Evidence for causal effects of lifetime smoking on risk for depression and schizophrenia: a Mendelian randomisation study.
Wootton Robyn E,Richmond Rebecca C,Stuijfzand Bobby G,Lawn Rebecca B,Sallis Hannah M,Taylor Gemma M J,Hemani Gibran,Jones Hannah J,Zammit Stanley,Davey Smith George,Munafò Marcus R
BACKGROUND:Smoking prevalence is higher amongst individuals with schizophrenia and depression compared with the general population. Mendelian randomisation (MR) can examine whether this association is causal using genetic variants identified in genome-wide association studies (GWAS). METHODS:We conducted two-sample MR to explore the bi-directional effects of smoking on schizophrenia and depression. For smoking behaviour, we used (1) smoking initiation GWAS from the GSCAN consortium and (2) we conducted our own GWAS of lifetime smoking behaviour (which captures smoking duration, heaviness and cessation) in a sample of 462690 individuals from the UK Biobank. We validated this instrument using positive control outcomes (e.g. lung cancer). For schizophrenia and depression we used GWAS from the PGC consortium. RESULTS:There was strong evidence to suggest smoking is a risk factor for both schizophrenia (odds ratio (OR) 2.27, 95% confidence interval (CI) 1.67-3.08, p < 0.001) and depression (OR 1.99, 95% CI 1.71-2.32, p < 0.001). Results were consistent across both lifetime smoking and smoking initiation. We found some evidence that genetic liability to depression increases smoking (β = 0.091, 95% CI 0.027-0.155, p = 0.005) but evidence was mixed for schizophrenia (β = 0.022, 95% CI 0.005-0.038, p = 0.009) with very weak evidence for an effect on smoking initiation. CONCLUSIONS:These findings suggest that the association between smoking, schizophrenia and depression is due, at least in part, to a causal effect of smoking, providing further evidence for the detrimental consequences of smoking on mental health.
Breast cancer risk factors and their effects on survival: a Mendelian randomisation study.
Escala-Garcia Maria,Morra Anna,Canisius Sander,Chang-Claude Jenny,Kar Siddhartha,Zheng Wei,Bojesen Stig E,Easton Doug,Pharoah Paul D P,Schmidt Marjanka K
BACKGROUND:Observational studies have investigated the association of risk factors with breast cancer prognosis. However, the results have been conflicting and it has been challenging to establish causality due to potential residual confounding. Using a Mendelian randomisation (MR) approach, we aimed to examine the potential causal association between breast cancer-specific survival and nine established risk factors for breast cancer: alcohol consumption, body mass index, height, physical activity, mammographic density, age at menarche or menopause, smoking, and type 2 diabetes mellitus (T2DM). METHODS:We conducted a two-sample MR analysis on data from the Breast Cancer Association Consortium (BCAC) and risk factor summary estimates from the GWAS Catalog. The BCAC data included 86,627 female patients of European ancestry with 7054 breast cancer-specific deaths during 15 years of follow-up. Of these, 59,378 were estrogen receptor (ER)-positive and 13,692 were ER-negative breast cancer patients. For the significant association, we used sensitivity analyses and a multivariable MR model. All risk factor associations were also examined in a model adjusted by other prognostic factors. RESULTS:Increased genetic liability to T2DM was significantly associated with worse breast cancer-specific survival (hazard ratio [HR] = 1.10, 95% confidence interval [CI] = 1.03-1.17, P value [P] = 0.003). There were no significant associations after multiple testing correction for any of the risk factors in the ER-status subtypes. For the reported significant association with T2DM, the sensitivity analyses did not show evidence for violation of the MR assumptions nor that the association was due to increased BMI. The association remained significant when adjusting by other prognostic factors. CONCLUSIONS:This extensive MR analysis suggests that T2DM may be causally associated with worse breast cancer-specific survival and therefore that treating T2DM may improve prognosis.
Modifiable pathways for colorectal cancer: a mendelian randomisation analysis.
The lancet. Gastroenterology & hepatology
BACKGROUND:Epidemiological studies have linked lifestyle, cardiometabolic, reproductive, developmental, and inflammatory factors to the risk of colorectal cancer. However, which specific factors affect risk and the strength of these effects are unknown. We aimed to examine the relationship between potentially modifiable risk factors and colorectal cancer. METHODS:We used a random-effects model to examine the relationship between 39 potentially modifiable risk factors and colorectal cancer in 26 397 patients with colorectal cancer and 41 481 controls (ie, people without colorectal cancer). These population data came from a genome-wide association study of people of European ancestry, which was amended to exclude UK BioBank data. In the model, we used genetic variants as instruments via two-sample mendelian randomisation to limit bias from confounding and reverse causation. We calculated odds ratios per genetically predicted SD unit increase in each putative risk factor (OR) for colorectal cancer risk. We did mendelian randomisation Egger regressions to identify evidence of potential violations of mendelian randomisation assumptions. A Bonferroni-corrected threshold of p=1·3 × 10 was considered significant, and p values less than 0·05 were considered to be suggestive of an association. FINDINGS:No putative risk factors were significantly associated with colorectal cancer risk after correction for multiple testing. However, suggestive associations with increased risk were noted for genetically predicted body fat percentage (OR 1·14 [95% CI 1·03-1·25]; p=0·0086), body-mass index (1·09 [1·01-1·17]; p=0·023), waist circumference (1·13 [1·02-1·26]; p=0·018), basal metabolic rate (1·10 [1·03-1·18]; p=0·0079), and concentrations of LDL cholesterol (1·14 [1·04-1·25]; p=0·0056), total cholesterol (1·09 [1·01-1·18]; p=0·025), circulating serum iron (1·17 [1·00-1·36]; p=0·049), and serum vitamin B12 (1·21 [1·04-1·42]; p=0·016), although potential pleiotropy among genetic variants used as instruments for vitamin B12 constrains the finding. A suggestive association was also noted between adult height and increased risk of colorectal cancer (OR 1·04 [95% CI 1·00-1·08]; p=0·032). Low blood selenium concentration had a suggestive association with decreased risk of colorectal cancer (OR 0·85 [95% CI 0·75-0·96]; p=0·0078) based on a single variant, as did plasma concentrations of interleukin-6 receptor subunit α (also based on a single variant; 0·98 [0·96-1·00]; p=0·035). Risk of colorectal cancer was not associated with any sex hormone or reproductive factor, serum calcium, or circulating 25-hydroxyvitamin D concentrations. INTERPRETATION:This analysis identified several modifiable targets for primary prevention of colorectal cancer, including lifestyle, obesity, and cardiometabolic factors, that should inform public health policy. FUNDING:Cancer Research UK, UK Medical Research Council Human Genetics Unit Centre, DJ Fielding Medical Research Trust, EU COST Action, and the US National Cancer Institute.
Mendelian randomisation highlights hypothyroidism as a causal determinant of idiopathic pulmonary fibrosis.
Zhang Yanan,Zhao Meng,Guo Ping,Wang Yanjun,Liu Lu,Zhao Jinghua,Gao Ling,Yuan Zhongshang,Xue Fuzhong,Zhao Jiajun
BACKGROUND:Although the association between hypothyroidism and idiopathic pulmonary fibrosis (IPF) is found in observational studies, it remains uncertain whether hypothyroidism causally influences IPF. METHODS:Two-sample Mendelian randomisation (MR) was conducted with hypothyroidism genome-wide association study (GWAS) data in the UK Biobank from 289,307 individuals (18,740 cases and 270,567 controls) and the largest GWAS summary statistics of IPF from 11,259 individuals (2,668 cases and 8,591 controls). Findings were verified using an independent validation dataset, as well as through different MR methods with different model assumptions. A multivariable MR based on Bayesian model averaging was further performed to evaluate whether hypothyroidism, even given several other comorbidities of IPF, remained to be the true causal one of IPF. FINDINGS:A positive causal effect of hypothyroidism on IPF was revealed (MR inverse-variance weighted [MR-IVW], odds ratio [OR]=1.125, 95% confidence interval [CI] 1.028-1.231; P=0.011), which was further verified in an independent validation set (MR-IVW, OR=1.229, 95% CI 1.054-1.432; P=0.008). The results were consistent from a variety of MR methods. Bidirectional analyses also indicated no reverse causation. Multivariable MR analysis showed hypothyroidism had the strongest marginal evidence (marginal inclusion probability=0.397, false discovery rate=0.025) compared with other comorbidities of IPF. INTERPRETATION:Our results illustrate the significant causal effect of hypothyroidism on IPF, which holds even given several other comorbidities of IPF. These findings may have an important insight into pathogenesis and possible future therapies of IPF. FUNDING:National Natural Science Foundation of China, the Natural Science Foundation of Shandong Province and the Young Scholars Program of Shandong University.
