A new Mendelian Randomization method to estimate causal effects of multivariable brain imaging exposures.
Ontology highlight
ABSTRACT:
SUBMITTER: Mo C
PROVIDER: S-EPMC8669774 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description: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.
| S-EPMC4325677 | biostudies-literature
Project description:Mendelian randomization (MR) is an instrumental variable approach used to infer causal relationships between exposures and outcomes and can apply to summary data from genome-wide association studies (GWAS). Since GWAS summary statistics are subject to estimation errors, most existing MR approaches suffer from measurement error bias, whose scale and direction are influenced by weak instrumental variables and GWAS sample overlap, respectively. We introduce MRBEE (MR using Bias-corrected Estimating Equation), a novel multivariable MR method capable of simultaneously removing measurement error bias and identifying horizontal pleiotropy. In simulations, we showed that MRBEE is capable of effectively removing measurement error bias in the presence of weak instrumental variables and sample overlap. In two independent real data analyses, we discovered that the causal effect of BMI on coronary artery disease risk is entirely mediated by blood pressure, and that existing MR methods may underestimate the causal effect of cannabis use disorder on schizophrenia risk compared to MRBEE. MRBEE possesses significant potential for advancing genetic research by providing a valuable tool to study causality between multiple risk factors and disease outcomes, particularly as a large number of GWAS summary statistics become publicly available.
| S-EPMC10103949 | biostudies-literature
Project description:BackgroundGlaucoma is hypothesized to originate in the brain but manifests as an eye disease as it possesses the common features of neurodegeneration diseases. But there is no evidence to demonstrate the primary brain changes in glaucoma patients. In the present study, we have used Mendelian randomization (MR) to understand the causal effect of brain alterations on glaucoma.MethodsOur MR study was carried out using summary statistics from genome-wide associations for 110 diffusion tensor imaging (DTI) measurements of white matter (WM) tracts (17,706 individuals), 101 brain region-of-interest (ROI) volumes (19,629 individuals), and glaucoma (8,591 cases, 210,201 control subjects). The causal relationship was evaluated by multiplicative random effects inverse variance weighted (IVW) method and verified by two other MR methods, including MR Egger, weighted median, and extensive sensitivity analyses.ResultsGenetic liability to fornix fractional anisotropy (FX.FA) (OR = 0.71, 95%CI = 0.56-0.88, P = 2.44 × 10-3), and uncinate fasciculus UNC.FA (OR = 0.65, 95%CI = 0.48-0.88, P = 5.57 × 10-3) was associated with a low risk of glaucoma. Besides, the right ventral diencephalon (OR = 1.72, 95%CI = 1.17-2.52, P = 5.64 × 10-3) and brain stem (OR = 1.35, 95%CI = 1.08-1.69, P = 8.94 × 10-3) were associated with the increased risk of glaucoma. No heterogeneity and pleiotropy were detected.ConclusionOur study suggests that the fornix and uncinate fasciculus degenerations and injures of the right ventral diencephalon and brain stem potentially increase the occurrence of glaucoma and reveal the existence of the brain-eye axis.
| S-EPMC9283577 | biostudies-literature
Project description:By adopting the extension approaches of Mendelian randomization, we successfully detected and prioritized the potential causal risk factors for BMD traits, which might provide us novel insights for treatment and intervention into bone-related complex traits and diseases.IntroductionOsteoporosis (OP) is a common metabolic skeletal disease characterized by reduced bone mineral density (BMD). The identified SNPs for BMD can only explain approximately 10% of the variability, and very few causal factors have been identified so far.MethodsThe Mendelian randomization (MR) approach enables us to assess the potential causal effect of a risk factor on the outcome by using genetic IVs. By using extension methods of MR-multivariable MR (mvMR) and MR based on Bayesian model averaging (MR-BMA)-we intend to estimate the causal relationship between fifteen metabolic risk factors for BMD and try to prioritize the most potential causal risk factors for BMD.ResultsOur analysis identified three risk factors T2D, FG, and HCadjBMI for FN BMD; four risk factors FI, T2D, HCadjBMI, and WCadjBMI for FA BMD; and three risk factors FI, T2D, and HDL cholesterol for LS BMD, and all risk factors were causally associated with heel BMD except for triglycerides and WCadjBMI. Consistent with the mvMR results, MR-BMA confirmed those risk factors as top risk factors for each BMD trait individually.ConclusionsBy combining MR approaches, we identified the potential causal risk factors for FN, FA, LS, and heel BMD individually and we also prioritized and ranked the potential causal risk factors for BMD, which might provide us novel insights for treatment and intervention into bone-related complex traits and diseases.
