scholarly journals Using a Two-Sample Mendelian Randomization Method in Assessing the Causal Relationships Between Human Blood Metabolites and Heart Failure

2021 ◽  
Vol 8 ◽  
Author(s):  
Zixian Wang ◽  
Shiyu Chen ◽  
Qian Zhu ◽  
Yonglin Wu ◽  
Guifeng Xu ◽  
...  
Keyword(s):  
Heart Failure ◽  
Human Blood ◽  
Large Scale ◽  
Genome Wide Association Study ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Meta Analysis ◽  
Sensitivity Analyses ◽  
Blood Metabolites ◽  
Causal Relationships

Background: Heart failure (HF) is the main cause of morbidity and mortality worldwide, and metabolic dysfunction is an important factor related to HF pathogenesis and development. However, the causal effect of blood metabolites on HF remains unclear.Objectives: Our chief aim is to investigate the causal relationships between human blood metabolites and HF risk.Methods: We used an unbiased two-sample Mendelian randomization (MR) approach to assess the causal relationships between 486 human blood metabolites and HF risk. Exposure information was obtained from Sample 1, which is the largest metabolome-based genome-wide association study (mGWAS) data containing 7,824 Europeans. Outcome information was obtained from Sample 2, which is based on the results of a large-scale GWAS meta-analysis of HF and contains 47,309 cases and 930,014 controls of Europeans. The inverse variance weighted (IVW) model was used as the primary two-sample MR analysis method and followed the sensitivity analyses, including heterogeneity test, horizontal pleiotropy test, and leave-one-out analysis.Results: We observed that 11 known metabolites were potentially related to the risk of HF after using the IVW method (P < 0.05). After adding another four MR models and performing sensitivity analyses, we found a 1-SD increase in the xenobiotics 4-vinylphenol sulfate was associated with ~22% higher risk of HF (OR [95%CI], 1.22 [1.07–1.38]).Conclusions: We revealed that the 4-vinylphenol sulfate may nominally increase the risk of HF by 22% after using a two-sample MR approach. Our findings may provide novel insights into the pathogenesis underlying HF and novel strategies for HF prevention.

scholarly journals Inflammatory Cytokines and Risk of Ischemic Stroke: A Mendelian Randomization Study

2022 ◽  
Vol 12 ◽  
Author(s):  
Yalan Li ◽  
Jun Lu ◽  
Jie Wang ◽  
Peizhi Deng ◽  
Changjiang Meng ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
Inflammatory Cytokines ◽  
Large Scale ◽  
Genome Wide Association Study ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Meta Analysis ◽  
Outcome Data ◽  
Sensitivity Analyses ◽  
A Genome

Background: Observational studies have revealed the association between some inflammatory cytokines and the occurrence of ischemic stroke, but the causal relationships remain unclear.Methods: We conducted a two-sample Mendelian randomization (MR) analysis to assess the causal effects of thirty inflammatory cytokines and the risk of ischemic stroke. For exposure data, we collected genetic variants associated with inflammatory cytokines as instrumental variables (IVs) from a genome-wide association study (GWAS) meta-analysis from Finland (sample size up to 8,293). For the outcome data, we collected summary data of ischemic stroke from a large-scale GWAS meta-analysis involved 17 studies (34,217 cases and 406,111 controls). We further performed a series of sensitivity analyses as validation of primary MR results.Results: According to the primary MR estimations and further sensitivity analyses, we established one robust association after Bonferroni correction: the odds ratio (95% CI) per unit change in genetically increased IL-4 was 0.84 (0.89–0.95) for ischemic stroke. The chemokine MCP3 showed a nominally significant association with ischemic stroke risk (OR: 0.93, 95% CI: 0.88–0.99, unadjusted p < 0.05). There was no evidence of a causal effect of other inflammatory cytokines and the risk of ischemic stroke.Conclusions: Our study suggested that genetically increased IL-4 levels showed a protective effect on the risk of ischemic stroke, which provides important new insights into the potential therapeutic target for preventing ischemic stroke.

scholarly journals Mendelian randomization study shows no causal effects of serum urate levels on the risk of MS

2020 ◽  
Vol 8 (1) ◽  
pp. e920
Author(s):  
Adil Harroud ◽  
J. Brent Richards ◽  
Sergio E. Baranzini
Keyword(s):  
Large Scale ◽  
Genome Wide Association Study ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Meta Analysis ◽  
Serum Urate ◽  
Sensitivity Analyses ◽  
Data Sets ◽  
A Genome ◽  
Mr Study

