scholarly journals Bias in two-sample Mendelian randomization when using heritable covariable-adjusted summary associations

2021 ◽  
Author(s):  
Fernando Pires Hartwig ◽  
Kate Tilling ◽  
George Davey Smith ◽  
Deborah A Lawlor ◽  
Maria Carolina Borges
Keyword(s):  
Waist Circumference ◽  
Genetic Variants ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Association Studies ◽  
Real Data ◽  
Sensitivity Analyses ◽  
Effect Estimate ◽  
Genome Wide Association Studies ◽  
Residual Confounding

Abstract Background Two-sample Mendelian randomization (MR) allows the use of freely accessible summary association results from genome-wide association studies (GWAS) to estimate causal effects of modifiable exposures on outcomes. Some GWAS adjust for heritable covariables in an attempt to estimate direct effects of genetic variants on the trait of interest. One, both or neither of the exposure GWAS and outcome GWAS may have been adjusted for covariables. Methods We performed a simulation study comprising different scenarios that could motivate covariable adjustment in a GWAS and analysed real data to assess the influence of using covariable-adjusted summary association results in two-sample MR. Results In the absence of residual confounding between exposure and covariable, between exposure and outcome, and between covariable and outcome, using covariable-adjusted summary associations for two-sample MR eliminated bias due to horizontal pleiotropy. However, covariable adjustment led to bias in the presence of residual confounding (especially between the covariable and the outcome), even in the absence of horizontal pleiotropy (when the genetic variants would be valid instruments without covariable adjustment). In an analysis using real data from the Genetic Investigation of ANthropometric Traits (GIANT) consortium and UK Biobank, the causal effect estimate of waist circumference on blood pressure changed direction upon adjustment of waist circumference for body mass index. Conclusions Our findings indicate that using covariable-adjusted summary associations in MR should generally be avoided. When that is not possible, careful consideration of the causal relationships underlying the data (including potentially unmeasured confounders) is required to direct sensitivity analyses and interpret results with appropriate caution.

scholarly journals Bias in two-sample Mendelian randomization when using heritable covariable-adjusted summary associations

2019 ◽  
Author(s):  
Fernando Pires Hartwig ◽  
Kate Tilling ◽  
George Davey Smith ◽  
Deborah A Lawlor ◽  
Maria Carolina Borges
Keyword(s):  
Waist Circumference ◽  
Simulation Study ◽  
Genetic Variants ◽  
Mendelian Randomization ◽  
Real Data ◽  
Effect Estimate ◽  
List Type ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Residual Confounding

AbstractBackgroundTwo-sample Mendelian randomization (MR) allows the use of freely accessible summary association results from genome-wide association studies (GWAS) to estimate causal effects of modifiable exposures on outcomes. Some GWAS adjust for heritable covariables in an attempt to estimate direct effects of genetic variants on the trait of interest. One, both or neither of the exposure GWAS and outcome GWAS may have been adjusted for covariables.MethodsWe performed a simulation study comprising different scenarios that could motivate covariable adjustment in a GWAS and analysed real data to assess the influence of using covariable-adjusted summary association results in two-sample MR.ResultsIn the absence of residual confounding between exposure and covariable, between exposure and outcome, and between covariable and outcome, using covariable-adjusted summary associations for two-sample MR eliminated bias due to horizontal pleiotropy. However, covariable adjustment led to bias in the presence of residual confounding (especially between the covariable and the outcome), even in the absence of horizontal pleiotropy (when the genetic variants would be valid instruments without covariable adjustment). In an analysis using real data from the Genetic Investigation of ANthropometric Traits (GIANT) consortium and UK Biobank, the causal effect estimate of waist circumference on blood pressure changed direction upon adjustment of waist circumference for body mass index.ConclusionsOur findings indicate that using covariable-adjusted summary associations in MR should generally be avoided. When that is not possible, careful consideration of the causal relationships underlying the data (including potentially unmeasured confounders) is required to direct sensitivity analyses and interpret results with appropriate caution.Key messagesSummary genetic associations from large genome-wide associations studies (GWAS) have been increasingly used in two-sample Mendelian randomization (MR) analyses.Many GWAS adjust for heritable covariates in an attempt to estimate direct genetic effects on the trait of interest.In an extensive simulation study, we demonstrate that using covariable-adjusted summary associations may bias MR analyses.The bias largely depends on the underlying causal structure, specially the presence of unmeasured common causes between the covariable and the outcome.Our findings indicate that using covariable-adjusted summary associations in MR should generally be avoided.

