Genome-wide Association Studies Datasets Research Articles

Telomere length and the risk of cardiovascular diseases: A Mendelian randomization study.

The causal direction and magnitude of the associations between telomere length (TL) and cardiovascular diseases (CVDs) remain uncertain due to susceptibility of reverse causation and confounding. This study aimed to investigate the associations between TL and CVDs using Mendelian randomization (MR). In this two-sample MR study, we identified 154 independent TL-associated genetic variants from a genome-wide association study (GWAS) consisting of 472,174 individuals (aged 40-69) in the UK Biobank. Summary level data of CVDs were obtained from different GWASs datasets. Methods of inverse variance weighted (IVW), Mendelian Randomization-Egger (MR-Egger), Mendelian Randomization robust adjusted profile score (MR-RAPS), maximum likelihood estimation, weighted mode, penalized weighted mode methods, and Mendelian randomization pleiotropy residual sum and outlier test (MR-PRESSO) were conducted to investigate the associations between TL and CVDs. Our findings indicated that longer TL was significantly associated with decreased risk of coronary atherosclerosis [odds ratio (OR), 0.85; 95% confidence interval (CI), 0.75-0.95; P = 4.36E-03], myocardial infarction (OR, 0.72; 95% CI, 0.63-0.83; P = 2.31E-06), ischemic heart disease (OR, 0.87; 95% CI, 0.78-0.97; P = 1.01E-02), stroke (OR, 0.87; 95% CI, 0.79-0.95; P = 1.60E-03), but an increased risk of hypertension (OR, 1.12; 95% CI, 1.02-1.23; P = 2.00E-02). However, there was no significant association between TL and heart failure (OR, 0.94; 95% CI, 0.87-1.01; P = 1.10E-01), atrial fibrillation (OR, 1.01; 95% CI, 0.93-1.11; P = 7.50E-01), or cardiac death (OR, 0.95; 95% CI, 0.82-1.10; P = 4.80E-01). Both raw and outlier corrected estimates from MR-PRESSO were consistent with those of IVW results. The sensitivity analyses showed no evidence of pleiotropy (MR-Egger intercept, P > 0.05), while Cochran's Q test and MR-Egger suggested different degrees of heterogeneity. Our MR study suggested that longer telomeres were associated with decreased risk of several CVDs, including coronary atherosclerosis, myocardial infarction, ischemic heart disease, and stroke, as well as an increased risk of hypertension. Future studies are still warranted to validate the results and investigate the mechanisms underlying these associations.

Causality of genetically determined metabolites on anxiety disorders: a two-sample Mendelian randomization study

