Polynomial Mendelian randomization reveals non-linear causal effects for obesity-related traits

Causal inference in health and disease: a review of the principles and applications of Mendelian randomization

Mendelian randomization (MR) is a genetic epidemiological technique that uses genetic variation to infer causal relationships between modifiable exposures and outcome variables. Conventional observational epidemiological studies are subject to bias from a range of sources; MR analyses can offer an advantage in that they are less prone to bias as they use genetic variants inherited at conception as "instrumental variables", which are proxies of an exposure. However, as with all research tools, MR studies must be carefully designed to yield valuable insights into causal relationships between exposures and outcomes, and to avoid biased or misleading results that undermine the validity of the causal inferences drawn from the study. In this review, we outline Mendel's laws of inheritance, the assumptions and principles that underlie MR, MR study designs and methods, and how MR analyses can be applied and reported. Using the example of serum phosphate concentrations on liability to kidney stone disease we illustrate how MR estimates may be visualized and, finally, we contextualize MR in bone and mineral research including exemplifying how this technique could be employed to inform clinical studies and future guidelines concerning BMD and fracture risk. This review provides a framework to enhance understanding of how MR may be used to triangulate evidence and progress research in bone and mineral metabolism as we strive to infer causal effects in health and disease.

Body mass index and all-cause mortality in HUNT and UK biobank studies: revised non-linear Mendelian randomisation analyses

OBJECTIVES

To estimate the shape of the causal relationship between body mass index (BMI) and mortality risk in a Mendelian randomisation framework.

DESIGN

Mendelian randomisation analyses of two prospective population-based cohorts.

SETTING

Individuals of European ancestries living in Norway or the UK.

PARTICIPANTS

56 150 participants from the Trøndelag Health Study (HUNT) in Norway and 366 385 participants from UK Biobank recruited by postal invitation.

OUTCOMES

All-cause mortality and cause-specific mortality (cardiovascular, cancer, non-cardiovascular non-cancer).

RESULTS

A previously published non-linear Mendelian randomisation analysis of these data using the residual stratification method suggested a J-shaped association between genetically predicted BMI and mortality outcomes with the lowest mortality risk at a BMI of around 25 kg/m2. However, the 'constant genetic effect' assumption required by this method is violated. The reanalysis of these data using the more reliable doubly-ranked stratification method provided some indication of a J-shaped relationship, but with much less certainty as there was less precision in estimates at the lower end of the BMI distribution. Evidence for a harmful effect of reducing BMI at low BMI levels was only present in some analyses, and where present, only below 20 kg/m2. A harmful effect of increasing BMI for all-cause mortality was evident above 25 kg/m2, for cardiovascular mortality above 24 kg/m2, for cancer mortality above 30 kg/m2 and for non-cardiovascular non-cancer mortality above 26 kg/m2. In UK Biobank, the association between genetically predicted BMI and mortality at high BMI levels was stronger in women than in men.

CONCLUSION

This research challenges findings from previous conventional observational epidemiology and Mendelian randomisation investigations that the lowest level of mortality risk is at a BMI level of around 25 kg/m2. Our results provide some evidence that reductions in BMI will increase mortality risk for a small proportion of the population, and clear evidence that increases in BMI will increase mortality risk for those with BMI above 25 kg/m2.

Non-linear Mendelian randomization: detection of biases using negative controls with a focus on BMI, Vitamin D and LDL cholesterol

Mendelian randomisation (MR) is an established technique in epidemiological investigation, using the principle of random allocation of genetic variants at conception to estimate the causal linear effect of an exposure on an outcome. Extensions to this technique include non-linear approaches that allow for differential effects of the exposure on the outcome depending on the level of the exposure. A widely used non-linear method is the residual approach, which estimates the causal effect within different strata of the non-genetically predicted exposure (i.e. the "residual" exposure). These "local" causal estimates are then used to make inferences about non-linear effects. Recent work has identified that this method can lead to estimates that are seriously biased, and a new method-the doubly-ranked method-has been introduced as a possibly more robust approach. In this paper, we perform negative control outcome analyses in the MR context. These are analyses with outcomes onto which the exposure should have no predicted causal effect. Using both methods we find clearly biased estimates in certain situations. We additionally examined a situation for which there are robust randomised controlled trial estimates of effects-that of low-density lipoprotein cholesterol (LDL-C) reduction onto myocardial infarction, where randomised trials have provided strong evidence of the shape of the relationship. The doubly-ranked method did not identify the same shape as the trial data, and for LDL-C and other lipids they generated some highly implausible findings. Therefore, we suggest there should be extensive simulation and empirical methodological examination of performance of both methods for NLMR under different conditions before further use of these methods. In the interim, use of NLMR methods needs justification, and a number of sanity checks (such as analysis of negative and positive control outcomes, sensitivity analyses excluding removal of strata at the extremes of the distribution, examination of biological plausibility and triangulation of results) should be performed.