scholarly journals A Novel Method for Mendelian Randomization Analyses With Pleiotropy and Linkage Disequilibrium in Genetic Variants From Individual Data

2021 ◽  
Vol 12 ◽  
Author(s):  
Yuquan Wang ◽  
Tingting Li ◽  
Liwan Fu ◽  
Siqian Yang ◽  
Yue-Qing Hu
Keyword(s):  
Linkage Disequilibrium ◽  
Genetic Variants ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Association Studies ◽  
Random Effect ◽  
Pleiotropic Effect ◽  
Genome Wide Association Studies ◽  
The Novel ◽  
Novel Method

Mendelian randomization makes use of genetic variants as instrumental variables to eliminate the influence induced by unknown confounders on causal estimation in epidemiology studies. However, with the soaring genetic variants identified in genome-wide association studies, the pleiotropy, and linkage disequilibrium in genetic variants are unavoidable and may produce severe bias in causal inference. In this study, by modeling the pleiotropic effect as a normally distributed random effect, we propose a novel mixed-effects regression model-based method PLDMR, pleiotropy and linkage disequilibrium adaptive Mendelian randomization, which takes linkage disequilibrium into account and also corrects for the pleiotropic effect in causal effect estimation and statistical inference. We conduct voluminous simulation studies to evaluate the performance of the proposed and existing methods. Simulation results illustrate the validity and advantage of the novel method, especially in the case of linkage disequilibrium and directional pleiotropic effects, compared with other methods. In addition, by applying this novel method to the data on Atherosclerosis Risk in Communications Study, we conclude that body mass index has a significant causal effect on and thus might be a potential risk factor of systolic blood pressure. The novel method is implemented in R and the corresponding R code is provided for free download.

scholarly journals Univariable and Multivariable Two-Sample Mendelian Randomization Investigating the Effects of Leisure Sedentary Behaviors on the Risk of Lung Cancer

2021 ◽  
Vol 12 ◽  
Author(s):  
Haoxin Peng ◽  
Xiangrong Wu ◽  
Yaokai Wen ◽  
Yiyuan Ao ◽  
Yutian Li ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Computer Use ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Association Studies ◽  
Sensitivity Analyses ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Sedentary Behaviors ◽  
Watching Tv

Background:Leisure sedentary behaviors (LSB) are widespread, and observational studies have provided emerging evidence that LSB play a role in the development of lung cancer (LC). However, the causal inference between LSB and LC remains unknown.Methods: We utilized univariable (UVMR) and multivariable two-sample Mendelian randomization (MVMR) analysis to disentangle the effects of LSB on the risk of LC. MR analysis was conducted with genetic variants from genome-wide association studies of LSB (408,815 persons from UK Biobank), containing 152 single-nucleotide polymorphisms (SNPs) for television (TV) watching, 37 SNPs for computer use, and four SNPs for driving, and LC from the International Lung Cancer Consortium (11,348 cases and 15,861 controls). Multiple sensitivity analyses were further performed to verify the causality.Results: UVMR demonstrated that genetically predisposed 1.5-h increase in LSB spent on watching TV increased the odds of LC by 90% [odds ratio (OR), 1.90; 95% confidence interval (CI), 1.44–2.50; p < 0.001]. Similar trends were observed for squamous cell lung cancer (OR, 1.97; 95%CI, 1.31–2.94; p = 0.0010) and lung adenocarcinoma (OR, 1.64; 95%CI 1.12–2.39; p = 0.0110). The causal effects remained significant after adjusting for education (OR, 1.97; 95%CI, 1.44–2.68; p < 0.001) and body mass index (OR, 1.86; 95%CI, 1.36–2.54; p < 0.001) through MVMR approach. No association was found between prolonged LSB spent on computer use and driving and LC risk. Genetically predisposed prolonged LSB was additionally correlated with smoking (OR, 1.557; 95%CI, 1.287–1.884; p < 0.001) and alcohol consumption (OR, 1.010; 95%CI, 1.004–1.016; p = 0.0016). Consistency of results across complementary sensitivity MR methods further strengthened the causality.Conclusion: Robust evidence was demonstrated for an independent, causal effect of LSB spent on watching TV in increasing the risk of LC. Further work is necessary to investigate the potential mechanisms.