Mediators of the association between educational attainment and type 2 diabetes mellitus: a two-step multivariable Mendelian randomisation study.
AIMS/HYPOTHESIS:Type 2 diabetes mellitus is a major health burden disproportionately affecting those with lower educational attainment (EA). We aimed to obtain causal estimates of the association between EA and type 2 diabetes and to quantify mediating effects of known modifiable risk factors. METHODS:We applied two-step, two-sample multivariable Mendelian randomisation (MR) techniques using SNPs as genetic instruments for exposure and mediators, thereby minimising bias due to confounding and reverse causation. We leveraged summary data on genome-wide association studies for EA, proposed mediators (i.e. BMI, blood pressure, smoking, television watching) and type 2 diabetes. The total effect of EA on type 2 diabetes was decomposed into a direct effect and indirect effects through multiple mediators. Additionally, traditional mediation analysis was performed in a subset of the National Health and Nutrition Examination Survey 2013-2014. RESULTS:EA was inversely associated with type 2 diabetes (OR 0.53 for each 4.2 years of schooling; 95% CI 0.49, 0.56). Individually, the largest contributors were BMI (51.18% mediation; 95% CI 46.39%, 55.98%) and television watching (50.79% mediation; 95% CI 19.42%, 82.15%). Combined, the mediators explained 83.93% (95% CI 70.51%, 96.78%) of the EA-type 2 diabetes association. Traditional analysis yielded smaller effects but showed consistent direction and priority ranking of mediators. CONCLUSIONS/INTERPRETATION:These results support a potentially causal protective effect of EA against type 2 diabetes, with considerable mediation by a number of modifiable risk factors. Interventions on these factors thus have the potential of substantially reducing the burden of type 2 diabetes attributable to low EA.
Lung function and cardiovascular disease: a two-sample Mendelian randomisation study.
Higbee Daniel H,Granell Raquel,Sanderson Eleanor,Davey Smith George,Dodd James W
The European respiratory journal
BACKGROUND:Observational studies suggest an association between reduced lung function and risk of coronary artery disease and ischaemic stroke, independent of shared cardiovascular risk factors such as cigarette smoking. We use the latest genetic epidemiological methods to determine whether impaired lung function is causally associated with an increased risk of cardiovascular disease. METHODS AND FINDINGS:Mendelian randomisation uses genetic variants as instrumental variables to investigate causation. Preliminary analysis used two-sample Mendelian randomisation with lung function single nucleotide polymorphisms. To avoid collider bias, the main analysis used single nucleotide polymorphisms for lung function identified from UKBiobank in a multivariable Mendelian randomisation model conditioning for height, body mass index and smoking.Multivariable Mendelian randomisation shows strong evidence that reduced forced vital capacity (FVC) causes increased risk of coronary artery disease (OR 1.32, 95% CI 1.19-1.46 per standard deviation). Reduced forced expiratory volume in 1 s (FEV) is unlikely to cause increased risk of coronary artery disease, as evidence of its effect becomes weak after conditioning for height (OR 1.08, 95% CI 0.89-1.30). There is weak evidence that reduced lung function increases risk of ischaemic stroke. CONCLUSION:There is strong evidence that reduced FVC is independently and causally associated with coronary artery disease. Although the mechanism remains unclear, FVC could be taken into consideration when assessing cardiovascular risk and considered a potential target for reducing cardiovascular events. FEV and airflow obstruction do not appear to cause increased cardiovascular events; confounding and collider bias may explain previous findings of a causal association.
Interpreting Mendelian-randomization estimates of the effects of categorical exposures such as disease status and educational attainment.
International journal of epidemiology
BACKGROUND: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 influence an individual's categorical exposure via their effects on liability, thus Mendelian-randomization analyses with categorical exposures will capture effects of liability that act independently of exposure category. METHODS AND RESULTS:We discuss how groups in which 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 body mass index measured before the minimum school leaving age (e.g. age 10 years), cannot indicate the effects of years in full-time education on this outcome. Using UK Biobank data, we show that a higher educational-attainment PGS is strongly associated with lower smoking initiation and higher odds of glasses use at age 15 years. 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 years before the reform likely had higher liability to educational attainment than those who were compelled to remain in education to age 16 years after the reform, and had higher income, lower pack-years of smoking, higher odds of glasses use and lower deprivation in adulthood. These results suggest that liability to educational attainment is associated with health and social outcomes independently of years in full-time education. CONCLUSIONS: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.
The causal effects of serum lipids and apolipoproteins on kidney function: multivariable and bidirectional Mendelian-randomization analyses.
International journal of epidemiology
BACKGROUND:The causal nature of the observed associations between serum lipids and apolipoproteins and kidney function are unclear. METHODS:Using two-sample and multivariable Mendelian randomization (MR), we examined the causal effects of serum lipids and apolipoproteins on kidney function, indicated by the glomerular-filtration rate estimated using creatinine (eGFRcrea) or cystatin C (eGFRcys) and the urinary albumin-to-creatinine ratio (UACR). We obtained lipid- and apolipoprotein-associated genetic variants from the Global Lipids Genetics Consortium (n = 331 368) and UK Biobank (n = 441 016), respectively, and kidney-function markers from the Trøndelag Health Study (HUNT; n = 69 736) and UK Biobank (n = 464 207). The reverse causal direction was examined using variants associated with kidney-function markers selected from recent genome-wide association studies. RESULTS:There were no strong associations between genetically predicted lipid and apolipoprotein levels with kidney-function markers. Some, but inconsistent, evidence suggested a weak association of higher genetically predicted atherogenic lipid levels [indicated by low-density lipoprotein cholesterol (LDL-C), triglycerides and apolipoprotein B] with increased eGFR and UACR. For high-density lipoprotein cholesterol (HDL-C), results differed between eGFRcrea and eGFRcys, but neither analysis suggested substantial effects. We found no clear evidence of a reverse causal effect of eGFR on lipid or apolipoprotein traits, but higher UACR was associated with higher LDL-C, triglyceride and apolipoprotein B levels. CONCLUSION:Our MR estimates suggest that serum lipid and apolipoprotein levels do not cause substantial changes in kidney function. A possible weak effect of higher atherogenic lipids on increased eGFR and UACR warrants further investigation. Processes leading to higher UACR may lead to more atherogenic lipid levels.
Telomere length and risk of idiopathic pulmonary fibrosis and chronic obstructive pulmonary disease: a mendelian randomisation study.