DeLIVR: a deep learning approach to IV regression for testing nonlinear causal effects in transcriptome-wide association studies

Transcriptome-wide association studies (TWAS) have been increasingly applied to identify (putative) causal genes for complex traits and diseases. TWAS can be regarded as a two-sample two-stage least squares method for instrumental variable (IV) regression for causal inference. The standard TWAS (called TWAS-L) only considers a linear relationship between a gene's expression and a trait in stage 2, which may lose statistical power when not true. Recently, an extension of TWAS (called TWAS-LQ) considers both the linear and quadratic effects of a gene on a trait, which however is not flexible enough due to its parametric nature and may be low powered for nonquadratic nonlinear effects. On the other hand, a deep learning (DL) approach, called DeepIV, has been proposed to nonparametrically model a nonlinear effect in IV regression. However, it is both slow and unstable due to the ill-posed inverse problem of solving an integral equation with Monte Carlo approximations. Furthermore, in the original DeepIV approach, statistical inference, that is, hypothesis testing, was not studied. Here, we propose a novel DL approach, called DeLIVR, to overcome the major drawbacks of DeepIV, by estimating a related but different target function and including a hypothesis testing framework. We show through simulations that DeLIVR was both faster and more stable than DeepIV. We applied both parametric and DL approaches to the GTEx and UK Biobank data, showcasing that DeLIVR detected additional 8 and 7 genes nonlinearly associated with high-density lipoprotein (HDL) cholesterol and low-density lipoprotein (LDL) cholesterol, respectively, all of which would be missed by TWAS-L, TWAS-LQ, and DeepIV; these genes include BUD13 associated with HDL, SLC44A2 and GMIP with LDL, all supported by previous studies.

Unravelling the complex causal effects of substance use behaviours on common diseases

BACKGROUND

Substance use behaviours (SUB) including smoking, alcohol consumption, and coffee intake are associated with many health outcomes. However, whether the health effects of SUB are causal remains controversial, especially for alcohol consumption and coffee intake.

METHODS

In this study, we assess 11 commonly used Mendelian Randomization (MR) methods by simulation and apply them to investigate the causal relationship between 7 SUB traits and health outcomes. We also combine stratified regression, genetic correlation, and MR analyses to investigate the dosage-dependent effects.

RESULTS

We show that smoking initiation has widespread risk effects on common diseases such as asthma, type 2 diabetes, and peripheral vascular disease. Alcohol consumption shows risk effects specifically on cardiovascular diseases, dyslipidemia, and hypertensive diseases. We find evidence of dosage-dependent effects of coffee and tea intake on common diseases (e.g., cardiovascular disease and osteoarthritis). We observe that the minor allele effect of rs4410790 (the top signal for tea intake level) is negative on heavy tea intake ( b ̂ G W A S = - 0.091 , s . e . = 0.007 , P = 4.90 × 10 - 35 ) but positive on moderate tea intake ( b ̂ G W A S = 0.034 , s . e . = 0.006 , P = 3.40 × 10 - 8 ) , compared to the non-tea-drinkers.

CONCLUSION

Our study reveals the complexity of the health effects of SUB and informs design for future studies aiming to dissect the causal relationships between behavioural traits and complex diseases.

Novel insights into causal effects of serum lipids and lipid-modifying targets on cholelithiasis

OBJECTIVE

Different serum lipids and lipid-modifying targets should affect the risk of cholelithiasis differently, however, whether such effects are causal is still controversial and we aimed to answer this question.

DESIGN

We prospectively estimated the associations of four serum lipids with cholelithiasis in UK Biobank using the Cox proportional hazard model, including total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C) and triglycerides (TG). Furthermore, we estimated the causal associations of the genetically predicted serum lipids with cholelithiasis in Europeans using the Mendelian randomisation (MR) design. Finally, both drug-target MR and colocalisation analyses were performed to estimate the lipid-modifying targets' effects on cholelithiasis, including HMGCR, NPC1L1, PCSK9, APOB, LDLR, ACLY, ANGPTL3, MTTP, PPARA, PPARD and PPARG.

RESULTS

We found that serum levels of LDL-C and HDL-C were inversely associated with cholelithiasis risk and such associations were linear. However, the serum level of TC was non-linearly associated with cholelithiasis risk where lower TC was associated with higher risk of cholelithiasis, and the serum TG should be in an inverted 'U-shaped' relationship with it. The MR analyses supported that lower TC and higher TG levels were two independent causal risk factors. The drug-target MR analysis suggested that HMGCR inhibition should reduce the risk of cholelithiasis, which was corroborated by colocalisation analysis.

CONCLUSION

Lower serum TC can causally increase the risk of cholelithiasis. The cholelithiasis risk would increase with the elevation of serum TG but would decrease when exceeding 2.57 mmol/L. The use of HMGCR inhibitors should prevent its risk.

