An integrative multi-context Mendelian randomization method for identifying risk genes across human tissues.

Mendelian randomization (MR) provides valuable assessments of the causal effect of exposure on outcome, yet the application of conventional MR methods for mapping risk genes encounters new challenges. One of the issues is the limited availability of expression quantitative trait loci (eQTLs) as instrumental variables (IVs), hampering the estimation of sparse causal effects. Additionally, the often context- or tissue-specific eQTL effects challenge the MR assumption of consistent IV effects across eQTL and GWAS data. To address these challenges, we propose a multi-context multivariable integrative MR framework, mintMR, for mapping expression and molecular traits as joint exposures. It models the effects of molecular exposures across multiple tissues in each gene region, while simultaneously estimating across multiple gene regions. It uses eQTLs with consistent effects across more than one tissue type as IVs, improving IV consistency. A major innovation of mintMR involves employing multi-view learning methods to collectively model latent indicators of disease relevance across multiple tissues, molecular traits, and gene regions. The multi-view learning captures the major patterns of disease relevance and uses these patterns to update the estimated tissue relevance probabilities. The proposed mintMR iterates between performing a multi-tissue MR for each gene region and joint learning the disease-relevant tissue probabilities across gene regions, improving the estimation of sparse effects across genes. We apply mintMR to evaluate the causal effects of gene expression and DNA methylation for 35 complex traits using multi-tissue QTLs as IVs. The proposed mintMR controls genome-wide inflation and offers insights into disease mechanisms.

Using genetic association data to guide drug discovery and development: Review of methods and applications.

Evidence on the validity of drug targets from randomized trials is reliable but typically expensive and slow to obtain. In contrast, evidence from conventional observational epidemiological studies is less reliable because of the potential for bias from confounding and reverse causation. Mendelian randomization is a quasi-experimental approach analogous to a randomized trial that exploits naturally occurring randomization in the transmission of genetic variants. In Mendelian randomization, genetic variants that can be regarded as proxies for an intervention on the proposed drug target are leveraged as instrumental variables to investigate potential effects on biomarkers and disease outcomes in large-scale observational datasets. This approach can be implemented rapidly for a range of drug targets to provide evidence on their effects and thus inform on their priority for further investigation. In this review, we present statistical methods and their applications to showcase the diverse opportunities for applying Mendelian randomization in guiding clinical development efforts, thus enabling interventions to target the right mechanism in the right population group at the right time. These methods can inform investigators on the mechanisms underlying drug effects, their related biomarkers, implications for the timing of interventions, and the population subgroups that stand to gain the most benefit. Most methods can be implemented with publicly available data on summarized genetic associations with traits and diseases, meaning that the only major limitations to their usage are the availability of appropriately powered studies for the exposure and outcome and the existence of a suitable genetic proxy for the proposed intervention.

Twins, income, and happiness: Evidence from China.

We estimate the causal effect of income on happiness using a unique dataset of Chinese twins. This allows us to address omitted variable bias and measurement errors. Our findings show that individual income has a large positive effect on happiness, with a doubling of income resulting in an increase of 0.26 scales or 0.37 SDs in the four-scale happiness measure. We also find that income matters most for males and the middle-aged. Our results highlight the importance of accounting for various biases when studying the relationship between socioeconomic status and subjective well-being.

simmrd: An open-source tool to perform simulations in Mendelian randomization.

Mendelian randomization (MR) has become a popular tool for inferring causality of risk factors on disease. There are currently over 45 different methods available to perform MR, reflecting this extremely active research area. It would be desirable to have a standard simulation environment to objectively evaluate the existing and future methods. We present simmrd, an open-source software for performing simulations to evaluate the performance of MR methods in a range of scenarios encountered in practice. Researchers can directly modify the simmrd source code so that the research community may arrive at a widely accepted framework for researchers to evaluate the performance of different MR methods.

Cognitive Behavioral Program for the Prevention of Depression in At-Risk Adolescents: Isolating the Effects of Dose.