Duckworth Anna,Gibbons Michael A,Allen Richard J,Almond Howard,Beaumont Robin N,Wood Andrew R,Lunnon Katie,Lindsay Mark A,Wain Louise V,Tyrrell Jess,Scotton Chris J
The Lancet. Respiratory medicine
BACKGROUND:Idiopathic pulmonary fibrosis (IPF) is a fatal lung disease accounting for 1% of UK deaths. In the familial form of pulmonary fibrosis, causal genes have been identified in about 30% of cases, and a majority of these causal genes are associated with telomere maintenance. Prematurely shortened leukocyte telomere length is associated with IPF and chronic obstructive pulmonary disease (COPD), a disease with similar demographics and shared risk factors. Using mendelian randomisation, we investigated evidence supporting a causal role for short telomeres in IPF and COPD. METHODS:Mendelian randomisation inference of telomere length causality was done for IPF (up to 1369 cases) and COPD (13 538 cases) against 435 866 controls of European ancestry in UK Biobank. Polygenic risk scores were calculated and two-sample mendelian randomisation analyses were done using seven genetic variants previously associated with telomere length, with replication analysis in an IPF cohort (2668 cases vs 8591 controls) and COPD cohort (15 256 cases vs 47 936 controls). FINDINGS:In the UK Biobank, a genetically instrumented one-SD shorter telomere length was associated with higher odds of IPF (odds ratio [OR] 4·19, 95% CI 2·33-7·55; p=0·0031) but not COPD (1·07, 0·88-1·30; p=0·51). Similarly, an association was found in the IPF replication cohort (12·3, 5·05-30·1; p=0·0015) and not in the COPD replication cohort (1·04, 0·71-1·53; p=0·83). Meta-analysis of the two-sample mendelian randomisation results provided evidence inferring that shorter telomeres cause IPF (5·81 higher odds of IPF, 95% CI 3·56-9·50; p=2·19 × 10). There was no evidence to infer that telomere length caused COPD (OR 1·07, 95% CI 0·90-1·27; p=0·46). INTERPRETATION:Cellular senescence is hypothesised as a major driving force in IPF and COPD; telomere shortening might be a contributory factor in IPF, suggesting divergent mechanisms in COPD. Defining a key role for telomere shortening enables greater focus in telomere-related diagnostics, treatments, and the search for a cure in IPF. Investigation of therapies that improve telomere length is warranted. FUNDING:Medical Research Council.
Bayesian mendelian randomization with study heterogeneity and data partitioning for large studies.
BMC medical research methodology
BACKGROUND:Mendelian randomization (MR) is a useful approach to causal inference from observational studies when randomised controlled trials are not feasible. However, study heterogeneity of two association studies required in MR is often overlooked. When dealing with large studies, recently developed Bayesian MR can be computationally challenging, and sometimes even prohibitive. METHODS:We addressed study heterogeneity by proposing a random effect Bayesian MR model with multiple exposures and outcomes. For large studies, we adopted a subset posterior aggregation method to overcome the problem of computational expensiveness of Markov chain Monte Carlo. In particular, we divided data into subsets and combined estimated causal effects obtained from the subsets. The performance of our method was evaluated by a number of simulations, in which exposure data was partly missing. RESULTS:Random effect Bayesian MR outperformed conventional inverse-variance weighted estimation, whether the true causal effects were zero or non-zero. Data partitioning of large studies had little impact on variations of the estimated causal effects, whereas it notably affected unbiasedness of the estimates with weak instruments and high missing rate of data. For the cases being simulated in our study, the results have indicated that the "divide (data) and combine (estimated subset causal effects)" can help improve computational efficiency, for an acceptable cost in terms of bias in the causal effect estimates, as long as the size of the subsets is reasonably large. CONCLUSIONS:We further elaborated our Bayesian MR method to explicitly account for study heterogeneity. We also adopted a subset posterior aggregation method to ease computational burden, which is important especially when dealing with large studies. Despite the simplicity of the model we have used in the simulations, we hope the present work would effectively point to MR studies that allow modelling flexibility, especially in relation to the integration of heterogeneous studies and computational practicality.
Mendelian randomization: can genetic epidemiology help redress the failures of observational epidemiology?
Ebrahim Shah,Davey Smith George
Establishing causal relationships between environmental exposures and common diseases is beset with problems of unresolved confounding, reverse causation and selection bias that may result in spurious inferences. Mendelian randomization, in which a functional genetic variant acts as a proxy for an environmental exposure, provides a means of overcoming these problems as the inheritance of genetic variants is independent of-that is randomized with respect to-the inheritance of other traits, according to Mendel's law of independent assortment. Examples drawn from exposures and outcomes as diverse as milk and osteoporosis, alcohol and coronary heart disease, sheep dip and farm workers' compensation neurosis, folate and neural tube defects are used to illustrate the applications of Mendelian randomization approaches in assessing potential environmental causes of disease. As with all genetic epidemiology studies there are problems associated with the need for large sample sizes, the non-replication of findings, and the lack of relevant functional genetic variants. In addition to these problems, Mendelian randomization findings may be confounded by other genetic variants in linkage disequilibrium with the variant under study, or by population stratification. Furthermore, pleiotropy of effect of a genetic variant may result in null associations, as may canalisation of genetic effects. If correctly conducted and carefully interpreted, Mendelian randomization studies can provide useful evidence to support or reject causal hypotheses linking environmental exposures to common diseases.
An atlas on risk factors for type 2 diabetes: a wide-angled Mendelian randomisation study.
Yuan Shuai,Larsson Susanna C
AIMS/HYPOTHESIS:The aim of this study was to use Mendelian randomisation (MR) to identify the causal risk factors for type 2 diabetes. METHODS:We first conducted a review of meta-analyses and review articles to pinpoint possible risk factors for type 2 diabetes. Around 170 possible risk factors were identified of which 97 risk factors with available genetic instrumental variables were included in MR analyses. To reveal more risk factors that were not included in our MR analyses, we conducted a review of published MR studies of type 2 diabetes. For our MR analyses, we used summary-level data from the DIAbetes Genetics Replication And Meta-analysis consortium (74,124 type 2 diabetes cases and 824,006 controls of European ancestry). Potential causal associations were replicated using the FinnGen consortium (11,006 type 2 diabetes cases and 82,655 controls of European ancestry). The inverse-variance weighted method was used as the main analysis. Multivariable MR analysis was used to assess whether the observed associations with type 2 diabetes were mediated by BMI. We used the Benjamini-Hochberg method that controls false discovery rate for multiple testing. RESULTS:We found evidence of causal associations between 34 exposures (19 risk factors and 15 protective factors) and type 2 diabetes. Insomnia was identified as a novel risk factor (OR 1.17 [95% CI 1.11, 1.23]). The other 18 risk factors were depression, systolic BP, smoking initiation, lifetime smoking, coffee (caffeine) consumption, plasma isoleucine, valine and leucine, liver alanine aminotransferase, childhood and adulthood BMI, body fat percentage, visceral fat mass, resting heart rate, and four plasma fatty acids. The 15 exposures associated with a decreased risk of type 2 diabetes were plasma alanine, HDL- and total cholesterol, age at menarche, testosterone levels, sex hormone binding globulin levels (adjusted for BMI), birthweight, adulthood height, lean body mass (for women), four plasma fatty acids, circulating 25-hydroxyvitamin D and education years. Eight associations remained after adjustment for adulthood BMI. We additionally identified 21 suggestive risk factors (p < 0.05), such as alcohol consumption, breakfast skipping, daytime napping, short sleep, urinary sodium, and certain amino acids and inflammatory factors. CONCLUSIONS/INTERPRETATION:The present study verified several previously reported risk factors and identified novel potential risk factors for type 2 diabetes. Prevention strategies for type 2 diabetes should be considered from multiple perspectives on obesity, mental health, sleep quality, education level, birthweight and smoking.
Estimating dose-response relationships for vitamin D with coronary heart disease, stroke, and all-cause mortality: observational and Mendelian randomisation analyses.