| S-EPMC7987914 | biostudies-literature
Project description:BackgroundPrevious Mendelian randomization studies have suggested that, while low-density lipoprotein cholesterol (LDL-c) and triglycerides are causally implicated in coronary artery disease (CAD) risk, high-density lipoprotein cholesterol (HDL-c) may not be, with causal effect estimates compatible with the null.Principal findingsThe causal effects of these three lipid fractions can be better identified using the extended methods of 'multivariable Mendelian randomization'. We employ this approach using published data on 185 lipid-related genetic variants and their associations with lipid fractions in 188,578 participants, and with CAD risk in 22,233 cases and 64,762 controls. Our results suggest that HDL-c may be causally protective of CAD risk, independently of the effects of LDL-c and triglycerides. Estimated causal odds ratios per standard deviation increase, based on 162 variants not having pleiotropic associations with either blood pressure or body mass index, are 1.57 (95% credible interval 1.45 to 1.70) for LDL-c, 0.91 (0.83 to 0.99, p-value = 0.028) for HDL-c, and 1.29 (1.16 to 1.43) for triglycerides.SignificanceSome interventions on HDL-c concentrations may influence risk of CAD, but to a lesser extent than interventions on LDL-c. A causal interpretation of these estimates relies on the assumption that the genetic variants do not have pleiotropic associations with risk factors on other pathways to CAD. If they do, a weaker conclusion is that genetic predictors of LDL-c, HDL-c and triglycerides each have independent associations with CAD risk.
| S-EPMC4193746 | biostudies-literature
Project description:ObjectivesObservational analyses suggest that high bone mineral density (BMD) is a risk factor for osteoarthritis (OA); it is unclear whether this represents a causal effect or shared aetiology and whether these relationships are body mass index (BMI)-independent. We performed bidirectional Mendelian randomization (MR) to uncover the causal pathways between BMD, BMI and OA.MethodsOne-sample (1S)MR estimates were generated by two-stage least-squares regression. Unweighted allele scores instrumented each exposure. Two-sample (2S)MR estimates were generated using inverse-variance weighted random-effects meta-analysis. Multivariable MR (MVMR), including BMD and BMI instruments in the same model, determined the BMI-independent causal pathway from BMD to OA. Latent causal variable (LCV) analysis, using weight-adjusted femoral neck (FN)-BMD and hip/knee OA summary statistics, determined whether genetic correlation explained the causal effect of BMD on OA.Results1SMR provided strong evidence for a causal effect of BMD estimated from heel ultrasound (eBMD) on hip and knee OA {odds ratio [OR]hip = 1.28 [95% confidence interval (CI) = 1.05, 1.57], p = 0.02, ORknee = 1.40 [95% CI = 1.20, 1.63], p = 3 × 10-5, OR per standard deviation [SD] increase}. 2SMR effect sizes were consistent in direction. Results suggested that the causal pathways between eBMD and OA were bidirectional (βhip = 1.10 [95% CI = 0.36, 1.84], p = 0.003, βknee = 4.16 [95% CI = 2.74, 5.57], p = 8 × 10-9, β = SD increase per doubling in risk). MVMR identified a BMI-independent causal pathway between eBMD and hip/knee OA. LCV suggested that genetic correlation (i.e. shared genetic aetiology) did not fully explain the causal effects of BMD on hip/knee OA.ConclusionsThese results provide evidence for a BMI-independent causal effect of eBMD on OA. Despite evidence of bidirectional effects, the effect of BMD on OA did not appear to be fully explained by shared genetic aetiology, suggesting a direct action of bone on joint deterioration.
| S-EPMC9365636 | biostudies-literature
Project description:Modern high-throughput experiments provide a rich resource to investigate causal determinants of disease risk. Mendelian randomization (MR) is the use of genetic variants as instrumental variables to infer the causal effect of a specific risk factor on an outcome. Multivariable MR is an extension of the standard MR framework to consider multiple potential risk factors in a single model. However, current implementations of multivariable MR use standard linear regression and hence perform poorly with many risk factors. Here, we propose a two-sample multivariable MR approach based on Bayesian model averaging (MR-BMA) that scales to high-throughput experiments. In a realistic simulation study, we show that MR-BMA can detect true causal risk factors even when the candidate risk factors are highly correlated. We illustrate MR-BMA by analysing publicly-available summarized data on metabolites to prioritise likely causal biomarkers for age-related macular degeneration.
