The Experts below are selected from a list of 11049 Experts worldwide ranked by ideXlab platform
Stephen Burgess - One of the best experts on this subject based on the ideXlab platform.
-
urate blood pressure and cardiovascular disease evidence from Mendelian Randomization and meta analysis of clinical trials
Hypertension, 2021Co-Authors: Dipender Gill, Stephen Burgess, Alan C Cameron, Daniel J Doherty, Ville Karhunen, Azmil H Abdulrahim, Martin Taylorrowan, Verena Zuber, Philip S TsaoAbstract:Serum urate has been implicated in hypertension and cardiovascular disease, but it is not known whether it is exerting a causal effect. To investigate this, we performed Mendelian Randomization ana...
-
fat mass and fat free mass in relation to cardiometabolic diseases a two sample Mendelian Randomization study
Journal of Internal Medicine, 2020Co-Authors: Susanna C. Larsson, Stephen BurgessAbstract:Fat mass and fat-free mass in relation to cardiometabolic diseases : a two-sample Mendelian Randomization study
-
contextualizing selection bias in Mendelian Randomization how bad is it likely to be
International Journal of Epidemiology, 2019Co-Authors: Apostolos Gkatzionis, Stephen BurgessAbstract:Background Selection bias affects Mendelian Randomization investigations when selection into the study sample depends on a collider between the genetic variant and confounders of the risk factor-outcome association. However, the relative importance of selection bias for Mendelian Randomization compared with other potential biases is unclear. Methods We performed an extensive simulation study to assess the impact of selection bias on a typical Mendelian Randomization investigation. We considered inverse probability weighting as a potential method for reducing selection bias. Finally, we investigated whether selection bias may explain a recently reported finding that lipoprotein(a) is not a causal risk factor for cardiovascular mortality in individuals with previous coronary heart disease. Results Selection bias had a severe impact on bias and Type 1 error rates in our simulation study, but only when selection effects were large. For moderate effects of the risk factor on selection, bias was generally small and Type 1 error rate inflation was not considerable. Inverse probability weighting ameliorated bias when the selection model was correctly specified, but increased bias when selection bias was moderate and the model was misspecified. In the example of lipoprotein(a), strong genetic associations and strong confounder effects on selection mean the reported null effect on cardiovascular mortality could plausibly be explained by selection bias. Conclusions Selection bias can adversely affect Mendelian Randomization investigations, but its impact is likely to be less than other biases. Selection bias is substantial when the effects of the risk factor and confounders on selection are particularly large.
-
Serum magnesium and calcium levels in relation to ischemic stroke Mendelian Randomization study
Neurology, 2019Co-Authors: Susanna Larsson, Stephen Burgess, Karl Michaëlsson, Matthew Traylor, Giorgio Boncoraglio, Christina Jern, Hugh MarkusAbstract:Objective To determine whether serum magnesium and calcium concentrations are causally associated with ischemic stroke or any of its subtypes using the Mendelian Randomization approach. Methods Analyses were conducted using summary statistics data for 13 single-nucleotide polymorphisms robustly associated with serum magnesium (n = 6) or serum calcium (n = 7) concentrations. The corresponding data for ischemic stroke were obtained from the MEGASTROKE consortium (34,217 cases and 404,630 noncases). Results In standard Mendelian Randomization analysis, the odds ratios for each 0.1 mmol/L (about 1 SD) increase in genetically predicted serum magnesium concentrations were 0.78 (95% confidence interval [CI] 0.69-0.89; p = 1.3 × 10 −4) for all ischemic stroke, 0.63 (95% CI 0.50-0.80; p = 1.6 × 10 −4) for cardioembolic stroke, and 0.60 (95% CI 0.44-0.82; p = 0.001) for large artery stroke; there was no association with small vessel stroke (odds ratio 0.90, 95% CI 0.67-1.20; p = 0.46). Only the association with cardioembolic stroke was robust in sensitivity analyses. There was no association of genetically predicted serum calcium concentrations with all ischemic stroke (per 0.5 mg/dL [about 1 SD] increase in serum calcium: odds ratio 1.03, 95% CI 0.88-1.21) or with any subtype. Conclusions This study found that genetically higher serum magnesium concentrations are associated with a reduced risk of cardioembolic stroke but found no significant association of genetically higher serum calcium concentrations with any ischemic stroke subtype.
