scholarly journals A Comprehensive Evaluation of Methods for Mendelian Randomization Using Realistic Simulations and an Analysis of 38 Biomarkers for Risk of Type-2 Diabetes

2019 ◽  
Author(s):  
Guanghao Qi ◽  
Nilanjan Chatterjee
Keyword(s):  
Type 2 Diabetes ◽  
Complex Traits ◽  
Type I Error ◽  
Mendelian Randomization ◽  
Realistic Model ◽  
Type I ◽  
Simulation Studies ◽  
Sample Sizes ◽  
Effect Size Distribution

AbstractBackgroundMendelian randomization (MR) has provided major opportunities for understanding the causal relationship among complex traits. Previous studies have often evaluated MR methods based on simulations that do not adequately reflect the data-generating mechanism in GWAS and there are often discrepancies in performance of MR methods in simulations and real datasets.MethodsWe use a simulation framework that generates data on full GWAS for two traits under realistic model for effect-size distribution coherent with heritability, co-heritability and polygenicity typically observed for complex traits. We further use recent data generated from GWAS of 38 biomarkers in the UK Biobank to investigate their causal effects on risk of type-2 diabetes using externally available GWAS summary-statistics.ResultsSimulation studies show that weighted mode and MRMix are the only two methods which maintain correct type-I error rate in a diverse set of scenarios. Between the two methods, MRMix tends to be more powerful for larger GWAS while the opposite being true for smaller sample sizes. Among the other methods, random-effect IVW, MR-Robust and MR-RAPS tend to perform best in maintaining low mean squared error when the InSIDE assumption is satisfied, but can produce large bias when InSIDE is violated. In real data analysis, some biomarkers showed major heterogeneity in estimates of their causal effects on risk of type-2 diabetes across the different methods, with patterns similar to those observed in simulation studies.ConclusionsRelative performance of different MR methods depends heavily on sample sizes of underlying GWAS, proportion of valid instruments and validity of the InSIDE assumption.Key MessagesMany previous simulations studies to evaluate Mendelian randomization methods do not adequately reflect the data-generating mechanism of genome-wide association studies (GWAS).We use a simulation framework that generates data on full GWASs under realistic model informed by recent studies on effect-size distribution. We also used very recent GWAS data available on a large number of biomarkers to evaluate their causal effect on type-2 diabetes using alternative methods.Among the 10 methods that were compared, relative performance of different methods depends heavily on sample sizes of underlying GWAS, proportion of valid instruments and validity of the InSIDE assumption.Weighted mode and MRMix are the only two methods that maintain correct type I error rate in a diverse set of scenarios.

scholarly journals A comprehensive evaluation of methods for Mendelian randomization using realistic simulations and an analysis of 38 biomarkers for risk of type 2 diabetes

2021 ◽  
Author(s):  
Guanghao Qi ◽  
Nilanjan Chatterjee
Keyword(s):  
Type 2 Diabetes ◽  
Mendelian Randomization ◽  
Association Studies ◽  
Real Data ◽  
Causal Effects ◽  
Type I ◽  
Genome Wide Association Studies ◽  
Simulation Studies ◽  
Sample Sizes

Abstract Background Previous studies have often evaluated methods for Mendelian randomization (MR) analysis based on simulations that do not adequately reflect the data-generating mechanisms in genome-wide association studies (GWAS) and there are often discrepancies in the performance of MR methods in simulations and real data sets. Methods We use a simulation framework that generates data on full GWAS for two traits under a realistic model for effect-size distribution coherent with the heritability, co-heritability and polygenicity typically observed for complex traits. We further use recent data generated from GWAS of 38 biomarkers in the UK Biobank and performed down sampling to investigate trends in estimates of causal effects of these biomarkers on the risk of type 2 diabetes (T2D). Results Simulation studies show that weighted mode and MRMix are the only two methods that maintain the correct type I error rate in a diverse set of scenarios. Between the two methods, MRMix tends to be more powerful for larger GWAS whereas the opposite is true for smaller sample sizes. Among the other methods, random-effect IVW (inverse-variance weighted method), MR-Robust and MR-RAPS (robust adjust profile score) tend to perform best in maintaining a low mean-squared error when the InSIDE assumption is satisfied, but can produce large bias when InSIDE is violated. In real-data analysis, some biomarkers showed major heterogeneity in estimates of their causal effects on the risk of T2D across the different methods and estimates from many methods trended in one direction with increasing sample size with patterns similar to those observed in simulation studies. Conclusion The relative performance of different MR methods depends heavily on the sample sizes of the underlying GWAS, the proportion of valid instruments and the validity of the InSIDE assumption. Down-sampling analysis can be used in large GWAS for the possible detection of bias in the MR methods.