Causal relationship between psychological factors and hepatocellular carcinoma as revealed by Mendelian randomization

PURPOSE

The impact of psychological factors on the incidence of hepatocellular carcinoma (HCC) in humans remains unclear. Mendelian randomization (MR) study is a novel approach aimed at unbiased detection of causal effects. Therefore, we conducted a two-sample MR to determine if there is a causal relationship between psychological distress (PD), participation in leisure/social activities of religious groups (LARG), and HCC.

METHODS

The genetic summary data of exposures and outcome were retrieved from genome-wide association studies (GWAS). We used PD and LARG as exposures and HCC as outcome. Five MR methods were used to investigate the causal relationship between PD, LARG, and HCC. The result of inverse variance weighted (IVW) method was deemed as principal result. Besides, we performed a comprehensive sensitivity analysis to verify the robustness of the results.

RESULTS

The IVW results showed that PD [odds ratio (OR) 1.006, 95% confidence interval (CI) 1.000-1.011, P = 0.033] and LARG (OR 0.994, 95% CI 0.988-1.000, P = 0.035) were causally associated with the incidence of HCC. Sensitivity analysis did not identify any bias in the results.

CONCLUSION

PD turned out to be a mild risk factor for HCC. In contrast, LARG is a protective factor for HCC. Therefore, it is highly recommended that people with PD are seeking positive leisure activities such as participation in formal religious social activities, which may help them reduce the risk of HCC.

Mendelian Randomization as a Tool for Cardiovascular Research: A Review

Importance

Mendelian randomization (MR) is a statistical approach that has become increasingly popular in the field of cardiovascular disease research. It offers a way to infer potentially causal relationships between risk factors and outcomes using observational data, which is particularly important in cases where randomized clinical trials are not feasible or ethical. With the growing availability of large genetic data sets, MR has become a powerful and accessible tool for studying the risk factors for cardiovascular disease.

Observations

MR uses genetic variation associated with modifiable exposures or risk factors to mitigate biases that affect traditional observational study designs. The approach uses genetic variants that are randomly assigned at conception as proxies for exposure to a risk factor, mimicking a randomized clinical trial. By comparing the outcomes of individuals with different genetic variants, researchers may draw causal inferences about the effects of specific risk factors on cardiovascular disease, provided assumptions are met that address (1) the association between each genetic variant and risk factor and (2) the association of the genetic variants with confounders and (3) that the association between each genetic variant and the outcome only occurs through the risk factor. Like other observational designs, MR has limitations, which include weak instruments that are not strongly associated with the exposure of interest, linkage disequilibrium where genetic instruments influence the outcome via correlated rather than direct effects, overestimated genetic associations, and selection and survival biases. In addition, many genetic databases and MR studies primarily include populations genetically similar to European reference populations; improved diversity of participants in these databases and studies is critically needed.

Conclusions and Relevance

This review provides an overview of MR methodology, including assumptions, strengths, and limitations. Several important applications of MR in cardiovascular disease research are highlighted, including the identification of drug targets, evaluation of potential cardiovascular risk factors, as well as emerging methodology. Overall, while MR alone can never prove a causal relationship beyond reasonable doubt, MR offers a rigorous approach for investigating possible causal relationships in observational data and has the potential to transform our understanding of the etiology and treatment of cardiovascular disease.

Poor sleep and shift work associate with increased blood pressure and inflammation in UK Biobank participants

Disrupted circadian rhythms have been linked to an increased risk of hypertension and cardiovascular disease. However, many studies show inconsistent findings and are not sufficiently powered for targeted subgroup analyses. Using the UK Biobank cohort, we evaluate the association between circadian rhythm-disrupting behaviours, blood pressure (SBP, DBP) and inflammatory markers in >350,000 adults with European white British ancestry. The independent U-shaped relationship between sleep length and SBP/DBP is most prominent with a low inflammatory status. Poor sleep quality and permanent night shift work are also positively associated with SBP/DBP. Although fully adjusting for BMI in the linear regression model attenuated effect sizes, these associations remain significant. Two-sample Mendelian Randomisation (MR) analyses support a potential causal effect of long sleep, short sleep, chronotype, daytime napping and sleep duration on SBP/DBP. Thus, in the current study, we present a positive association between circadian rhythm-disrupting behaviours and SBP/DBP regulation in males and females that is largely independent of age.

Guidelines for performing Mendelian randomization investigations: update for summer 2023

This paper provides guidelines for performing Mendelian randomization investigations. It is aimed at practitioners seeking to undertake analyses and write up their findings, and at journal editors and reviewers seeking to assess Mendelian randomization manuscripts. The guidelines are divided into ten sections: motivation and scope, data sources, choice of genetic variants, variant harmonization, primary analysis, supplementary and sensitivity analyses (one section on robust statistical methods and one on other approaches), extensions and additional analyses, data presentation, and interpretation. These guidelines will be updated based on feedback from the community and advances in the field. Updates will be made periodically as needed, and at least every 24 months.