-
contextualizing selection bias in Mendelian Randomization how bad is it likely to be
arXiv: Applications, 2018Co-Authors: Apostolos Gkatzionis, Stephen BurgessAbstract:Selection bias affects Mendelian Randomization investigations when selection into the study sample depends on a collider between the genetic variant and confounders of the risk factor-outcome association. However, the relative importance of selection bias for Mendelian Randomization compared to other potential biases is unclear. We performed an extensive simulation study to assess the impact of selection bias on a typical Mendelian Randomization investigation. Selection bias had a severe impact on bias and Type 1 error rates in our simulation study, but only when selection effects were large. For moderate effects of the risk factor on selection, bias was generally small and Type 1 error rate inflation was not considerable. The magnitude of bias was also affected by the strength of confounder-risk factor and confounder-outcome associations, the structure of the causal diagram and selection frequency. The use of inverse probability weighting ameliorated bias when the selection model was correctly specified, but increased bias when selection bias was moderate and the model was misspecified. Finally, we investigated whether selection bias may explain a recently reported finding that lipoprotein(a) is not a causal risk factor for cardiovascular mortality in individuals with previous coronary heart disease.
George Davey Smith - One of the best experts on this subject based on the ideXlab platform.
-
interpreting Mendelian Randomization estimates of the effects of categorical exposures such as disease status and educational attainment
medRxiv, 2020Co-Authors: George Davey Smith, Neil M Davies, Laurence J Howe, Matthew J TudballAbstract:Abstract Mendelian Randomization has been previously used to estimate the effects of binary and ordinal categorical exposures - e.g. type 2 diabetes or educational attainment defined by qualification - on outcomes. Binary and categorical phenotypes can be modelled in terms of liability, an underlying latent continuous variable with liability thresholds separating individuals into categories. Genetic variants typically influence an individual’s categorical exposure via their effects on liability, thus Mendelian Randomization analyses with categorical exposures will capture effects of liability which act independent of exposure category. We discuss how groups where the categorical exposure is invariant can be used to detect liability effects acting independently of exposure category. For example, associations between an adult educational attainment polygenic score (PGS) and BMI measured before the minimum school leaving age (e.g. age 10), cannot indicate the effects of years in full-time education on this outcome. Using UK Biobank data, we show that a higher education PGS is strongly associated with lower smoking initiation and higher glasses use at age 15. These associations were replicated in sibling models. An orthogonal approach using the raising of the school leaving age (ROSLA) policy change found that individuals who chose to remain in education to age 16 before the reform likely had higher liability to educational attainment than those who were compelled to remain in education to 16 after the reform, and had higher income, decreased cigarette smoking, higher glasses use and lower deprivation in adulthood. These results suggest that liability to educational attainment associates with health and social outcomes independent of years in full-time education. Mendelian Randomization studies with non-continuous exposures should be interpreted in terms of liability, which may affect the outcome via changes in exposure category and/or independently.
-
Mendelian Randomization analysis of the causal effect of adiposity on hospital costs.
Journal of health economics, 2020Co-Authors: Padraig Dixon, Sean Harrison, Neil M Davies, William Hollingworth, George Davey SmithAbstract:Estimates of the marginal effect of measures of adiposity such as body mass index (BMI) on healthcare costs are important for the formulation and evaluation of policies targeting adverse weight profiles. Most estimates of this association are affected by endogeneity bias. We use a novel identification strategy exploiting Mendelian Randomization - random germline genetic variation modelled using instrumental variables - to identify the causal effect of BMI on inpatient hospital costs. Using data on over 300,000 individuals, the effect size per person per marginal unit of BMI per year varied according to specification, including £21.22 (95% confidence interval (CI): £14.35-£28.07) for conventional inverse variance weighted models to £18.85 (95% CI: £9.05-£28.65) for penalized weighted median models. Effect sizes from Mendelian Randomization models were larger in most cases than non-instrumental variable multivariable adjusted estimates (£13.47, 95% CI: £12.51-£14.43). There was little evidence of non-linearity. Within-family estimates, intended to address dynastic biases, were imprecise.
