scholarly journals Testosterone and socioeconomic position: Mendelian Randomization in 306,248 men and women participants of UK Biobank

2020 ◽  
Author(s):  
Sean Harrison ◽  
Neil M Davies ◽  
Laura D Howe ◽  
Amanda Hughes
Keyword(s):  
Socioeconomic Position ◽  
Risk Taking ◽  
Genetic Variants ◽  
Employment Status ◽  
Mendelian Randomization ◽  
Uk Biobank ◽  
Genome Wide ◽  
Causal Impact ◽  
Residual Confounding ◽  
Randomization Analysis

AbstractMen with more advantaged socioeconomic position (SEP) and better health have been observed to have higher levels of testosterone. It is unclear whether these associations arise because testosterone has a causal impact on SEP and health. In 306,248 participants of UK Biobank, we performed sex- stratified genome-wide association analysis to identify genetic variants associated with testosterone. Using the identified variants, we performed Mendelian randomization analysis of the influence of testosterone on socioeconomic position, including income, employment status, area-level deprivation, and educational qualifications; on health, including self-rated health and BMI, and on risk-taking behaviour. We found little evidence that testosterone affected socioeconomic position, health, or risk-taking. Our results therefore suggest it is unlikely that testosterone meaningfully affects these outcomes in men or women. Differences between Mendelian randomization and multivariable-adjusted estimates suggest previously reported associations with socioeconomic position and health may be due to residual confounding or reverse causation.

scholarly journals Testosterone and socioeconomic position: Mendelian randomization in 306,248 men and women in UK Biobank

2021 ◽  
Vol 7 (31) ◽  
pp. eabf8257
Author(s):  
Sean Harrison ◽  
Neil M. Davies ◽  
Laura D. Howe ◽  
Amanda Hughes
Keyword(s):  
Socioeconomic Position ◽  
Risk Taking ◽  
Employment Status ◽  
Mendelian Randomization ◽  
Uk Biobank ◽  
Genome Wide ◽  
Causal Impact ◽  
Residual Confounding ◽  
Randomization Analysis ◽  
Neighborhood Level

Men with more advantaged socioeconomic position (SEP) have been observed to have higher levels of testosterone. It is unclear whether these associations arise because testosterone has a causal impact on SEP. In 306,248 participants of UK Biobank, we performed sex-stratified genome-wide association analysis to identify genetic variants associated with testosterone. Using the identified variants, we performed Mendelian randomization analysis of the influence of testosterone on socioeconomic position, including income, employment status, neighborhood-level deprivation, and educational qualifications; on health, including self-rated health and body mass index; and on risk-taking behavior. We found little evidence that testosterone affected socioeconomic position, health, or risk-taking. Our results therefore suggest that it is unlikely that testosterone meaningfully affects these outcomes in men or women. Differences between Mendelian randomization and multivariable-adjusted estimates suggest that previously reported associations with socioeconomic position and health may be due to residual confounding or reverse causation.

scholarly journals Estimating the causal effect of genetic liability to prevalent disease on hospital costs using Mendelian Randomization

2020 ◽  
Author(s):  
Padraig Dixon ◽  
Sean Harrison ◽  
William Hollingworth ◽  
Neil M Davies ◽  
George Davey Smith
Keyword(s):  
Genetic Variants ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Hospital Costs ◽  
Disease Status ◽  
Uk Biobank ◽  
Inpatient Hospital ◽  
Genome Wide ◽  
Split Sample ◽  
Randomization Analysis

BACKGROUND Accurate measurement of the effects of disease status on healthcare cost is important in the pragmatic evaluation of interventions but is complicated by endogeneity biases due to omitted variables and reverse causality. Mendelian Randomization, the use of random perturbations in germline genetic variation as instrumental variables, can avoid these limitations. We report a novel Mendelian Randomization analysis of the causal effect of liability to disease on healthcare costs. METHODS We used Mendelian Randomization to model the causal impact on inpatient hospital costs of liability to six highly prevalent diseases: asthma, eczema, migraine, coronary heart disease, type 2 diabetes, and major depressive disorder. We identified genetic variants from replicated genome-wide associations studies and estimated their association with inpatient hospital costs using data from UK Biobank, a large prospective cohort study of individuals linked to records of hospital care. We assessed potential violations of the instrumental variable assumptions, particularly the exclusion restriction (i.e. variants affecting costs through alternative paths). We also conducted new genome wide association studies of hospital costs within the UK Biobank cohort as a further split sample sensitivity analysis. RESULTS We analyzed data on 307,032 individuals. Genetic variants explained only a small portion of the variance in each disease phenotype. Liability to coronary heart disease had substantial impacts (mean per person per year increase in costs from allele score Mendelian Randomization models: 712 pounds sterling (95% confidence interval: 238 pounds to 1,186 pounds)) on inpatient hospital costs in causal analysis, but other results were imprecise. There was concordance of findings across varieties of sensitivity analyses, including stratification by sex, and those obtained from the split sample analysis. CONCLUSION A novel Mendelian Randomization analysis of the causal effect of liability to disease on healthcare cost demonstrates that this type of analysis is feasible and informative in this context. There was concordance across data sources and across methods bearing different assumptions. Selection into the relatively healthy UK Biobank cohort and the modest proportion of variance in disease status accounted for by the allele scores reduced the precision of our estimates. We therefore could not exclude the possibility of substantial costs due to these diseases.