BackgroundAlthough anxiety disorders are one of the most prevalent mental disorders, their underlying biological mechanisms have not yet been fully elucidated. In recent years, genetically determined metabolites (GDMs) have been used to reveal the biological mechanisms of mental disorders. However, this strategy has not been applied to anxiety disorders. Herein, we explored the causality of GDMs on anxiety disorders through Mendelian randomization study, with the overarching goal of unraveling the biological mechanisms.MethodsA two-sample Mendelian randomization (MR) analysis was implemented to assess the causality of GDMs on anxiety disorders. A genome-wide association study (GWAS) of 486 metabolites was used as the exposure, whereas four different GWAS datasets of anxiety disorders were the outcomes. Notably, all datasets were acquired from publicly available databases. A genetic instrumental variable (IV) was used to explore the causality between the metabolite and anxiety disorders for each metabolite. The MR Steiger filtering method was implemented to examine the causality between metabolites and anxiety disorders. The standard inverse variance weighted (IVW) method was first used for the causality analysis, followed by three additional MR methods (the MR-Egger, weighted median, and MR-PRESSO (pleiotropy residual sum and outlier) methods) for sensitivity analyses in MR analysis. MR-Egger intercept, and Cochran’s Q statistical analysis were used to evaluate possible heterogeneity and pleiotropy. Bonferroni correction was used to determine the causative association features (P < 1.03 × 10–4). Furthermore, metabolic pathways analysis was performed using the web-based MetaboAnalyst 5.0 software. All statistical analysis were performed in R software. The STROBE-MR checklist for the reporting of MR studies was used in this study.ResultsIn MR analysis, 85 significant causative relationship GDMs were identified. Among them, 11 metabolites were overlapped in the four different datasets of anxiety disorders. Bonferroni correction showing1-linoleoylglycerophosphoethanolamine (ORfixed-effect IVW = 1.04; 95% CI 1.021–1.06; Pfixed-effect IVW = 4.3 × 10–5) was the most reliable causal metabolite. Our results were robust even without a single SNP because of a “leave-one-out” analysis. The MR-Egger intercept test indicated that genetic pleiotropy had no effect on the results (intercept = − 0.0013, SE = 0.0006, P = 0.06). No heterogeneity was detected by Cochran’s Q test (MR-Egger. Q = 7.68, P = 0.742; IVW. Q = 12.12, P = 0.436). A directionality test conducted by MR Steiger confirmed our estimation of potential causal direction (P < 0.001). In addition, two significant pathways, the “primary bile acid biosynthesis” pathway (P = 0.008) and the “valine, leucine, and isoleucine biosynthesis” pathway (P = 0.03), were identified through metabolic pathway analysis.ConclusionThis study provides new insights into the causal effects of GDMs on anxiety disorders by integrating genomics and metabolomics. The metabolites that drive anxiety disorders may be suited to serve as biomarkers and also will help to unravel the biological mechanisms of anxiety disorders.

RNA Modification-Related Genetic Variants in Genomic Loci Associated with Bone Mineral Density and Fracture.

Genome-wide association studies (GWASs) have identified more than 500 loci for bone mineral density (BMD), but functional variants in these loci are less known. The aim of this study was to identify RNA modification-related SNPs (RNAm-SNPs) for BMD in GWAS loci. We evaluated the association of RNAm-SNPs with quantitative heel ultrasound BMD (eBMD) in 426,824 individuals, femoral neck (FN) and lumbar spine (LS) BMD in 32,961 individuals and fracture in ~1.2 million individuals. Furthermore, we performed functional enrichment, QTL and Mendelian randomization analyses to support the functionality of the identified RNAm-SNPs. We found 300 RNAm-SNPs significantly associated with BMD, including 249 m6A-, 28 m1A-, 3 m5C-, 7 m7G- and 13 A-to-I-related SNPs. m6A-SNPs in OP susceptibility genes, such as WNT4, WLS, SPTBN1, SEM1, FUBP3, LRP5 and JAG1, were identified and functional enrichment for m6A-SNPs in the eBMD GWAS dataset was detected. eQTL signals were found for nearly half of the identified RNAm-SNPs, and the affected gene expression was associated with BMD and fracture. The RNAm-SNPs were also associated with the plasma levels of proteins in cytokine-cytokine receptor interaction, PI3K-Akt signaling, NF-kappa B signaling and MAPK signaling pathways. Moreover, the plasma levels of proteins (CCL19, COL1A1, CTSB, EFNA5, IL19, INSR, KDR, LIFR, MET and PLXNB2) in these pathways were found to be associated with eBMD in Mendelian randomization analysis. This study identified functional variants and potential causal genes for BMD and fracture in GWAS loci and suggested that RNA modification may play an important role in osteoporosis.

