Exposing the Causal Effect of Body Mass Index on the Risk of Type 2 Diabetes Mellitus: A Mendelian Randomization Study

Liang, Cheng; Zhuang, He; Ju, Hong; Yang, Shuo; Han, Junwei; Tan, Renjie; Hu, Yang

doi:10.3389/fgene.2019.00094

Cited by 58 publications

(47 citation statements)

References 57 publications

(80 reference statements)

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“…The estimate of UA on the risk of MS was evaluated using each SNP singly via the Wald ratio (Cheng et al, 2019). We used the random effects inverse variance weighted method to perform the two-sample MR analysis by pooling all of the estimates.…”

Section: Statistical Analysesmentioning

confidence: 99%

Serum Uric Acid Level and Multiple Sclerosis: A Mendelian Randomization Study

et al. 2020

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Previous observational studies have shown that the serum uric acid (UA) level is decreased in persons with multiple sclerosis (MS). We used the two-sample Mendelian randomization (MR) method to determine whether the serum UA level is causally associated with the risk of MS. We screened 26 single-nucleotide polymorphisms (SNPs) in association with serum UA level (p < 5 × 10 −8) from a large genome-wide meta-analysis involving 110,347 individuals. The SNP outcome effects were obtained from two large international genetic studies of MS involving 38,589 individuals and 27,148 individuals. A total of 18 SNPs, including nine proxy SNPs, were included in the MR analysis. The estimate based on SNP rs12498742 that explained the largest proportion of variance showed that the odds ratio (OR) of UA (per mg/dl increase) for MS was 1.00 [95% confidence interval (CI) 0.90-1.11; p = 0.96]. The main MR analysis based on the random effects inverse variance weighted method showed that the pooled OR was 1.05 (95% CI 0.92-1.19; p = 0.50). Although there was no evidence of net horizontal pleiotropy in MR-Egger regression (p = 0.48), excessive heterogeneity was found via Cochran's Q statistic (p = 9.6 × 10 −4). The heterogeneity showed a substantial decrease after exclusion of two outlier SNPs (p = 0.17). The pooled ORs for the other MR methods ranged from 0.89 (95% CI 0.65-1.20; p = 0.45) to 1.05 (95% CI 0.96-1.14; p = 0.29). The results of sensitivity analyses and additional analyses all showed similar pooled estimates. MR analyses by using 81 MS-associated SNPs as instrumental variables showed that genetically predicted risk of MS was not significantly associated with serum UA level. The pooled OR was 1.00 (95% CI 0.99-1.02; p = 0.74) for the main MR analysis. This MR study does not support a causal effect of genetically determined serum UA level on the risk of MS, nor does it support a causal effect of genetically determined risk of MS on serum UA level.

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Section: Statistical Analysesmentioning

confidence: 99%

Serum Uric Acid Level and Multiple Sclerosis: A Mendelian Randomization Study

et al. 2020

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“…MR analysis is an IV-based framework, which requires summarized GWAS data. In recent years, MR analysis has helped us to identify lots of causal effects, such as body mass index and C-reactive protein increase the risk of type 2 diabetes (Cheng et al, 2019c;Zhuang et al, 2019b). Here, the number of the case and control for GWAS data is very important for the estimation.…”

Section: Discussionmentioning

confidence: 99%

“…To avoid over-precise estimates due to genetic pleiotropy, we should remove these SNPs with potential linkage disequilibrium (LD) relationships. The analogous method has been used in the MR analysis of causal effect of phenotype on T2DM (Cheng et al, 2019c;Zhuang et al, 2019b). To remove SNPs with LDs, we ranked significant SNPs of IL-18 based on P-values.…”

