A putative causal relationship between genetically determined female body shape and posttraumatic stress disorder.
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ABSTRACT:
SUBMITTER: Polimanti R
PROVIDER: S-EPMC5702961 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundTelomere maintenance is increasingly recognized as being fundamental to glioma oncogenesis with longer leukocyte telomere length (LTL) reported to increase risk of glioma. To gain further insight into the relationship between telomere genetics and risk of glioma, we conducted several complementary analyses, using genome-wide association studies data on LTL (78 592 individuals) and glioma (12 488 cases and 18 169 controls).MethodsWe performed both classical and summary Mendelian randomization (SMR), coupled with heterogeneity in dependent instruments tests, at genome-wide significant LTL loci to examine if an association was mediated by the same causal variant in glioma. To prioritize genes underscoring glioma-LTL associations, we analyzed gene expression and DNA methylation data.ResultsGenetically increased LTL was significantly associated with increased glioma risk, random-effects inverse variance weighted ORs per 1 SD unit increase in the putative risk factor (odds ratio [OR]SD) 4.79 (95% confidence interval: 2.11-10.85; P = 1.76 × 10-4). SMR confirmed the previously reported LTL associations at 3q26.2 (TERC; PSMR = 1.33 × 10-5), 5p15.33 (TERT; PSMR = 9.80 × 10-27), 10q24.33 (STN1 alias OBFC1; PSMR = 4.31 × 10-5), and 20q13.3 (STMN3/RTEL1; PSMR = 2.47 × 10-4) glioma risk loci. Our analysis implicates variation at 1q42.12 (PSMR = 1.55 × 10-2), 6p21.3 (PSMR = 9.76 × 10-3), 6p22.2 (PSMR = 5.45 × 10-3), 7q31.33 (PSMR = 6.52 × 10-3), and 11q22.3 (PSMR = 8.89 × 10-4) as risk factors for glioma risk. While complicated by patterns of linkage disequilibrium, genetic variation involving PARP1, PRRC2A, CARMIL1, POT1, and ATM-NPAT1 was implicated in the etiology of glioma.ConclusionsThese observations extend the role of telomere-related genes in the development of glioma.
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Project description:ObjectiveTo analyze the interactions between maternal gestational diabetes mellitus (GDM) and genetically determined maternal body mass index (BMI) during pregnancy on offspring childhood obesity.Research design and methodsA total of 1114 Chinese mother-child pairs (560 GDM and 554 non-GDM) were included between August 2009 and July 2011. Maternal genetic risk score (GRS) of BMI during pregnancy was derived on the basis of 12 single nucleotide polymorphisms identified from a genome-wide association study. Offspring's BMI, BMI-for-age z score, weight, weight-for-age z score, waist circumference, sum of skinfolds, and body fat percentage during childhood were measured or calculated.ResultsMaternal GRS of BMI during pregnancy significantly interacted with maternal GDM status on childhood risks of overweight and obesity (all P for interaction <.05). After multivariable adjustment, per unit of GRS was associated with a 24% (P<.001) and a 28% (P<.001) increased risk of overweight and obesity among children of GDM mothers, whereas no significant associations were observed among children of mothers without GDM. In addition, per unit GRS of BMI during pregnancy was significantly associated with 0.16 kg/m2 higher BMI (P=.002), 0.09 higher BMI-for-age z score (P=.002), 0.24 kg higher weight (P=.04), 0.06 higher weight-for-age z score (P=.02), 0.28 cm higher waist circumference (P=.03), 0.94 mm higher sum of skinfolds (P=.004), and 0.37% higher body fat percentage (P=.03) among children of GDM mothers. There were no significant associations between maternal GRS of BMI during pregnancy and offspring's obesity-related outcomes among children of mothers without GDM.ConclusionOur findings for the first time indicate that maternal GDM status may modify the relation between genetically determined maternal BMI during pregnancy and offspring's obesity risk during childhood.