ScGWAS: landscape of trait-cell type associations by integrating single-cell transcriptomics-wide and genome-wide association studies

BackgroundThe rapid accumulation of single-cell RNA sequencing (scRNA-seq) data presents unique opportunities to decode the genetically mediated cell-type specificity in complex diseases. Here, we develop a new method, scGWAS, which effectively leverages scRNA-seq data to achieve two goals: (1) to infer the cell types in which the disease-associated genes manifest and (2) to construct cellular modules which imply disease-specific activation of different processes.ResultsscGWAS only utilizes the average gene expression for each cell type followed by virtual search processes to construct the null distributions of module scores, making it scalable to large scRNA-seq datasets. We demonstrated scGWAS in 40 genome-wide association studies (GWAS) datasets (average sample size N ≈ 154,000) using 18 scRNA-seq datasets from nine major human/mouse tissues (totaling 1.08 million cells) and identified 2533 trait and cell-type associations, each with significant modules for further investigation. The module genes were validated using disease or clinically annotated references from ClinVar, OMIM, and pLI variants.ConclusionsWe showed that the trait-cell type associations identified by scGWAS, while generally constrained to trait-tissue associations, could recapitulate many well-studied relationships and also reveal novel relationships, providing insights into the unsolved trait-tissue associations. Moreover, in each specific cell type, the associations with different traits were often mediated by different sets of risk genes, implying disease-specific activation of driving processes. In summary, scGWAS is a powerful tool for exploring the genetic basis of complex diseases at the cell type level using single-cell expression data.

Prioritized candidate causal haplotype blocks in plant genome-wide association studies.

Genome wide association studies (GWAS) can play an essential role in understanding genetic basis of complex traits in plants and animals. Conventional SNP-based linear mixed models (LMM) that marginally test single nucleotide polymorphisms (SNPs) have successfully identified many loci with major and minor effects in many GWAS. In plant, the relatively small population size in GWAS and the high genetic diversity found in many plant species can impede mapping efforts on complex traits. Here we present a novel haplotype-based trait fine-mapping framework, HapFM, to supplement current GWAS methods. HapFM uses genotype data to partition the genome into haplotype blocks, identifies haplotype clusters within each block, and then performs genome-wide haplotype fine-mapping to prioritize the candidate causal haplotype blocks of trait. We benchmarked HapFM, GEMMA, BSLMM, GMMAT, and BLINK in both simulated and real plant GWAS datasets. HapFM consistently resulted in higher mapping power than the other GWAS methods in high polygenicity simulation setting. Moreover, it resulted in smaller mapping intervals, especially in regions of high LD, achieved by prioritizing small candidate causal blocks in the larger haplotype blocks. In the Arabidopsis flowering time (FT10) datasets, HapFM identified four novel loci compared to GEMMA's results, and the average mapping interval of HapFM was 9.6 times smaller than that of GEMMA. In conclusion, HapFM is tailored for plant GWAS to result in high mapping power on complex traits and improved on mapping resolution to facilitate crop improvement.

Mendelian Randomization Study on Causal Association of Pyroglutamine with COVID-19

BackgroundGlutamine family amino acids such as glutamate, pyroglutamate, and glutamine have been shown to play important roles in COVID-19. However, it is still unclear about the role of pyroglutamate in COVID-19. Thus, we use a two-sample Mendelian randomization (MR) study to identify the genetic causal link between blood pyroglutamine levels and COVID-19 risk.MethodsPyroglutamine genetic instrumental variables (IVs) were chosen from the largest pyroglutamine-associated genome-wide association studies (GWAS). The largest COVID-19 GWAS dataset was employed to evaluate the causal link between blood pyroglutamine levels and COVID-19 risk using two-sample MR analysis.ResultsWe found no significant pleiotropy or heterogeneity of pyroglutamine-associated genetic IVs in COVID-19 GWAS. Interestingly, we found that as pyroglutamine genetically increased, the risk of COVID-19 decreased using inverse variance weighted (IVW) (Beta = − 0.644, p = 0.003; OR = 0.525, 95% CI [0.346–0.798]) and weighted median (Beta = − 0.609, p = 0.013; OR = 0.544, 95% CI [0.337–0.878]).ConclusionOur analysis suggests a causal link between genetically increased pyroglutamine and reduced risk of COVID-19. Thus, pyroglutamine may be a protective factor for patients with COVID-19.