The lancet. Diabetes & endocrinology
BACKGROUND:Randomised trials of vitamin D supplementation for cardiovascular disease and all-cause mortality have generally reported null findings. However, generalisability of results to individuals with low vitamin D status is unclear. We aimed to characterise dose-response relationships between 25-hydroxyvitamin D (25[OH]D) concentrations and risk of coronary heart disease, stroke, and all-cause mortality in observational and Mendelian randomisation frameworks. METHODS:Observational analyses were undertaken using data from 33 prospective studies comprising 500 962 individuals with no known history of coronary heart disease or stroke at baseline. Mendelian randomisation analyses were performed in four population-based cohort studies (UK Biobank, EPIC-CVD, and two Copenhagen population-based studies) comprising 386 406 middle-aged individuals of European ancestries, including 33 546 people who developed coronary heart disease, 18 166 people who had a stroke, and 27 885 people who died. Primary outcomes were coronary heart disease, defined as fatal ischaemic heart disease (International Classification of Diseases 10th revision code I20-I25) or non-fatal myocardial infarction (I21-I23); stroke, defined as any cerebrovascular disease (I60-I69); and all-cause mortality. FINDINGS:Observational analyses suggested inverse associations between incident coronary heart disease, stroke, and all-cause mortality outcomes with 25(OH)D concentration at low 25(OH)D concentrations. In population-wide genetic analyses, there were no associations of genetically-predicted 25(OH)D with coronary heart disease, stroke, or all-cause mortality. However, for the participants with vitamin D deficiency (25[OH]D concentration <25 nmol/L), genetic analyses provided strong evidence for an inverse association with all-cause mortality (odds ratio [OR] per 10 nmol/L increase in genetically-predicted 25[OH]D concentration 0·69 [95% CI 0·59-0·80]; p<0·0001) and non-significant inverse associations for stroke (0·85 [0·70-1·02], p=0·09) and coronary heart disease (0·89 [0·76-1·04]; p=0·14). A finer stratification of participants found inverse associations between genetically-predicted 25(OH)D concentrations and all-cause mortality up to around 40 nmol/L. INTERPRETATION:Stratified Mendelian randomisation analyses suggest a causal relationship between 25(OH)D concentrations and mortality for individuals with low vitamin D status. Our findings have implications for the design of vitamin D supplementation trials, and potential disease prevention strategies. FUNDING:British Heart Foundation, Medical Research Council, National Institute for Health Research, Health Data Research UK, Cancer Research UK, and International Agency for Research on Cancer.
Associations between plasma fatty acid concentrations and schizophrenia: a two-sample Mendelian randomisation study.
The lancet. Psychiatry
BACKGROUND:Although studies suggest that concentrations of omega-3 and omega-6 fatty acids are lower in individuals with schizophrenia, evidence for beneficial effects of fatty acid supplementation is scarce. Therefore, in this study, we aimed to determine whether omega-3 and omega-6 fatty acid concentrations are causally related to schizophrenia. METHODS:We did a two-sample Mendelian randomisation study, using deidentified summary-level data that were publicly available. Exposure-outcome relationships were evaluated using the inverse variance weighted two-sample Mendelian randomisation method using results from genome-wide association studies (GWASs) of fatty acid concentrations and schizophrenia. GWAS results were available for European (fatty acids) and European and Asian (schizophrenia) ancestry samples. Overall age and gender information were not calculable from the summary-level GWAS results. Weighted median, weighted mode, and Mendelian randomisation Egger regression methods were used as sensitivity analyses. To address underlying mechanisms, further analyses were done using single instruments within the FADS gene cluster and ELOVL2 gene locus. FADS gene cluster and ELOVL2 gene causal effects on schizophrenia were calculated by dividing the single nucleotide polymorphism (SNP)-schizophrenia effect estimate by the SNP-fatty acid effect estimate with standard errors derived using the first term from a delta method expansion for the ratio estimate. Multivariable Mendelian randomisation was used to estimate direct effects of omega-3 fatty acids on schizophrenia, independent of omega-6 fatty acids, lipoproteins (ie, HDL and LDL), and triglycerides. FINDINGS:Mendelian randomisation analyses indicated that long-chain omega-3 and long-chain omega-6 fatty acid concentrations were associated with a lower risk of schizophrenia (eg, inverse variance weighted odds ratio [OR] 0·83 [95% CI 0·75-0·92] for docosahexaenoic acid). By contrast, there was weak evidence that short-chain omega-3 and short-chain omega-6 fatty acids were associated with an increased risk of schizophrenia (eg, inverse variance weighted OR 1·07 [95% CI 0·98-1·18] for α-linolenic acid). Effects were consistent across the sensitivity analyses and the FADS single-SNP analyses, suggesting that long-chain omega-3 and long-chain omega-6 fatty acid concentrations were associated with lower risk of schizophrenia (eg, OR 0·74 [95% CI 0·58-0·96] for docosahexaenoic acid) whereas short-chain omega-3 and short-chain omega-6 fatty acid concentrations were associated with an increased risk of schizophrenia (eg, OR 1·08 [95% CI 1·02-1·15] for α-linolenic acid). By contrast, estimates from the ELOVL2 single-SNP analyses were more imprecise and compatible with both risk-increasing and protective effects for each of the fatty acid measures. Multivariable Mendelian randomisation indicated that the protective effect of docosahexaenoic acid on schizophrenia persisted after conditioning on other lipids, although evidence was slightly weaker (multivariable inverse variance weighted OR 0·84 [95% CI 0·71-1·01]). INTERPRETATION:Our results are compatible with the protective effects of long-chain omega-3 and long-chain omega-6 fatty acids on schizophrenia, suggesting that people with schizophrenia might have difficulty converting short-chain polyunsaturated fatty acids to long-chain polyunsaturated fatty acids. Further studies are required to determine whether long-chain polyunsaturated fatty acid supplementation or diet enrichment might help prevent onset of schizophrenia. FUNDING:National Institute for Health Research Biomedical Research Centre at University Hospitals Bristol and Weston NHS Foundation Trust and the University of Bristol.
Association of telomere length with risk of rheumatoid arthritis: a meta-analysis and Mendelian randomization.
Zeng Zhen,Zhang Wanting,Qian Yu,Huang Huijun,Wu David J H,He Zhixing,Ye Ding,Mao Yingying,Wen Chengping
Rheumatology (Oxford, England)
OBJECTIVE:To evaluate the telomere length (TL) in patients with RA relative to that in controls and to test whether TL is causally associated with risk of RA. METHODS:Systematic review and meta-analysis of relevant literature was conducted to evaluate the association between TL and RA. Standardized mean differences with 95% CIs of TL in RA patients relative to controls were pooled using fixed or random-effects models. TL-related single-nucleotide polymorphisms were selected from a genome-wide association study of 37 684 individuals, and summary statistics of RA were obtained from a genome-wide association study meta-analysis including 14 361 RA patients and 43 923 controls. Mendelian randomization was performed using the inverse-variance weighted, weighted-median and likelihood-based methods. Sensitivity analyses were performed to test the robustness of the association. RESULTS:In the meta-analysis of 911 RA patients and 2498 controls, we found that patients with RA had a significantly shorter TL compared with controls (standardized mean differences = -0.50; 95% CI -0.88, -0.11; P = 0.012). In the Mendelian randomization analysis, we found that genetically predicted longer TL was associated with a reduced risk of RA [odds ratio = 0.68; 95% CI 0.54, 0.86; P = 0.002 using the inverse-variance weighted method]. Sensitivity analyses using alternative Mendelian randomization approaches yielded similar findings, suggesting the robustness of the causal association. CONCLUSION:Our study provides evidence for a negative causal association of TL with risk of RA. Further studies are warranted to elucidate the underlying mechanism for the role of telomeres in the development of RA.
Coffee consumption and cancer risk: a Mendelian randomisation study.
