies (GWAS) summary data from FinnGen and the IEU Open GWAS project.

Results

Our results showed no significant relationship between all 3 CMV infections (unspecified cytomegaloviral diseases, anti-CMV IgG levels, and maternal CMV) and ASD.

Conclusions

Our results indicate that CMV infection does not significantly increase ASD risk. These results show that the relationship between CMV infection and ASD remains elusive and needs to be further clarified.

SUBMITTER: Zhang M

PROVIDER: S-EPMC10394766 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature

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Publications

Two-sample Mendelian randomization study does not reveal a significant relationship between cytomegalovirus (CMV) infection and autism spectrum disorder.

Zhang Mengna M Ming Ying Y Du Yunling Y Xin Ziyuan Z

BMC psychiatry 20230802 1

<h4>Background</h4>Autism spectrum disorder (ASD) is a neurodevelopmental disorder affecting ~ 2% of children worldwide and is characterized by repetitive, stereotypical behaviours and impaired expressive communication. Cytomegalovirus (CMV) is considered a risk factor for ASD; however, published studies are usually limited by covering too few events and have different conclusions, indicating that the relationship between CMV infection and ASD remains elusive.<h4>Methods</h4>To investigate the a ...[more]

PMID: 37533011

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Project description:BackgroundAlterations in surface area (SA) in specific regions of the cortex have been reported in many individuals with autism spectrum disorder (ASD), however, the genetic background between ASD and SA is still unclear. This study estimated the genetic correlation and causal effect of ASD and cortical SA.MethodsSummarized data of genome-wide association studies (GWAS) were separately downloaded from the Psychiatric Genomics Consortium (18,381 cases of ASD, and 27,969 controls) and the Enhancing Neuroimaging Genetics through Meta-Analysis Consortium (33,992 participants of Europeans). We used Linkage disequilibrium score regression (LDSC) and Heritability Estimation from Summary Statistics (HESS) to calculate the heritability of each trait. As for the genetic correlation between ASD and SA, LDSC was used for global correlation and HESS was used to examine the local genetic covariance further. We used three Mendelian randomization (MR) methods, Inverse-variance weighted, MR-Egger, and weighted median to estimate the causal relationship.ResultsLDSC observed a nominal significant genetic correlation (rg = 0.1229, P-value = 0.0346) between ASD and SA of the rostral anterior cingulate gyrus whereas analysis through HESS did not reveal any significant loci having genetic covariance. Based on MR results, statistically meaningful estimations were found in the following areas, postcentral cortex (β (SE) = 21.82 (7.84) mm, 95% CI: 6.46 to 37.19 mm, PIVW = 5.38 × 10- 3, PFDR = 3.09 × 10- 2), posterior cingulate gyrus (β (SE) = 6.23 (2.69) mm, 95% CI: 0.96 to 11.49 mm, PIVW = 2.05 × 10- 2, PFDR = 4.26 × 10- 2), supramarginal gyrus (β (SE) = 19.25 (8.43) mm, 95% CI: 29.29 to 35.77 mm, PIVW = 2.24 × 10- 2, PFDR = 4.31 × 10- 2).ConclusionOur results provided genetic evidence to support the opinion that individuals with ASD tend to develop differences in cortical SA of special areas. The findings contributed to understanding the genetic relationship between ASD and cortical SA.

Project description:Background: Previous observational studies have reported an association between psychiatric traits and cardiovascular diseases (CVDs). In this two-sample Mendelian randomization (MR) study, we aimed to investigate the causality between psychiatric traits and CVDs. Methods: Single-nucleotide polymorphisms (SNPs) associated with autism spectrum disorder (ASD), neuroticism, and subjective well-being at genome-wide significance (P < 1 × 10-8) were identified from genome-wide association studies. Summary-level data of the outcomes, including coronary artery disease (CAD), myocardial infarction (MI), atrial fibrillation (AF), and heart failure (HF), were obtained from several largest datasets. The inverse-variance weighted (IVW) method was used as our main analyses to conduct this MR study. Sensitivity analyses included the weighted median, the MR-robust adjusted profile score (MR-RAPS), and the MR pleiotropy residual sum and outlier (MR-PRESSO) method. Repeated MR analyses using a more relaxed threshold (P < 1 × 10-6) for instruments selection and multivariable MR analyses were also applied to evaluate the robustness of results. Results: The MR analyses showed that genetic predisposition to ASD was associated with a higher risk of AF [odds ratio (OR), 1.109; 95% confidence interval (CI), 1.023-1.201; P = 0.011] and HF (OR, 1.138; 95% CI, 1.036-1.251; P = 0.007). Neuroticism was casually associated with an increased risk of AF (OR, 1.201; 95% CI, 1.037-1.392; P = 0.015), whereas subjective well-being had a protective effect on HF (OR, 0.732; 95% CI, 0.574-0.933; P = 0.012). No other causal association between psychiatric traits and CVDs was observed. Consistent results were obtained in sensitivity analyses. Conclusion: This study provided evidence of causal associations of ASD with a higher risk of AF and HF. Besides, neuroticism was casually associated with an increased risk of AF, and subjective well-being was associated with a decreased risk of HF.

