Exome-Wide Sequencing Study Identified Genetic Variants Associated With Sarcopenic Obesity.

Sarcopenic obesity (SO) is an age-related disease characterized by the coexistence of excessive adiposity and low muscle mass or function. Although obesity and sarcopenia are heritable conditions, the genetic determinants of SO have not been fully understood. We conducted a large-scale exome-wide association analysis of SO in a sequenced sample of 2 887 cases and 113 284 controls and an imputed sample of 4 003 cases and 161 990 controls in the UK Biobank cohort. Single-variant association analysis identified one locus 1q41 (lead SNP rs1417066, LYPLAL1-AS1, odds ratio [OR] = 1.15, 95% confidence interval [CI] = [1.11-1.19], p = 1.75 × 10-14) that was significantly associated with SO at the exome-wide significance level (p < 1 × 10-8). Colocalization analysis in the Genotype-Tissue Expression expression quantitative trait locus database showed that LYPLAL1-AS1 was colocalized with SO in multiple musculoskeletal-related tissues. Gene-based burden test of rare loss-of-function variants identified 5 genes at the gene-wise significance level (p < 4.3 × 10-6): PDE3B (OR = 2.48, p = 1.10 × 10-6), MYOZ3 (OR = 25.49, p = 1.41 × 10-7), SLC15A3 (OR = 4.75, p = 6.82 × 10-7), RNF130 (OR = 25.83, p = 4.07 × 10-6), and TNK2 (OR = 4.25, p = 8.75 × 10-8). Overall, our study uncovered the genetic effects of both common and rare variants on SO susceptibility, expanded existing knowledge of the genetic architecture of SO, and improved understanding of the genetic mechanisms underlying SO.

Screening Plasma Proteins for the Putative Drug Targets for Carpal Tunnel Syndrome.

Carpal tunnel syndrome (CTS) is one of the most common work-related musculoskeletal disorders. The present study sought to identify putative causal proteins for CTS. We conducted a two-sample Mendelian randomization (MR) analysis to evaluate the causal association between 2859 plasma proteins (N = 35,559) and CTS (N = 1,239,680) based on the published GWAS summary statistics. Then we replicated the significant associations using an independent plasma proteome GWAS (N = 10,708). Sensitivity analyses were conducted to validate the robustness of MR results. Multivariate MR and mediation analyses were conducted to evaluate the mediation effects of body mass index (BMI), type 2 diabetes (T2D), and arm tissue composition on the association between putative causal proteins and CTS. Colocalization analysis was used to examine whether the identified proteins and CTS shared causal variant(s). Finally, we evaluated druggability of the identified proteins. Ten plasma proteins were identified as putative causal markers for CTS, including sCD14, PVR, LTOR3, CTSS, SIGIRR, IFNL3, ASPN, TM11D, ASIP, and ITIH1. Sensitivity analyses and reverse MR analysis validated the robustness of their causal effects. Arm tissue composition, BMI, and T2D may play a fully/partial mediating role in the causal relationships of ASIP, TM11D, IFNL3, PVR, and LTOR3 with CTS. The association of ASPN and sCD14 with CTS were supported by colocalization analysis. Druggability assessment demonstrated that sCD14, CTSS, TM11D, and IFNL3 were potential drug therapeutic targets. The present study identified several potential plasma proteins that were causally associated with CTS risk, providing new insights into the pathogenesis of protein-mediated CTS and offering potential targets for new therapies.

Effect of vitamin D3 supplementation in winter on physical performance of university students: a one-month randomized controlled trial.