Clinical nutrition (Edinburgh, Scotland)
BACKGROUND:Coffee contains many bioactive chemicals and associations with cancer have been reported in observational studies. In this Mendelian randomisation (MR) study we investigated the causal associations of coffee consumption with a broad range of cancers. MATERIALS AND METHODS:Twelve independent genetic variants proxied coffee consumption. Genetically-predicted risk of any cancer (59,647 cases) and 22 site-specific cancers was estimated in European-descent individuals in UK Biobank. Univariable and multivariable MR analyses were conducted. RESULTS:Genetically-predicted coffee consumption was not associated with risk of any cancer in the main analysis (OR 1.05, 95% CI 0.98-1.14, p = 0.183) but was associated with an increased risk of digestive system cancer (OR 1.28, 95% CI 1.09-1.51, p = 0.003), driven by a strong association with oesophageal cancer (OR 2.79, 95% CI 1.73-4.50, p = 2.5×10). This association was consistent after adjustment for genetically-predicted body mass index, smoking and alcohol consumption. There was no strong evidence supporting a causal relationship between genetically-predicted coffee consumption and the majority of cancers studied. However, genetically-predicted coffee consumption was associated with increased risk of multiple myeloma (OR 2.25, 95% CI 1.30-3.89, p = 0.004) and reduced ovarian cancer risk (OR 0.63, 95% CI 0.43-0.93, p = 0.020). CONCLUSIONS:This MR study provides strong support for a causal association of coffee consumption with oesophageal cancer, but not for the majority of cancer types, and the underlying mechanisms require investigation.
Mendelian randomization: potential use of genetics to enable causal inferences regarding HIV-associated biomarkers and outcomes.
He Weijing,Castiblanco John,Walter Elizabeth A,Okulicz Jason F,Ahuja Sunil K
Current opinion in HIV and AIDS
PURPOSE OF REVIEW:It is unknown whether biomarkers simply correlate with or are causal for HIV-associated outcomes. Mendelian randomization is a genetic epidemiologic approach used to disentangle causation from association. Here, we discuss the potential use of Mendelian randomization for differentiating whether biomarkers are correlating with or causal for HIV-associated outcomes. RECENT FINDINGS:Mendelian randomization refers to the random allocation of alleles at the time of gamete formation. In observational epidemiology, this refers to the use of genetic variants to estimate a causal effect between a modifiable risk factor and an outcome of interest. A formal Mendelian randomization study using a genetic marker as a proxy for the biomarker has not been conducted in the HIV field. However, in the postgenomic era, this approach is being used increasingly. Examples are evidence for the causal role of BMI in blood pressure and noncausal role of C-reactive protein in coronary heart disease. We discuss the conceptual framework, uses, and limitations of Mendelian randomization in the context of HIV infection as well as specific biomarkers (IL-6, C-reactive protein) and genetic determinants (e.g., in CCR5, chemokine, and DARC genes) that associate with HIV-related outcomes. SUMMARY:Making the distinction between correlation and causality has particular relevance when a biomarker (e.g., IL-6) is potentially modifiable, in which case a biomarker-guided targeted treatment strategy may be feasible. Although the tenets of Mendelian randomization rest on strong assumptions, and conducting a Mendelian randomization study in HIV infection presents many challenges, it may offer the potential to identify causal biomarkers for HIV-associated outcomes.
Mendelian randomization studies in coronary artery disease.
Jansen Henning,Samani Nilesh J,Schunkert Heribert
European heart journal
Epidemiological research over the last 50 years has discovered a plethora of biomarkers (including molecules, traits or other diseases) that associate with coronary artery disease (CAD) risk. Even the strongest association detected in such observational research precludes drawing conclusions about the causality underlying the relationship between biomarker and disease. Mendelian randomization (MR) studies can shed light on the causality of associations, i.e whether, on the one hand, the biomarker contributes to the development of disease or, on the other hand, the observed association is confounded by unrecognized exogenous factors or due to reverse causation, i.e. due to the fact that prevalent disease affects the level of the biomarker. However, conclusions from a MR study are based on a number of important assumptions. A prerequisite for such studies is that the genetic variant employed affects significantly the biomarker under investigation but has no effect on other phenotypes that might confound the association between the biomarker and disease. If this biomarker is a true causal risk factor for CAD, genotypes of the variant should associate with CAD risk in the direction predicted by the association of the biomarker with CAD. Given a random distribution of exogenous factors in individuals carrying respective genotypes, groups represented by the genotypes are highly similar except for the biomarker of interest. Thus, the genetic variant converts into an unconfounded surrogate of the respective biomarker. This scenario is nicely exemplified for LDL cholesterol. Almost every genotype found to increase LDL cholesterol level by a sufficient amount has also been found to increase CAD risk. Pending a number of conditions that needed to be fulfilled by the genetic variant under investigation (e.g. no pleiotropic effects) and the experimental set-up of the study, LDL cholesterol can be assumed to act as the functional component that links genotypes and CAD risk and, more importantly, it can be assumed that any modulation of LDL cholesterol-by whatever mechanism-would have similar effects on disease risk. Therefore, MR analysis has tremendous potential for identifying therapeutic targets that are likely to be causal for CAD. This review article discusses the opportunities and challenges of MR studies for CAD, highlighting several examples that involved multiple biomarkers, including various lipid and inflammation traits as well as hypertension, diabetes mellitus, and obesity.
A causal relationship between childhood obesity and risk of osteoarthritis: results from a two-sample Mendelian randomization analysis.
Annals of medicine
PURPOSE:It has been found that childhood obesity (CO) may play an important role in the onset and progression of osteoarthritis (OA). Thus we conducted this mendelian randomisation analysis (MR) to evaluate the causal association between childhood obesity and osteoarthritis. METHODS:Instrumental variables (IVs) were obtained from publicly available genome-wide association study datasets. The leave-one-out sensitivity test, MR Pleiotropy RESidual Sum and Outlier test (MR-PRESSO), and Cochran's test were used to confirm the heterogeneity and pleiotropy of identified IVs, then five different models, including the inverse variance weighted model (IVW), weighted median estimator model (WME), weighted model-based method (WM), MR-Egger regression model (MER), and MR-Robust Adjusted Profile Score (MRAPS) were applied in this MR analysis. RESULTS:After excluding all outliers identified by the MR-PRESSO test, no evident directional pleiotropy was found. Significant heterogeneity was found in the secondary MR and as a result, the multiplicative random-effect model was used. Significant causal association between CO and OA (OR 1.0075, 95% CI [1.0054, 1.0010], = 8.12 × 10). The secondary MR also revealed that CO was causally associated with knee OA (OR 1.1067, 95% CI [1.0769, 1.1373], = 3.30 × 10) and hip OA (OR 1.1272, 95% CI [1.0610, 1.1976], = 1.07 × 10). The accuracy and robustness of these findings were confirmed by sensitivity tests. CONCLUSION:There appears to be a causal relationship between childhood obesity and OA. Our results indicate that individuals with a history of childhood obesity require specific clinical attention to prevent the development of knee and hip OA.
Evaluating the Causal Effects of TIMP-3 on Ischaemic Stroke and Intracerebral Haemorrhage: A Mendelian Randomization Study.
