scholarly journals Mendelian randomization analysis revealed causal effects from gut microbiota to abdominal obesity

2020 ◽  
Author(s):  
Qian Xu ◽  
Shan-Shan Zhang ◽  
Yu-Fang Pei ◽  
Jing-Jing Ni ◽  
Lei Zhang ◽  
...  
Keyword(s):  
Gut Microbiota ◽  
Causal Relationship ◽  
Abdominal Obesity ◽  
Mendelian Randomization ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Summary Statistics ◽  
Replication Sample ◽  
Discovery Sample ◽  
Increased Risk

ABSTRACTAlthough recent studies have revealed the association between the gut microbiota and obesity, the causality remains elusive. We performed a Mendelian Randomization (MR) analysis to determine whether there is a causal relationship between gut microbiota and abdominal obesity. We used a two-sample MR approach to assess the causal effect from gut microbiota to obesity based on genome-wide association studies (GWAS) summary statistics. The GWAS summary statistics of gut microbiota obtained from UK-twins cohort (N=1,126) were used as discovery sample exposure, and the GWAS summary statistics from the Genetic Environmental Microbial (GEM) project (N=1,098) were used as replication sample exposure. Trunk fat mass (TFM) summary statistics from the UK Biobank (UKB) cohort (N=330,762) were used as outcome. Bacteria were grouped into taxa features at family level. A total of 16 families were analyzed in the discovery sample. Family Barnesiellaceae was associated with TFM at the nominal significance level (b=-3.81×10−4, P=1.96×10−3). The causal association was successfully replicated in the replication sample (b=-7.34×10−3, P =2.77×10−2). Our findings provided evidence of causal relationship from microbiota to fat development, and may be helpful in selecting potential causal bacteria for manipulating candidate gut microbiota to therapy obesity.IMPORTANCEObesity, as a global public health problem, is one of the most important risk factors contributing to the overall global burden of disease, and is associated with an increased risk of cardiovascular disease, type 2 diabetes, and certain cancers. Recent studies have shown that gut microbiota is closely related to the development of obesity, but the causal relationship is unclear. Therefore, it is necessary to identify the causality between gut microbiota and obesity. The significance of our research is in identifying the causal relationship from specific bacteria to fat development, which will provide the new insights into the microbiota mediated the fat development mechanism.

scholarly journals Causal Inference Between Chronic Periodontitis and Chronic Kidney Disease: A Bidirectional Mendelian Randomization Analysis in a European Population

2021 ◽  
Vol 12 ◽  
Author(s):  
Jie Yang ◽  
Tianyi Chen ◽  
Yahong Zhu ◽  
Mingxia Bai ◽  
Xingang Li
Keyword(s):  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Causal Inference ◽  
Causal Relationship ◽  
Instrumental Variables ◽  
Chronic Periodontitis ◽  
Mendelian Randomization ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Summary Statistics

BackgroundPrevious epidemiological studies have shown significant associations between chronic periodontitis (CP) and chronic kidney disease (CKD), but the causal relationship remains uncertain. Aiming to examine the causal relationship between these two diseases, we conducted a bidirectional two-sample Mendelian randomization (MR) analysis with multiple MR methods.MethodsFor the casual effect of CP on CKD, we selected seven single-nucleotide polymorphisms (SNPs) specific to CP as genetic instrumental variables from the genome-wide association studies (GWAS) in the GLIDE Consortium. The summary statistics of complementary kidney function measures, i.e., estimated glomerular filtration rate (eGFR) and blood urea nitrogen (BUN), were derived from the GWAS in the CKDGen Consortium. For the reversed causal inference, six SNPs associated with eGFR and nine with BUN from the CKDGen Consortium were included and the summary statistics were extracted from the CLIDE Consortium.ResultsNo significant causal association between genetically determined CP and eGFR or BUN was found (all p > 0.05). Based on the conventional inverse variance-weighted method, one of seven instrumental variables supported genetically predicted CP being associated with a higher risk of eGFR (estimate = 0.019, 95% CI: 0.012–0.026, p < 0.001).ConclusionEvidence from our bidirectional causal inference does not support a causal relation between CP and CKD risk and therefore suggests that associations reported by previous observational studies may represent confounding.

scholarly journals Association of 25 hydroxyvitamin D concentration with risk of COVID-19: a Mendelian randomization study

