BackgroundGrowing evidence has shown that alterations in gut microbiota composition are associated with multiple autoimmune diseases (ADs). However, it is unclear whether these associations reflect a causal relationship.ObjectiveTo reveal the causal association between gut microbiota and AD, we conducted a two-sample Mendelian randomization (MR) analysis.Materials and MethodsWe assessed genome-wide association study (GWAS) summary statistics for gut microbiota and six common ADs, namely, systemic lupus erythematosus, rheumatoid arthritis, inflammatory bowel disease, multiple sclerosis, type 1 diabetes (T1D), and celiac disease (CeD), from published GWASs. Two-sample MR analyses were first performed to identify causal bacterial taxa for ADs in discovery samples. Significant bacterial taxa were further replicated in independent replication outcome samples. A series of sensitivity analyses was performed to validate the robustness of the results. Finally, a reverse MR analysis was performed to evaluate the possibility of reverse causation.ResultsCombining the results from the discovery and replication stages, we identified one causal bacterial genus, Bifidobacterium. A higher relative abundance of the Bifidobacterium genus was associated with a higher risk of T1D [odds ratio (OR): 1.605; 95% CI, 1.339–1.922; PFDR = 4.19 × 10−7] and CeD (OR: 1.401; 95% CI, 1.139–1.722; PFDR = 2.03 × 10−3), respectively. Further sensitivity analyses validated the robustness of the above associations. The results of reverse MR analysis showed no evidence of reverse causality from T1D and CeD to the Bifidobacterium genus.ConclusionThis study implied a causal relationship between the Bifidobacterium genus and T1D and CeD, thus providing novel insights into the gut microbiota-mediated development mechanism of ADs.

Evidence supports the observational associations of gut microbiota with a variety of psychiatric disorders, but the causal nature of such associations remains obscure. Aiming to comprehensively investigate their causal relationship and to identify specific causal microbe taxa for psychiatric diseases, we conducted a two-sample Mendelian randomization (MR) analysis of gut microbiome with 15 psychiatric diseases. Specifically, the microbiome genome-wide association study (GWAS) in 18,473 individuals from the MiBioGen study was used as exposure sample, and the GWAS for 15 psychiatric diseases was used as outcome samples. One-hundred ninety bacterial taxa from six levels were available for analysis. At a multiple-testing corrected significance level (phylum P < 5.56 × 10–3, class P < 3.33 × 10–3, order P < 2.63 × 10–3, family P < 1.67 × 10–3, genus P < 4.90 × 10–4, and species P < 3.33 × 10–3), the following eight causal associations from seven bacterial features (one phylum + three classes + one order + one family + one species) were identified: family Prevotellaceae with autism spectrum disorder (P = 5.31 × 10–4), class Betaproteobacteria with bipolar disorder (P = 1.53 × 10–3), class Actinobacteria with schizophrenia (P = 1.33 × 10–3), class Bacteroidia and order Bacteroidales with Tourette syndrome (P = 2.51 × 10–3 and 2.51 × 10–3), phylum Actinobacteria and class Actinobacteria with extroversion (P = 8.22 × 10–4 and 1.09 × 10–3), and species Clostridium innocuum with neuroticism (P = 8.92 × 10–4). Sensitivity analysis showed no evidence of reverse causality, pleiotropy, and heterogeneity. Our findings offered novel insights into the gut microbiota–mediated development mechanism of psychiatric disorders.

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Background Observational studies have demonstrated associations between plasma proteins and obesity, but evidence of causal relationship remains to be studied. Methods To evaluate the causal relationship between plasma proteins and body composition, we conducted a two-sample Mendelian randomization (MR) analysis based on the genome-wide association study (GWAS) summary statistics of 23 body composition traits and 2,656 plasma proteins. We then performed hierarchical cluster analysis to evaluate the structure and pattern of the identified causal associations, and performed gene ontology enrichment analysis to explore the functional relevance of the identified proteins. Results We identified 430 putatively causal effects of 96 plasma proteins on 22 body composition traits (except obesity status) with strong MR evidence (P < 2.53 × 10 −6, at a Bonferroni-corrected threshold). The top 3 causal associations are FST (follistatin) on trunk fat-free mass (Beta = -0.63, SE = 0.04, P = 2.00 × 10 -63), IGFBP1 (insulin-like growth factor-binding protein 1) on trunk fat-free mass (Beta = -0.54, SE = 0.03, P = 1.79 × 10 -57) and RSPO3 (r-spondin-3) on WHR (waist circumference/hip circumference) (Beta = 0.01, SE= 4.47 × 10 -4, P = 5.45 × 10 -60), respectively. Further clustering analysis and pathway analysis demonstrated that the pattern of causal effect to fat mass and fat-free mass may be different. Conclusion Our findings may provide evidence for causal relationships from plasma proteins to various body composition traits and provide basis for further targeted functional studies.

Background Age at natural menopause (ANM) is an important index for women's health. Either early or late ANM is associated with a series of adverse outcomes later in life. Despite being an inheritable trait, its genetic determinant has not yet been fully understood. Methods Aiming to better characterize the genetic architecture of ANM, we conducted genome-wide association study (GWAS) meta-analyses in European-specific as well as trans-ancestry samples by using GWAS summary statistics from the following 3 large studies: the Reproductive Genetics Consortium (ReproGen, N=69,626), the UK Biobank cohort (UKBB, N=111,593) and the BioBank Japan Project (BBJ, N=43,861), followed by a series of bioinformatical assessments and functional annotations. Results By integrating the summary statistics from the 3 GWAS of up to 225,200 participants, this largest meta-analysis identified 49 novel loci and 3 secondary signals that were associated with ANM at the genome-wide significance level (P<5×10 -8). No population specificity or heterogeneity was observed at most of the associated loci. Functional annotations prioritized 90 candidate genes at the newly identified loci. Among the 26 traits that were genetically correlated with ANM, hormone replacement therapy (HRT) exerted a causal relationship, implying a causal pattern by which HRT was determined by ANM. Conclusion Our findings improved our understanding of the etiology of female menopause, as well as shed light on potential new therapies for abnormal menopause.