Frontiers in genetics
Since tissue inhibitors of matrix metalloproteinase 3 (TIMP-3) was reported to be a potential risk factor of atherosclerosis, aneurysm, hypertension, and post-ischaemic neuronal injury, it may also be a candidate risk factor of stress. Therefore, this study was designed to explore the causal role of TIMP-3 in the risk of ischaemic stroke (IS) and intracerebral haemorrhage (ICH), which are the two main causes of stress via this Mendelian Randomisation (MR) study. The summarised data of TIMP-3 level in circulation was acquired from the Cooperative Health Research in the Region of Augsburg public database and the outcome of IS and ICH was obtained from genome-wide association studies conducted by MEGASTROKE and the International Stroke Genetics Consortium, respectively. Five statistical methods including inverse-variance weighting, weighted-median analysis, MR-Egger regression, MR Pleiotropy RESidual Sum and Outlier test, and MR-Robust Adjusted Profile Score were applied to evaluate the causal role of TIMP-3 in the occurrence of IS and ICH. Inverse-variance weighting was applied for assessing causality. Furthermore, heterogeneity and pleiotropic tests were utilised to confirm the reliability of this study. We found that TIMP-3 could be a positively causal relationship with the incidence of IS (OR = 1.026, 95% CI: 1.007-1.046, = 0.0067), especially for the occurrence of small vessel stroke (SVS; OR = 1.045, 95% CI: 1.016-1.076, = 0.0024). However, the causal effects of TIMP-3 on another IS subtype cardioembolic stroke (CES; OR = 1.049, 95% CI: 1.006-1.094, = 0.024), large artery stroke (LAS; OR = 1.0027, 95% CI: 0.9755-1.0306, = 0.849) and ICH (OR = 0.9900, 95% CI: 0.9403-1.0423, = 0.701), as well as ICH subtypes were not observed after Bonferroni corrections ( = 0.00714). Our results revealed that high levels of circulating TIMP-3 causally increased the risk of developing IS and SVS, but not CES, LAS, ICH, and all ICH subtypes. Further investigation is required to elucidate the underlying mechanism.
Genetic predisposition to COVID-19 may increase the risk of hypertension disorders in pregnancy: A two-sample Mendelian randomization study.
Tan Jiang-Shan,Liu Ning-Ning,Guo Ting-Ting,Hu Song,Hua Lu
AIMS:The aim of this study was to apply the Mendelian randomization (MR) design to explore the potential causal association between COVID-19 and the risk of hypertension disorders in pregnancy. METHODS:Our primary genetic instrument comprised 8 single-nucleotide polymorphisms (SNPs) associated with COVID-19 at genome-wide significance. Data on the associations between the SNPs and the risk of hypertension disorders in pregnancy were obtained from study based on a very large cohort of European population. The random-effects inverse-variance weighted method was conducted for the main analyses, with a complementary analysis of the weighted median and MR-Egger approaches. RESULTS:Using IVW, we found that genetically predicted COVID-19 was significantly positively associated with hypertension disorders in pregnancy, with an odds ratio (OR) of 1.111 [95% confidence interval (CI) 1.042-1.184; P = 0.001]. Weighted median regression also showed directionally similar estimates [OR 1.098 (95% CI, 1.013-1.190), P = 0.023]. Both funnel plots and MR-Egger intercepts suggest no directional pleiotropic effects observed. CONCLUSIONS:Our findings provide direct evidence that there is a shared genetic predisposition so that patients infected with COVID-19 may be causally associated with increased risk of hypertension disorders in pregnancy.
Mendelian randomisation for mediation analysis: current methods and challenges for implementation.
European journal of epidemiology
Mediation analysis seeks to explain the pathway(s) through which an exposure affects an outcome. Traditional, non-instrumental variable methods for mediation analysis experience a number of methodological difficulties, including bias due to confounding between an exposure, mediator and outcome and measurement error. Mendelian randomisation (MR) can be used to improve causal inference for mediation analysis. We describe two approaches that can be used for estimating mediation analysis with MR: multivariable MR (MVMR) and two-step MR. We outline the approaches and provide code to demonstrate how they can be used in mediation analysis. We review issues that can affect analyses, including confounding, measurement error, weak instrument bias, interactions between exposures and mediators and analysis of multiple mediators. Description of the methods is supplemented by simulated and real data examples. Although MR relies on large sample sizes and strong assumptions, such as having strong instruments and no horizontally pleiotropic pathways, our simulations demonstrate that these methods are unaffected by confounders of the exposure or mediator and the outcome and non-differential measurement error of the exposure or mediator. Both MVMR and two-step MR can be implemented in both individual-level MR and summary data MR. MR mediation methods require different assumptions to be made, compared with non-instrumental variable mediation methods. Where these assumptions are more plausible, MR can be used to improve causal inference in mediation analysis.
Mendelian Randomization and Type 2 Diabetes.
Swerdlow Daniel I
Cardiovascular drugs and therapy
Type 2 diabetes (T2DM) is a common, complex disease that poses a substantial burden on individual and population health, but we have relatively limited understanding of its underlying pathophysiology. Observational studies have highlighted large numbers of risk factors for T2DM, some of which are modifiable through behavioural or pharmacological intervention. Determining which of these risk factors plays a causal role in the development of T2DM has been a challenge, but Mendelian randomisation (MR) studies are harnessing genetic data in population studies to offer new insights. Using evolving analytical methods, MR studies continue to address questions of causality related to T2DM, including exploring the roles of adiposity, blood lipids and inflammation. The causal roles of a number of important modifiable risk factors have been confirmed by MR studies, while the relevance of others has been called into question. As more MR studies are conducted, methods are developed and refined in order to make the most efficient and reliable use of available genetic and phenotypic data. In this review, the design and findings of some important MR studies related to T2DM are explored and their relevance for translation to clinical practice considered.
Systemic inflammatory regulators and risk of Alzheimer's disease: a bidirectional Mendelian-randomization study.
International journal of epidemiology
BACKGROUND:Systemic inflammation has been suggested to be associated with Alzheimer's-disease progression, although whether it is a cause or a downstream effect is still controversial. This study aims to assess the effect of systemic inflammatory regulators on Alzheimer's disease within a bidirectional Mendelian-randomization design. METHODS:Genetic associations with Alzheimer's disease were obtained from the largest and most up-to-date genome-wide association study (GWAS) (cases and proxy cases: 71 880; controls: 383 378) and with inflammatory regulators from two recent GWASs on the human proteome and cytokines. Estimates were obtained by inverse-variance weighting with sensitivity analyses using MR-Egger, weighted median and MR-PRESSO. Possible bias due to selective survival and competing risk was also considered. RESULTS:None of 41 systemic inflammatory regulators was associated with risk of Alzheimer's disease with consistent results in validation analysis. Conversely, Alzheimer's disease was suggestively associated with five systemic inflammatory regulators, i.e. basic fibroblast growth factor, granulocyte-colony-stimulating factor, interferon gamma, interleukin-13 and interleukin-7. CONCLUSION:The systemic inflammatory regulators considered did not appear to be associated with the risk of Alzheimer's disease. Conversely, specific systemic inflammatory regulators may be downstream effects of Alzheimer's disease or consequences of common factors causing both inflammation and Alzheimer's disease.
Effect of long-term exposure to lower low-density lipoprotein cholesterol beginning early in life on the risk of coronary heart disease: a Mendelian randomization analysis.
Ference Brian A,Yoo Wonsuk,Alesh Issa,Mahajan Nitin,Mirowska Karolina K,Mewada Abhishek,Kahn Joel,Afonso Luis,Williams Kim Allan,Flack John M
Journal of the American College of Cardiology
OBJECTIVES:The purpose of this study was to estimate the effect of long-term exposure to lower plasma low-density lipoprotein cholesterol (LDL-C) on the risk of coronary heart disease (CHD). BACKGROUND:LDL-C is causally related to the risk of CHD. However, the association between long-term exposure to lower LDL-C beginning early in life and the risk of CHD has not been reliably quantified. METHODS:We conducted a series of meta-analyses to estimate the effect of long-term exposure to lower LDL-C on the risk of CHD mediated by 9 polymorphisms in 6 different genes. We then combined these Mendelian randomization studies in a meta-analysis to obtain a more precise estimate of the effect of long-term exposure to lower LDL-C and compared it with the clinical benefit associated with the same magnitude of LDL-C reduction during treatment with a statin. RESULTS:All 9 polymorphisms were associated with a highly consistent reduction in the risk of CHD per unit lower LDL-C, with no evidence of heterogeneity of effect (I(2) = 0.0%). In a meta-analysis combining nonoverlapping data from 312,321 participants, naturally random allocation to long-term exposure to lower LDL-C was associated with a 54.5% (95% confidence interval: 48.8% to 59.5%) reduction in the risk of CHD for each mmol/l (38.7 mg/dl) lower LDL-C. This represents a 3-fold greater reduction in the risk of CHD per unit lower LDL-C than that observed during treatment with a statin started later in life (p = 8.43 × 10(-19)). CONCLUSIONS:Prolonged exposure to lower LDL-C beginning early in life is associated with a substantially greater reduction in the risk of CHD than the current practice of lowering LDL-C beginning later in life.