-
the causes and consequences of alzheimer s disease phenome wide evidence from Mendelian Randomization
medRxiv, 2019Co-Authors: Roxanna Korologoulinden, Laura D Howe, George Davey Smith, Louise A C Millard, Yoav Benshlomo, Dylan M Williams, Emma L Anderson, Evangelia Stergiakouli, Neil M DaviesAbstract:Objective: To identify causal risk factors for Alzheimer9s disease and clarify which may instead be modified by emerging Alzheimer9s disease pathophysiology. Method: We performed a phenome-wide association study (PheWAS) of a polygenic risk score (5x10-8) for Alzheimer9s disease with a wide range of phenotypes in the UK Biobank, stratified by age tertiles. We also investigated the association between the polygenic risk score for Alzheimer9s disease and previously implicated risk factors. Using two-sample bidirectional Mendelian Randomization, we then estimated the size of causal effects of both previously implicated risk factors and those identified by the PheWAS on the risk of Alzheimer9s disease. Results: Genetic liability for Alzheimer9s disease was associated with red blood cell indices and cognitive measures in the youngest age tertile. In the middle and older age tertiles, higher genetic liability for Alzheimer9s disease was associated with medical history (e.g. atherosclerosis, use of cholesterol-lowering medications), physical measures (e.g. body fat measures), blood cell indices (e.g. erythrocyte distribution width), cognition (e.g. fluid intelligence score) and lifestyle (e.g. self-reported moderate activity and daytime napping). In follow-up analyses using Mendelian Randomization, we replicated established risk factors for Alzheimer9s disease (e.g. fluid intelligence score, education) and identified several novel risk factors (e.g. forced vital capacity, self-reported moderate physical activity and daytime napping). Conclusion: Genetic liability for Alzheimer9s disease is associated with over 160 phenotypes. However, findings from Mendelian Randomization analyses imply that most of these associations are likely to be caused by increased genetic risk for Alzheimer9s disease or selection, rather than a cause of the disease.
-
within family Mendelian Randomization studies
Human Molecular Genetics, 2019Co-Authors: Neil M Davies, Ben Brumpton, Laurence J Howe, Alexandra Havdahl, David Evans, George Davey SmithAbstract:Mendelian Randomization (MR) is increasingly used to make causal inferences in a wide range of fields, from drug development to etiologic studies. Causal inference in MR is possible because of the process of genetic inheritance from parents to offspring. Specifically, at gamete formation and conception, meiosis ensures random allocation to the offspring of one allele from each parent at each locus, and these are unrelated to most of the other inherited genetic variants. To date, most MR studies have used data from unrelated individuals. These studies assume that genotypes are independent of the environment across a sample of unrelated individuals, conditional on covariates. Here we describe potential sources of bias, such as transmission ratio distortion, selection bias, population stratification, dynastic effects and assortative mating that can induce spurious or biased SNP-phenotype associations. We explain how studies of related individuals such as sibling pairs or parent-offspring trios can be used to overcome some of these sources of bias, to provide potentially more reliable evidence regarding causal processes. The increasing availability of data from related individuals in large cohort studies presents an opportunity to both overcome some of these biases and also to evaluate familial environmental effects.
-
adhd genetic liability and physical health outcomes a two sample Mendelian Randomization study
bioRxiv, 2019Co-Authors: Beate Leppert, George Davey Smith, Lucy Riglin, Christina Dardani, Ajay Kumar Thapar, James R Staley, Kate Tilling, Anita Thapar, Evie StergiakouliAbstract:Abstract Objective Attention-deficit/hyperactivity disorder (ADHD) has been associated with a broad range of physical health problems, including cardiometabolic, neurological and immunological conditions. Determining whether ADHD plays a causal role in these associations is of great importance for treatment and prevention but also because comorbid health problems further increase the serious social and economic impacts of ADHD on individuals and their families. Methods We used a two-sample Mendelian Randomization (MR) approach to examine the causal relationships between genetic liability for ADHD and previously implicated physical health conditions. 11 genetic variants associated with ADHD were obtained from the latest summary statistics. Consistent effects obtained from IVW, weighted median and MR Egger methods were taken forward for sensitivity analysis, including bidirectional MR and multivariable MR (MVMR). Results We found evidence of a causal effect of genetic liability for ADHD on childhood obesity (OR:1.29 (95% CI:1.02,1.63)) and coronary artery disease (CAD) (OR:1.11 (95% CI:1.03,1.19)) with consistent results across different MR approaches. There was further evidence for a bidirectional relationship between genetic liability for ADHD and childhood obesity. The effect of genetic liability for ADHD on CAD was independent of smoking heaviness but was attenuated when simultaneously controlling for childhood obesity. There was little evidence for a causal effect on other cardiometabolic, immunological, neurological disorders and lung cancer. Conclusion Our findings strengthen the argument for early treatment and support for children with ADHD and their families and especially promoting physical activity and providing them with dietary advice to reduce future risk for developing CAD. Key Message Epidemiological studies have reported observational associations between ADHD and adult onset physical health outcomes. Mendelian Randomization can be used to assess causal associations for ADHD on health outcomes that would traditionally require long term follow-up and may suffer confounding We found that genetic liability for ADHD was associated with coronary artery disease and there was evidence for a bidirectional association between genetic liability for ADHD and childhood obesity Multivariable Mendelian Randomization suggests that the link between genetic liability and coronary artery disease might partially act through childhood obesity but was independent of smoking heaviness There was little evidence of a causal of ADHD on other cardiometabolic and immunological diseases.