Peripheral level of CD33 and Alzheimer’s disease: a bidirectional two-sample Mendelian randomization study

Increased expression of CD33 in the brain has been suggested to be associated with increased amyloid plaque burden, while the peripheral level of CD33 in Alzheimer’s disease (AD) patients and its role in AD remain unclear. The current study aimed to systematically explore the bidirectional relationship between peripheral CD33 and AD. Genome-wide association study (GWAS) datasets of AD (Ncases: 21982; Ncontrols: 41944), blood CD33 mRNA level, the plasma CD33 protein level, and CD33 expression on immune-cell subtypes were obtained from GWASs conducted in the European population. Eligible IVs were extracted from the GWASs. MR estimates were calculated by inverse-variance weighting (IVW) and other sensitivity analyses. The main statistical analyses were conducted using TwoSampleMR (v.0.5.5) in R package (V.4.1.2).In the forward MR analysis (CD33 as exposure and AD as outcome), the IVW results indicated that elevated blood CD33 mRNA level (OR [95% CI] = 1.156[1.080, 1.238], p = 3.25e-05), elevated serum CD33 protein level (OR [95% CI] = 1.08 [1.031, 1.139], p = 1.6e-03) and increased CD33's expression on immune cell subtypes (p < 0.05) were all leading to a higher risk of AD. And sensitivity analyses supported these findings. While the reverse MR analysis (AD as exposure and CD33 as outcome) indicated that AD was not leading to the elevation of CD33's protein level in the blood (p > 0.05). In conclusion, our results indicated that elevated peripheral expression of CD33 was causal to the development of AD. Future studies are needed to work on developing CD33 as a biomarker and therapeutic target in AD.

Investigating genetic causal relationships between blood pressure and anxiety, depressive symptoms, neuroticism and subjective well-being

BackgroundHigh blood pressure is a leading cardiovascular disease risk factor and considered to be associated with psychological factors. However, the causal relationships between blood pressure and anxiety, depressive symptoms, neuroticism...

Genetically predicted body fat mass and distribution with diabetic kidney disease: A two-sample Mendelian randomization study.

The aim of this study is to apply a Mendelian randomization (MR) design to investigate the potential causal associations between the body mass index (BMI), body fat mass such as trunk fat mass and waist circumference (WC), and diabetic kidney disease (DKD). A two-sample MR study was conducted to obtain exposure and outcome data from previously published studies. The instrumental variables for BMI, trunk fat mass, and WC were selected from genome-wide association study datasets based on summary-level statistics. The random-effects inverse-variance weighted (IVW) method was used for the main analyses, and the weighted median and MR-Egger approaches were complementary. In total, three MR methods suggested that genetically predicted BMI, trunk fat mass, and WC were positively associated with DKD. Using IVW, we found evidence of causal relationships between BMI [odds ratio (OR) = 1.99; 95% confidence interval (CI), 1.47–2.69; p = 7.89 × 10−6], trunk fat mass (OR = 1.80; 95% CI, 1.28–2.53; p = 6.84 × 10−4), WC (OR = 2.48; 95% CI, 1.40–4.42; p = 1.93 × 10−3), and DKD. MR-Egger and weighted median regression also showed directionally similar estimates. Both funnel plots and MR-Egger intercepts showed no directional pleiotropic effects involving the aforementioned variables and DKD. Our MR analysis supported the causal effect of BMI, trunk fat mass, and WC on DKD. Individuals can substantially reduce DKD risk by reducing body fat mass and modifying their body fat distribution.

The association between vitamin D and uterine fibroids: A mendelian randomization study.