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.

scholarly journals Depression and Osteoporosis: A Mendelian Randomization Study

2021 ◽  
Author(s):  
Bin He ◽  
Qiong Lyu ◽  
Lifeng Yin ◽  
Muzi Zhang ◽  
Zhengxue Quan ◽  
...  
Keyword(s):  
Observational Studies ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Association Studies ◽  
Meta Analysis ◽  
Sensitivity Analyses ◽  
European Ancestry ◽  
Genome Wide Association Studies ◽  
Reverse Causality ◽  
Mineral Density

AbstractObservational studies suggest a link between depression and osteoporosis, but these may be subject to confounding and reverse causality. In this two-sample Mendelian randomization analysis, we included the large meta-analysis of genome-wide association studies for depression among 807,553 individuals (246,363 cases and 561,190 controls) of European descent, the large meta-analysis to identify genetic variants associated with femoral neck bone mineral density (FN-BMD), forearm BMD (FA-BMD) and lumbar spine BMD (LS-BMD) among 53,236 individuals of European ancestry, and the GWAS summary data of heel BMD (HE-BMD) and fracture among 426,824 individuals of European ancestry. The results revealed that genetic predisposition towards depression showed no causal effect on FA-BMD (beta-estimate: 0.091, 95% confidence interval [CI] − 0.088 to 0.269, SE:0.091, P value = 0.320), FN-BMD (beta-estimate: 0.066, 95% CI − 0.016 to 0.148, SE:0.042, P value = 0.113), LS-BMD (beta-estimate: 0.074, 95% CI − 0.029 to 0.177, SE:0.052, P value = 0.159), HE-BMD (beta-estimate: 0.009, 95% CI − 0.043 to 0.061, SE:0.027, P value = 0.727), or fracture (beta-estimate: 0.008, 95% CI − 0.071 to 0.087, SE:0.041, P value = 0.844). These results were also confirmed by multiple sensitivity analyses. Contrary to the findings of observational studies, our results do not reveal a causal role of depression in osteoporosis or fracture.

scholarly journals Circulating vitamin D concentrations and risk of breast and prostate cancer: a Mendelian randomization study

2018 ◽  
Vol 48 (5) ◽  
pp. 1416-1424 ◽  
Author(s):  
Xia Jiang ◽  
Niki L Dimou ◽  
Kawthar Al-Dabhani ◽  
Sarah J Lewis ◽  
Richard M Martin ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Prostate Cancer ◽  
Vitamin D ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Association Studies ◽  
Sensitivity Analyses ◽  
Genome Wide Association Studies ◽  
Common Genetic Variants ◽  
Breast And Prostate Cancer

Abstract Background Observational studies have suggested an association between circulating vitamin D concentrations [25(OH)D] and risk of breast and prostate cancer, which was not supported by a recent Mendelian randomization (MR) analysis comprising 15 748 breast and 22 898 prostate-cancer cases. Demonstrating causality has proven challenging and one common limitation of MR studies is insufficient power. Methods We aimed to determine whether circulating concentrations of vitamin D are causally associated with the risk of breast and prostate cancer, by using summary-level data from the largest ever genome-wide association studies conducted on vitamin D (N = 73 699), breast cancer (Ncase = 122 977) and prostate cancer (Ncase = 79 148). We constructed a stronger instrument using six common genetic variants (compared with the previous four variants) and applied several two-sample MR methods. Results We found no evidence to support a causal association between 25(OH)D and risk of breast cancer [OR per 25 nmol/L increase, 1.02 (95% confidence interval: 0.97–1.08), P = 0.47], oestrogen receptor (ER)+ [1.00 (0.94–1.07), P = 0.99] or ER− [1.02 (0.90–1.16), P = 0.75] subsets, prostate cancer [1.00 (0.93–1.07), P = 0.99] or the advanced subtype [1.02 (0.90–1.16), P = 0.72] using the inverse-variance-weighted method. Sensitivity analyses did not reveal any sign of directional pleiotropy. Conclusions Despite its almost five-fold augmented sample size and substantially improved statistical power, our MR analysis does not support a causal effect of circulating 25(OH)D concentrations on breast- or prostate-cancer risk. However, we can still not exclude a modest or non-linear effect of vitamin D. Future studies may be designed to understand the effect of vitamin D in subpopulations with a profound deficiency.