Simon G Thompson - One of the best experts on this subject based on the ideXlab platform.
-
a review of instrumental variable estimators for Mendelian Randomization
Statistical Methods in Medical Research, 2017Co-Authors: Stephen Burgess, Dylan S Small, Simon G ThompsonAbstract:Instrumental variable analysis is an approach for obtaining causal inferences on the effect of an exposure (risk factor) on an outcome from observational data. It has gained in popularity over the past decade with the use of genetic variants as instrumental variables, known as Mendelian Randomization. An instrumental variable is associated with the exposure, but not associated with any confounder of the exposure–outcome association, nor is there any causal pathway from the instrumental variable to the outcome other than via the exposure. Under the assumption that a single instrumental variable or a set of instrumental variables for the exposure is available, the causal effect of the exposure on the outcome can be estimated. There are several methods available for instrumental variable estimation; we consider the ratio method, two-stage methods, likelihood-based methods, and semi-parametric methods. Techniques for obtaining statistical inferences and confidence intervals are presented. The statistical prop...
-
interpreting findings from Mendelian Randomization using the mr egger method
European Journal of Epidemiology, 2017Co-Authors: Stephen Burgess, Simon G ThompsonAbstract:Mendelian Randomization-Egger (MR-Egger) is an analysis method for Mendelian Randomization using summarized genetic data. MR-Egger consists of three parts: (1) a test for directional pleiotropy, (2) a test for a causal effect, and (3) an estimate of the causal effect. While conventional analysis methods for Mendelian Randomization assume that all genetic variants satisfy the instrumental variable assumptions, the MR-Egger method is able to assess whether genetic variants have pleiotropic effects on the outcome that differ on average from zero (directional pleiotropy), as well as to provide a consistent estimate of the causal effect, under a weaker assumption—the InSIDE (INstrument Strength Independent of Direct Effect) assumption. In this paper, we provide a critical assessment of the MR-Egger method with regard to its implementation and interpretation. While the MR-Egger method is a worthwhile sensitivity analysis for detecting violations of the instrumental variable assumptions, there are several reasons why causal estimates from the MR-Egger method may be biased and have inflated Type 1 error rates in practice, including violations of the InSIDE assumption and the influence of outlying variants. The issues raised in this paper have potentially serious consequences for causal inferences from the MR-Egger approach. We give examples of scenarios in which the estimates from conventional Mendelian Randomization methods and MR-Egger differ, and discuss how to interpret findings in such cases.
-
sensitivity analyses for robust causal inference from Mendelian Randomization analyses with multiple genetic variants
Epidemiology, 2017Co-Authors: Tove Fall, Erik Ingelsson, Jack Bowden, Stephen Burgess, Simon G ThompsonAbstract:Mendelian Randomization investigations are becoming more powerful and simpler to perform, due to the increasing size and coverage of genome-wide association studies and the increasing availability of summarized data on genetic associations with risk factors and disease outcomes. However, when using multiple genetic variants from different gene regions in a Mendelian Randomization analysis, it is highly implausible that all the genetic variants satisfy the instrumental variable assumptions. This means that a simple instrumental variable analysis alone should not be relied on to give a causal conclusion. In this article, we discuss a range of sensitivity analyses that will either support or question the validity of causal inference from a Mendelian Randomization analysis with multiple genetic variants. We focus on sensitivity analyses of greatest practical relevance for ensuring robust causal inferences, and those that can be undertaken using summarized data. Aside from cases in which the justification of the instrumental variable assumptions is supported by strong biological understanding, a Mendelian Randomization analysis in which no assessment of the robustness of the findings to violations of the instrumental variable assumptions has been made should be viewed as speculative and incomplete. In particular, Mendelian Randomization investigations with large numbers of genetic variants without such sensitivity analyses should be treated with skepticism.