Uterine fibroids (UFs), the most common benign gynecological tumor, can bring severe negative impacts on a woman’s life quality. Vitamin D, is thought to play an important role in regulating cell proliferation and differentiation. In recent years, several studies suggested that higher level of vitamin D has a negative effect on the occurrence of UFs, but the results of studies on the relationship between them are conflicting and further evidence needs to be studied. Here in, we used a two-sample Mendelian Randomization (2SMR) study to explore the causal relationship between genetically predicted vitamin D levels and the risk of UFs. The exposure data comes from a genome-wide association study (GWAS) summary dataset consisting of 441,291 individuals, which includes datasets from United Kingdom Biobank, FinnGen Biobank and the corresponding consortia. Single-nucleotide polymorphisms (SNPs) associated with vitamin D at a significant level of p < 5 × 10−8 and low linkage disequilibrium (LD) level (r2 < 0.01) were selected. The outcome data comes from a GWAS dataset of IEU analysis of United Kingdom Biobank phenotypes consisting of 7,122 UFs cases and 455,811 controls. Our inverse-variance weight (IVW) analysis results support the causal association of genetically predicted vitamin D with the risk of UFs (OR = 0.995,95% CI = 0.990-0.999, p = 0.024). In addition, heterogeneity and pleiotropy were not observed in statistical models. In summary, our results indicate that elevated serum vitamin D levels are in strong relationship with reduction of the risk of UFs, which indicates that the clinical treatment of UFs may have a new and excellent option.

Causal relationship between insomnia and tuberculosis: A bi-directional Mendelian randomization analysis.

Previous observational studies appear to have established a bi-directional association between sleep disorders and tuberculosis. However, their conclusions are prone to be biased by confounding effects and reverse causation due to the nature of observational studies. Mendelian randomization (MR) approach provides unconfounded estimates of causal effects and overcomes the limitations of observational studies. We performed a bi-directional MR analysis to clarify whether there existed a causal effect of insomnia on tuberculosis, or tuberculosis on insomnia. In forward-direction MR, we chose genome-wide significant (P < .5 × 10-8) and independent (r2 < 0.001) single-nucleotide polymorphisms (SNPs) as instrumental variants (IVs), then extracted effect estimates of these IVs in tuberculosis genome-wide association study (GWAS) dataset to explore causal effect of genetically proxied insomnia on tuberculosis using inverse variance-weighted (IVW), MR-Egger, and weighted median methods. Additionally, we examined robustness and pleiotropy of effect estimates by heterogeneity and sensitivity analysis. Similarly, we investigated causal effect of genetically proxied tuberculosis on insomnia in reverse-direction MR. We revealed no causal relationship between genetically proxied insomnia and tuberculosis using 15 SNPs in forward-direction MR (IVW OR 5.305 [0.100-281.341], P = .410) and reverse-direction MR analysis (ORs and P values were not applicable due to no eligible SNPs in GWAS), with insignificant heterogeneity (Q = 22.6, I2 < 0.001, P = .066) and pleiotropy (intercept = 0.032, SE = 0.057, P = .592) in effect estimates. Our bi-directional MR analysis affirms no causal effect of insomnia on tuberculosis, or tuberculosis on insomnia.

Advances in Stroke: Genetics, Genomics, Precision Medicine.

Advances in Stroke: Genetics, Genomics, Precision Medicine.

Mendelian randomization analysis of the causal association of bone mineral density and fracture with multiple sclerosis.