ObjectiveTo examine whether lifelong genetically increased serum urate levels, a potent antioxidant, contribute to MS susceptibility using Mendelian randomization (MR).MethodsThis 2-sample MR study included 25 independent genetic variants strongly associated with serum urate levels in a genome-wide association study meta-analysis of 140,949 individuals. Effects on the risk of MS were assessed with summary statistics from 3 large-scale MS genetic data sets totaling 61,667 MS cases and 86,806 controls from the International MS Genetic Consortium. Multiple sensitivity analyses were performed to evaluate the assumptions of MR and remove potentially pleiotropic variants.ResultsUsing inverse-variance weighted MR, we found no evidence for a causal effect of serum urate level on the risk of MS in any of the cohorts (MS1: OR 0.99 per each mg/dL unit increase in urate, 95% CI 0.89–1.08, p = 0.76; MS2: OR = 0.99, 95% CI 0.89–1.11, p = 0.90; MS3: OR = 1.00, 95% CI 0.98–1.2, p = 0.91). Pleiotropy robust MR methods yielded consistent estimates.ConclusionThis MR study does not support a clinically relevant causal effect of serum urate levels on the risk of MS.

scholarly journals Causal Association Between Heart Failure and Alzheimer’s Disease: A Two-Sample Bidirectional Mendelian Randomization Study

2022 ◽  
Vol 12 ◽  
Author(s):  
Chenglin Duan ◽  
Jingjing Shi ◽  
Guozhen Yuan ◽  
Xintian Shou ◽  
Ting Chen ◽  
...  
Keyword(s):  
Heart Failure ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Sensitivity Analysis ◽  
Causal Relationship ◽  
Observational Studies ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Causal Relationships ◽  
Simple Mode

Background: Traditional observational studies have demonstrated an association between heart failure and Alzheimer’s disease. The strengths of observational studies lie in their speed of implementation, cost, and applicability to rare diseases. However, observational studies have several limitations, such as uncontrollable confounders. Therefore, we employed Mendelian randomization of genetic variants to evaluate the causal relationships existing between AD and HF, which can avoid these limitations.Materials and Methods: A two-sample bidirectional MR analysis was employed. All datasets were results from the UK’s Medical Research Council Integrative Epidemiology Unit genome-wide association study database, and we conducted a series of control steps to select the most suitable single-nucleotide polymorphisms for MR analysis, for which five primary methods are offered. We reversed the functions of exposure and outcomes to explore the causal direction of HF and AD. Sensitivity analysis was used to conduct several tests to avoid heterogeneity and pleiotropic bias in the MR results.Results: Our MR studies did not support a meaningful causal relationship between AD on HF (MR-Egger, p = 0.634 > 0.05; weighted median (WM), p = 0.337 > 0.05; inverse variance weighted (IVW), p = 0.471 > 0.05; simple mode, p = 0.454 > 0.05; weighted mode, p = 0.401 > 0.05). At the same time, we did not find a significant causal relationship between HF and AD with four of the methods (MR-Egger, p = 0.195 > 0.05; IVW, p = 0.0879 > 0.05; simple mode, p = 0.170 > 0.05; weighted mode, p = 0.110 > 0.05), but the WM method indicated a significant effect of HF on AD (p = 0.025 < 0.05). Because the statistical powers of IVW and MR-Egger are more than that of WM, we think that there is no causal effect of HF on AD. Sensitivity analysis and horizontal pleiotropy were not detected in the MR analysis.Conclusion: Our results did not provide significant evidence indicating any causal relationships between HF and AD in the European population. Therefore, more large-scale datasets or datasets related to similar factors are expected for further MR analysis.

scholarly journals Using the MR-Base platform to investigate risk factors and drug targets for thousands of phenotypes

2019 ◽  
Vol 4 ◽  
pp. 113 ◽  
Author(s):  
Venexia M Walker ◽  
Neil M Davies ◽  
Gibran Hemani ◽  
Jie Zheng ◽  
Philip C Haycock ◽  
...  
Keyword(s):  
Risk Factors ◽  
Web Application ◽  
Drug Targets ◽  
Genome Wide Association Study ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
R Package ◽  
Sensitivity Analyses ◽  
Link Type ◽  
Study Results

Mendelian randomization (MR) estimates the causal effect of exposures on outcomes by exploiting genetic variation to address confounding and reverse causation. This method has a broad range of applications, including investigating risk factors and appraising potential targets for intervention. MR-Base has become established as a freely accessible, online platform, which combines a database of complete genome-wide association study results with an interface for performing Mendelian randomization and sensitivity analyses. This allows the user to explore millions of potentially causal associations. MR-Base is available as a web application or as an R package. The technical aspects of the tool have previously been documented in the literature. The present article is complementary to this as it focuses on the applied aspects. Specifically, we describe how MR-Base can be used in several ways, including to perform novel causal analyses, replicate results and enable transparency, amongst others. We also present three use cases, which demonstrate important applications of Mendelian randomization and highlight the benefits of using MR-Base for these types of analyses.