2020 ◽  
Author(s):  
Di Liu ◽  
Qiuyue Tian ◽  
Jie Zhang ◽  
Haifeng Hou ◽  
Wei Wang ◽  
...  
Keyword(s):  
Causal Relationship ◽  
Mendelian Randomization ◽  
Association Studies ◽  
Sensitivity Analyses ◽  
Genome Wide Association Studies ◽  
Causal Association ◽  
Hydroxyvitamin D ◽  
Increased Risk ◽  
25Ohd Concentration ◽  
25 Hydroxyvitamin D

Background In observational studies, 25 hydroxyvitamin D (25OHD) concentration has been associated with an increased risk of Coronavirus disease 2019 (COVID-19). However, it remains unclear whether this association is causal. Methods We performed a two-sample Mendelian randomization (MR) to explore the causal relationship between 25OHD concentration and COVID-19, using summary data from the genome-wide association studies (GWASs) and using 25OHD concentration-related SNPs as instrumental variables (IVs). Results MR analysis did not show any evidence of a causal association of 25OHD concentration with COVID-19 susceptibility and severity (odds ratio [OR]=1.136, 95% confidence interval [CI] 0.988-1.306, P=0.074; OR=0.889, 95% CI 0.549-1.439, P=0.632). Sensitivity analyses using different instruments and statistical models yielded similar findings, suggesting the robustness of the causal association. No obvious pleiotropy bias and heterogeneity were observed. Conclusion The MR analysis showed that there might be no linear causal relationship of 25OHD concentration with COVID-19 susceptibility and severity.

scholarly journals Investigating causality in associations between education and smoking: A two-sample Mendelian randomization study

2017 ◽  
Author(s):  
Suzanne H. Gage ◽  
Jack Bowden ◽  
George Davey Smith ◽  
Marcus R. Munafo
Keyword(s):  
Smoking Cessation ◽  
Educational Attainment ◽  
Mendelian Randomization ◽  
Association Studies ◽  
Smoking Initiation ◽  
Self Report ◽  
Genome Wide Association Studies ◽  
Summary Statistics ◽  
Increased Risk ◽  
Higher Educational Attainment

AbstractBackgroundLower educational attainment is associated with increased rates of smoking, but ascertaining causality is challenging. We used two-sample Mendelian randomization (MR) analyses of summary statistics to examine whether educational attainment is causally related to smoking.Methods and FindingsWe used summary statistics from genome-wide association studies of educational attainment and a range of smoking phenotypes (smoking initiation, cigarettes per day, cotinine levels and smoking cessation). Various complementary MR techniques (inverse-variance weighted regression, MR Egger, weighted-median regression) were used to test the robustness of our results. We found broadly consistent evidence across these techniques that higher educational attainment leads to reduced likelihood of smoking initiation, reduced heaviness of smoking among smokers (as measured via self-report and cotinine levels), and greater likelihood of smoking cessation among smokers.ConclusionsOur findings indicate a causal association between low educational attainment and increased risk of smoking, and may explain the observational associations between educational attainment and adverse health outcomes such as risk of coronary heart disease.

scholarly journals Association of 25 Hydroxyvitamin D Concentration with Risk of COVID-19: A Mendelian Randomization Study

2020 ◽  
Author(s):  
Di Liu ◽  
Qiuyue Tian ◽  
Jie Zhang ◽  
Haifeng Hou ◽  
Wei Wang ◽  
...  
Keyword(s):  
Causal Relationship ◽  
Mendelian Randomization ◽  
Association Studies ◽  
Sensitivity Analyses ◽  
Genome Wide Association Studies ◽  
Causal Association ◽  
Hydroxyvitamin D ◽  
Increased Risk ◽  
25Ohd Concentration ◽  
25 Hydroxyvitamin D

Abstract Background: Coronavirus disease 2019 (COVID-19) has caused a large global pandemic. In observational studies, 25 hydroxyvitamin D (25OHD) concentration has been associated with an increased risk of Coronavirus disease 2019 (COVID-19). However, it remains unclear whether this association is causal.Methods: We performed a two-sample Mendelian randomization (MR) to explore the causal relationship between 25OHD concentration and COVID-19, using summary data from the genome-wide association studies (GWASs) and using 25OHD concentration-related SNPs as instrumental variables (IVs). Results: MR analysis did not show any evidence of a causal association of 25OHD concentration with COVID-19 susceptibility and severity (odds ratio [OR]=1.136, 95% confidence interval [CI] 0.988-1.306, P=0.074; OR=0.889, 95% CI 0.549-1.439, P=0.632). Sensitivity analyses using different instruments and statistical models yielded similar findings, suggesting the robustness of the causal association. No obvious pleiotropy bias and heterogeneity were observed.Conclusions: The MR analysis showed that there might be no linear causal relationship of 25OHD concentration with COVID-19 susceptibility and severity.