scholarly journals Using Mendelian randomization to determine causal effects of maternal pregnancy (intrauterine) exposures on offspring outcomes: Sources of bias and methods for assessing them

2017 ◽  
Vol 2 ◽  
pp. 11 ◽  
Author(s):  
Deborah A. Lawlor ◽  
Rebecca Richmond ◽  
Nicole Warrington ◽  
George McMahon ◽  
George Davey Smith ◽  
...  
Keyword(s):  
Genetic Variants ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Strong Association ◽  
Simulated Data ◽  
Real Data ◽  
Causal Effects ◽  
Sensitivity Analyses ◽  
Specific Situation ◽  
Exclusion Restriction

Mendelian randomization (MR), the use of genetic variants as instrumental variables (IVs) to test causal effects, is increasingly used in aetiological epidemiology. Few of the methodological developments in MR have considered the specific situation of using genetic IVs to test the causal effect of exposures in pregnant women on postnatal offspring outcomes. In this paper, we describe specific ways in which the IV assumptions might be violated when MR is used to test such intrauterine effects. We highlight the importance of considering the extent to which there is overlap between genetic variants in offspring that influence their outcome with genetic variants used as IVs in their mothers. Where there is overlap, and particularly if it generates a strong association of maternal genetic IVs with offspring outcome via the offspring genotype, the exclusion restriction assumption of IV analyses will be violated. We recommend a set of analyses that ought to be considered when MR is used to address research questions concerned with intrauterine effects on post-natal offspring outcomes, and provide details of how these can be undertaken and interpreted. These additional analyses include the use of genetic data from offspring and fathers, examining associations using maternal non-transmitted alleles, and using simulated data in sensitivity analyses (for which we provide code). We explore the extent to which new methods that have been developed for exploring violation of the exclusion restriction assumption in the two-sample setting (MR-Egger and median based methods) might be used when exploring intrauterine effects in one-sample MR. We provide a list of recommendations that researchers should use when applying MR to test the effects of intrauterine exposures on postnatal offspring outcomes and use an illustrative example with real data to demonstrate how our recommendations can be applied and subsequent results appropriately interpreted.

scholarly journals Examining the Causal Inference of Leptin and Soluble Plasma Leptin Receptor Levels on Schizophrenia: A Mendelian Randomization Study

2021 ◽  
Vol 12 ◽  
Author(s):  
Guoqing Chen ◽  
Qiuling Wang ◽  
Ranran Xue ◽  
Xia Liu ◽  
Hao Yu
Keyword(s):  
Large Scale ◽  
Leptin Receptor ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Association Studies ◽  
Sensitivity Analyses ◽  
European Ancestry ◽  
Genome Wide Association Studies ◽  
Unit Increase ◽  
Plasma Leptin

Background: Observational studies that have supported the role of the leptin level in schizophrenia (SCZ) risk are conflicting. Therefore, we performed a two-sample Mendelian randomization (MR) analysis to investigate whether the circulating leptin and soluble plasma leptin receptor (sOB-R) levels play a causal role in SCZ risk.Methods: We first selected five independent single-nucleotide polymorphisms (SNPs) associated with the circulating leptin level and three independent SNPs associated with the sOB-R level from two genome-wide association studies (GWASs) of European individuals. Then, we extracted their associations with SCZ using a large-scale GWAS that consisted of 40,675 patients with SCZ and 64,643 controls of European ancestry. We performed an MR analysis using the inverse variance-weighted (IVW) method to examine the causal effect of leptin on SCZ risk. Moreover, we performed sensitivity analyses to verify our MR results using the weighted median and MR-Egger methods.Results: According to the IVW method, genetically predicted circulating leptin levels were not associated with SCZ risk (OR = 1.98, for per 1-SD unit increase in leptin level; 95% CI, 0.87–4.53; p = 0.10). In addition, the sOB-R level showed no causal effect on the SCZ risk using IVW (OR = 0.98 for per 1-SD unit increase in sOB-R level; 95% CI, 0.97–1.00; p = 0.06). Our sensitivity analysis results confirmed our MR findings.Conclusions: By estimating the causal effect of leptin on SCZ risk using the MR methods, we identified no effect of genetically predicted circulating leptin or the sOB-R level on SCZ. As such, our study suggests that leptin might not be a risk factor for SCZ.