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Project description:The association between physical appearance and income has been of central interest in social science. However, most previous studies often measured physical appearance using classical proxies from subjective opinions based on surveys. In this study, we use novel data, called CAESAR, which contains three-dimensional (3D) whole-body scans to mitigate possible reporting and measurement errors. We demonstrate the existence of significant nonclassical reporting errors in the reported heights and weights by comparing them with measured counterparts, and show that these discrete measurements are too sparse to provide a complete description of the body shape. Instead, we use a graphical autoencoder to obtain intrinsic features, consisting of human body shapes directly from 3D scans and estimate the relationship between body shapes and family income. We also take into account a possible issue of endogenous body shapes using proxy variables and control functions. The estimation results reveal a statistically significant relationship between physical appearance and family income and that these associations differ across genders. This supports the hypothesis on the physical attractiveness premium in labor market outcomes and its heterogeneity across genders.
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Project description:BackgroundThe causal effects of gut microbiome and the development of posttraumatic stress disorder (PTSD) are still unknown. This study aimed to clarify their potential causal association using mendelian randomization (MR).MethodsThe summary-level statistics for gut microbiome were retrieved from a genome-wide association study (GWAS) of the MiBioGen consortium. As to PTSD, the Freeze 2 datasets were originated from the Psychiatric Genomics Consortium Posttraumatic Stress Disorder Working Group (PGC-PTSD), and the replicated datasets were obtained from FinnGen consortium. Single nucleotide polymorphisms meeting MR assumptions were selected as instrumental variables. The inverse variance weighting (IVW) method was employed as the main approach, supplemented by sensitivity analyses to evaluate potential pleiotropy and heterogeneity and ensure the robustness of the MR results. We also performed reverse MR analyses to explore PTSD's causal effects on the relative abundances of specific features of the gut microbiome.ResultsIn Freeze 2 datasets from PGC-PTSD, eight bacterial traits revealed a potential causal association between gut microbiome and PTSD (IVW, all P < 0.05). In addition, Genus.Dorea and genus.Sellimonas were replicated in FinnGen datasets, in which eight bacterial traits revealed a potential causal association between gut microbiome and the occurrence of PTSD. The heterogeneity and pleiotropy analyses further supported the robustness of the IVW findings, providing additional evidence for their reliability.ConclusionOur study provides the potential causal impact of gut microbiomes on the development of PTSD, shedding new light on the understanding of the dysfunctional gut-brain axis in this disorder. Our findings present novel evidence and call for investigations to confirm the association between their links, as well as to illuminate the underlying mechanisms.
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Project description:Observational studies suggest that posttraumatic stress disorder (PTSD) increases risk for various autoimmune diseases. Insights into shared biology and causal relationships between these diseases may inform intervention approaches to PTSD and co-morbid autoimmune conditions. We investigated the shared genetic contributions and causal relationships between PTSD, 18 autoimmune diseases, and 3 immune/inflammatory biomarkers. Univariate MiXeR was used to contrast the genetic architectures of phenotypes. Genetic correlations were estimated using linkage disequilibrium score regression. Bi-directional, two-sample Mendelian randomization (MR) was performed using independent, genome-wide significant single nucleotide polymorphisms; inverse variance weighted and weighted median MR estimates were evaluated. Sensitivity analyses for uncorrelated (MR PRESSO) and correlated horizontal pleiotropy (CAUSE) were also performed. PTSD was considerably more polygenic (10,863 influential variants) than autoimmune diseases (median 255 influential variants). However, PTSD evidenced significant genetic correlation with nine autoimmune diseases and three inflammatory biomarkers. PTSD had putative causal effects on autoimmune thyroid disease (p = 0.00009) and C-reactive protein (CRP) (p = 4.3 × 10-7). Inferences were not substantially altered by sensitivity analyses. Additionally, the PTSD-autoimmune thyroid disease association remained significant in multivariable MR analysis adjusted for genetically predicted inflammatory biomarkers as potential mechanistic pathway variables. No autoimmune disease had a significant causal effect on PTSD (all p values > 0.05). Although causal effect models were supported for associations of PTSD with CRP, shared pleiotropy was adequate to explain a putative causal effect of CRP on PTSD (p = 0.18). In summary, our results suggest a significant genetic overlap between PTSD, autoimmune diseases, and biomarkers of inflammation. PTSD has a putative causal effect on autoimmune thyroid disease, consistent with existing epidemiologic evidence. A previously reported causal effect of CRP on PTSD is potentially confounded by shared genetics. Together, results highlight the nuanced links between PTSD, autoimmune disorders, and associated inflammatory signatures, and suggest the importance of targeting related pathways to protect against disease and disability.