scholarly journals Parathyroid Hormone and Bone Mineral Density: A Mendelian Randomization Study

2020 ◽  
Vol 105 (11) ◽  
Author(s):  
Zihao Qu ◽  
Fangkun Yang ◽  
Jianqiao Hong ◽  
Wei Wang ◽  
Shigui Yan
Keyword(s):  
Bone Mineral Density ◽  
Parathyroid Hormone ◽  
Bone Mineral ◽  
Large Scale ◽  
Genome Wide Association Study ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Age Groups ◽  
Mineral Density ◽  
Serum Pth

Abstract Purpose Accumulating evidence implicates parathyroid hormone (PTH) in the development of osteoporosis. However, the causal effect of PTH on bone mineral density (BMD) remains unclear. Thus, this study is aimed at exploring the association between the concentrations of serum PTH and BMD. Methods The instrumental variables for PTH were selected from a large-scale genome-wide association study (GWAS; n = 29 155). Outcomes included BMD of the forearm (FA; n = 8143), femoral neck (FN; n = 33 297), lumbar spine (LS; n = 32 735), heel (HL; n = 394 929), and risk of fractures in these bones (n = 361 194). Furthermore, the BMD of 5 different age groups: 15 years or younger (n = 11 807), 15–30 (n = 4180), 30–45 (n = 10 062), 45–60 (n = 18 805), and 60 years or older (n = 22 504) were extracted from a GWAS meta-analysis study. The analyses were performed using the 2-sample Mendelian randomization method. Results Mendelian randomization analysis revealed that the level of serum PTH was inversely associated with BMD of FA (95% CI: -0.763 to -0.016), FN (95% CI: -0.669 to -0.304), and LS (95% CI: -0.667 to -0.243). A causal relationship between serum PTH levels and BMD was observed in individuals aged 30–45 (95% CI: -0.888 to -0.166), 45–60 (95% CI: -0.758 to -0.232), and over 60 years (95% CI: -0.649 to -0.163). Main Conclusions This study demonstrated that the concentrations of serum PTH is inversely associated with BMD of several bones. Further analysis revealed site- and age-specific correlations between serum PTH levels and BMD, which implies that the levels of serum PTH contribute to the development of osteoporosis.

scholarly journals Iron Status May Not Affect Amyotrophic Lateral Sclerosis: A Mendelian Randomization Study

2021 ◽  
Vol 12 ◽  
Author(s):  
Jiahao Cai ◽  
Xiong Chen ◽  
Hongxuan Wang ◽  
Zixin Wei ◽  
Mei Li ◽  
...  
Keyword(s):  
Amyotrophic Lateral Sclerosis ◽  
Genome Wide Association Study ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Iron Status ◽  
Transferrin Saturation ◽  
Outcome Data ◽  
Sensitivity Analyses ◽  
A Genome ◽  
Lateral Sclerosis

BackgroundObservational studies have shown an association of increased iron status with a higher risk of amyotrophic lateral sclerosis (ALS). Iron status might be a novel target for ALS prevention if a causal relationship exists. We aimed to reveal the causality between iron status and ALS incidence using a large two-sample Mendelian randomization (MR).MethodsSingle nucleotide polymorphisms (SNPs) for iron status were identified from a genome-wide association study (GWAS) on 48,972 individuals. The outcome data came from the largest ALS GWAS to date (20,806 cases; 59,804 controls). We conducted conservative analyses (using SNPs with concordant change of biomarkers of iron status) and liberal analyses (using SNPs associated with at least one of the biomarkers of iron status), with inverse variance weighted (IVW) method as the main analysis. We then performed sensitivity analyses including weighted median, MR-Egger and MR-pleiotropy residual sum and outlier, as well as leave-one-out analysis to detect pleiotropy.ResultsIn the conservative analyses, we found no evidence of association between four biomarkers of iron status and ALS using IVW method with odds ratio (OR) 1.00 [95% confidence interval (CI): 0.90–1.11] per standard deviation (SD) increase in iron, 0.96 (95% CI: 0.77–1.21) in ferritin, 0.99 (95% CI: 0.92–1.07) in transferrin saturation, and 1.04 (95% CI: 0.93–1.16) in transferrin. Findings from liberal analyses were similar, and sensitivity analyses suggested no pleiotropy detected (all p > 0.05).ConclusionOur findings suggest no causal effect between iron status and risk of ALS. Efforts to change the iron status to decrease ALS incidence might be impractical.