The current study estimated effects of intervention dose (attendance) of a cognitive behavioral prevention (CBP) program on depression-free days (DFD) in adolescent offspring of parents with a history of depression. As part of secondary analyses of a multi-site randomized controlled trial, we analyzed the complete intention-to-treat sample of 316 at-risk adolescents ages 13-17. Youth were randomly assigned to the CBP program plus usual care (n=159) or to usual care alone (n=157). The CBP program involved 8 weekly acute sessions and 6 monthly continuation sessions. Results showed that higher CBP program dose predicted more DFDs, with a key threshold of approximately 75% of a full dose in analyses employing instrumental variable methodology to control multiple channels of bias. Specifically, attending at more than 75% of acute phase sessions led to 45.3 more DFDs over the 9-month period post randomization, which accounted for over 12% of the total follow-up days. Instrument sets were informed by study variables and external data including weather and travel burden. In contrast, conventional analysis methods failed to find a significant dose-outcome relation. Application of the instrumental variable approach, which better controls the influence of confounding, demonstrated that higher CBP program dose resulted in more DFDs.

Mendelian randomization: causal inference leveraging genetic data.

Mendelian randomization (MR) leverages genetic information to examine the causal relationship between phenotypes allowing for the presence of unmeasured confounders. MR has been widely applied to unresolved questions in epidemiology, making use of summary statistics from genome-wide association studies on an increasing number of human traits. However, an understanding of essential concepts is necessary for the appropriate application and interpretation of MR. This review aims to provide a non-technical overview of MR and demonstrate its relevance to psychiatric research. We begin with the origins of MR and the reasons for its recent expansion, followed by an overview of its statistical methodology. We then describe the limitations of MR, and how these are being addressed by recent methodological advances. We showcase the practical use of MR in psychiatry through three illustrative examples - the connection between cannabis use and psychosis, the link between intelligence and schizophrenia, and the search for modifiable risk factors for depression. The review concludes with a discussion of the prospects of MR, focusing on the integration of multi-omics data and its extension to delineating complex causal networks.

Using instrumental variables to correct for bias in real-world cohort studies of the effects of disease-modifying treatment in MS.

BACKGROUND: Estimating the effect of disease-modifying treatment of MS in observational studies is impaired by bias from unmeasured confounders, in particular indication bias. OBJECTIVE: To show how instrumental variables (IVs) reduce bias. METHODS: All patients with relapsing onset of MS 1996-2010, identified by the nationwide Danish Multiple Sclerosis Registry, were followed from onset. Exposure was treatment index throughout the first 12 years from onset, defined as a cumulative function of months without and with medium- or high-efficacy treatment, and outcomes were hazard ratios (HRs) per unit treatment index for sustained Expanded Disability Scale Score (EDSS) 4 and 6 adjusted for age at onset and sex, without and with an IV. We used the onset cohort (1996-2000; 2001-2005; 2006-2010) as an IV because treatment index increased across the cohorts. RESULTS: We included 6014 patients. With conventional Cox regression, HRs for EDSS 4 and 6 were 1.15 [95% CI: 1.13-1.18] and 1.17 [1.13-1.20] per unit treatment index. Only with IVs, we confirmed a beneficial effect of treatment with HRs of 0.86 [0.81-0.91] and 0.82 [0.74-0.90]. CONCLUSION: The use of IVs eliminates indication bias and confirms that treatment is effective in delaying disability. IVs could, under some circumstances, be an alternative to marginal structural models.

A Bayesian semi-parametric scalar-on-function regression with measurement error using instrumental variables.