BACKGROUND: There is epidemiological evidence which suggests an association between 25-hydroxyvitamin D [25(OH)D] levels and bone and muscle function; however, it is unclear whether vitamin D supplementation has an added benefit beyond bone health. Here, we investigated the effects of vitamin D3 supplementation (1 month) on physical performance in Chinese university students in winter. METHODS: One hundred and seventeen eligible subjects with 25(OH)D (19.2 ± 7.8 ng/mL) were randomly assigned to either vitamin D3 supplement (N = 56; 1000 IU/day) or the control (N = 61) group for 1 month. Pre- and post-measurements included: 1) serum levels of 25(OH)D; 2) musculoskeletal and pulmonary function [vertical jump height (VJH) and right handgrip strength (RHS), forced vital capacity (FVC), and forced expiratory volume at 1s (FEV1)]; 3) bone turnover markers [parathyroid hormone (PTH), n-terminal osteocalcin (N-MID), and calcium]; 4) hemoglobin-related parameters [hemoglobin (Hb), hematocrit (HCT), red blood cells (RBC), and red cell distribution width (RDW)]; 5) lipid parameters [total triglycerides (TG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C)]; 6) Fatigue-related indicators [serum creatine kinase (CK), lactate dehydrogenase (LDH), and total testosterone (T)]. In addition, aerobic capacity was assessed by measuring maximal oxygen uptake (VO2max) at baseline. RESULTS: During wintertime, supplementation with 1000 IU/d of vitamin D3 significantly increased serum 25(OH)D levels (from 18.85 ± 7.04 to 26.98 ± 5.88 ng/mL, p < 0.05), accompanied by a decrease of PTH (p < 0.05). However, vitamin D3 supplementation did not significantly impact the physical performance, serum lipid parameters, and bone turnover markers of students. Furthermore, 25(OH)D was found to be positively correlated with VJH and negatively correlated with PTH and TC at the beginning and end of the study (p < 0.05). In addition, the multiple linear regression analysis showed that 25(OH)D combined with athletic, gender, height, weight, Hb, and FVC could account for 84.0% of the VO2max value. CONCLUSIONS: The study demonstrated that one-month of 1000 IU/d of vitamin D3 supplementation during the winter had beneficial effects on 25(OH)D status and PTH. However, vitamin D3 intervention was not sufficient to improve physical performance. Furthermore, 25(OH)D levels combined with athletic, Hb and FVC could be a predictor of VO2max.

Causal associations between type 1 diabetes and COVID-19 infection and prognosis: a two-sample Mendelian randomization study.

INTRODUCTION: It has been suggested that type 1 diabetes was associated with increased COVID-19 morbidity and mortality. However, their causal relationship is still unclear. Herein, we performed a two-sample Mendelian randomization (MR) to investigate the causal effect of type 1 diabetes on COVID-19 infection and prognosis. RESEARCH DESIGN AND METHODS: The summary statistics of type 1 diabetes were obtained from two published genome-wide association studies of European population, one as a discovery sample including 15 573 cases and 158 408 controls, and the other data as a replication sample consisting of 5913 cases and 8828 controls. We first performed a two-sample MR analysis to evaluate the causal effect of type 1 diabetes on COVID-19 infection and prognosis. Then, reverse MR analysis was conducted to determine whether reverse causality exists. RESULTS: MR analysis results showed that the genetically predicted type 1 diabetes was associated with higher risk of severe COVID-19 (OR=1.073, 95% CI: 1.034 to 1.114, pFDR=1.15×10-3) and COVID-19 death (OR=1.075, 95% CI: 1.033 to 1.119, pFDR=1.15×10-3). Analysis of replication dataset showed similar results, namely a positive association between type 1 diabetes and severe COVID-19 (OR=1.055, 95% CI: 1.029 to 1.081, pFDR=1.59×10-4), and a positively correlated association with COVID-19 death (OR=1.053, 95% CI: 1.026 to 1.081, pFDR=3.50×10-4). No causal association was observed between type 1 diabetes and COVID-19 positive, hospitalized COVID-19, the time to the end of COVID-19 symptoms in the colchicine treatment group and placebo treatment group. Reverse MR analysis showed no reverse causality. CONCLUSIONS: Type 1 diabetes had a causal effect on severe COVID-19 and death after COVID-19 infection. Further mechanistic studies are needed to explore the relationship between type 1 diabetes and COVID-19 infection and prognosis.

Proteome and genome integration analysis of obesity.