scholarly journals The Parkinson’s Disease Mendelian Randomization Research Portal

2019 ◽  
Author(s):  
Alastair J Noyce ◽  
Sara Bandres-Ciga ◽  
Jonggeol Kim ◽  
Karl Heilbron ◽  
Demis Kia ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Association Studies ◽  
Sensitivity Analyses ◽  
P Value ◽  
Phenotypic Traits ◽  
Genome Wide Association Studies ◽  
Summary Statistics

ABSTRACTBackgroundMendelian randomization (MR) is a method for exploring observational associations to find evidence of causality.ObjectiveTo apply MR between multiple risk factors/phenotypic traits (exposures) and Parkinson’s disease (PD) in a large, unbiased manner, and to create a public resource for research.MethodsWe used two-sample MR in which the summary statistics relating to SNPs from genome wide association studies (GWASes) of 5,839 exposures curated on MR Base were used to assess causal relationships with PD. We selected the highest quality exposure GWASes for this report (n=401). For the disease outcome, summary statistics from the largest published PD GWAS were used. For each exposure, the causal effect on PD was assessed using the inverse variance weighted (IVW) method, followed by a range of sensitivity analyses. We used a false discovery rate (FDR) corrected p-value of <0.05 from the IVW analysis to prioritize traits of interest.ResultsWe observed evidence for causal associations between twelve exposures and risk of PD. Of these, nine were causal effects related to increasing adiposity and decreasing risk of PD. The remaining top exposures that affected PD risk were tea drinking, time spent watching television and forced vital capacity, but the latter two appeared to be biased by violations of underlying MR assumptions.DiscussionWe present a new platform which offers MR analyses for a total of 5,839 GWASes versus the largest PD GWASes available (https://pdgenetics.shinyapps.io/pdgenetics/). Alongside, we report further evidence to support a causal role for adiposity on lowering the risk of PD.

scholarly journals No Causal Association Between Adiponectin and the Risk of Rheumatoid Arthritis: A Mendelian Randomization Study

2021 ◽  
Vol 12 ◽  
Author(s):  
Hanzhu Chen ◽  
Shuai Mi ◽  
Jiahao Zhu ◽  
Weidong Jin ◽  
Yasong Li ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Genetic Correlation ◽  
Causal Relationship ◽  
Large Scale ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Association Studies ◽  
Sensitivity Analyses ◽  
Reverse Direction ◽  
Genome Wide Association Studies

Background: Accumulating evidence from observational studies suggested that circulating adiponectin levels are associated with the risk of rheumatoid arthritis (RA), but the causality remains unknown. We aimed to assess the causal relationship of adiponectin with RA risk.Methods: Based on summary statistics from large-scale genome-wide association studies (GWAS), we quantified the genetic correlation between adiponectin and RA. Then bidirectional Mendelian randomization (MR) analysis was performed to assess the causal relationship. Twenty single-nucleotide polymorphisms (SNPs) associated with adiponectin were selected as instrumental variables from a recent GWAS (n = 67,739). We applied theses SNPs to a large-scale GWAS for RA (14,361 cases and 43,923 controls) with replication using RA data from the FinnGen consortium (6,236 cases and 147,221 controls) and the UK Biobank (5,201 cases and 457,732 controls). The inverse-variance weighted (IVW) and multiple pleiotropy-robust methods were used for two-sample MR analyses.Results: Our analyses showed no significant genetic correlation between circulating adiponectin levels and RA [rG = 0.127, 95% confidence interval (CI): –0.012 to 0.266, P = 0.074]. In MR analyses, genetically predicted adiponectin levels were not significantly associated with the RA risk (odds ratio: 0.98, 95% CI: 0.88–1.09, P = 0.669). In the reverse direction analysis, there is little evidence supporting an association of genetic susceptibility to RA with adiponectin (β: 0.007, 95% CI: –0.003 to 0.018, P = 0.177). Replication analyses and sensitivity analyses using different models yielded consistent results.Conclusions: Our findings provided no evidence to support the causal effect of adiponectin levels on RA risk and of RA on circulating adiponectin levels.

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