scholarly journals Evaluating the effects of alcohol and tobacco use on cardiovascular disease using multivariable Mendelian randomization

2019 ◽  
Author(s):  
Daniel B. Rosoff ◽  
George Davey Smith ◽  
Nehal Mehta ◽  
Toni-Kim Clarke ◽  
Falk W. Lohoff
Keyword(s):  
Risk Factors ◽  
Cardiovascular Disease ◽  
Alcohol Use ◽  
Tobacco Use ◽  
Mendelian Randomization ◽  
Association Studies ◽  
Density Lipoprotein ◽  
Genome Wide Association Studies ◽  
Cvd Risk ◽  
Increased Risk

ABSTRACTAlcohol and tobacco use, two major modifiable risk factors for cardiovascular disease (CVD), are often consumed together. Using large publicly available genome-wide association studies (results from > 940,000 participants), we conducted two-sample multivariable Mendelian randomization (MR) to simultaneously assess the independent effects of alcohol and tobacco use on CVD risk factors and events. We found genetic instruments associated with increased alcohol use, controlling for tobacco use, associated with increased high-density-lipoprotein-cholesterol (HDL-C), decreased triglycerides, but not with coronary heart disease (CHD), myocardial infarction (MI), nor stroke; and instruments for increased tobacco use, controlling for alcohol use, associated with decreased HDL-C, increased triglycerides, and increased risk of CHD and MI. Exploratory analysis found associations with HDL-C, LDL-C, and intermediate-density-lipoprotein metabolites. Consistency of results across complementary methods accommodating different MR assumptions strengthened causal inference, providing strong genetic evidence for the causal effects of modifiable lifestyle risk factors on CVD risk.

scholarly journals Leukocyte telomere length and amyotrophic lateral sclerosis: a Mendelian randomization study

2021 ◽  
Author(s):  
Kailin Xia ◽  
Linjing Zhang ◽  
Gan Zhang ◽  
Yajun Wang ◽  
Tao Huang ◽  
...  
Keyword(s):  
Amyotrophic Lateral Sclerosis ◽  
Telomere Length ◽  
Causal Relationship ◽  
Mendelian Randomization ◽  
Association Studies ◽  
Sensitivity Analyses ◽  
Genome Wide Association Studies ◽  
Leukocyte Telomere Length ◽  
Weighted Median ◽  
Lateral Sclerosis

Abstract Background Observational studies have suggested that telomere length is associated with amyotrophic lateral sclerosis (ALS). However, it remains unclear whether this association is causal. We employed a two-sample Mendelian randomization (MR) approach to explore the causal relationship between leukocyte telomere length (LTL) and ALS based on the most cited and most recent and largest LTL genome-wide association studies (GWASs) that measured LTL with the Southern blot method (n = 9190) and ALS GWAS summary data (n = 80,610). We adopted the inverse variance weighted (IVW) method to examine the effect of LTL on ALS and used the weighted median method, simple median method, MR Egger method and MR PRESSO method to perform sensitivity analyses. Results We found that genetically determined longer LTL was inversely associated with the risk of ALS (OR = 0.846, 95% CI: 0.744–0.962, P = 0.011), which was mainly driven by rs940209 in the OBFC1 gene, suggesting a potential effect of OBFC1 on ALS. In sensitivity analyses, that was confirmed in MR Egger method (OR = 0.647,95% CI = 0.447–0.936, P = 0.050), and a similar trend was shown with the weighted median method (OR = 0.893, P = 0.201) and simple median method (OR = 0.935 P = 0.535). The MR Egger analyses did not suggest directional pleiotropy, showing an intercept of 0.025 (P = 0.168). Neither the influence of instrumental outliers nor heterogeneity was found. Conclusions Our results suggest that genetically predicted longer LTL has a causal relationship with a lower risk of ALS and underscore the importance of protecting against telomere loss in ALS.