-
multivariable Mendelian Randomization the use of pleiotropic genetic variants to estimate causal effects
American Journal of Epidemiology, 2015Co-Authors: Stephen Burgess, Simon G ThompsonAbstract:A conventional Mendelian Randomization analysis assesses the causal effect of a risk factor on an outcome by usinggeneticvariantsthataresolelyassociatedwiththeriskfactorofinterestasinstrumental variables.However, in somecases,suchasthecaseoftriglyceridelevelasariskfactorforcardiovasculardisease,itmaybedifficulttofind a relevant genetic variant that is not also associated with related risk factors, such as other lipid fractions. Such a variant is known as pleiotropic. In this paper, we propose an extension of Mendelian Randomization that uses multiple genetic variants associated with several measured risk factors to simultaneously estimate the causal effect of each of the risk factors on the outcome. This “multivariable Mendelian Randomization” approach is similar to the simultaneous assessment of several treatments in afactorial randomized trial. In this paper, methods forestimating thecausal effects are presentedand compared usingreal andsimulated data, andthe assumptions necessaryfora valid multivariable Mendelian Randomization analysis are discussed. Subject to these assumptions, we demonstrate that triglyceride-related pathways have a causal effect on the risk of coronary heart disease independent of the effects of low-density lipoprotein cholesterol and high-density lipoprotein cholesterol. causal inference; epidemiologic methods; instrumental variables; lipid fractions; Mendelian Randomization; pleiotropy
-
use of allele scores as instrumental variables for Mendelian Randomization
International Journal of Epidemiology, 2013Co-Authors: Stephen Burgess, Simon G ThompsonAbstract:Background An allele score is a single variable summarizing multiple genetic variants associated with a risk factor. It is calculated as the total number of risk factor-increasing alleles for an individual (unweighted score), or the sum of weights for each allele corresponding to estimated genetic effect sizes (weighted score). An allele score can be used in a Mendelian Randomization analysis to estimate the causal effect of the risk factor on an outcome. Methods Data were simulated to investigate the use of allele scores in Mendelian Randomization where conventional instrumental variable techniques using multiple genetic variants demonstrate ‘weak instrument’ bias. The robustness of estimates using the allele score to misspecification (for example non-linearity, effect modification) and to violations of the instrumental variable assumptions was assessed. Results Causal estimates using a correctly specified allele score were unbiased with appropriate coverage levels. The estimates were generally robust to misspecification of the allele score, but not to instrumental variable violations, even if the majority of variants in the allele score were valid instruments. Using a weighted rather than an unweighted allele score increased power, but the increase was small when genetic variants had similar effect sizes. Naive use of the data under analysis to choose which variants to include in an allele score, or for deriving weights, resulted in substantial biases. Conclusions Allele scores enable valid causal estimates with large numbers of genetic variants. The stringency of criteria for genetic variants in Mendelian Randomization should be maintained for all variants in an allele score.
Fernando Pires Hartwig - One of the best experts on this subject based on the ideXlab platform.
-
avoiding dynastic assortative mating and population stratification biases in Mendelian Randomization through within family analyses
Nature Communications, 2020Co-Authors: Sean Harrison, Ben Michael Brumpton, Fernando Pires Hartwig, Eleanor Sanderson, Karl Heilbron, Gunnhild Aberge Vie, Yoonsu Cho, Laura D HoweAbstract:Estimates from Mendelian Randomization studies of unrelated individuals can be biased due to uncontrolled confounding from familial effects. Here we describe methods for within-family Mendelian Randomization analyses and use simulation studies to show that family-based analyses can reduce such biases. We illustrate empirically how familial effects can affect estimates using data from 61,008 siblings from the Nord-Trondelag Health Study and UK Biobank and replicated our findings using 222,368 siblings from 23andMe. Both Mendelian Randomization estimates using unrelated individuals and within family methods reproduced established effects of lower BMI reducing risk of diabetes and high blood pressure. However, while Mendelian Randomization estimates from samples of unrelated individuals suggested that taller height and lower BMI increase educational attainment, these effects were strongly attenuated in within-family Mendelian Randomization analyses. Our findings indicate the necessity of controlling for population structure and familial effects in Mendelian Randomization studies.
