Using genetic variants as instrumental variables to assess causal relationships

2018 ◽  
pp. 15-16
Author(s):  
MISSING-VALUE MISSING-VALUE
Keyword(s):  
Instrumental Variables ◽  
Genetic Variants ◽  
Causal Relationships

scholarly journals Factorial Mendelian randomization: using genetic variants to assess interactions

2019 ◽  
Vol 49 (4) ◽  
pp. 1147-1158 ◽  
Author(s):  
Jessica M B Rees ◽  
Christopher N Foley ◽  
Stephen Burgess
Keyword(s):  
Risk Factors ◽  
Instrumental Variables ◽  
Genetic Variants ◽  
Mendelian Randomization ◽  
Real Data ◽  
Uk Biobank ◽  
Pharmacological Interventions ◽  
Natural Break ◽  
Using Data ◽  
Randomization Analysis

Abstract Background Factorial Mendelian randomization is the use of genetic variants to answer questions about interactions. Although the approach has been used in applied investigations, little methodological advice is available on how to design or perform a factorial Mendelian randomization analysis. Previous analyses have employed a 2 × 2 approach, using dichotomized genetic scores to divide the population into four subgroups as in a factorial randomized trial. Methods We describe two distinct contexts for factorial Mendelian randomization: investigating interactions between risk factors, and investigating interactions between pharmacological interventions on risk factors. We propose two-stage least squares methods using all available genetic variants and their interactions as instrumental variables, and using continuous genetic scores as instrumental variables rather than dichotomized scores. We illustrate our methods using data from UK Biobank to investigate the interaction between body mass index and alcohol consumption on systolic blood pressure. Results Simulated and real data show that efficiency is maximized using the full set of interactions between genetic variants as instruments. In the applied example, between 4- and 10-fold improvement in efficiency is demonstrated over the 2 × 2 approach. Analyses using continuous genetic scores are more efficient than those using dichotomized scores. Efficiency is improved by finding genetic variants that divide the population at a natural break in the distribution of the risk factor, or else divide the population into more equal-sized groups. Conclusions Previous factorial Mendelian randomization analyses may have been underpowered. Efficiency can be improved by using all genetic variants and their interactions as instrumental variables, rather than the 2 × 2 approach.

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 Using multiple genetic variants as instrumental variables for modifiable risk factors

2011 ◽  
Vol 21 (3) ◽  
pp. 223-242 ◽  
Author(s):  
Tom M Palmer ◽  
Debbie A Lawlor ◽  
Roger M Harbord ◽  
Nuala A Sheehan ◽  
Jon H Tobias ◽  
...  
Keyword(s):  
Risk Factors ◽  
Risk Factor ◽  
Missing Data ◽  
Instrumental Variables ◽  
Genetic Variants ◽  
Association Studies ◽  
Mendelian Randomisation ◽  
Modifiable Risk Factors ◽  
Genome Wide Association Studies ◽  
Finite Sample

Mendelian randomisation analyses use genetic variants as instrumental variables (IVs) to estimate causal effects of modifiable risk factors on disease outcomes. Genetic variants typically explain a small proportion of the variability in risk factors; hence Mendelian randomisation analyses can require large sample sizes. However, an increasing number of genetic variants have been found to be robustly associated with disease-related outcomes in genome-wide association studies. Use of multiple instruments can improve the precision of IV estimates, and also permit examination of underlying IV assumptions. We discuss the use of multiple genetic variants in Mendelian randomisation analyses with continuous outcome variables where all relationships are assumed to be linear. We describe possible violations of IV assumptions, and how multiple instrument analyses can be used to identify them. We present an example using four adiposity-associated genetic variants as IVs for the causal effect of fat mass on bone density, using data on 5509 children enrolled in the ALSPAC birth cohort study. We also use simulation studies to examine the effect of different sets of IVs on precision and bias. When each instrument independently explains variability in the risk factor, use of multiple instruments increases the precision of IV estimates. However, inclusion of weak instruments could increase finite sample bias. Missing data on multiple genetic variants can diminish the available sample size, compared with single instrument analyses. In simulations with additive genotype-risk factor effects, IV estimates using a weighted allele score had similar properties to estimates using multiple instruments. Under the correct conditions, multiple instrument analyses are a promising approach for Mendelian randomisation studies. Further research is required into multiple imputation methods to address missing data issues in IV estimation.

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

2019 ◽  
Author(s):  
Qing Cheng ◽  
Yi Yang ◽  
Xingjie Shi ◽  
Kar-Fu Yeung ◽  
Can Yang ◽  
...  
Keyword(s):  
Risk Factors ◽  
Body Mass Index ◽  
Linkage Disequilibrium ◽  
Body Mass ◽  
Genetic Variants ◽  
Mendelian Randomization ◽  
Alternative Methods ◽  
Summary Statistics ◽  
Causal Relationships ◽  
Disease Outcomes