Project description:BackgroundIn recent years, observational studies have provided evidence supporting a potential association between autism spectrum disorder (ASD) and gut microbiota. However, the causal effect of gut microbiota on ASD remains unknown.MethodsWe identified the summary statistics of 206 gut microbiota from the MiBioGen study, and ASD data were obtained from the latest Psychiatric Genomics Consortium Genome-Wide Association Study (GWAS). We then performed Mendelian randomization (MR) to determine a causal relationship between the gut microbiota and ASD using the inverse variance weighted (IVW) method, simple mode, MR-Egger, weighted median, and weighted model. Furthermore, we used Cochran's Q test, MR-Egger intercept test, Mendelian Randomization Pleiotropy RESidual Sum and Outlier (MR-PRESSO), and leave-one-out analysis to identify heterogeneity and pleiotropy. Moreover, the Benjamin-Hochberg approach (FDR) was employed to assess the strength of the connection between exposure and outcome. We performed reverse MR analysis on the gut microbiota that were found to be causally associated with ASD in the forward MR analysis to examine the causal relationships. The enrichment analyses were used to analyze the biological function at last.ResultsBased on the results of IVW results, genetically predicted family Prevotellaceae and genus Turicibacter had a possible positive association with ASD (IVW OR=1.14, 95% CI: 1.00-1.29, P=3.7×10-2), four gut microbiota with a potential protective effect on ASD: genus Dorea (OR=0.81, 95% CI: 0.69-0.96, P=1.4×10-2), genus Ruminiclostridium5 (OR=0.81, 95% CI: 0.69-0.96, P=1.5×10-2), genus Ruminococcus1 (OR=0.83, 95% CI: 0.70-0.98, P=2.8×10-2), and genus Sutterella (OR=0.82, 95% CI: 0.68-0.99, P=3.6×10-2). After FDR multiple-testing correction we further observed that there were two gut microbiota still have significant relationship with ASD: family Prevotellaceae (IVW OR=1.24; 95% CI: 1.09-1.40, P=9.2×10-4) was strongly positively correlated with ASD and genus RuminococcaceaeUCG005 (IVW OR=0.78, 95% CI: 0.67-0.89, P=6.9×10-4) was strongly negatively correlated with ASD. The sensitivity analysis excluded the influence of heterogeneity and horizontal pleiotropy.ConclusionOur findings reveal a causal association between several gut microbiomes and ASD. These results deepen our comprehension of the role of gut microbiota in ASD's pathology, providing the foothold for novel ideas and theoretical frameworks to prevent and treat this patient population in the future.

Project description:BackgroundsMany studies have shown particulate matter has emerged as one of the major environmental risk factors for diabetes; however, studies on the causal relationship between particulate matter 2.5 (PM2.5) and diabetes based on genetic approaches are scarce. The study estimated the causal relationship between diabetes and PM2.5 using two sample mendelian randomization (TSMR).MethodsWe collected genetic data from European ancestry publicly available genome wide association studies (GWAS) summary data through the MR-BASE repository. The IEU GWAS information output PM2.5 from the Single nucleotide polymorphisms (SNPs) GWAS pipeline using pheasant-derived variables (Consortium = MRC-IEU, sample size: 423,796). The annual relationship of PM2.5 (2010) were modeled for each address using a Land Use Regression model developed as part of the European Study of Cohorts for Air Pollution Effects. Diabetes GWAS information (Consortium = MRC-IEU, sample size: 461,578) were used, and the genetic variants were used as the instrumental variables (IVs). We performed three representative Mendelian Randomization (MR) methods: Inverse Variance Weighted regression (IVW), Egger, and weighted median for causal relationship using genetic variants. Furthermore, we used a novel method called MR Mixture to identify outlier SNPs.ResultsFrom the IVW method, we revealed the causal relationship between PM2.5 and diabetes (Odds ratio [OR]: 1.041, 95% CI: 1.008-1.076, P = 0.016), and the finding was substantiated by the absence of any directional horizontal pleiotropy through MR-Egger regression (β = 0.016, P = 0.687). From the IVW fixed-effect method (i.e., one of the MR machine learning mixture methods), we excluded outlier SNP (rs1537371) and showed the best predictive model (AUC = 0.72) with a causal relationship between PM2.5 and diabetes (OR: 1.028, 95% CI: 1.006-1.049, P = 0.012).ConclusionWe identified the hypothesis that there is a causal relationship between PM2.5 and diabetes in the European population, using MR methods.

Dataset Information

Two-sample Mendelian randomization study does not reveal a significant relationship between cytomegalovirus (CMV) infection and autism spectrum disorder.

Background

Methods

Results

Conclusions

Publications

Two-sample Mendelian randomization study does not reveal a significant relationship between cytomegalovirus (CMV) infection and autism spectrum disorder.

Similar Datasets

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