Multiple sclerosis (MS) is a neurodegenerative disorder and an autoimmune disease. Until now, observational studies have indicated the association of bone mineral density (BMD) and fracture with the risk of MS. However, these studies indicated inconsistent findings. Until now, genome-wide association studies (GWAS) have been conducted in BMD, fracture, and MS, which provide large-scale datasets to investigate the causal association of BMD and fracture with the risk of MS using the Mendelian randomization (MR) study. Here, we performed an MR study to clarify the causal association between BMD/fracture and the risk of MS using large-scale publicly available GWAS datasets from BMD, fracture, and MS. We first evaluated the bidirectional causal effects of BMD and MS. The main analysis method inverse-variance weighted (IVW) showed no significant causal effect of BMD on the risk of MS (β = 0.058, and p = 1.98E-01), and MS on the risk of BMD (β = −0.001, and p = 7.83E-01). We then evaluated the bidirectional causal effects of fracture and MS. However, we only identified a significant causal effect of fracture on the risk of MS using IVW (β = −0.375, p = 0.002), but no significant causal effect of MS on the risk of the fracture using IVW (β = 0.011, p = 2.39E-01). Therefore, our main analysis method IVW only found a significant causal effect of fracture on MS using the threshold for the statistically significant association p < 0.05/4 = 0.0125. Meanwhile, multivariable MR analyses showed that the causal effect of fracture on MS was independent of smoking, drinking, and obesity, but dependent on BMD. In summary, our MR analysis demonstrates that genetically increased fracture may reduce the risk of MS. Our findings should be further verified and the underlying mechanisms should be further evaluated by future studies.

Association between telomere length and insomnia: A mendelian randomization and colocalization study

BackgroundPrevious studies have suggested a potential association between sleep and telomere length (TL), but its genetic basis remains unclear. In this study, we aimed to explore the genetic correlation and potential causal association between TL and insomnia. MethodsThe genome-wide association study (GWAS) datasets of TL and insomnia-related traits were used, including insomnia, snoring, daytime dozing and napping. Based on the polygenic risk scores (PRS) of TL, linear regression and linkage disequilibrium score (LDSC) regression were used to preliminarily explore the association between TL and insomnia parameters in the UK Biobank cohort. Then, we investigated the causal association between TL and insomnia by mendelian randomization (MR) analysis and colocalization analysis. ResultsIn the UK Biobank cohort, the association between TL and insomnia was observed in the female samples (t = 2.968, P = 3.00 × 10−3). LDSC detected a genetic correlation between short TL and insomnia (Rg = −9.27 × 10−2, P = 8.00 × 10−4). We found no evidence supporting significant causal association between insomnia and TL in IVW method (b = −5.95 × 10−3, P = 0.57), with horizontal pleiotropy and heterogeneity tests indicating the validity of our MR study. Finally, rs12638862 was classified as colocalized by COLOC (PP4 = 0.99), and TERC may be involved in regulating the association between insomnia and TL. ConclusionsOur study found no evidence for causal association between insomnia and TL in individuals of European ancestry. We detected a candidate gene associated with both insomnia and TL, providing novel clues for understanding the roles of this association.

Genetic correlation between female infertility and mental health and lifestyle factors: A linkage disequilibrium score regression study.

Background and AimsFemale fertility is a heterogeneous condition of complete psychosocial and physical well‐being. Observational studies have revealed that women with infertility have varying degrees of poor mental status and lifestyle choices in varying degrees. However, the genetic contribution to female infertility remains elusive. Our study aimed to explore the genetic correlations between female infertility and mental health and lifestyle factors.MethodsThe genome‐wide association study (GWAS) data sets of characteristics related to mental health and lifestyle were obtained from the IEU OpenGWAS database. The GWAS data sets of female infertility were derived from the Finggen database. Linkage disequilibrium score regression was performed to systematically estimate the pairwise genetic correlations between female infertility and a set of mental health‐ and lifestyle‐related traits.ResultsThe genetic correlation analysis revealed a moderate and positive genetic correlation between depressive symptoms, major depressive disorder, and female infertility. Similarly, worry and the personality trait of neuroticism displayed a moderate positive genetic correlation with female infertility. Adversely, a negative and moderate genetic correlation was observed between strenuous sports or exercises and female infertility.ConclusionThe study demonstrated genetic correlations between female infertility and mental health status, including depression, worry, and neuroticism. Additionally, we observed that females with better physical activity may have reduced risks of female infertility. These findings would serve as a fundamental resource for understanding the genetic mechanisms of the effects of mental health and lifestyle factors on female infertility.