Daytime sleepiness, night sleep changes and ALS: a Mendelian randomization study

2020 ◽  
Author(s):  
Gan Zhang ◽  
Linjing Zhang ◽  
Tao Huang ◽  
Dongsheng Fan
Keyword(s):  
Sleep Duration ◽  
Daytime Sleepiness ◽  
Mendelian Randomization ◽  
Association Studies ◽  
Sleep Efficiency ◽  
Genome Wide Association Studies ◽  
Night Sleep ◽  
Inverse Variance ◽  
Point Increase ◽  
Increased Risk

Abstract Background Observational studies have indicated that there is a high prevalence of daytime sleepiness and night sleep changes in amyotrophic lateral sclerosis (ALS). However, the actual relation between these symptoms and ALS remains unclear. We aimed to determine whether daytime sleepiness and night sleep changes have an effect on ALS. Methods We used 2-sample mendelian randomization to estimate the effects of daytime sleepiness, sleep efficiency, number of sleep episodes and sleep duration on ALS. Summary statistics we used was from resent and large genome-wide association studies on the traits we chosen (n = 85,670–452,071) and ALS (cases n = 20,806, controls n = 59,804). Inverse variance weighted method was used as the main method for assessing causality. Results A genetically predicted 1-point increase in the assessment of daytime sleepiness was significantly associated with an increased risk of ALS (inverse-variance-weighted (IVW) odds ratio = 2.70, 95% confidence interval (CI): 1.27–5.76; P = 0.010). ALS was not associated with a genetically predicted 1-SD increase in sleep efficiency (IVW 1.01, 0.64–1.58; P = 0.973), Number of sleep episodes (IVW 1.02, 0.80–1.30; P = 0.859) or sleep duration (IVW 1.00, 1.00–1.01; P = 0.250). Conclusions Our results provide novel evidence that daytime sleepiness causes an increase in the risk of ALS and indicate that daytime sleepiness may be inherent in preclinical and clinical ALS patients, rather than simply affected by potential influencing factors.

scholarly journals MR-LDP: a two-sample Mendelian randomization for GWAS summary statistics accounting for linkage disequilibrium and horizontal pleiotropy

2020 ◽  
Vol 2 (2) ◽  
Author(s):  
Qing Cheng ◽  
Yi Yang ◽  
Xingjie Shi ◽  
Kar-Fu Yeung ◽  
Can Yang ◽  
...  
Keyword(s):  
Risk Factors ◽  
Linkage Disequilibrium ◽  
Genetic Variants ◽  
Mendelian Randomization ◽  
Association Studies ◽  
Alternative Methods ◽  
Genome Wide Association Studies ◽  
Summary Statistics ◽  
Causal Relationships ◽  
Disease Outcomes

Abstract The proliferation of genome-wide association studies (GWAS) has prompted the use of two-sample Mendelian randomization (MR) with genetic variants as instrumental variables (IVs) for drawing reliable causal relationships between health risk factors and disease outcomes. However, the unique features of GWAS demand that MR methods account for both linkage disequilibrium (LD) and ubiquitously existing horizontal pleiotropy among complex traits, which is the phenomenon wherein a variant affects the outcome through mechanisms other than exclusively through the exposure. Therefore, statistical methods that fail to consider LD and horizontal pleiotropy can lead to biased estimates and false-positive causal relationships. To overcome these limitations, we proposed a probabilistic model for MR analysis in identifying the causal effects between risk factors and disease outcomes using GWAS summary statistics in the presence of LD and to properly account for horizontal pleiotropy among genetic variants (MR-LDP) and develop a computationally efficient algorithm to make the causal inference. We then conducted comprehensive simulation studies to demonstrate the advantages of MR-LDP over the existing methods. Moreover, we used two real exposure–outcome pairs to validate the results from MR-LDP compared with alternative methods, showing that our method is more efficient in using all-instrumental variants in LD. By further applying MR-LDP to lipid traits and body mass index (BMI) as risk factors for complex diseases, we identified multiple pairs of significant causal relationships, including a protective effect of high-density lipoprotein cholesterol on peripheral vascular disease and a positive causal effect of BMI on hemorrhoids.

scholarly journals High Level of Uromodulin Increases the Risk of Hypertension: A Mendelian Randomization Study