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 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 Causal effect of renal function on venous thromboembolism: a two-sample Mendelian randomization investigation

2021 ◽  
Author(s):  
Shuai Yuan ◽  
Maria Bruzelius ◽  
Susanna C. Larsson
Keyword(s):  
Renal Function ◽  
Venous Thromboembolism ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Meta Analysis ◽  
Genome Wide Association Studies ◽  
Uk Biobank ◽  
Genome Wide ◽  
Increased Risk ◽  
Mr Study

AbstractWhether renal function is causally associated with venous thromboembolism (VTE) is not yet fully elucidated. We conducted a two-sample Mendelian randomization (MR) study to determine the causal effect of renal function, measured as estimated glomerular filtration rate (eGFR), on VTE. Single-nucleotide polymorphisms associated with eGFR were selected as instrumental variables at the genome-wide significance level (p < 5 × 10−8) from a meta-analysis of 122 genome-wide association studies including up to 1,046,070 individuals. Summary-level data for VTE were obtained from the FinnGen consortium (6913 VTE cases and 169,986 non-cases) and UK Biobank study (4620 VTE cases and 356,574 non-cases). MR estimates were calculated using the random-effects inverse-variance weighted method and combined using fixed-effects meta-analysis. Genetically predicted decreased eGFR was significantly associated with an increased risk of VTE in both FinnGen and UK Biobank. For one-unit decrease in log-transformed eGFR, the odds ratios of VTE were 2.93 (95% confidence interval (CI) 1.25, 6.84) and 4.46 (95% CI 1.59, 12.5) when using data from FinnGen and UK Biobank, respectively. The combined odds ratio was 3.47 (95% CI 1.80, 6.68). Results were consistent in all sensitivity analyses and no horizontal pleiotropy was detected. This MR-study supported a casual role of impaired renal function in VTE.

scholarly journals Genetic predictors of participation in optional components of UK Biobank

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Jessica Tyrrell ◽  
Jie Zheng ◽  
Robin Beaumont ◽  
Kathryn Hinton ◽  
Tom G. Richardson ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Genetic Variants ◽  
Genetic Factors ◽  
Mendelian Randomization ◽  
Genetic Correlations ◽  
Mr Studies ◽  
Uk Biobank ◽  
Factors Affecting ◽  
Participation Bias ◽  
Genetic Predictors

AbstractLarge studies such as UK Biobank are increasingly used for GWAS and Mendelian randomization (MR) studies. However, selection into and dropout from studies may bias genetic and phenotypic associations. We examine genetic factors affecting participation in four optional components in up to 451,306 UK Biobank participants. We used GWAS to identify genetic variants associated with participation, MR to estimate effects of phenotypes on participation, and genetic correlations to compare participation bias across different studies. 32 variants were associated with participation in one of the optional components (P < 6 × 10−9), including loci with links to intelligence and Alzheimer’s disease. Genetic correlations demonstrated that participation bias was common across studies. MR showed that longer educational duration, older menarche and taller stature increased participation, whilst higher levels of adiposity, dyslipidaemia, neuroticism, Alzheimer’s and schizophrenia reduced participation. Our effect estimates can be used for sensitivity analysis to account for selective participation biases in genetic or non-genetic analyses.

Associations of observational and genetically determined caffeine intake with coronary artery disease and diabetes

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
A Said ◽  
Y.J Van De Vegte ◽  
N Verweij ◽  
P Van Der Harst
Keyword(s):  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Mendelian Randomization ◽  
Cox Regression ◽  
Caffeine Intake ◽  
Uk Biobank ◽  
Genome Wide ◽  
Artery Disease ◽  
Genetically Determined