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Project description:Intelligence predicts important life and health outcomes, but the biological mechanisms underlying differences in intelligence are not yet understood. The use of genetically determined metabotypes (GDMs) to understand the role of genetic and environmental factors, and their interactions, in human complex traits has been recently proposed. However, this strategy has not been applied to human intelligence. Here we implemented a two-sample Mendelian randomization (MR) analysis using GDMs to assess the causal relationships between genetically determined metabolites and human intelligence. The standard inverse-variance weighted (IVW) method was used for the primary MR analysis and three additional MR methods (MR-Egger, weighted median, and MR-PRESSO) were used for sensitivity analyses. Using 25 genetic variants as instrumental variables (IVs), our study found that 5-oxoproline was associated with better performance in human intelligence tests (PIVW?=?9.25?×?10-5). The causal relationship was robust when sensitivity analyses were applied (PMR-Egger?=?0.0001, PWeighted median?=?6.29?×?10-6, PMR-PRESSO?=?0.0007), and repeated analysis yielded consistent result (PIVW?=?0.0087). Similarly, also dihomo-linoleate (20:2n6) and p-acetamidophenylglucuronide showed robust association with intelligence. Our study provides novel insight by integrating genomics and metabolomics to estimate causal effects of genetically determined metabolites on human intelligence, which help to understanding of the biological mechanisms related to human intelligence.
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Project description:Previous studies have shown that metabolites play an important role in phenotypic regulation. However, the causal relationship between metabolites and multiple myeloma has not been adequately investigated. Here, we attempt to explore the causal effects of genetically determined blood metabolites on multiple myeloma. The large-scale public blood metabolites and multiple myeloma datasets from independently published genome-wide association studies (GWAS) were used to explore the causal relationship between each genetically determined blood metabolite and multiple myeloma through inverse variance weighted (IVW), weighted median, MR-Egger and mode-based estimation methods. Sensitivity tests were performed to evaluate the stability and reliability of the results by MR-Egger regression and leave-one-out methods. Metabolic pathway analysis was further explored using filtered data. Statistical analyses were all performed in R. Among 452 metabolites, ten known metabolites and three unknown metabolites had significant causal relationship with multiple myeloma (P < 0.05). Four known metabolites, 3-methyl-2-oxovalenate, oxidized bilirubin, isovalerylcarnitine and glutamine carnitine, reached statistical significance in IVW models. Metabolic pathways analysis identified four significant pathways. The occurrence of multiple myeloma may have a causal relationship with these four metabolites, and there are four metabolic pathways that are also related to the occurrence of multiple myeloma. This can provide new ideas for exploring early screening and treatment of multiple myeloma.
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Project description:BackgroundThe amygdala is a subcortical structure involved in socioemotional and associative fear learning processes relevant for understanding the mechanisms of posttraumatic stress disorder (PTSD). Research in animals indicates that the amygdala is a heterogeneous structure in which the basolateral and centromedial divisions are susceptible to stress. While the amygdala complex is implicated in the pathophysiology of PTSD, little is known about the specific contributions of the individual nuclei that constitute the amygdala complex.MethodsMilitary veterans (n = 355), including military veterans with PTSD (n = 149) and trauma-exposed control subjects without PTSD (n = 206), underwent high-resolution T1-weighted anatomical scans. Automated FreeSurfer segmentation of the amygdala yielded 9 structures: basal, lateral, accessory basal, anterior amygdaloid, and central, medial, cortical, and paralaminar nuclei, along with the corticoamygdaloid transition zone. Subregional volumes were compared between groups using ordinary-least-squares regression with relevant demographic and clinical regressors followed by 3-dimensional shape analysis of whole amygdala.ResultsPTSD was associated with smaller left and right lateral and paralaminar nuclei, but with larger left and right central, medial, and cortical nuclei (p < .05, false discovery rate corrected). Shape analyses revealed lower radial distance in anterior bilateral amygdala and lower Jacobian determinant in posterior bilateral amygdala in PTSD compared with control subjects.ConclusionsAlterations in select amygdala subnuclear volumes and regional shape distortions are associated with PTSD in military veterans. Volume differences of the lateral nucleus and the centromedial complex associated with PTSD demonstrate a subregion-specific pattern that is consistent with their functional roles in fear learning and fear expression behaviors.