scholarly journals Mendelian randomization analysis of the association between human blood cell traits and uterine polyps

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Shuliu Sun ◽  
Yan Liu ◽  
Lanlan Li ◽  
Minjie Jiao ◽  
Yufen Jiang ◽  
...  
Keyword(s):  
Human Blood ◽  
Genome Wide Association Study ◽  
Mendelian Randomization ◽  
Random Effect ◽  
Sensitivity Analyses ◽  
Endometrial Polyps ◽  
Inverse Variance ◽  
Weighted Median ◽  
The Uk ◽  
Uterine Polyps

AbstractHuman blood cells (HBCs) play essential roles in multiple biological processes but their roles in development of uterine polyps are unknown. Here we implemented a Mendelian randomization (MR) analysis to investigate the effects of 36 HBC traits on endometrial polyps (EPs) and cervical polyps (CPs). The random-effect inverse-variance weighted method was adopted as standard MR analysis and three additional MR methods (MR-Egger, weighted median, and MR-PRESSO) were used for sensitivity analyses. Genetic instruments of HBC traits was extracted from a large genome-wide association study of 173,480 individuals, while data for EPs and CPs were obtained from the UK Biobank. All samples were Europeans. Using genetic variants as instrumental variables, our study found that both eosinophil count (OR 0.85, 95% CI 0.79–0.93, P = 1.06 × 10−4) and eosinophil percentage of white cells (OR 0.84, 95% CI 0.77–0.91, P = 2.43 × 10−5) were associated with decreased risk of EPs. The results were robust in sensitivity analyses and no evidences of horizontal pleiotropy were observed. While we found no significant associations between HBC traits and CPs. Our findings suggested eosinophils might play important roles in the pathogenesis of EPs. Besides, out study provided novel insight into detecting uterine polyps biomarkers using genetic epidemiology approaches.

scholarly journals Body mass index and height and risk of cutaneous melanoma: Mendelian randomization analyses

2020 ◽  
Vol 49 (4) ◽  
pp. 1236-1245 ◽  
Author(s):  
Jean Claude Dusingize ◽  
Catherine M Olsen ◽  
Jiyuan An ◽  
Nirmala Pandeya ◽  
Matthew H Law ◽  
...  
Keyword(s):  
Body Mass Index ◽  
Body Mass ◽  
Genome Wide Association Study ◽  
Mendelian Randomization ◽  
Meta Analysis ◽  
Positive Association ◽  
Sensitivity Analyses ◽  
Nucleotide Polymorphisms ◽  
Melanoma Risk ◽  
Lifestyle Risk Factors

Abstract Background Height and body mass index (BMI) have both been positively associated with melanoma risk, although findings for BMI have been less consistent than height. It remains unclear, however, whether these associations reflect causality or are due to residual confounding by environmental and lifestyle risk factors. We re-evaluated these associations using a two-sample Mendelian randomization (MR) approach. Methods We identified single nucleotide polymorphisms (SNPs) for BMI and height from separate genome-wide association study (GWAS) meta-analyses. We obtained melanoma SNPs from the most recent melanoma GWAS meta-analysis comprising 12 874 cases and 23 203 controls. We used the inverse variance-weighted estimator to derive separate causal risk estimates across all SNP instruments for BMI and height. Results Based on the combined estimate derived from 730 SNPs for BMI, we found no evidence of an association between genetically predicted BMI and melanoma [odds ratio (OR) per one standard deviation (1 SD) (4.6 kg/m2) increase in BMI 1.00, 95% confidence interval (CI): 0.91–1.11]. In contrast, we observed a positive association between genetically-predicted height (derived from a pooled estimate of 3290 SNPs) and melanoma risk [OR 1.08, 95% CI: 1.02–1.13, per 1 SD (9.27 cm) increase in height]. Sensitivity analyses using two alternative MR methods yielded similar results. Conclusions These findings provide no evidence for a causal association between higher BMI and melanoma, but support the notion that height is causally associated with melanoma risk. Mechanisms through which height influences melanoma risk remain unclear, and it remains possible that the effect could be mediated through diverse pathways including growth factors and even socioeconomic status.

scholarly journals The use of two-sample methods for Mendelian randomization analyses on single large datasets