Frailty index and risk of cardiovascular diseases: a mendelian randomization study.

BackgroundPrevious epidemiological evidence has suggested that frailty status might be associated with cardiovascular diseases (CVDs). However, the exact causality remains unestablished. In this study, we employed Mendelian randomization and sought to investigate the potential causality in association of frailty index (FI) with cardiovascular outcomes [coronary artery disease (CAD), myocardial infarction (MI), atrial fibrillation (AF), and heart failure (HF)].MethodsIndependent single nucleotide polymorphisms (SNPs) at genome-wide significance for FI were obtained from a recent genome-wide association study (GWAS) meta-analysis of European descent (n=175,226). The association of these SNPs with CVDs was examined in summary statistics from corresponding GWASs of European descent (CAD: 184,305 cases and 60,801 controls; MI: 184,305 cases and 43,676 controls; AF: 1,030,836 cases and 60,620 controls; and HF: 977,323 cases and 47,309 controls). Replication analyses were performed using GWAS datasets from FinnGen.ResultsIn the meta-analysis of inverse-variance weighted estimates from different data sources, genetically determined higher FI conferred an odds ratio (OR) of 1.46 [95% confidence interval (CI): 1.13 to 1.87; P=0.003] for CAD, 1.62 (95% CI: 1.21 to 2.17, P=0.001) for MI, and 1.46 (95% CI: 1.24 to 1.72; P=4.89×10−6) for HF. However, FI failed to be potentially influential on AF risk (OR, 1.43; 95% CI: 0.93 to 1.66; P=0.107). Several complementary analyses also received broadly concordant results.ConclusionsWe have provided genetic evidence of a causal association between FI and the risk of CAD, MI, and HF. Further studies are warranted to clarify whether FI is causally related to AF risk.

New Distance-Based approach for Genome-Wide Association Studies.

With the rise of genome-wide association studies (GWAS), the analysis of typical GWAS data sets with thousands of single-nucleotide polymorphisms (SNPs) has become crucial in biomedicine research. Here, we propose a new method to identify SNPs related to disease in case-control studies. The method, based on genetic distances between individuals, takes into account the possible population substructure, and avoids the issues of multiple testing. The method provides two ordered lists of SNPs; one with SNPs which minor alleles can be considered risk alleles for the disease, and another one with SNPs which minor alleles can be considered as protective. These two lists provide a useful tool to help the researcher to decide where to focus attention in a first stage.

Causal relationships of obesity on musculoskeletal chronic pain: A two-sample Mendelian randomization study.

BackgroundThe association between obesity and musculoskeletal chronic pain has attracted much attention these days; however, the causal relationship between them is uncertain. Hence, this study performed a Mendelian randomization (MR) analysis to investigate the causal effects of body mass index (BMI), waist circumference (WC), hip circumference (HC), and waist-to-hip ratio (WHR) on knee pain, hip pain, and back pain.Materials and methodsThe summary data for obesity and musculoskeletal chronic pain came from the genome-wide association study datasets. Significant and independent (p < 5 × 10−8; r2 < 0.001, kb = 10,000) single-nucleotide polymorphisms were extracted for MR analysis. The inverse variance weighted (IVW) and other methods were used for MR analysis, while sensitivity analyses were conducted to test the reliability and stability.ResultsThe positive causal effects of BMI on knee pain (odds ratio (OR) = 1.049; 95% CI: 1.034 to 1.063; p = 9.88 × 10−12), hip pain (OR = 1.034; 95% CI: 1.024 to 1.044; p = 1.38 × 10−12), and back pain (OR = 1.022; 95% CI: 1.007 to 1.038; p = 0.004) were observed. WC and HC were also positively associated with knee pain (WC: OR = 1.057; 95% CI: 1.041 to 1.072; p = 1.54 × 10−13; HC: OR = 1.034; 95% CI: 1.017 to 1.052; p = 1.32 × 10−4) and hip pain (WC: OR = 1.031; 95% CI: 1.020 to 1.042; p = 2.61 × 10−8; HC: OR = 1.027; 95% CI: 1.018 to 1.035; p = 5.48 × 10−10) but not back pain. No causal relationship was found between WHR and musculoskeletal chronic pain. The results were robust according to sensitivity tests.ConclusionsThis study revealed that BMI was positively related to knee, hip, and back pain and that WC and HC were positively associated with knee and hip pain, while WHR was not related to any type of musculoskeletal chronic pain.