2021 ◽  
Vol 8 ◽  
Author(s):  
Ruilian You ◽  
Lanlan Chen ◽  
Lubin Xu ◽  
Dingding Zhang ◽  
Haitao Li ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Causal Relationship ◽  
Mendelian Randomization ◽  
Association Studies ◽  
Meta Analysis ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Inverse Variance ◽  
The Uk ◽  
High Level

Background: The association of uromodulin and hypertension has been observed in clinical studies, but not proven by a causal relationship. We conducted a two-sample Mendelian randomization (MR) analysis to investigate the causal relationship between uromodulin and blood pressure.Methods: We selected single nucleotide polymorphisms (SNPs) related to urinary uromodulin (uUMOD) and serum uromodulin (sUMOD) from a large Genome-Wide Association Studies (GWAS) meta-analysis study and research in PubMed. Six datasets based on the UK Biobank and the International Consortium for Blood Pressure (ICBP) served as outcomes with a large sample of hypertension (n = 46,188), systolic blood pressure (SBP, n = 1,194,020), and diastolic blood pressure (DBP, n = 1,194,020). The inverse variance weighted (IVW) method was performed in uUMOD MR analysis, while methods of IVW, MR-Egger, Weighted median, and Mendelian Randomization Pleiotropy RESidual Sum and Outlier (MR-PRESSO) were utilized on sUMOD MR analysis.Results: MR analysis of IVM showed the odds ratio (OR) of the uUMOD to hypertension (“ukb-b-14057” and “ukb-b-14177”) is 1.04 (95% Confidence Interval (CI), 1.03-1.04, P < 0.001); the effect sizes of the uUMOD to SBP are 1.10 (Standard error (SE) = 0.25, P = 8.92E-06) and 0.03 (SE = 0.01, P = 2.70E-04) in “ieu-b-38” and “ukb-b-20175”, respectively. The β coefficient of the uUMOD to DBP is 0.88 (SE = 0.19, P = 4.38E-06) in “ieu-b-39” and 0.05 (SE = 0.01, P = 2.13E-10) in “ukb-b-7992”. As for the sUMOD, the OR of hypertension (“ukb-b-14057” and “ukb-b-14177”) is 1.01 (95% CI 1.01–1.02, all P < 0.001). The β coefficient of the SBP is 0.37 (SE = 0.07, P = 1.26E-07) in “ieu-b-38” and 0.01 (SE = 0.003, P = 1.04E-04) in “ukb-b-20175”. The sUMOD is causally associated with elevated DBP (“ieu-b-39”: β = 0.313, SE = 0.050, P = 3.43E-10; “ukb-b-7992”: β = 0.018, SE = 0.003, P = 8.41E-09).Conclusion: Our results indicated that high urinary and serum uromodulin levels are potentially detrimental in elevating blood pressure, and serve as a causal risk factor for hypertension.

scholarly journals An iterative approach to detect pleiotropy and perform Mendelian Randomization analysis using GWAS summary statistics

2020 ◽  
Author(s):  
Xiaofeng Zhu ◽  
Xiaoyin Li ◽  
Rong Xu ◽  
Tao Wang
Keyword(s):  
Complex Traits ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Association Studies ◽  
Real Data ◽  
Supplementary Information ◽  
Genome Wide Association Studies ◽  
Summary Statistics ◽  
Causal Relationships ◽  
Multiple Traits

Abstract Motivation The overall association evidence of a genetic variant with multiple traits can be evaluated by cross-phenotype association analysis using summary statistics from genome-wide association studies. Further dissecting the association pathways from a variant to multiple traits is important to understand the biological causal relationships among complex traits. Results Here, we introduce a flexible and computationally efficient Iterative Mendelian Randomization and Pleiotropy (IMRP) approach to simultaneously search for horizontal pleiotropic variants and estimate causal effect. Extensive simulations and real data applications suggest that IMRP has similar or better performance than existing Mendelian Randomization methods for both causal effect estimation and pleiotropic variant detection. The developed pleiotropy test is further extended to detect colocalization for multiple variants at a locus. IMRP will greatly facilitate our understanding of causal relationships underlying complex traits, in particular, when a large number of genetic instrumental variables are used for evaluating multiple traits. Availability and implementation The software IMRP is available at https://github.com/XiaofengZhuCase/IMRP. The simulation codes can be downloaded at http://hal.case.edu/∼xxz10/zhu-web/ under the link: MR Simulations software. Supplementary information Supplementary data are available at Bioinformatics online.

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