Wearable devices such as the ActiGraph are now commonly used in research to monitor or track physical activity. This trend corresponds with the growing need to assess the relationships between physical activity and health outcomes, such as obesity, accurately. Device-based physical activity measures are best treated as functions when assessing their associations with scalar-valued outcomes such as body mass index. Scalar-on-function regression (SoFR) is a suitable regression model in this setting. Most estimation approaches in SoFR assume that the measurement error in functional covariates is white noise. Violating this assumption can lead to underestimating model parameters. There are limited approaches to correcting measurement errors for frequentist methods and none for Bayesian methods in this area. We present a non-parametric Bayesian measurement error-corrected SoFR model that relaxes all the constraining assumptions often involved with these models. Our estimation relies on an instrumental variable allowing a time-varying biasing factor, a significant departure from the current generalized method of moment (GMM) approach. Our proposed method also permits model-based grouping of the functional covariate following measurement error correction. This grouping of the measurement error-corrected functional covariate allows additional ease of interpretation of how the different groups differ. Our method is easy to implement, and we demonstrate its finite sample properties in extensive simulations. Finally, we applied our method to data from the National Health and Examination Survey to assess the relationship between wearable device-based measures of physical activity and body mass index in adults in the United States.

Using instruments for selection to adjust for selection bias in Mendelian randomization.

Selection bias is a common concern in epidemiologic studies. In the literature, selection bias is often viewed as a missing data problem. Popular approaches to adjust for bias due to missing data, such as inverse probability weighting, rely on the assumption that data are missing at random and can yield biased results if this assumption is violated. In observational studies with outcome data missing not at random, Heckman's sample selection model can be used to adjust for bias due to missing data. In this paper, we review Heckman's method and a similar approach proposed by Tchetgen Tchetgen and Wirth (2017). We then discuss how to apply these methods to Mendelian randomization analyses using individual-level data, with missing data for either the exposure or outcome or both. We explore whether genetic variants associated with participation can be used as instruments for selection. We then describe how to obtain missingness-adjusted Wald ratio, two-stage least squares and inverse variance weighted estimates. The two methods are evaluated and compared in simulations, with results suggesting that they can both mitigate selection bias but may yield parameter estimates with large standard errors in some settings. In an illustrative real-data application, we investigate the effects of body mass index on smoking using data from the Avon Longitudinal Study of Parents and Children.

Elucidating the causal landscape: Mendelian randomization analysis of lifestyle and physiological factors in stress urinary incontinence.

PURPOSE: To explore the potential causal links between obesity, type 2 diabetes (T2D), and lifestyle choices (such as smoking, alcohol and coffee consumption, and vigorous physical activity) on stress urinary incontinence (SUI), this study employs a Mendelian Randomization approach. This research aims to clarify these associations, which have been suggested but not conclusively established in prior observational studies. METHODS: Genetic instruments associated with the exposures at the genome-wide significance (p < 5 × 10-8) were selected from corresponding genome-wide association studies. Summary-level data for SUI, was obtained from the UK Biobank. A two-sample MR analysis was employed to estimate causal effects, utilizing the inverse-variance weighted (IVW) method as the primary analytical approach. Complementary sensitivity analyses including MR-PRESSO, MR-Egger, and weighted median methods were performed. The horizontal pleiotropy was detected by using MR-Egger intercept and MR-PRESSO methods, and the heterogeneity was assessed using Cochran's Q statistics. RESULTS: Our findings demonstrate a significant causal relationship between higher body mass index (BMI) and the risk of SUI, with increased abdominal adiposity (WHRadjBMI) similarly linked to SUI. Smoking initiation is also causally associated with an elevated risk. However, our analysis did not find definitive causal connections for other factors, including T2D, alcohol consumption, coffee intake, and vigorous physical activity. CONCLUSIONS: These findings provide valuable insights for clinical strategies targeting SUI, suggesting a need for heightened awareness and potential intervention in individuals with higher BMI, WHR, and smoking habits. Further research is warranted to explore the complex interplay between genetic predisposition and lifestyle choices in the pathogenesis of SUI.

Mendelian Randomization analysis of the causal effect of cigarette smoking on hospital costs.