Assessment of the Causal Effect of IgG N-Glycosylation Level on Risk of Dementia: A 2-Sample Mendelian Randomization Study.

Previous prospective studies highlighted aberrant immunoglobulin G (IgG) N-glycosylation as a risk factor for dementia [such as Alzheimer's disease (AD) and vascular dementia (VaD)]. It is unclear whether this association is causal or explained by confounding or reverse causation. The aim is to examine the association of genetically predicted IgG N-glycosylation with dementia using 2-sample Mendelian randomization (MR). Independent genetic variants for IgG N-glycosylation traits were selected as instrument variables from published genome-wide association studies (GWAS) among individuals of European ancestry. We extracted their corresponding summary statistics from large-scale clinically diagnosed AD GWAS dataset and FinnGen biobank VaD GWAS dataset. The inverse variance weighted (IVW) was performed to calculate the effect estimates. Meanwhile, multiple sensitivity analyses were used to assess horizontal pleiotropy and outliers. There were no associations of genetically predicted IgG N-glycosylation traits with the risk of AD and VaD using the IVW method (all Bonferroni corrected p > 0.0013). These estimates of four additional sensitivity analyses methods were consistent with the IVW estimates in terms of direction and magnitude. Additionally, the MR-PRESSO global test and the intercept of MR-Egger regression indicated no evidence of horizontal pleiotropy. Meanwhile, the heterogeneity test showed no significant heterogeneity using the Cochran Q statistic. The leave-one-out sensitivity analyses also did not detect any significant change. Our MR study did not support evidence for the hypothesis that IgG N-glycosylation level may be causally associated with the risk of dementia.

Pathway Analysis of Genome Wide Association Studies (GWAS) Data Associated with Male Infertility

Background: Infertility is a common condition affecting approximately 10–20% of the reproductive age population. Idiopathic infertility cases are thought to have a genetic basis, but the underlying causes are largely unknown. However, the genetic basis underlying male infertility in humans is only partially understood. The Purpose of the study is to understand the current state of research on the genetics of male infertility and its association with significant biological mechanisms. Results: We performed an Identify Candidate Causal SNPs and Pathway (ICSN Pathway) analysis using a genome-wide association study (GWAS) dataset, and NCBI-PubMed search which included 632 SNPs in GWAS and 451 SNPs from the PubMed server, respectively. The ICSN Pathway analysis produced three hypothetical biological mechanisms associated with male infertility: (1) rs8084 and rs7192→HLA-DRA→inflammatory pathways and cell adhesion; rs7550231 and rs2234167→TNFRSF14→TNF Receptor Superfamily Member 14→T lymphocyte proliferation and activation; rs1105879 and rs2070959→UGT1A6→UDP glucuronosyltransferase family 1 member A6→Metabolism of Xenobiotics, androgen, estrogen, retinol, and carbohydrates. Conclusions: We believe that our results may be helpful to study the genetic mechanisms of male infertility. Pathway-based methods have been applied to male infertility GWAS datasets to investigate the biological mechanisms and reported some novel male infertility risk pathways. This pathway analysis using GWAS dataset suggests that the biological process related to inflammation and metabolism might contribute to male infertility susceptibility. Our analysis suggests that genetic contribution to male infertility operates through multiple genes affecting common inflammatory diseases interacting in functional pathways.