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Project description:ABO blood types and their corresponding antigens have long been assumed to be related to different human diseases. So far, smaller studies on the relationship between mental disorders and blood types yielded contradicting results. In this study we analyzed the association between ABO blood types and lifetime major depressive disorder (MDD). We performed a pooled analysis with data from 26 cohorts that are part of the MDD working group of the Psychiatric Genomics Consortium (PGC). The dataset included 37,208 individuals of largely European ancestry of which 41.6% were diagnosed with lifetime MDD. ABO blood types were identified using three single nucleotide polymorphisms in the ABO gene: rs505922, rs8176746 and rs8176747. Regression analyses were performed to assess associations between the individual ABO blood types and MDD diagnosis as well as putative interaction effects with sex. The models were adjusted for sex, cohort and the first ten genetic principal components. The percentage of blood type A was slightly lower in cases than controls while blood type O was more prominent in cases. However, these differences were not statistically significant. Our analyses found no evidence of an association between ABO blood types and major depressive disorder.
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Project description:BackgroundDiabetes mellitus (DM) stands as the most prevalent endocrine abnormality affecting the physiological systems and organs and impairing the male reproductive functions. Type 2 Diabetes Mellitus (T2DM), accounting for about 90-95% of DM, is closely associated with male infertility. However, the magnitude of the causal relationships between T2DM and male infertility remains unclear. The current investigation was to explore the causal relationship between T2DM and male infertility utilizing the Mendelian Randomization (MR) analysis.MethodsA two-sample MR (2SMR) analysis was conducted to investigate the causal relationship between T2DM and male infertility in the European population from the genome-wide association study (GWAS) summary data that was publicly accessible. GWAS for T2DM and male infertility were extracted from the IEU Open GWAS Project database, with the resulting data encompassing 680 cases and 72,799 controls as the outcome data. Five MR methods were employed for the 2SMR analyses, namely the MR-Egger, weighted median estimation (WME), weighted mode (WM), inverse-variance weighted (IVW), and simple mode. The primary analytical technique utilized in this study was the IVW method, and a multivariate MR analysis was executed to examine the potential mediating influences of T2DM on male infertility.ResultsFollowing were the odds ratios (ORs) and associated 95% CIs derived from IVW (fixed effects), MR-Egger, WM, WME, and simple mode approaches: 0.824 (95% CI 0.703-0.966), 0.726 (95% CI 0.527-1.001), 0.827 (95% CI 0.596-1.150), 0.841 (95% CI 0.654-1.082), and 0.875 (95% CI 0.544-1.405), respectively. The outcomes of the heterogeneity tests were P=0.378 and P=0.384, respectively, implying no heterogeneity. Egger-intercept outcomes were P=0.374, highlighting the absence of pleiotropy. The stability of the results was affirmed through the leave-one-out analysis. Notably, all F-values surpassed 10, indicating the absence of weak bias attributed to instrument variables(IVs).ConclusionsThis research furnishes evidence supporting a causal association between T2DM and male infertility. These insights offer a foundation for future investigations aiming to establish the association between genetically predicted T2DM and male infertility. These outcomes suggest the significance of active monitoring and proactive measures for preventing infertility in male individuals with T2DM. Furthermore, careful consideration is required for individuals of reproductive age to prevent and treat T2DM.
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