ABSTRACT: The prevalence of obesity has increased worldwide in recent decades. Genetic factors are now known to play a substantial role in the predisposition to obesity and may contribute up to 70% of the risk for obesity. Technological advancements during the last decades have allowed the identification of many hundreds of genetic markers associated with obesity. However, the transformation of current genetic variant-obesity associations into biological knowledge has been proven challenging. Genomics and proteomics are complementary fields, as proteomics extends functional analyses. Integrating genomic and proteomic data can help to bridge a gap in knowledge regarding genetic variant-obesity associations and to identify new drug targets for the treatment of obesity. We provide an overview of the published papers on the integrated analysis of proteomic and genomic data in obesity and summarize four mainstream strategies: overlap, colocalization, Mendelian randomization, and proteome-wide association studies. The integrated analyses identified many obesity-associated proteins, such as leptin, follistatin, and adenylate cyclase 3. Despite great progress, integrative studies focusing on obesity are still limited. There is an increased demand for large prospective cohort studies to identify and validate findings, and further apply these findings to the prevention, intervention, and treatment of obesity. In addition, we also discuss several other potential integration methods.

Causal Effects of Plasma Proteome on Osteoporosis and Osteoarthritis.

The two-sample Mendelian randomization (MR) study revealed a causal association of plasma proteins with osteoporosis (OP) and osteoarthritis (OA). Bone mineral density (BMD) is the gold standard for the clinical assessment of OP. Recent studies have shown that plasma proteins play an essential role in the regulation of bone development. However, the causal association of plasma proteins with BMD and OA remains unclear. We estimated the effects of 2889 plasma proteins on 2 BMD phenotypes and 6 OA phenotypes using two-sample MR analysis based on the genome-wide association study summary statistics. Then, we performed sensitivity analysis and reverse-direction MR analysis to evaluate the robustness of the MR analysis results, followed by gene ontology (GO) enrichment analysis and KEGG pathway analysis to explore the functional relevance of the identified plasma proteins. Overall, we observed a total of 257 protein-estimated heel BMD associations, 17 protein-total-body BMD associations, 2 protein-all-OA associations, and 2 protein-knee-OA associations at PFDR < 0.05. Reverse-direction MR analysis demonstrated that there was little evidence of the causal association of BMD and OA with plasma proteins. GO enrichment analysis and KEGG pathway analysis identified multiple pathways, which may be involved in the development of OP and OA. Our findings recognized plasma proteins that could be used to regulate changes in OP and OA, thus, providing new insights into protein-mediated mechanisms of bone development.

Efficient identification of trait-associated loss-of-function variants in the UK Biobank cohort by exome-sequencing based genotype imputation.

The large-scale open access whole-exome sequencing (WES) data of the UK Biobank ~200,000 participants is accelerating a new wave of genetic association studies aiming to identify rare and functional loss-of-function (LoF) variants associated with complex traits and diseases. We proposed to merge the WES genotypes and the genome-wide genotyping (GWAS) genotypes of 167,000 UKB homogeneous European participants into a combined reference panel, and then to impute 241,911 UKB homogeneous European participants who had the GWAS genotypes only. We then used the imputed data to replicate association identified in the discovery WES sample. The average imputation accuracy measure r2 is modest to high for LoF variants at all minor allele frequency intervals: 0.942 at MAF interval (0.01, 0.5), 0.807 at (1.0 × 10-3 , 0.01), 0.805 at (1.0 × 10-4 , 1.0 × 10-3 ), 0.664 at (1.0 × 10-5 , 1.0 × 10-4 ) and 0.410 at (0, 1.0 × 10-5 ). As applications, we studied associations of LoF variants with estimated heel BMD and four lipid traits. In addition to replicating dozens of previously reported genes, we also identified three novel associations, two genes PLIN1 and ANGPTL3 for high-density-lipoprotein cholesterol and one gene PDE3B for triglycerides. Our results highlighted the strength of WES based genotype imputation as well as provided useful imputed data within the UKB cohort.

Mendelian randomization analysis identified causal Association of Childhood Obesity with adult major depressive disorder.