Abstract Background Caffeine is the most widely consumed psychostimulant and is associated with lower risk of coronary artery disease (CAD) and type 2 diabetes (T2D). However, whether these associations are causal remains unknown. Objectives This study aimed to identify genetic variants associated with caffeine intake, and to investigate possible causal links between genetically determined caffeine intake and CAD or T2D. Additionally, we aimed to replicate previous observational findings between caffeine intake and CAD or T2D. Methods Genome wide associated studies (GWAS) were performed on caffeine intake from coffee, tea or both in 407,072 UK Biobank participants. Identified variants were used in a two-sample Mendelian randomization (MR) approach to investigate evidence for causal links between caffeine intake and CAD in CARDIoGRAMplusC4D (60,801 cases; 123,504 controls) or T2D in DIAGRAM (26,676 cases; 132,532 controls). Observational associations were tested within UK Biobank using Cox regression analyses. Results Moderate observational caffeine intakes from coffee or tea were associated with lower risks of CAD or T2D compared to no or high intake, with the lowest risks at intakes of 120–180 mg/day from coffee for CAD (HR=0.77 [95% CI: 0.73–0.82; P&lt;1e-16]), and 300–360 mg/day for T2D (HR=0.76 [95% CI: 0.67–0.86]; P=1.57e-5). GWAS identified 51 novel genetic loci associated with caffeine intake, enriched for central nervous system genes. In contrast to observational analyses, MR analyses in CARDIoGRAMplusC4D and DIAGRAM yielded no evidence for causal links between caffeine intake and the development of CAD or T2D. Conclusions MR analyses indicate caffeine intake might not protect against CAD or T2D, despite protective associations in observational analyses. Manhattan_plot_CaffeineIntake Funding Acknowledgement Type of funding source: None

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 Coffee Consumption and Cardiovascular Diseases: A Mendelian Randomization Study

Nutrients ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 2218
Author(s):  
Shuai Yuan ◽  
Paul Carter ◽  
Amy M. Mason ◽  
Stephen Burgess ◽  
Susanna C. Larsson
Keyword(s):  
Cardiovascular Disease ◽  
Cardiovascular Diseases ◽  
Intracerebral Hemorrhage ◽  
Genetic Variants ◽  
Odds Ratio ◽  
Observational Studies ◽  
Mendelian Randomization ◽  
Coffee Consumption ◽  
Uk Biobank ◽  
The Uk

Coffee consumption has been linked to a lower risk of cardiovascular disease in observational studies, but whether the associations are causal is not known. We conducted a Mendelian randomization investigation to assess the potential causal role of coffee consumption in cardiovascular disease. Twelve independent genetic variants were used to proxy coffee consumption. Summary-level data for the relations between the 12 genetic variants and cardiovascular diseases were taken from the UK Biobank with up to 35,979 cases and the FinnGen consortium with up to 17,325 cases. Genetic predisposition to higher coffee consumption was not associated with any of the 15 studied cardiovascular outcomes in univariable MR analysis. The odds ratio per 50% increase in genetically predicted coffee consumption ranged from 0.97 (95% confidence interval (CI), 0.63, 1.50) for intracerebral hemorrhage to 1.26 (95% CI, 1.00, 1.58) for deep vein thrombosis in the UK Biobank and from 0.86 (95% CI, 0.50, 1.49) for subarachnoid hemorrhage to 1.34 (95% CI, 0.81, 2.22) for intracerebral hemorrhage in FinnGen. The null findings remained in multivariable Mendelian randomization analyses adjusted for genetically predicted body mass index and smoking initiation, except for a suggestive positive association for intracerebral hemorrhage (odds ratio 1.91; 95% CI, 1.03, 3.54) in FinnGen. This Mendelian randomization study showed limited evidence that coffee consumption affects the risk of developing cardiovascular disease, suggesting that previous observational studies may have been confounded.

scholarly journals Smoking, DNA methylation and lung function: a Mendelian randomization analysis to investigate causal relationships

2019 ◽  
Author(s):  
Emily Jamieson ◽  
Roxanna Korologou-Linden ◽  
Robyn E. Wootton ◽  
Anna L. Guyatt ◽  
Thomas Battram ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Lung Function ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Forced Expiratory Volume ◽  
Multiple Trait ◽  
Genome Wide ◽  
Causal Variants ◽  
Association Data ◽  
Randomization Analysis

AbstractWhether smoking-associated DNA methylation has a causal effect on lung function has not been thoroughly evaluated. We investigated the causal effects of 474 smoking-associated CpGs on forced expiratory volume in one second (FEV1) in two-sample Mendelian randomization (MR) using methylation quantitative trait loci and genome-wide association data for FEV1. We found evidence of a possible causal effect for DNA methylation on FEV1 at 18 CpGs (p<1.2×10−4). Replication analysis supported a causal effect at three CpGs (cg21201401 (ZGPAT), cg19758448 (PGAP3) and cg12616487 (AHNAK) (p<0.0028). DNA methylation did not clearly mediate the effect of smoking on FEV1, although DNA methylation at some sites may influence lung function via effects on smoking. Using multiple-trait colocalization, we found evidence of shared causal variants between lung function, gene expression and DNA methylation. Findings highlight potential therapeutic targets for improving lung function and possibly smoking cessation, although large, tissue-specific datasets are required to confirm these results.

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