INTRODUCTION: Knowledge of the impact of smoking on healthcare costs is important for establishing the external effects of smoking and for evaluating policies intended to modify this behavior. Conventional analysis of this association is difficult because of omitted variable bias, reverse causality, and measurement error. METHODS: We approached these challenges using a Mendelian Randomization study design; genetic variants associated with smoking behaviors were used in instrumental variables models with inpatient hospital costs (calculated from electronic health records) as the outcome. We undertook genome wide association studies to identify genetic variants associated with smoking initiation and a composite smoking index (reflecting cumulative health impacts of smoking) on up to 300,045 individuals (mean age: 57 years at baseline, range 39 to 72 years) in the UK Biobank. We followed individuals up for a mean of six years. RESULTS: Genetic liability to initiate smoking (ever versus never smoking) was estimated to increase mean per-patient annual inpatient hospital costs by £477 (95% confidence interval (CI): £187 to £766). A one-unit change in genetic liability to the composite smoking index (range: 0-4.0) increased inpatient hospital costs by £204 (95% CI: £105 to £303) per unit increase in this index. There was some evidence that the composite smoking index causal models violated the instrumental variable assumptions, and all Mendelian Randomization models were estimated with considerable uncertainty. Models conditioning on risk tolerance were not robust to weak instrument bias. CONCLUSIONS: Our findings have implications for the potential cost-effectiveness of smoking interventions. IMPLICATIONS: We report the first Mendelian Randomization analysis of the causal effect of smoking on healthcare costs. Using two distinct smoking phenotypes, we identified substantial impacts of smoking on inpatient hospital costs, although the causal models were associated with considerable uncertainty. These results could be used alongside other evidence on the impact of smoking to evaluate the cost-effectiveness of anti-smoking interventions and to understand the scale of externalities associated with this behaviour.

Non-classical measurement error in instrumental variables estimation: An application to the medical care costs of obesity.

Estimates of the impact of body mass index and obesity on health and labor market outcomes often use instrumental variables estimation (IV) to mitigate bias due to endogeneity. When these studies rely on survey data that include self- or proxy-reported height and weight, there is non-classical measurement error due to the tendency of individuals to under-report their own weight. Mean reverting errors in weight do not cause IV to be asymptotically biased per se, but may result in bias if instruments are correlated with additive error in weight. We demonstrate the conditions under which IV is biased when there is non-classical measurement error and derive bounds for this bias conditional on instrument strength and the severity of mean-reverting error. We show that improvements in instrument relevance alone cannot eliminate IV bias, but reducing the correlation between weight and reporting error mitigates the bias. A solution we consider is regression calibration (RC) of endogenous variables with external validation data. In simulations, we find IV estimation paired with RC can produce consistent estimates when correctly specified. Even when RC fails to match the covariance structure of reporting error, there is still a reduction in asymptotic bias.

Estimating the short-term effect of PM2.5 on the mortality of cardiovascular diseases based on instrumental variables.

BACKGROUND: PM2.5 can induce and aggravate the occurrence and development of cardiovascular diseases (CVDs). The objective of our study is to estimate the causal effect of PM2.5 on mortality rates associated with CVDs using the instrumental variables (IVs) method. METHODS: We extracted daily meteorological, PM2.5 and CVDs death data from 2016 to 2020 in Binzhou. Subsequently, we employed the general additive model (GAM), two-stage predictor substitution (2SPS), and control function (CFN) to analyze the association between PM2.5 and daily CVDs mortality. RESULTS: The 2SPS estimated the association between PM2.5 and daily CVDs mortality as 1.14% (95% CI: 1.04%, 1.14%) for every 10 µg/m3 increase in PM2.5. Meanwhile, the CFN estimated this association to be 1.05% (95% CI: 1.02%, 1.10%). The GAM estimated it as 0.85% (95% CI: 0.77%, 1.05%). PM2.5 also exhibited a statistically significant effect on the mortality rate of patients with ischaemic heart disease, myocardial infarction, or cerebrovascular accidents (P < 0.05). However, no significant association was observed between PM2.5 and hypertension. CONCLUSION: PM2.5 was significantly associated with daily CVDs deaths (excluding hypertension). The estimates from the IVs method were slightly higher than those from the GAM. Previous studies based on GAM may have underestimated the impact of PM2.5 on CVDs.

Using Instrumental Variables to Measure Causation over Time in Cross-Lagged Panel Models.