-
within family studies for Mendelian Randomization avoiding dynastic assortative mating and population stratification biases
bioRxiv, 2019Co-Authors: Sean Harrison, Ben Michael Brumpton, Laura D Howe, Fernando Pires Hartwig, Eleanor Sanderson, Amanda HughesAbstract:Mendelian Randomization (MR) is a widely-used method for causal inference using genetic data. Mendelian Randomization studies of unrelated individuals may be susceptible to bias from family structure, for example, through dynastic effects which occur when parental genotypes directly affect offspring phenotypes. Here we describe methods for within-family Mendelian Randomization and through simulations show that family-based methods can overcome bias due to dynastic effects. We illustrate these issues empirically using data from 61,008 siblings from the UK Biobank and Nord-Trondelag Health Study. Both within-family and population-based Mendelian Randomization analyses reproduced established effects of lower BMI reducing risk of diabetes and high blood pressure. However, while MR estimates from population-based samples of unrelated individuals suggested that taller height and lower BMI increase educational attainment, these effects largely disappeared in within-family MR analyses. We found differences between population-based and within-family based estimates, indicating the importance of controlling for family effects and population structure in Mendelian Randomization studies.
-
bias in Mendelian Randomization due to assortative mating
Genetic Epidemiology, 2018Co-Authors: Fernando Pires Hartwig, Neil M Davies, George Davey SmithAbstract:Mendelian Randomization (MR) has been increasingly used to strengthen causal inference in observational epidemiology. Methodological developments in the field allow detecting and/or adjusting for different potential sources of bias, mainly bias due to horizontal pleiotropy (or "off-target" genetic effects). Another potential source of bias is nonrandom matching between spouses (i.e., assortative mating). In this study, we performed simulations to investigate the bias caused in MR by assortative mating. We found that bias can arise due to either cross-trait assortative mating (i.e., assortment on two phenotypes, such as highly educated women selecting taller men) or single-trait assortative mating (i.e., assortment on a single phenotype), even if the exposure and outcome phenotypes are not the phenotypes under assortment. The simulations also indicate that bias due to assortative mating accumulates over generations and that MR methods robust to horizontal pleiotropy are also affected by this bias. Finally, we show that genetic data from mother-father-offspring trios can be used to detect and correct for this bias.
-
lactase persistence and body mass index the contribution of Mendelian Randomization
Clinical Chemistry, 2018Co-Authors: Fernando Pires Hartwig, George Davey SmithAbstract:Nutritional epidemiology is one of the most challenging fields in epidemiology with respect to causal inference. Eating or not eating a particular food often occurs within a much wider dietary habit pattern, thus making it difficult to statistically disentangle the effects of individual foods. Dietary intake is also difficult to measure, and in large population-based studies, self-reported information is often the only feasible option. Intake may also vary over time, raising the need for repeated measurements to investigate the effects of long-term (and changing) dietary patterns. Longitudinal studies are also required to overcome the possibility of reverse causation, given that disease status often has a major influence on dietary behaviors. Diet is also often correlated with lifestyle and socioeconomic factors, which may be unknown or difficult to measure; therefore, the possibility of residual confounding is often nonnegligible. Randomized controlled trials could in principle be used to overcome the limitations of observational studies in nutritional epidemiology. However, it is difficult to achieve high adherence to behavioral interventions, especially in healthy individuals. Moreover, extended follow-up time is required because the health effects of a dietary intervention are unlikely to be acute. The participants of trials are often highly selected subgroups of the population, which may limit the generalizability of the findings. Finally, it is difficult (and often impossible) to blind participants of a behavioral intervention regarding their intervention status. In this issue of Clinical Chemistry , the Mendelian Randomization of Dairy Consumption Working Group (1) attempted to overcome the above limitations and estimate the causal effect of dairy intake on body mass index (BMI)4 using Mendelian Randomization (MR). MR uses 1 or more genetic variants robustly associated with modifiable exposures as instrumental variables (IVs) to assess the causal effect of such exposures on a given outcome. Valid causal inference from MR …
-
inflammatory biomarkers and risk of schizophrenia a 2 sample Mendelian Randomization study