MR-Corr2: a two-sample Mendelian randomization method that accounts for correlated horizontal pleiotropy using correlated instrumental variants

2021 ◽  
Author(s):  
Qing Cheng ◽  
Tingting Qiu ◽  
Xiaoran Chai ◽  
Baoluo Sun ◽  
Yingcun Xia ◽  
...  
Keyword(s):  
Complex Traits ◽  
Error Control ◽  
Type I Error ◽  
Mendelian Randomization ◽  
Association Studies ◽  
Bivariate Normal Distribution ◽  
Supplementary Information ◽  
Type I ◽  
Genome Wide Association Studies ◽  
The Impact

Abstract Motivation Mendelian randomization (MR) is a valuable tool to examine the causal relationships between health risk factors and outcomes from observational studies. Along with the proliferation of genome-wide association studies, a variety of two-sample MR methods for summary data have been developed to account for horizontal pleiotropy (HP), primarily based on the assumption that the effects of variants on exposure (γ) and HP (α) are independent. In practice, this assumption is too strict and can be easily violated because of the correlated HP. Results To account for this correlated HP, we propose a Bayesian approach, MR-Corr2, that uses the orthogonal projection to reparameterize the bivariate normal distribution for γ and α, and a spike-slab prior to mitigate the impact of correlated HP. We have also developed an efficient algorithm with paralleled Gibbs sampling. To demonstrate the advantages of MR-Corr2 over existing methods, we conducted comprehensive simulation studies to compare for both type-I error control and point estimates in various scenarios. By applying MR-Corr2 to study the relationships between exposure–outcome pairs in complex traits, we did not identify the contradictory causal relationship between HDL-c and CAD. Moreover, the results provide a new perspective of the causal network among complex traits. Availability and implementation The developed R package and code to reproduce all the results are available at https://github.com/QingCheng0218/MR.Corr2. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Association of serum 25-hydroxyvitamin D with metabolic syndrome and type 2 diabetes: a one sample Mendelian randomization study

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Jing Xiao ◽  
Jingyi Lv ◽  
Shiyu Wang ◽  
Yang Zhou ◽  
Lunwen Chen ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Metabolic Syndrome ◽  
Vitamin D ◽  
Vitamin D Deficiency ◽  
Mendelian Randomization ◽  
Middle Aged ◽  
Hydroxyvitamin D ◽  
25 Hydroxyvitamin D ◽  
Randomization Analysis

Abstract Background Vitamin D deficiency has been associated with type 2 diabetes (T2D) and metabolic syndrome (MS) and its components. However, it is unclear whether a low concentration of vitamin D is the cause or consequence of these health conditions. Thus, this study aimed to evaluate the association of vitamin D concentrations and its genetic risk scores (GRSs) with MS and its component diseases, such as T2D, in middle-aged and elderly participants from rural eastern China. Methods A subset of 2393 middle-aged and elderly individuals were selected from 70,458 participants of the Nantong Chronic Diseases Study of 2017–2018 in China. We used two 25-hydroxyvitamin D (25[OH]D) synthesis single-nucleotide polymorphisms (SNPs) (DHCR7-rs12785878 and CYP2R1-rs10741657) and two 25(OH) D metabolism SNPs (GC-rs2282679 and CYP24A1-rs6013897) for creating GRSs, which were used as instrumental variables to assess the effect of genetically lowered 25(OH) D concentrations on MS and T2D based on the Wald ratio. F statistics were used to validate that the four SNPs genetically determined 25(OH) D concentrations. Results Compared to vitamin D sufficient individuals, individuals with vitamin D insufficiency had an odds ratio (OR [95% confidence interval {CI}]) of MS of 1.30 (1.06–1.61) and of T2D of 1.32 (1.08–1.64), individuals with vitamin D deficiency had an ORs (95% CI) of MS of 1.50 (1.24–1.79) and of T2D of 1.47 (1.12–1.80), and those with vitamin D severe deficiency had an ORs (95% CI) of MS of 1.52 (1.29–1.85) and of T2D of 1.54 (1.27–1.85). Mendelian randomization analysis showed a 25-nmol/L decrease in genetically instrumented serum 25(OH) D concentrations using the two synthesis SNPs (DHCR7 and CYP2R1 genes) associated with the risk of T2D and abnormal diastolic blood pressure (DBP) with ORs of 1.10 (95%CI: 1.02–1.45) for T2D and 1.14 (95%CI: 1.03–1.43) for DBP. Conclusions This one sample Mendelian randomization analysis shows genetic evidence for a causal role of lower 25(OH) D concentrations in promoting of T2D and abnormal DBP in middle-aged and elderly participants from rural China.