BACKGROUND: Childhood obesity is associated with adult major depressive disorder (MDD), but their causality is not clear. METHODS: We performed a two-sample Mendelian randomization (MR) analysis to explore the causality of childhood body mass index (BMI) and childhood obesity on MDD, followed by a multivariable MR (MVMR) analysis to investigate the potential role of adult BMI in mediating such effect. We accessed genome-wide association summary statistics of childhood BMI, childhood obesity, adult BMI and adult MDD from the Early Growth Genetics consortium (nBMI  = 47 541, nobesity  = 24 160), the Genetic Investigation of Anthropometric Traits consortium (nadult_BMI  = âˆ¼700 000) and the Psychiatric Genomics consortium (nMDD  = 500 199), respectively. The MR-PRESSO test was performed to remove SNPs with potential pleiotropic effect. The MR analysis was performed by inverse-variance weighted test. Further sensitivity analyses, including the MR-Egger intercept test and leave-one-out analysis, were performed to evaluate the reliability of the results. RESULTS: Our study found that childhood obesity might increase the odds of developing MDD in adults (OR = 1.03, 95% CI: 1.01-1.06, p = 2.6 × 10-3 ). Children with higher BMI were more likely to develop MDD in adulthood, with an OR of 1.12 per standard deviation score (SDS) increase in BMI (95% CI: 1.07-1.17, p = 4.4 × 10-7 ). Sensitivity analyses verified the reliability of the causality between childhood BMI/obesity and MDD. Further MVMR results revealed that the impact of childhood BMI on MDD risk was predominantly mediated by adult BMI. CONCLUSION: Our findings provided evidence of a causal relationship between childhood BMI/obesity and adult MDD, thus providing new insights into the prevention of MDD.

Causal relationship between gut microbiota and serum vitamin D: evidence from genetic correlation and Mendelian randomization study.

BACKGROUND: The gastrointestinal microbiota is emerging as an important mediator in intestinal metabolism, such as vitamin D absorption. METHODS: To elucidate the causality of microbiota and vitamin D, we used linkage disequilibrium score (LDSC) regression and two-sample Mendelian randomization (MR) methods with largest genome-wide association study (GWAS) summary statistics to identify specific taxa that are linked to serum 25-hydroxyvitamin D (25(OH)D). RESULTS: We found that Ruminiclostridium9 was significantly genetically correlated with 25(OH)D at nominal significance (rg = 0.43, P = 0.04). Applying the inverse variance weighted (IVW) method, we identified that doubling the genetic liability of abundance of Erysipelotrichia, Erysipelotrichaceae and Erysipelotrichales reduced the concentration of 25(OH)D by 0.06 standard deviation (SD) (ßIVW = -0.06, s.e. = 0.01, P = 1.48 × 10-6, PFDR = 1.93 × 10-4) and, in turn, one SD increment in genetically determined serum 25(OH)D caused a 0.16 SD decrease in the relative abundance of Phascolarctobacterium (ßIVW = -0.16, s.e. = 0.04, P = 2.48 × 10-4, PFDR = 0.02) after removing pleiotropic instruments and outliers. Moreover, four MR methods were also used to evaluate causality, the results of which supported these findings. Leave-one-out analyses showed that the results were robust with regard to alterations in the single nucleotide polymorphisms (SNPs) we selected. CONCLUSIONS: In conclusion, our results support the hypothesis that the gut microbiota mediates the absorption of serum vitamin D supplementation and interacts with it closely. These microbiota are potential therapeutic targets for promoting serum vitamin D homeostasis.

Mendelian Randomization Analysis Reveals Causal Effects of Plasma Proteome on Body Composition Traits.

CONTEXT: Observational studies have demonstrated associations between plasma proteins and obesity, but evidence of causal relationship remains to be studied. OBJECTIVE: We aimed to evaluate the causal relationship between plasma proteins and body composition. METHODS: We conducted a 2-sample Mendelian randomization (MR) analysis based on the genome-wide association study (GWAS) summary statistics of 23 body composition traits and 2656 plasma proteins. We then performed hierarchical cluster analysis to evaluate the structure and pattern of the identified causal associations, and we 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 follistatin (FST) on trunk fat-free mass (Beta = -0.63, SE = 0.04, P = 2.00 × 10-63), insulin-like growth factor-binding protein 1 (IGFBP1) on trunk fat-free mass (Beta = -0.54, SE = 0.03, P = 1.79 × 10-57) and r-spondin-3 (RSPO3) 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.