2020 ◽  
Author(s):  
Cosetta Minelli ◽  
M. Fabiola Del Greco ◽  
Diana A. van der Plaat ◽  
Jack Bowden ◽  
Nuala A. Sheehan ◽  
...  
Keyword(s):  
Mendelian Randomization ◽  
Causal Effect ◽  
Meta Analysis ◽  
Effect Estimate ◽  
List Type ◽  
Causal Relationships ◽  
Uk Biobank ◽  
Current Availability ◽  
Association Data ◽  
Very High

AbstractBackgroundWith genome-wide association data for many exposures and outcomes now available from large biobanks, one-sample Mendelian randomization (MR) is increasingly used to investigate causal relationships. Many robust MR methods are available to address pleiotropy, but these assume independence between the gene-exposure and gene-outcome association estimates. Unlike in two-sample MR, in one-sample MR the two estimates are obtained from the same individuals, and the assumption of independence does not hold in the presence of confounding.MethodsWith simulations mimicking a typical study in UK Biobank we assessed the performance, in terms of bias and precision of the MR estimate, of the fixed-effect and (multiplicative) random-effects meta-analysis method, weighted median estimator, weighted mode estimator and MR-Egger regression, used in both one-sample and two-sample data. We considered scenarios differing for: presence/absence of a true causal effect; amount of confounding; presence and type of pleiotropy (none, balanced or directional).ResultsEven in the presence of substantial correlation due to confounding, all methods performed well when used in one-sample MR except for MR-Egger, which resulted in bias reflecting direction and magnitude of the confounding. Such bias was much reduced in the presence of very high variability in instrumental strength across variants (I2GX of 97%).ConclusionsTwo-sample MR methods can be safely used for one-sample MR performed within large biobanks, expect for MR-Egger. MR-Egger is not recommended for one-sample MR unless the correlation between the gene-exposure and gene-outcome estimates due to confounding can be kept low, or the variability in instrumental strength is very high.Key MessagesCurrent availability of phenotypic and genetic data from large biobanks, such as UK Biobank, has led to increasing use of one-sample Mendelian randomization (MR) to investigate causal relationships in epidemiological researchRobust MR methods have been developed to address pleiotropy, but they assume independence between the gene-exposure and gene-outcome association estimates; this holds in two-sample MR but not in one-sample MRWe illustrate the practical implications, in terms of bias and precision of the MR causal effect estimate, of using robust two-sample methods in one-sample MR studies performed within large biobanksTwo-sample MR methods can be safely used for one-sample MR performed within large biobanks, expect for MR-Egger regressionMR-Egger is not recommended for one-sample MR unless the correlation between the gene-exposure and gene-outcome estimates due to confounding can be kept low, or the variability in instrumental strength is very high

scholarly journals Plasma Proteomics of Renal Function: A Trans-ethnic Meta-analysis and Mendelian Randomization Study

2021 ◽  
pp. ASN.2020071070
Author(s):  
Pamela Matías-García ◽  
Rory Wilson ◽  
Qi Guo ◽  
Shaza Zaghlool ◽  
James Eales ◽  
...  
Keyword(s):  
Renal Function ◽  
Plasma Proteins ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Meta Analysis ◽  
Kidney Tissue ◽  
Renal Tissue ◽  
Cross Sectional Study ◽  
Causal Relationships ◽  
Cross Sectional

Background: Studies on the relationship between renal function and the human plasma proteome have identified several potential biomarkers. However, investigations have been conducted largely in European populations, and causality of the associations between plasma proteins and kidney function has never been addressed. Methods: A cross-sectional study of 993 plasma proteins among 2,882 participants in four studies of European and admixed ancestries (KORA, INTERVAL, HUNT, QMDiab) identified trans-ethnic associations between eGFR/CKD and proteomic biomarkers. For the replicated associations, two-sample bidirectional Mendelian randomization (MR) was used to investigate potential causal relationships. Publicly available datasets and transcriptomic data from independent studies were used to examine the association between gene expression in kidney tissue and eGFR . Results: Fifty-seven plasma proteins were associated with eGFR, including one novel protein. Twenty-three of these were additionally associated with CKD. The strongest inferred causal effect was the positive effect of eGFR on testican-2, in line with the known biological role of this protein and the expression of its protein-coding gene (SPOCK2) in renal tissue. We also observed suggestive evidence of an effect of melanoma inhibitory activity (MIA), carbonic anhydrase III, and cystatin-M on eGFR. Conclusions: In a discovery-replication setting, we identified 57 proteins trans-ethnically associated with eGFR. The revealed causal relationships are an important stepping-stone in establishing testican-2 as a clinically relevant physiological marker of kidney disease progression, and point to additional proteins warranting further investigation.

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