scholarly journals Association of leukocyte telomere length with chronic kidney disease in East Asians with type 2 diabetes: A Mendelian randomization study

2021 ◽  
Author(s):  
Resham L Gurung ◽  
Rajkumar Dorajoo ◽  
Yiamunaa M ◽  
Ling Wang ◽  
Sylvia Liu ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Telomere Length ◽  
Mendelian Randomization ◽  
Random Effect ◽  
Leukocyte Telomere Length ◽  
Increased Risk ◽  
Genetically Determined

Abstract Background Chronic kidney disease (CKD) is common among type 2 diabetes (T2D) and increases the risk of kidney failure and cardiovascular diseases. Shorter leukocyte telomere length is associated with CKD in patients with T2D. We previously reported single nucleotide polymorphisms (SNPs) associated with leukocyte telomere length in Asian population. In this study, we elucidated the association of these SNPs with CKD in patients with T2D using Mendelian randomization (MR) approach. Methods The cross-sectional association of 16 leukocyte telomere length SNPs with CKD, defined as an estimated glomerular filtration rate of less than 60 ml/min/1.73m2 was assessed among 4,768 (1,628 cases, 3,140 controls) participants in the Singapore Study of Macro-angiopathy and Micro-vascular Reactivity in Type 2 Diabetes and Diabetic Nephropathy cohorts. MR analysis was performed using the random-effect inverse-variance weighted (IVW) method, the weighted median, MR-Egger and Radial MR adjusted for age and sex-stratified by cohorts and ethnicity (Chinese and Malays), then meta-analysed. Results Genetically determined shorter leukocyte telomere length was associated with increased risk of CKD in patients with T2D (meta-IVW adjusted odds ratio = 1.51 [95% confidence interval, 1.12 - 2.12; P = 0.007; Phet= 0.547]). Similar results were obtained following sensitivity analysis. MR-Egger analysis (intercept) suggested no evidence of horizontal pleiotropy (β  =  0.010, P = 0.751). Conclusions Our findings suggest that genetically determined leukocyte telomere length is associated with CKD in patients with T2D. Further studies are warranted to elucidate the causal role of telomere length in CKD progression.

scholarly journals REMAXINT: a two-mode clustering-based method for statistical inference on two-way interaction

2021 ◽  
Author(s):  
Zaheer Ahmed ◽  
Alberto Cassese ◽  
Gerard van Breukelen ◽  
Jan Schepers
Keyword(s):  
Null Hypothesis ◽  
Type I Error ◽  
Type I ◽  
The Novel ◽  
Simulation Studies ◽  
Test Statistic ◽  
Clustering Model ◽  
Novel Method ◽  
Main Effects ◽  
Empirical Case Studies

AbstractWe present a novel method, REMAXINT, that captures the gist of two-way interaction in row by column (i.e., two-mode) data, with one observation per cell. REMAXINT is a probabilistic two-mode clustering model that yields two-mode partitions with maximal interaction between row and column clusters. For estimation of the parameters of REMAXINT, we maximize a conditional classification likelihood in which the random row (or column) main effects are conditioned out. For testing the null hypothesis of no interaction between row and column clusters, we propose a $$max-F$$ m a x - F test statistic and discuss its properties. We develop a Monte Carlo approach to obtain its sampling distribution under the null hypothesis. We evaluate the performance of the method through simulation studies. Specifically, for selected values of data size and (true) numbers of clusters, we obtain critical values of the $$max-F$$ m a x - F statistic, determine empirical Type I error rate of the proposed inferential procedure and study its power to reject the null hypothesis. Next, we show that the novel method is useful in a variety of applications by presenting two empirical case studies and end with some concluding remarks.