Environmental risk factors in multiple sclerosis: bridging Mendelian randomization and observational studies.
Journal of neurology
Multiple sclerosis (MS) is a complex disease with both genetic variants and environmental factors involved in disease susceptibility. The main environmental risk factors associated with MS in observational studies include obesity, vitamin D deficiency, Epstein-Barr virus infection and smoking. As modifying these environmental and lifestyle factors may enable prevention, it is important to pinpoint causal links between these factors and MS. Leveraging genetics through the Mendelian randomization (MR) paradigm is an elegant way to inform prevention strategies in MS. In this review, we summarize MR studies regarding the impact of environmental factors on MS susceptibility, thereby paying attention to quality criteria which will aid readers in interpreting any MR studies. We draw parallels and differences with observational studies and randomized controlled trials and look forward to the challenges that such work presents going forward.
An introduction to Mendelian randomization with applications in neurology.
Allman Phillip H,Aban Inmaculada B,Tiwari Hemant K,Cutter Gary R
Multiple sclerosis and related disorders
Mendelian randomization studies have become increasingly common due to the maturation of genome-wide association studies and its potential to ascertain causal relationships. With the increasing use of this method comes the need for medical practitioners and clinicians to develop an understanding of its rationale, limitations, and interpretation. Mendelian randomization attempts to ascertain a causal relationship between some risk factor of interest and some outcome or disease of interest. It exploits Mendel's law on the random assortment of genetic variants. This random assortment of genetic variants mimics the main principle of randomization used in clinical trials; with the genetic variant replacing the randomly allocated treatment. In this paper we provide a readable introduction to the rationale behind Mendelian randomization and its limitations. We also discuss and interpret several examples of Mendelian randomization analyses which pertain to neurological diseases.
Genetic Causal Association between Iron Status and Osteoarthritis: A Two-Sample Mendelian Randomization.
Objective: Observational studies have shown the association between iron status and osteoarthritis (OA). However, due to difficulties of determining sequential temporality, their causal association is still elusive. Based on the summary data of genome-wide association studies (GWASs) of a large-scale population, this study explored the genetic causal association between iron status and OA. Methods: First, we took a series of quality control steps to select eligible instrumental SNPs which were strongly associated with exposure. The genetic causal association between iron status and OA was analyzed using the two-sample Mendelian randomization (MR). Inverse-variance weighted (IVW), MR-Egger, weighted median, simple mode, and weighted mode methods were used for analysis. The results were mainly based on IVW (random effects), followed by sensitivity analysis. IVW and MR-Egger were used for heterogeneity testing. MR-Egger was also used for pleiotropy testing. Leave-one-SNP-out analysis was used to identify single nucleotide polymorphisms (SNPs) with potential impact. Maximum likelihood, penalized weighted median, and IVW (fixed effects) were performed to further validate the reliability of results. Results: IVW results showed that transferrin saturation had a positive causal association with knee osteoarthritis (KOA), hip osteoarthritis (HOA) and KOA or HOA (p < 0.05, OR > 1), and there was a negative causal association between transferrin and HOA and KOA or HOA (p < 0.05, OR < 1). The results of heterogeneity test showed that our IVW analysis results were basically free of heterogeneity (p > 0.05). The results of the pleiotropy test showed that there was no pleiotropy in our IVW analysis (p > 0.05). The analysis results of maximum likelihood, penalized weighted median and IVW (fixed effects) were consistent with our IVW results. No genetic causal association was found between serum iron and ferritin and OA. Conclusions: This study provides evidence of the causal association between iron status and OA, which provides novel insights to the genetic research of OA.
MendelianRandomization: an R package for performing Mendelian randomization analyses using summarized data.
Yavorska Olena O,Burgess Stephen
International journal of epidemiology
MendelianRandomization is a software package for the R open-source software environment that performs Mendelian randomization analyses using summarized data. The core functionality is to implement the inverse-variance weighted, MR-Egger and weighted median methods for multiple genetic variants. Several options are available to the user, such as the use of robust regression, fixed- or random-effects models and the penalization of weights for genetic variants with heterogeneous causal estimates. Extensions to these methods, such as allowing for variants to be correlated, can be chosen if appropriate. Graphical commands allow summarized data to be displayed in an interactive graph, or the plotting of causal estimates from multiple methods, for comparison. Although the main method of data entry is directly by the user, there is also an option for allowing summarized data to be incorporated from the PhenoScanner database of genotype-phenotype associations. We hope to develop this feature in future versions of the package. The R software environment is available for download from [https://www.r-project.org/]. The MendelianRandomization package can be downloaded from the Comprehensive R Archive Network (CRAN) within R, or directly from [https://cran.r-project.org/web/packages/MendelianRandomization/]. Both R and the MendelianRandomization package are released under GNU General Public Licenses (GPL-2|GPL-3).
'Mendelian randomization': can genetic epidemiology contribute to understanding environmental determinants of disease?
Smith George Davey,Ebrahim Shah
International journal of epidemiology
Associations between modifiable exposures and disease seen in observational epidemiology are sometimes confounded and thus misleading, despite our best efforts to improve the design and analysis of studies. Mendelian randomization-the random assortment of genes from parents to offspring that occurs during gamete formation and conception-provides one method for assessing the causal nature of some environmental exposures. The association between a disease and a polymorphism that mimics the biological link between a proposed exposure and disease is not generally susceptible to the reverse causation or confounding that may distort interpretations of conventional observational studies. Several examples where the phenotypic effects of polymorphisms are well documented provide encouraging evidence of the explanatory power of Mendelian randomization and are described. The limitations of the approach include confounding by polymorphisms in linkage disequilibrium with the polymorphism under study, that polymorphisms may have several phenotypic effects associated with disease, the lack of suitable polymorphisms for studying modifiable exposures of interest, and canalization-the buffering of the effects of genetic variation during development. Nevertheless, Mendelian randomization provides new opportunities to test causality and demonstrates how investment in the human genome project may contribute to understanding and preventing the adverse effects on human health of modifiable exposures.
Multivariable Mendelian randomization: the use of pleiotropic genetic variants to estimate causal effects.
Burgess Stephen,Thompson Simon G
American journal of epidemiology
A conventional Mendelian randomization analysis assesses the causal effect of a risk factor on an outcome by using genetic variants that are solely associated with the risk factor of interest as instrumental variables. However, in some cases, such as the case of triglyceride level as a risk factor for cardiovascular disease, it may be difficult to find 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 a factorial randomized trial. In this paper, methods for estimating the causal effects are presented and compared using real and simulated data, and the assumptions necessary for a 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.
Association between COVID-19 and telomere length: A bidirectional Mendelian randomization study.
Journal of medical virology
Several traditional observational studies suggested an association between COVID-19 and leukocyte telomere length (LTL), a biomarker for biological age. However, whether there was a causal association between them remained unclear. We aimed to investigate whether genetically predicted COVID-19 is related to the risk of LTL, and vice versa. We performed bidirectional Mendelian randomization (MR) study using summary statistics from the genome-wide association studies of critically ill COVID-19 (n = 1 388 342) and LTL (n = 472 174) of European ancestry. The random-effects inverse-variance weighted estimation method was applied as the primary method with several other estimators as complementary methods. Using six single-nucleotide polymorphisms (SNPs) of genome-wide significance as instrumental variables for critically ill COVID-19, we did not find a significant association of COVID-19 on LTL (β = 0.0075, 95% confidence interval [CI]: -0.018 to 0.021, p = 0.733). Likewise, using 97 SNPs of genome-wide significance as instrumental variables for LTL, we did not find a significant association of LTL on COVID-19 (odds ratio = 1.00, 95% CI: 0.79-1.28, p = 0.973). Comparable results were obtained using MR-Egger regression, weighted median, and weighted mode approaches. We did not find evidence to support a causal association between COVID-19 and LTL in either direction.