Cross-lagged panel models (CLPMs) are commonly used to estimate causal influences between two variables with repeated assessments. The lagged effects in a CLPM depend on the time interval between assessments, eventually becoming undetectable at longer intervals. To address this limitation, we incorporate instrumental variables (IVs) into the CLPM with two study waves and two variables. Doing so enables estimation of both the lagged (i.e., "distal") effects and the bidirectional cross-sectional (i.e., "proximal") effects at each wave. The distal effects reflect Granger-causal influences across time, which decay with increasing time intervals. The proximal effects capture causal influences that accrue over time and can help infer causality when the distal effects become undetectable at longer intervals. Significant proximal effects, with a negligible distal effect, would imply that the time interval is too long to estimate a lagged effect at that time interval using the standard CLPM. Through simulations and an empirical application, we demonstrate the impact of time intervals on causal inference in the CLPM and present modeling strategies to detect causal influences regardless of the time interval in a study. Furthermore, to motivate empirical applications of the proposed model, we highlight the utility and limitations of using genetic variables as IVs in large-scale panel studies.

Modifiable risk factors mediate the effect of gastroesophageal reflux disease on stroke and subtypes: A Mendelian randomization study.

OBJECTIVES: Previous observational studies have suggested that gastroesophageal reflux disease (GERD) increases the risk of stroke, but the specific underlying mechanisms are unclear. We investigated the causal associations of GERD with stroke and its subtypes using Mendelian randomization (MR), and evaluated the potential mediating effects of modifiable stroke risk factors in the causal pathway. METHODS: Genetic instrumental variables for GERD were extracted from the latest genome-wide association study (GWAS) summary level data. We initially performed two-sample MR to examine the association of GERD with stroke and its subtypes, including ischemic stroke, intracranial hemorrhage, and the major subtypes of ischemic stroke. Two-step MR was further employed to investigate the mediating effect of 15 risk factors in the causal pathway. RESULTS: We found significant causal associations of genetically predicted GERD with increased risk of stroke (OR: 1.22 95% CI: 1.126-1.322), ischemic stroke (OR: 1.19 95% CI: 1.098-1.299), and large-artery stroke (OR: 1.49 95% CI: 1.214-1.836). Replication and sensitivity analyses yielded consistent effect directions and similar estimates. Further mediation analyses indicated that hypertension (HTN), systolic blood pressure (SBP), and type 2 diabetes (T2D) mediated 36.0%, 9.0%, and 15.8% of the effect of GERD on stroke; 42.9%, 10.8%, and 21.4% for ischemic stroke, and 23.3%; 7.9%, and 18.7% for large-artery stroke, respectively. CONCLUSIONS: This study supports that GERD increases susceptibility to stroke, ischemic stroke, and large-artery stroke, and is partially mediated by HTN, SBP, and T2D.

How Do Attitudes of Adult Children Toward Long-Term Care Change with Education? International Evidence.

Global demographic trends indicate that the world population is aging and education acquisition is increasing. For the first time in history, people are expected to spend more years as adults with living parents than as a parent of teenage children, and the average years of schooling have increased dramatically over the past several decades for many countries. Additionally, family-provided care is still the most important form of care to meet care demands worldwide. As strong filial norms could affect older adults' long-term care decision-making, understanding the link between filial obligations and education is critical under these trends. Using individual data from the World Values Survey and an instrumental variables strategy to account for endogeneity, this study finds that adult children with higher education levels have lower filial beliefs. Since population aging is expected to increase the demand for long-term care services, and education can reduce the supply of family-provided long-term care services, countries must start addressing this gap.

Native American ancestry and breast cancer risk in Colombian and Mexican women: ruling out potential confounding through ancestry-informative markers.