JAMA Psychiatry, 2017Co-Authors: Fernando Pires Hartwig, Jack Bowden, George Davey Smith, Maria Carolina Borges, Bernardo L HortaAbstract:Importance Positive associations between inflammatory biomarkers and risk of psychiatric disorders, including schizophrenia, have been reported in observational studies. However, conventional observational studies are prone to bias, such as reverse causation and residual confounding, thus limiting our understanding of the effect (if any) of inflammatory biomarkers on schizophrenia risk. Objective To evaluate whether inflammatory biomarkers have an effect on the risk of developing schizophrenia. Design, Setting, and Participants Two-sample Mendelian Randomization study using genetic variants associated with inflammatory biomarkers as instrumental variables to improve inference. Summary association results from large consortia of candidate gene or genome-wide association studies, including several epidemiologic studies with different designs, were used. Gene-inflammatory biomarker associations were estimated in pooled samples ranging from 1645 to more than 80 000 individuals, while gene-schizophrenia associations were estimated in more than 30 000 cases and more than 45 000 ancestry-matched controls. In most studies included in the consortia, participants were of European ancestry, and the prevalence of men was approximately 50%. All studies were conducted in adults, with a wide age range (18 to 80 years). Exposures Genetically elevated circulating levels of C-reactive protein (CRP), interleukin-1 receptor antagonist (IL-1Ra), and soluble interleukin-6 receptor (sIL-6R). Main Outcomes and Measures Risk of developing schizophrenia. Individuals with schizophrenia or schizoaffective disorders were included as cases. Given that many studies contributed to the analyses, different diagnostic procedures were used. Results The pooled odds ratio estimate using 18 CRP genetic instruments was 0.90 (random effects 95% CI, 0.84-0.97; P = .005) per 2-fold increment in CRP levels; consistent results were obtained using different Mendelian Randomization methods and a more conservative set of instruments. The odds ratio for sIL-6R was 1.06 (95% CI, 1.01-1.12; P = .02) per 2-fold increment. Estimates for IL-1Ra were inconsistent among instruments, and pooled estimates were imprecise and centered on the null. Conclusions and Relevance Under Mendelian Randomization assumptions, our findings suggest a protective effect of CRP and a risk-increasing effect of sIL-6R (potentially mediated at least in part by CRP) on schizophrenia risk. It is possible that such effects are a result of increased susceptibility to early life infection.
Neil M Davies - One of the best experts on this subject based on the ideXlab platform.
-
interpreting Mendelian Randomization estimates of the effects of categorical exposures such as disease status and educational attainment
medRxiv, 2020Co-Authors: George Davey Smith, Neil M Davies, Laurence J Howe, Matthew J TudballAbstract:Abstract Mendelian Randomization has been previously used to estimate the effects of binary and ordinal categorical exposures - e.g. type 2 diabetes or educational attainment defined by qualification - on outcomes. Binary and categorical phenotypes can be modelled in terms of liability, an underlying latent continuous variable with liability thresholds separating individuals into categories. Genetic variants typically influence an individual’s categorical exposure via their effects on liability, thus Mendelian Randomization analyses with categorical exposures will capture effects of liability which act independent of exposure category. We discuss how groups where the categorical exposure is invariant can be used to detect liability effects acting independently of exposure category. For example, associations between an adult educational attainment polygenic score (PGS) and BMI measured before the minimum school leaving age (e.g. age 10), cannot indicate the effects of years in full-time education on this outcome. Using UK Biobank data, we show that a higher education PGS is strongly associated with lower smoking initiation and higher glasses use at age 15. These associations were replicated in sibling models. An orthogonal approach using the raising of the school leaving age (ROSLA) policy change found that individuals who chose to remain in education to age 16 before the reform likely had higher liability to educational attainment than those who were compelled to remain in education to 16 after the reform, and had higher income, decreased cigarette smoking, higher glasses use and lower deprivation in adulthood. These results suggest that liability to educational attainment associates with health and social outcomes independent of years in full-time education. Mendelian Randomization studies with non-continuous exposures should be interpreted in terms of liability, which may affect the outcome via changes in exposure category and/or independently.
-
Mendelian Randomization analysis of the causal effect of adiposity on hospital costs.