Joint Genome-Wide Association Analyses Identified 49 Novel Loci For Age at Natural Menopause.

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.

Mendelian Randomization Analysis Reveals Causal Effects of the Human Gut Microbiota on Abdominal Obesity.

BACKGROUND: Although recent studies have revealed an association between the composition of the gut microbiota and obesity, whether specific gut microbiota cause obesity has not been determined. OBJECTIVES: The aim of this study is to determine the causal relationship between specific gut microbiota and abdominal obesity. Based on genome-wide association study (GWAS) summary statistics, we performed a 2-sample Mendelian randomization (MR) analysis to evaluate whether the gut microbiota affects abdominal obesity. METHODS: Gut microbiota GWAS in 1126 twin pairs (age range, 18-89 years; 89% were females) from the TwinsUK study were used as exposure data. The primary outcome tested was trunk fat mass (TFM) GWAS in 492,805 participants (age range, 40-69 years; 54% were females) from the UK Biobank. The gut microbiota were classified at family, genus, and species levels. A feature was defined as a distinct family, genus, or species. MR analysis was mainly performed by an inverse variance-weighted test or Wald ratio test, depending on the number of instrumental variables (IVs) involved. A sensitivity analysis was performed on significant results by a weighted median test and a weighted genetic risk score (GRS) analysis. RESULTS: Results of MR analyses provided evidence of a causal association between 3 microbiota features and TFM, including 1 family [Lachnosiraceae; P = 0.02; ß = 0.001 (SEE, 4.28 × 10-4)], 1 genus [Bifidobacterium; P = 5.0 × 10-9; ß = -0.08 (SEE, 0.14)], and 1 species [Prausnitzii; P = 0.03; ß = -0.007 (SEE, 0.003)]. Both the weighted median test and GRS analysis successfully validated the association of the genetically predicted family, Lachnosiraceae (Pweighted median = 0.03; PGRS = 0.004). CONCLUSIONS: Our findings provided evidence of a causal association between gut microbiota and TFM in UK adults and identified specific bacteria taxa that may regulate the fat metabolism, thus offering new direction for the treatment of obesity.

Pleiotropic genomic variants at 17q21.31 associated with bone mineral density and body fat mass: a bivariate genome-wide association analysis.

Osteoporosis and obesity are two severe complex diseases threatening public health worldwide. Both diseases are under strong genetic determinants as well as genetically correlated. Aiming to identify pleiotropic genes underlying obesity and osteoporosis, we performed a bivariate genome-wide association (GWA) meta-analysis of hip bone mineral density (BMD) and total body fat mass (TBFM) in 12,981 participants from seven samples, and followed by in silico replication in the UK biobank (UKB) cohort sample (N = 217,822). Combining the results from discovery meta-analysis and replication sample, we identified one novel locus, 17q21.31 (lead SNP rs12150327, NC_000017.11:g.44956910G > A, discovery bivariate P = 4.83 × 10-9, replication P = 5.75 × 10-5) at the genome-wide significance level (ɑ = 5.0 × 10-8), which may have pleiotropic effects to both hip BMD and TBFM. Functional annotations highlighted several candidate genes, including KIF18B, C1QL1, and PRPF19 that may exert pleiotropic effects to the development of both body mass and bone mass. Our findings can improve our understanding of the etiology of osteoporosis and obesity, as well as shed light on potential new therapies.

Identification of pleiotropic loci underlying hip bone mineral density and trunk lean mass.

Bone mineral density (BMD) and lean body mass (LBM) not only have a considerable heritability each, but also are genetically correlated. However, common genetic determinants shared by both traits are largely unknown. In the present study, we performed a bivariate genome-wide association study (GWAS) meta-analysis of hip BMD and trunk lean mass (TLM) in 11,335 subjects from 6 samples, and performed replication in estimated heel BMD and TLM in 215,234 UK Biobank (UKB) participants. We identified 2 loci that nearly attained the genome-wide significance (GWS, p < 5.0 × 10-8) level in the discovery GWAS meta-analysis and that were successfully replicated in the UKB sample: 11p15.2 (lead SNP rs12800228, discovery p = 2.88 × 10-7, replication p = 1.95 × 10-4) and 18q21.32 (rs489693, discovery p = 1.67 × 10-7, replication p = 1.17 × 10-3). The above 2 pleiotropic loci may play a pleiotropic role for hip BMD and TLM development. So our findings provide useful insights that further enhance our understanding of genetic interplay between BMD and LBM.