Mendelian randomization studies on atherosclerotic cardiovascular disease: evidence and limitations.
Hu Qin,Hao Panpan,Liu Qiji,Dong Mei,Gong Yaoqin,Zhang Cheng,Zhang Yun
Science China. Life sciences
Epidemiological research has revealed a galaxy of biomarkers, such as genes, molecules or traits, which are associated with increased risk of atherosclerotic cardiovascular diseases (ASCVD). However, the etiological basis remains poorly characterized. Mendelian randomization (MR) involves the use of observational genetic data to ascertain the roles of disease-associated risk factors and, in particular, differentiate those reflecting the presence or severity of a disease from those contributing causally to a disease. Over the past decade, MR has evolved into a fruitful approach to clarifying the causal relation of a biomarker with ASCVD and to verifying potential therapeutic targets for ASCVD. In this review, we selected high-quality MR studies on ASCVD, examined the causal relationship of a series of biomarkers with ASCVD, and elucidated the role of MR in validating biomarkers as a therapeutic target by comparing the results from MR studies and randomized clinical trials (RCTs) for the treatment of ASCVD. The good agreement between the results derived by MR and RCTs suggests that MR could be performed as a screening process before novel drug development. However, when designing and interpreting a MR study, the assumptions and limitations inherent in this approach should be taken into account. Novel methodological developments, such as sensitivity analysis, will help to strengthen the validity of MR studies.
Mendelian Randomization Studies in Stroke: Exploration of Risk Factors and Drug Targets With Human Genetic Data.
Georgakis Marios K,Gill Dipender
Elucidating the causes of stroke is key to developing effective preventive strategies. The Mendelian randomization approach leverages genetic variants related to an exposure of interest to investigate the effects of varying that exposure on disease risk. The random allocation of genetic variants at conception reduces confounding from environmental factors and thus strengthens causal inference, analogous to treatment allocation in a randomized controlled trial. With the recent explosion in the availability of human genetic data, Mendelian randomization has proven a valuable tool for studying risk factors for stroke. In this review, we provide an overview of recent developments in the application of Mendelian randomization to unravel the pathophysiology of stroke subtypes and identify therapeutic targets for clinical translation. The approach has offered novel insight into the differential effects of risk factors and antihypertensive, lipid-lowering, and anticoagulant drug classes on risk of stroke subtypes. Analyses have further facilitated the prioritization of novel drug targets, such as for inflammatory pathways underlying large artery atherosclerotic stroke and for the coagulation cascade that contributes to cardioembolic stroke. With continued methodological advances coupled with the rapidly increasing availability of genetic data related to a broad range of stroke phenotypes, the potential for Mendelian randomization in this context is expanding exponentially.
Mendelian randomization in pharmacogenomics: The unforeseen potentials.
Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie
Mendelian randomization (MR) is an epidemiological method that uses genetic variants to proxy an exposure predicting its causal association with an outcome. It occupies a valuable niche between observational studies and randomized trials. MR applications expanded lately, facilitated by the availability of big data, to include disease risk causation prediction, supporting evidence of prior observational data, identifying new drug targets, and drug repurposing. Concurrently, the last decade witnessed the growth of pharmacogenomics (PGx) research as a cornerstone in precision medicine. PGx research, conducted at discovery and implementation levels, resulted in validated PGx biomarkers and tests. Despite many clinically relevant PGx associations that could be translated into clinical applications, worldwide implementation is lagging far behind. The current review examines the intersection zones between MR and PGx research. MR can provide supporting evidence that allows generalizing PGx findings supporting its implementation. Interchangeability, PGx research can fuel MR studies with libraries of genetic variants of validated biological relevance. Furthermore, PGx and MR exhibit a synergistic relationship in drug discovery that can accelerate identifying new targets and repurposing old drugs. Interdisciplinary research applied by PGx researchers, epidemiologists with MR experience, and data scientists' collaborations can unlock unforeseen opportunities in accelerating precision medicine acquisition.
Cannabis use and risk of schizophrenia: a Mendelian randomization study.
Cannabis use is observationally associated with an increased risk of schizophrenia, but whether the relationship is causal is not known. Using a genetic approach, we took 10 independent genetic variants previously identified to associate with cannabis use in 32 330 individuals to determine the nature of the association between cannabis use and risk of schizophrenia. Genetic variants were employed as instruments to recapitulate a randomized controlled trial involving two groups (cannabis users vs nonusers) to estimate the causal effect of cannabis use on risk of schizophrenia in 34 241 cases and 45 604 controls from predominantly European descent. Genetically-derived estimates were compared with a meta-analysis of observational studies reporting ever use of cannabis and risk of schizophrenia or related disorders. Based on the genetic approach, use of cannabis was associated with increased risk of schizophrenia (odds ratio (OR) of schizophrenia for users vs nonusers of cannabis: 1.37; 95% confidence interval (CI), 1.09-1.67; P-value=0.007). The corresponding estimate from observational analysis was 1.43 (95% CI, 1.19-1.67; P-value for heterogeneity =0.76). The genetic markers did not show evidence of pleiotropic effects and accounting for tobacco exposure did not alter the association (OR of schizophrenia for users vs nonusers of cannabis, adjusted for ever vs never smoker: 1.41; 95% CI, 1.09-1.83). This adds to the substantial evidence base that has previously identified cannabis use to associate with increased risk of schizophrenia, by suggesting that the relationship is causal. Such robust evidence may inform public health messages about cannabis use, especially regarding its potential mental health consequences.
Using Mendelian Randomization to Improve the Design of Randomized Trials.
Cold Spring Harbor perspectives in medicine
Randomized controlled trials and Mendelian randomization studies are two study designs that provide randomized evidence in human biological and medical research. Both exploit the power of randomization to provide unconfounded estimates of causal effect. However, randomized trials and Mendelian randomization studies have very different study designs and scientific objectives. As a result, despite sometimes being referred to as "nature's randomized trial," a Mendelian randomization study cannot be used to replace a randomized trial but instead provides complementary information. In this review, we explain the similarities and differences between randomized trials and Mendelian randomization studies, and suggest several ways that Mendelian randomization can be used to directly inform and improve the design of randomized trials illustrated with practical examples. We conclude by describing how Mendelian randomization studies can employ the principles of trial design to be framed as "naturally randomized trials" that can provide a template for the design of future randomized trials evaluating therapies directed against genetically validated targets.
Mendelian randomization: genetic anchors for causal inference in epidemiological studies.
Davey Smith George,Hemani Gibran
Human molecular genetics
Observational epidemiological studies are prone to confounding, reverse causation and various biases and have generated findings that have proved to be unreliable indicators of the causal effects of modifiable exposures on disease outcomes. Mendelian randomization (MR) is a method that utilizes genetic variants that are robustly associated with such modifiable exposures to generate more reliable evidence regarding which interventions should produce health benefits. The approach is being widely applied, and various ways to strengthen inference given the known potential limitations of MR are now available. Developments of MR, including two-sample MR, bidirectional MR, network MR, two-step MR, factorial MR and multiphenotype MR, are outlined in this review. The integration of genetic information into population-based epidemiological studies presents translational opportunities, which capitalize on the investment in genomic discovery research.
Inflammatory Biomarkers and Risk of Schizophrenia: A 2-Sample Mendelian Randomization Study.
Hartwig Fernando Pires,Borges Maria Carolina,Horta Bernardo Lessa,Bowden Jack,Davey Smith George
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.