scholarly journals Gallstone disease, diabetes, calcium, triglycerides, smoking and alcohol consumption and pancreatitis risk: Mendelian randomization study

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Shuai Yuan ◽  
Edward L. Giovannucci ◽  
Susanna C. Larsson
Keyword(s):  
Type 2 Diabetes ◽  
Chronic Pancreatitis ◽  
Alcohol Consumption ◽  
Mendelian Randomization ◽  
Gallstone Disease ◽  
Smoking Initiation ◽  
Disease Type ◽  
Uk Biobank ◽  
Increased Risk

AbstractWe conducted a Mendelian randomization study to determine the potential causal associations of gallstone disease, diabetes, serum calcium, triglyceride levels, smoking and alcohol consumption with acute and chronic pancreatitis. Genetic variants associated with the exposures at p < 5 × 10−8 were selected from corresponding genome-wide association studies. Summary-level data for pancreatitis were obtained from the FinnGen consortium and UK Biobank. Univariable and multivariable Mendelian randomization analyses were performed and results from FinnGen and UK Biobank were combined using the fixed-effects meta-analysis method. Genetic predisposition to gallstone disease, type 2 diabetes and smoking initiation was associated with an increased risk of acute pancreatitis. The combined odds ratios (ORs) were 1.74 (95% confidence interval (CI), 1.57, 1.93) for gallstone disease, 1.14 (95% CI, 1.06, 1.21) for type 2 diabetes and 1.56 (95% CI, 1.32, 1.83) for smoking initiation. The association for type 2 diabetes attenuated after adjustment for gallstone disease. Genetic predisposition to gallstone disease and smoking initiation as well as higher genetically predicted serum calcium and triglyceride levels were associated with an increased risk of chronic pancreatitis. The combined ORs of chronic pancreatitis were 1.27 (95% CI, 1.08, 1.50) for gallstone disease, 1.86 (95% CI, 1.43, 2.43) for smoking initiation, 2.20 (95% CI, 1.30, 3.72) for calcium and 1.47 (95% CI, 1.23, 1.76) for triglycerides. This study provides evidence in support that gallstone disease, type 2 diabetes, smoking and elevated calcium and triglyceride levels are causally associated with the risk of acute or chronic pancreatitis.

scholarly journals Evaluation of glycemic traits in susceptibility to COVID-19 risk: a Mendelian randomization study

BMC Medicine ◽  
2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Shiu Lun Au Yeung ◽  
Jie V Zhao ◽  
C Mary Schooling
Keyword(s):  
Type 2 Diabetes ◽  
Genetic Predisposition ◽  
Mendelian Randomization ◽  
Fasting Glucose ◽  
Association Studies ◽  
Sensitivity Analyses ◽  
Genome Wide Association Studies ◽  
Inverse Association ◽  
Wide Confidence Interval

Abstract Background Observational studies suggest poorer glycemic traits and type 2 diabetes associated with coronavirus disease 2019 (COVID-19) risk although these findings could be confounded by socioeconomic position. We conducted a two-sample Mendelian randomization to clarify their role in COVID-19 risk and specific COVID-19 phenotypes (hospitalized and severe cases). Method We identified genetic instruments for fasting glucose (n = 133,010), 2 h glucose (n = 42,854), glycated hemoglobin (n = 123,665), and type 2 diabetes (74,124 cases and 824,006 controls) from genome wide association studies and applied them to COVID-19 Host Genetics Initiative summary statistics (17,965 COVID-19 cases and 1,370,547 population controls). We used inverse variance weighting to obtain the causal estimates of glycemic traits and genetic predisposition to type 2 diabetes in COVID-19 risk. Sensitivity analyses included MR-Egger and weighted median method. Results We found genetic predisposition to type 2 diabetes was not associated with any COVID-19 phenotype (OR: 1.00 per unit increase in log odds of having diabetes, 95%CI 0.97 to 1.04 for overall COVID-19; OR: 1.02, 95%CI 0.95 to 1.09 for hospitalized COVID-19; and OR: 1.00, 95%CI 0.93 to 1.08 for severe COVID-19). There were no strong evidence for an association of glycemic traits in COVID-19 phenotypes, apart from a potential inverse association for fasting glucose albeit with wide confidence interval. Conclusion We provide some genetic evidence that poorer glycemic traits and predisposition to type 2 diabetes unlikely increase the risk of COVID-19. Although our study did not indicate glycemic traits increase severity of COVID-19, additional studies are needed to verify our findings.