BACKGROUND: Latin American and Hispanic women are less likely to develop breast cancer (BC) than women of European descent. Observational studies have found an inverse relationship between the individual proportion of Native American ancestry and BC risk. Here, we use ancestry-informative markers to rule out potential confounding of this relationship, estimating the confounder-free effect of Native American ancestry on BC risk. METHODS AND STUDY POPULATION: We used the informativeness for assignment measure to select robust instrumental variables for the individual proportion of Native American ancestry. We then conducted separate Mendelian randomization (MR) analyses based on 1401 Colombian women, most of them from the central Andean regions of Cundinamarca and Huila, and 1366 Mexican women from Mexico City, Monterrey and Veracruz, supplemented by sensitivity and stratified analyses. RESULTS: The proportion of Colombian Native American ancestry showed a putatively causal protective effect on BC risk (inverse variance-weighted odds ratio [OR] = 0.974 per 1% increase in ancestry proportion, 95% confidence interval [CI] 0.970-0.978, p = 3.1 × 10-40). The corresponding OR for Mexican Native American ancestry was 0.988 (95% CI 0.987-0.990, p = 1.4 × 10-44). Stratified analyses revealed a stronger association between Native American ancestry and familial BC (Colombian women: OR = 0.958, 95% CI 0.952-0.964; Mexican women: OR = 0.973, 95% CI 0.969-0.978), and stronger protective effects on oestrogen receptor (ER)-positive BC than on ER-negative and triple-negative BC. CONCLUSIONS: The present results point to an unconfounded protective effect of Native American ancestry on BC risk in both Colombian and Mexican women which appears to be stronger for familial and ER-positive BC. These findings provide a rationale for personalised prevention programmes that take genetic ancestry into account, as well as for future admixture mapping studies.

Estimating the Effect of a Treatment When There Is Nonadherence in a Trial.

Randomized trials offer a powerful strategy for estimating the effect of a treatment on an outcome. However, interpretation of trial results can be complicated when study subjects do not take the treatment to which they were assigned; this is referred to as nonadherence. Prior authors have described instrumental variable approaches to analyze trial data with nonadherence; under their approaches, the initial assignment to treatment is used as an instrument. However, their approaches require the assumption that initial assignment to treatment has no direct effect on the outcome except via the actual treatment received (i.e., the exclusion restriction), which may be implausible. We propose an approach to identification of a causal effect of treatment in a trial with 1-sided nonadherence without assuming exclusion restriction. The proposed approach leverages the study subjects initially assigned to control status as an unexposed reference population; we then employ a bespoke instrumental variable analysis, where the key assumption is "partial exchangeability" of the association between a covariate and an outcome in the treatment and control arms. We provide a formal description of the conditions for identification of causal effects, illustrate the method using simulations, and provide an empirical application.

An efficient and robust approach to Mendelian randomization with measured pleiotropic effects in a high-dimensional setting.

Valid estimation of a causal effect using instrumental variables requires that all of the instruments are independent of the outcome conditional on the risk factor of interest and any confounders. In Mendelian randomization studies with large numbers of genetic variants used as instruments, it is unlikely that this condition will be met. Any given genetic variant could be associated with a large number of traits, all of which represent potential pathways to the outcome which bypass the risk factor of interest. Such pleiotropy can be accounted for using standard multivariable Mendelian randomization with all possible pleiotropic traits included as covariates. However, the estimator obtained in this way will be inefficient if some of the covariates do not truly sit on pleiotropic pathways to the outcome. We present a method that uses regularization to identify which out of a set of potential covariates need to be accounted for in a Mendelian randomization analysis in order to produce an efficient and robust estimator of a causal effect. The method can be used in the case where individual-level data are not available and the analysis must rely on summary-level data only. It can be used where there are any number of potential pleiotropic covariates up to the number of genetic variants less one. We show the results of simulation studies that demonstrate the performance of the proposed regularization method in realistic settings. We also illustrate the method in an applied example which looks at the causal effect of urate plasma concentration on coronary heart disease.

Discussion on: Instrumented difference-in-differences, by Ting Ye, Ashkan Ertefaie, James Flory, Sean Hennessy and Dylan S. Small.

I discuss the assumptions needed for identification of average treatment effects and local average treatment effects in instrumented difference-in-differences (IDID), and the possible trade-offs between assumptions of standard IV and those needed for the new proposal IDID, in one- and two-sample settings. I also discuss the interpretation of the estimands identified under monotonicity. I conclude by suggesting possible extensions to the estimation method, by outlining a strategy to use data-adaptive estimation of the nuisance parameters, based on recent developments.