Prediction of Clinical Outcome in Endometrial Carcinoma Based on a 3-lncRNA Signature.

Endometrial carcinoma (EC) is one of the common gynecological cancers with increasing incidence and revived mortality recently. Given the heterogeneity of tumors and the complexity of lncRNAs, a panel of lncRNA biomarkers might be more precise and stable for prognosis. In the present study, we developed a new lncRNA model to predict the prognosis of patients with EC. EC-associated differentially expressed long noncoding RNAs (lncRNAs) were identified from The Cancer Genome Atlas (TCGA). Univariate COX regression and least absolute shrinkage and selection operator (LASSO) model were selected to find the 8-independent prognostic lncRNAs of EC patient. Furthermore, the risk score of the 3-lncRNA signature for overall survival (OS) was identified as CTD-2377D24.6 expression × 0.206 + RP4-616B8.5 × 0.341 + RP11-389G6.3 × 0.343 by multivariate Cox regression analysis. According to the median cutoff value of this prognostic signature, the EC samples were divided into two groups, high-risk set (3-lncRNAs at high levels) and low-risk set (3-lncRNAs at low levels), and the Kaplan-Meier survival curves demonstrated that the low-risk set had a higher survival rate than the high-risk set. In addition, the 3-lncRNA signature was closely linked with histological subtype (p = 0.0001), advanced clinical stage (p = 0.011), and clinical grade (p < 0.0001) in EC patients. Our clinical samples also confirmed that RP4-616B8.5, RP11-389G6.3, and CTD-2377D24.6 levels were increased in tumor tissues by qRT-PCR and in situ hybridization. Intriguingly, the p-value of combined 3-lncRNAs was lower than that of each lncRNA, indicating that the 3-lncRNA signature also showed higher performance in EC tissue than paracancerous. Functional analysis revealed that cortactin might be involved in the mechanism of 3-lncRNA signatures. These findings provide the first hint that a panel of lncRNAs may play a critical role in the initiation and metastasis of EC, indicating a new signature for early diagnosis and therapeutic strategy of uterine corpus endometrial carcinoma.

Gut Microbiota and Psychiatric Disorders: A Two-Sample Mendelian Randomization Study.

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.

Causal Relationship Between Gut Microbiota and Autoimmune Diseases: A Two-Sample Mendelian Randomization Study.

Background: Growing 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. Objective: To reveal the causal association between gut microbiota and AD, we conducted a two-sample Mendelian randomization (MR) analysis. Materials and Methods: We 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. Results: Combining 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. Conclusion: This 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.

Three pleiotropic loci associated with bone mineral density and lean body mass.

Both bone mineral density (BMD) and lean body mass (LBM) are important physiological measures with strong genetic determination. Besides, BMD and LBM might have common genetic factors. Aiming to identify pleiotropic genomic loci underlying BMD and LBM, we performed bivariate genome-wide association study meta-analyses of femoral neck bone mineral density and LBM at arms and legs, and replicated in the large-scale UK Biobank cohort sample. Combining the results from discovery meta-analysis and replication sample, we identified three genomic loci at the genome-wide significance level (p < 5.0 × 10-8): 2p23.2 (lead SNP rs4477866, discovery p = 3.47 × 10-8, replication p = 1.03 × 10-4), 16q12.2 (rs1421085, discovery p = 2.04 × 10-9, replication p = 6.47 × 10-14) and 18q21.32 (rs11152213, discovery p = 3.47 × 10-8, replication p = 6.69 × 10-6). Our findings not only provide useful insights into lean mass and bone mass development, but also enhance our understanding of the potential genetic correlation between BMD and LBM.