AbstractThe proliferation of genome-wide association studies (GWAS) has prompted the use of two-sample Mendelian randomization (MR) with genetic variants as instrumental variables (IV) 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 propose a probabilistic model for MR analysis to identify the casual 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). MR-LDP utilizes a computationally efficient parameter-expanded variational Bayes expectation-maximization (PX-VBEM) algorithm to estimate the parameter of interest and further calibrates the evidence lower bound (ELBO) for a likelihood ratio test. We then conducted comprehensive simulation studies to demonstrate the advantages of MR-LDP over the existing methods in terms of both type-I error control and point estimates. Moreover, we used two real exposure-outcome pairs (CAD-CAD and Height-Height; CAD for coronary artery disease) 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 (HDL-C) on peripheral vascular disease (PVD), and a positive causal effect of body mass index (BMI) on hemorrhoids.

scholarly journals Pleiotropy-robust Mendelian Randomization

2016 ◽  
Author(s):  
Hans van Kippersluis ◽  
Cornelius A Rietveld
Keyword(s):  
Sensitivity Analysis ◽  
Instrumental Variables ◽  
Genetic Variants ◽  
Large Scale ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Pleiotropic Effects ◽  
List Type ◽  
Exclusion Restriction ◽  
The Uk

AbstractBackgroundThe potential of Mendelian Randomization studies is rapidly expanding due to (i) the growing power of GWAS meta-analyses to detect genetic variants associated with several exposures, and (ii) the increasing availability of these genetic variants in large-scale surveys. However, without a proper biological understanding of the pleiotropic working of genetic variants, a fundamental assumption of Mendelian Randomization (the exclusion restriction) can always be contested.MethodsWe build upon and synthesize recent advances in the econometric literature on instrumental variables (IV) estimation that test and relax the exclusion restriction. Our Pleiotropy-robust Mendelian Randomization (PRMR) method first estimates the degree of pleiotropy, and in turn corrects for it. If a sample exists for which the genetic variants do not affect the exposure, and pleiotropic effects are homogenous, PRMR obtains unbiased estimates of causal effects in case of pleiotropy.ResultsSimulations show that existing MR methods produce biased estimators for realistic forms of pleiotropy. Under the aforementioned assumptions, PRMR produces unbiased estimators. We illustrate the practical use of PRMR by estimating the causal effect of (i) cigarettes smoked per day on Body Mass Index (BMI); (ii) prostate cancer on self-reported health, and (iii) educational attainment on BMI in the UK Biobank data.ConclusionsPRMR allows for instrumental variables that violate the exclusion restriction due to pleiotropy, and corrects for pleiotropy in the estimation of the causal effect. If the degree of pleiotropy is unknown, PRMR can still be used as a sensitivity analysis.Key messagesIf genetic variants have pleiotropic effects, causal estimates of Mendelian Randomization studies will be biased.Pleiotropy-robust Mendelian Randomization (PRMR) produces unbiased causal estimates in case (i) a subsample can be identified for which the genetic variants do not affect the exposure, and (ii) pleiotropic effects are homogenous.If such a subsample does not exist, PRMR can still routinely be reported as a sensitivity analysis in any MR analysis.If pleiotropic effects are not homogenous, PRMR can be used as an informal test to gauge the exclusion restriction.

scholarly journals Testing the principles of Mendelian randomization: Opportunities and complications on a genomewide scale

2017 ◽  
Author(s):  
M Taylor ◽  
KE Tansey ◽  
DA Lawlor ◽  
J Bowden ◽  
DM Evans ◽  
...  
Keyword(s):  
Genetic Variants ◽  
Mendelian Randomization ◽  
Phenotypic Correlation ◽  
Causal Relationships ◽  
Biological Phenomena ◽  
Parents And Children ◽  
Multiple Variables ◽  
Using Data ◽  
Avon Longitudinal Study ◽  
Gwas Catalog

ABSTRACTBackgroundMendelian randomization (MR) uses genetic variants as instrumental variables to assess whether observational associations between exposures and disease reflect causal relationships. MR requires genetic variants to be independent of factors that confound observational associations.MethodsUsing data from the Avon Longitudinal Study of Parents and Children, associations within and between 121 phenotypes and 13,720 genetic variants (from the NHGRI-EBI GWAS catalog) were examined to assess the validity of MR assumptions.ResultsAmongst 7,260 pairwise comparisons between the 121 phenotypes, 2,188 (30%) provided evidence of association, where 363 were expected at the 5% level (observed:expected ratio=6.03; 95% CI: 5.42, 6.70; χ2=9682.29; d.f. =1, P≤1x10-50). Amongst 1,660,120 pairwise associations between phenotypes and genotypes, 86,748 (5.2%) gave evidence of association at the same threshold, where 83,006 were expected (observed:expected ratio=1.05; 95% CI: 1.04, 1.05; χ2=117.57; d.f. =1, P=2.15x10-27). Amongst 1,171,764 pairwise associations between the phenotypes and LD pruned independent genetic variants, 60,136 (5.1%) gave evidence of association, where 58,588 were expected (observed:expected ratio=1.03; 95% CI: 1.03, 1.08; χ2= 43.05; d.f. = 1, P=5.33x10-11).ConclusionThese results confirm previously observed patterns of phenotypic correlation. They also provide evidence of a substantially lower level of association between genetic variants and phenotypes, with residual inflation the likely product of indistinguishable real genetic association, multiple variables measuring the same biological phenomena, or pleiotropy. These results reflect the favorable properties of genetic instruments for estimating causal relationships, but confirm the need for functional information or analytical methods to account for pleiotropic events.

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