The genetic architecture of age at menarche and its causal effects on other traits.

Age at menarche (AAM) is a sign of puberty of females. It is a heritable trait associated with various adult diseases. However, the genetic mechanism that determines AAM and links it to disease risk is poorly understood. Aiming to uncover the genetic basis for AAM, we conducted a joint association study in up to 438,089 women from 3 genome-wide association studies of European and East Asian ancestries. A series of bioinformatical analyses and causal inference were then followed to explore in-depth annotations at the associated loci and infer the causal relationship between AAM and other complex traits/diseases. This largest meta-analysis identified a total of 21 novel AAM associated loci at the genome wide significance level (P < 5.0 × 10-8), 4 of which were European ancestry-specific loci. Functional annotations prioritized 33 candidate genes at newly identified loci. Significant genetic correlations were observed between AAM and 67 complex traits. Further causal inference demonstrated the effects of AAM on 13 traits, including forced vital capacity (FVC), high blood pressure, age at first live birth, etc, indicating that earlier AAM causes lower FVC, worse lung function, hypertension and earlier age at first (last) live birth. Enrichment analysis identified 5 enriched tissues, including the hypothalamus middle, hypothalamo hypophyseal system, neurosecretory systems, hypothalamus and retina. Our findings may provide useful insights that elucidate the mechanisms determining AAM and the genetic interplay between AAM and some traits of women.

Mendelian randomization analysis reveals causal effects of blood lipidome on gestational diabetes mellitus.

BACKGROUND: Observational studies have revealed associations between maternal lipid metabolites and gestational diabetes mellitus (GDM). However, whether these associations are causal remain uncertain. OBJECTIVE: To evaluate the causal relationship between lipid metabolites and GDM. METHODS: A two-sample Mendelian randomization (MR) analysis was performed based on summary statistics. Sensitivity analyses, validation analyses and reverse MR analyses were conducted to assess the robustness of the MR results. Additionally, a phenome-wide MR (Phe-MR) analysis was performed to evaluate potential side effects of the targeted lipid metabolites. RESULTS: A total of 295 lipid metabolites were included in this study, 29 of them had three or more instrumental variables (IVs) suitable for sensitivity analyses. The ratio of triglycerides to phosphoglycerides (TG_by_PG) was identified as a potential causal biomarker for GDM (inverse variance weighted (IVW) estimate: odds ratio (OR) = 2.147, 95% confidential interval (95% CI) 1.415-3.257, P = 3.26e-4), which was confirmed by validation and reverse MR results. Two other lipid metabolites, palmitoyl sphingomyelin (d18:1/16:0) (PSM(d18:1/16:0)) (IVW estimate: OR = 0.747, 95% CI 0.583-0.956, P = 0.021) and triglycerides in very small very low-density lipoprotein (XS_VLDL_TG) (IVW estimate: OR = 2.948, 95% CI 1.197-5.215, P = 0.015), were identified as suggestive potential biomarkers for GDM using a conventional cut-off P-value of 0.05. Phe-MR results indicated that lowering TG_by_PG had detrimental effects on two diseases but advantageous effects on the other 13 diseases. CONCLUSION: Genetically predicted elevated TG_by_PG are causally associated with an increased risk of GDM. Side-effect profiles indicate that TG_by_PG might be a target for GDM prevention, though caution is advised due to potential adverse effects on other conditions.

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.

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.

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.

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.

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.

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.

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.

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.