Section: Data Processingmentioning

confidence: 99%

“…With the development of Genome-Wide Association Studies (GWAS) and identification of molecular mechanism in recent years (Li et al, 2015;Zhou et al, 2017a,b;Tang et al, 2018;Tan et al, 2019), Mendelian randomization (MR) analysis is widely used to expose the causal effect of phenotypes on the development of diseases. For example, body mass index and C-reaction protein are identified as the causal effect of the development of type 2 diabetes (Cheng et al, 2018b(Cheng et al, , 2019c. Meanwhile, some negative associations are also exposed, such as associations between infant length and type 2 diabetes (Zhuang et al, 2019a).…”

Section: Introductionmentioning

confidence: 99%

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A Mendelian Randomization Analysis to Expose the Causal Effect of IL-18 on Osteoporosis Based on Genome-Wide Association Study Data

Kou

Zhou

et al. 2020

Front. Bioeng. Biotechnol.

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Accumulating evidence showed that Interleukin (IL) level is associated with Osteoporosis. Whereas, most of these associations are based on observational studies. Thus, their causality was still unclear. Mendelian randomization (MR) is a widely used statistical framework that uses genetic instrumental variables (IVs) to explore the causality of intermediate phenotype with disease. To classify their causality, we conducted a MR analysis to investigate the effect of IL-18 level on the risk of Osteoporosis. First, based on summarized genome-wide association study (GWAS) data, 8 independent IL-18 SNPs reaching genome-wide significance were deemed as IVs. Next, Simple median method was used to calculate the pooled odds ratio (OR) of these 8 SNPs for the assessment of IL-8 on the risk of Osteoporosis. Then, MR-Egger regression was utilized to detect potential bias due to the horizontal pleiotropy of these IVs. As a result of simple median method, we get the SE (−0.001; 95% CI −0.002 to 0; P = 0.042), which means low IL-18 level could increases the risk of the development of Osteoporosis. The low intercept (0; 95% CI −0.001 to 0; P = 0.59) shows there is no bias due to the horizontal pleiotropy of the IVs.

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“…A focus of this case study is the distribution of body mass index (BMI), a well-characterized quantitative trait with established heritability [13][14][15] and also a known marker for multiple complex diseases and all cause mortality, e.g. [16][17][18][19][20] . Herein, BMI is used both as a surrogate measure of dissimilarly between cohorts and the primary phenotype under investigation.…”

mentioning

confidence: 99%

Non-random sampling leads to biased estimates of transcriptome association

Foulkes

Balasubramanian

Qian

et al. 2020

Sci Rep

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Integration of independent data resources across -omics platforms offers transformative opportunity for novel clinical and biological discoveries. However, application of emerging analytic methods in the context of selection bias represents a noteworthy and pervasive challenge. We hypothesize that combining differentially selected samples for integrated transcriptome analysis will lead to bias in the estimated association between predicted expression and the trait. Our results are based on in silico investigations and a case example focused on body mass index across four well-described cohorts apparently derived from markedly different populations. Our findings suggest that integrative analysis can lead to substantial relative bias in the estimate of association between predicted expression and the trait. The average estimate of association ranged from 51.3% less than to 96.7% greater than the true value for the biased sampling scenarios considered, while the average error was − 2.7% for the unbiased scenario. The corresponding 95% confidence interval coverage rate ranged from 46.4% to 69.5% under biased sampling, and was equal to 75% for the unbiased scenario. Inverse probability weighting with observed and estimated weights is applied as one corrective measure and appears to reduce the bias and improve coverage. These results highlight a critical need to address selection bias in integrative analysis and to use caution in interpreting findings in the presence of different sampling mechanisms between groups.

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Exposing the Causal Effect of Body Mass Index on the Risk of Type 2 Diabetes Mellitus: A Mendelian Randomization Study

Cited by 58 publications

References 57 publications

Serum Uric Acid Level and Multiple Sclerosis: A Mendelian Randomization Study

Serum Uric Acid Level and Multiple Sclerosis: A Mendelian Randomization Study

A Mendelian Randomization Analysis to Expose the Causal Effect of IL-18 on Osteoporosis Based on Genome-Wide Association Study Data

Non-random sampling leads to biased estimates of transcriptome association

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