| S-EPMC6946691 | biostudies-literature
Project description:IntroductionFor many exposures present across the life course, the effect of the exposure may vary over time. Multivariable Mendelian randomization (MVMR) is an approach that can assess the effects of related risk factors using genetic variants as instrumental variables. Recently, MVMR has been used to estimate the effects of an exposure during distinct time periods.MethodsWe investigated the behaviour of estimates from MVMR in a simulation study for different time-varying causal scenarios. We also performed an applied analysis to consider how MVMR estimates of body mass index on systolic blood pressure vary depending on the time periods considered.ResultsEstimates from MVMR in the simulation study were close to the true values when the outcome model was correctly specified: i.e. when the outcome was a discrete function of the exposure at the precise time points at which the exposure was measured. However, in more realistic cases, MVMR estimates were misleading. For example, in one scenario, MVMR estimates for early life were clearly negative despite the true causal effect being constant and positive. In the applied example, estimates were highly variable depending on the time period in which genetic associations with the exposure were estimated.ConclusionsThe poor performance of MVMR to study time-varying causal effects can be attributed to model misspecification and violation of the exclusion restriction assumption. We would urge caution about quantitative conclusions from such analyses and even qualitative interpretations about the direction, or presence or absence, of a causal effect during a given time period.
| S-EPMC10244034 | biostudies-literature
Project description:IntroductionThe correlation between dyslipidemia and periodontitis is revealed through epidemiological studies. However, the results are affected by several confounding factors. This study aims to elucidate the genetic causal association between circulating lipid traits and periodontitis by two-sample Mendelian randomization (MR) analysis.MethodsAfter the different screening processes, two cohorts of circulating lipid traits from the UK Biobank were used as exposure data, including five circulating lipid traits. The Periodontitis cohort was selected from the GeneLifestyle Interactions in Dental Endpoints (GLIDE) consortium as outcome data. In univariable MR, the inverse variance weighted (IVW) was used in conjunction with six additional analytical methods to assess causality. The Cochran Q test, IGX 2 statistic, MR-PRESSO, and MR-Egger intercept were used to quantify heterogeneity and pleiotropy. The multivariable MR-IVW (MVMR-IVW) and MVMR-robust were mainly used as analytical methods in the multiple MR analyses.ResultsThe IVW estimates showed that genetically predicted Apolipoprotein A1 (apo A1) [odds ratio (OR)=1.158, 95% confidence interval (CI)=1.007-1.331, P-value=0.040] was potentially associated with the risk of periodontitis, but the statistical power of the results was low. Multivariable MR analysis did not reveal any significant causal relationship between apo A1 and periodontitis (OR=0.72, 95% CI=0.36-1.41, P-value=0.34). In the validation cohort, there was also no significant causal relationship between apo A1 and periodontitis (OR=1.079, 95% CI=0.903-1.290, P-value=0.401). Meanwhile, genetically predicted Apolipoprotein B (apo B), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and triglyceride (TG) (all P-values>0.05) were not significantly associated with the risk of periodontitis causal inference.ConclusionThis MR analysis was unable to provide genetic evidence for the influence of these five circulating lipid traits on periodontitis. However, a more extensive study with a more comprehensive circulating lipid profile and periodontitis data is needed due to study limitations.
| S-EPMC10424932 | biostudies-literature
Project description:Background and aimsStudying the consequences of addictive behaviours is challenging, with understanding causal relationships from observational data being particularly difficult. For example, people who smoke or drink excessively are often systematically different from those who do not, are less likely to participate in research and may misreport their behaviours when they do. Furthermore, the direction of causation between an addictive behaviour and outcome may be unclear. Mendelian randomization (MR) offers potential solutions to these problems.MethodsWe describe MR's principles and the criteria under which it is valid. We identify challenges and potential solutions in its application (illustrated using two applied examples) and describe methodological extensions in its application.ResultsMR is subject to certain assumptions, and requires the availability of appropriate genetic data, large sample sizes and careful design and conduct. However, it has already been applied successfully to the addiction literature. The relationship between alcohol consumption (proxied by a variant in the ADH1B gene) and cardiovascular risk has been investigated, finding that alcohol consumption increases risk, with no evidence of a cardioprotective effect at moderate consumption levels. In addition, heaviness of smoking (proxied by a variant in the CHRNA5-A3-B4 gene cluster) and risk of depression and schizophrenia have been investigated, with no evidence of a causal effect of smoking on depression but some evidence of a causal effect on schizophrenia.ConclusionsMendelian randomization analyses are already producing robust evidence for addiction-related practice and policy. As genetic variants associated with addictive behaviours are identified, the potential for Mendelian randomization analyses will grow. Methodological developments are also increasing its applicability.
| S-EPMC5873430 | biostudies-literature