Mutation of the 5' untranslated region stem-loop mRNA structure reduces type I collagen deposition and arterial stiffness in male obese mice

2021 ◽  
Author(s):  
Francisco I. Ramirez-Perez ◽  
Makenzie L. Woodford ◽  
Mariana Morales-Quinones ◽  
Zachary I. Grunewald ◽  
Francisco J Cabral-Amador ◽  
...  
Keyword(s):  
Insulin Resistance ◽  
Type 2 Diabetes ◽  
Arterial Stiffness ◽  
Type I Collagen ◽  
Type I ◽  
Collagen Deposition ◽  
Wild Type ◽  
Stem Loop ◽  
Arterial Stiffening

Arterial stiffening, a characteristic feature of obesity and type 2 diabetes, contributes to the development and progression of cardiovascular diseases (CVD). Currently, no effective prophylaxis or therapeutics is available to prevent or treat arterial stiffening. A better understanding of the molecular mechanisms underlying arterial stiffening is vital to identify newer targets and strategies to reduce CVD burden. A major contributor to arterial stiffening is increased collagen deposition. In the 5' untranslated regions of mRNAs encoding for type I collagen, an evolutionally conserved stem-loop (SL) structure plays an essential role in its stability and post-transcriptional regulation. Here, we show that feeding a high fat/high sucrose (HFHS) diet for 28 weeks increases adiposity, insulin resistance, and blood pressure in male wild-type littermates. Moreover, arterial stiffness, assessed in vivo via aortic pulse wave velocity, and ex vivo using atomic force microscopy in aortic explants or pressure myography in isolated femoral and mesenteric arteries, was also increased in those mice. Notably, all these indices of arterial stiffness, along with collagen type I levels in the vasculature, were reduced in HFHS-fed mice harboring a mutation in the 5'SL structure, relative to wild-type littermates. This protective vascular phenotype in 5'SL-mutant mice did not associate with a reduction in insulin resistance or blood pressure. These findings implicate the 5'SL structure as a putative therapeutic target to prevent or reverse arterial stiffening and CVD associated with obesity and type 2 diabetes.

Comprehensive Review on Neuro-Degenerative Type 3DM

2021 ◽  
Vol 18 ◽  
Author(s):  
Chandani V. Chandarana ◽  
Salona Roy
Keyword(s):  
Type 2 Diabetes ◽  
Insulin Signaling ◽  
Insulin Signalling ◽  
Amyloid Β ◽  
Diabetes Mellitus Type 1 ◽  
Current Data ◽  
The Body ◽  
Type I ◽  
Therapeutic Benefits

: Alzheimer disease (AD) is thought to be the metabolic illness raised by defective insulin signaling, insulin resistance, and low insulin levels in the brain, according to a growing body of research. The "Type 3 diabetes" has been postulated for AD because reduced insulin signalling has molecular and physiological consequences that are comparable to Type I and Type 2 diabetes mellitus (Type 1 DM and Type 2 DM, respectively). The similarities between type 2 diabetes and Alzheimer's disease suggest that these clinical trials might yield therapeutic benefits. However, it's important to note that lowering your risk of Alzheimer's dementia, whether you have diabetes or not, is still a multidimensional process involving factors like exercise, smoking, alcohol, food, and mental challenge. The current aim is to show the relationship between T3D and AD being based on both the processing of amyloid-β (Aβ) precursor protein toxicity and the clearance of Aβ are the result of an impaired insulin signaling. The brain's metabolism with its high lipid content and energy needs, places excess demands on mitochondria and appears more susceptible to oxidative damage than the rest of the body. Current data suggests that increased oxidative stress relates to amyloid-β (Aβ) pathology and onset of AD.

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