Journal of health economics, 2020Co-Authors: Padraig Dixon, Sean Harrison, Neil M Davies, William Hollingworth, George Davey SmithAbstract:Estimates of the marginal effect of measures of adiposity such as body mass index (BMI) on healthcare costs are important for the formulation and evaluation of policies targeting adverse weight profiles. Most estimates of this association are affected by endogeneity bias. We use a novel identification strategy exploiting Mendelian Randomization - random germline genetic variation modelled using instrumental variables - to identify the causal effect of BMI on inpatient hospital costs. Using data on over 300,000 individuals, the effect size per person per marginal unit of BMI per year varied according to specification, including £21.22 (95% confidence interval (CI): £14.35-£28.07) for conventional inverse variance weighted models to £18.85 (95% CI: £9.05-£28.65) for penalized weighted median models. Effect sizes from Mendelian Randomization models were larger in most cases than non-instrumental variable multivariable adjusted estimates (£13.47, 95% CI: £12.51-£14.43). There was little evidence of non-linearity. Within-family estimates, intended to address dynastic biases, were imprecise.
-
the causes and consequences of alzheimer s disease phenome wide evidence from Mendelian Randomization
medRxiv, 2019Co-Authors: Roxanna Korologoulinden, Laura D Howe, George Davey Smith, Louise A C Millard, Yoav Benshlomo, Dylan M Williams, Emma L Anderson, Evangelia Stergiakouli, Neil M DaviesAbstract:Objective: To identify causal risk factors for Alzheimer9s disease and clarify which may instead be modified by emerging Alzheimer9s disease pathophysiology. Method: We performed a phenome-wide association study (PheWAS) of a polygenic risk score (5x10-8) for Alzheimer9s disease with a wide range of phenotypes in the UK Biobank, stratified by age tertiles. We also investigated the association between the polygenic risk score for Alzheimer9s disease and previously implicated risk factors. Using two-sample bidirectional Mendelian Randomization, we then estimated the size of causal effects of both previously implicated risk factors and those identified by the PheWAS on the risk of Alzheimer9s disease. Results: Genetic liability for Alzheimer9s disease was associated with red blood cell indices and cognitive measures in the youngest age tertile. In the middle and older age tertiles, higher genetic liability for Alzheimer9s disease was associated with medical history (e.g. atherosclerosis, use of cholesterol-lowering medications), physical measures (e.g. body fat measures), blood cell indices (e.g. erythrocyte distribution width), cognition (e.g. fluid intelligence score) and lifestyle (e.g. self-reported moderate activity and daytime napping). In follow-up analyses using Mendelian Randomization, we replicated established risk factors for Alzheimer9s disease (e.g. fluid intelligence score, education) and identified several novel risk factors (e.g. forced vital capacity, self-reported moderate physical activity and daytime napping). Conclusion: Genetic liability for Alzheimer9s disease is associated with over 160 phenotypes. However, findings from Mendelian Randomization analyses imply that most of these associations are likely to be caused by increased genetic risk for Alzheimer9s disease or selection, rather than a cause of the disease.
-
within family Mendelian Randomization studies
Human Molecular Genetics, 2019Co-Authors: Neil M Davies, Ben Brumpton, Laurence J Howe, Alexandra Havdahl, David Evans, George Davey SmithAbstract:Mendelian Randomization (MR) is increasingly used to make causal inferences in a wide range of fields, from drug development to etiologic studies. Causal inference in MR is possible because of the process of genetic inheritance from parents to offspring. Specifically, at gamete formation and conception, meiosis ensures random allocation to the offspring of one allele from each parent at each locus, and these are unrelated to most of the other inherited genetic variants. To date, most MR studies have used data from unrelated individuals. These studies assume that genotypes are independent of the environment across a sample of unrelated individuals, conditional on covariates. Here we describe potential sources of bias, such as transmission ratio distortion, selection bias, population stratification, dynastic effects and assortative mating that can induce spurious or biased SNP-phenotype associations. We explain how studies of related individuals such as sibling pairs or parent-offspring trios can be used to overcome some of these sources of bias, to provide potentially more reliable evidence regarding causal processes. The increasing availability of data from related individuals in large cohort studies presents an opportunity to both overcome some of these biases and also to evaluate familial environmental effects.
-
software application profile mrrobust a tool for performing two sample summary Mendelian Randomization analyses
International Journal of Epidemiology, 2019Co-Authors: Wesley Spiller, Neil M Davies, Tom PalmerAbstract:Motivation In recent years, Mendelian Randomization analysis using summary data from genome-wide association studies has become a popular approach for investigating causal relationships in epidemiology. The mrrobust Stata package implements several of the recently developed methods.