Are Mendelian randomization investigations immune from bias due to reverse causation?

Burgess, Stephen; Swanson, Sonja A.; Labrecque, Jeremy

doi:10.1007/s10654-021-00726-8

Cited by 72 publications

(76 citation statements)

References 23 publications

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“…If the liability does not cause the outcome, a null effect will be correctly estimated. 19 The Wald Ratio MR estimate of the effect of X k on Y , the liability effect , is the effect of increasing L such that X k increases by one unit. We extend this to an outcome measured at multiple timepoints in Appendix 2.…”

Section: Methodsmentioning

confidence: 99%

“…21 More recently, it has been questioned how appropriate MR is when applied to exposures that vary over time. [22][23][24] Labreque & Swanson propose one possible definition of a lifetime effect that might be of interest: the effect of increasing the exposure by one unit at each timepoint throughout the lifecourse. 22 In order to estimate this effect using MR, this implies time-invariant exposures and genotype-exposure associations, consistent with parallel exposure trajectories (a one unit change in trajectory at time 𝑡 will be the same as a one unit change in trajectory at time 𝑡 + 1).…”

Section: Mendelian Randomization Applied To One Measure Of An Exposure That Varies Over Timementioning

confidence: 99%

“…Concerns have also been raised that MR with time-varying exposures may be biased if a feedback mechanism exists where genetic factors influence predisposition to an outcome, which in turn influences the exposure at a subsequent time point. 23 For example, where instruments for coronary heart disease (CHD) relate to C-reactive protein (CRP) because the instruments for CHD relate to developing atheroma, which in turn increases CRP.…”

Section: Mendelian Randomization Applied To One Measure Of An Exposure That Varies Over Timementioning

confidence: 99%

“…It does not matter whether genotype-exposure associations are time-varying or time-invariant; the null hypothesis tested by MR is that the liability 𝐿 does not cause the outcome, i.e., there is no part of the trajectory which causes the outcome. If the liability does not cause the outcome, a null effect will be correctly estimated by MR. 23 The Wald Ratio MR estimate of the effect of 𝑋 𝑘 on 𝑌, the liability effect, is the effect of increasing 𝐿 such that 𝑋 𝑘 increases by 1 unit. We extend this to an outcome measured at multiple timepoints in Appendix 2.…”

Section: Mendelian Randomisationmentioning

confidence: 99%

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Interpretation of mendelian randomization using one measure of an exposure that varies over time

Morris

Heron

Sanderson

et al. 2021

Preprint

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BackgroundMendelian randomization (MR) is a powerful tool through which the causal effects of modifiable exposures on outcomes can be estimated from observational data. Most exposures vary throughout the life course, but MR is commonly applied to one measurement of an exposure (e.g., weight measured once between ages 40 and 60). It has been argued that MR provides biased causal effect estimates when applied to one measure of an exposure that varies over time.MethodsWe propose an approach that emphasises the liability that causes the entire exposure trajectory. We demonstrate this approach using simulations and an applied example.ResultsWe show that rather than estimating the direct or total causal effect of changing the exposure value at a given time, MR estimates the causal effect of changing the liability as induced by a specific genotype that gives rise to the exposure at that time. As such, results from MR conducted at different time points are expected to differ (unless the liability of exposure is constant over time), as we demonstrate by estimating the effect of BMI measured at different ages on systolic blood pressure.ConclusionsPractitioners should not interpret MR results as timepoint-specific direct or total causal effects, but as the effect of changing the liability that causes the entire exposure trajectory. Estimates of how the effects of a genetic variant on an exposure vary over time are needed to interpret timepoint-specific causal effects.

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Section: Methodsmentioning

confidence: 99%

Section: Mendelian Randomization Applied To One Measure Of An Exposure That Varies Over Timementioning

confidence: 99%

Section: Mendelian Randomization Applied To One Measure Of An Exposure That Varies Over Timementioning

confidence: 99%

Section: Mendelian Randomisationmentioning

confidence: 99%

See 2 more Smart Citations

Interpretation of mendelian randomization using one measure of an exposure that varies over time

Morris

Heron

Sanderson

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…MR offers a number of advantages over observational epidemiology. First, although reverse causality cannot be completely avoided, MR can still avoid the bias caused by reverse causality to a certain extent ( 12 ). Second, MR studies are relatively immune to common behavioral, physiological, and socioeconomic confounders owing to random assignment of alleles at meiosis.…”

Section: Introductionmentioning

confidence: 99%

Review of Mendelian Randomization Studies on Ovarian Cancer

et al. 2021

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Ovarian cancer (OC) is one of the deadliest gynecological cancers worldwide. Previous observational epidemiological studies have revealed associations between modifiable environmental risk factors and OC risk. However, these studies are prone to confounding, measurement error, and reverse causation, undermining robust causal inference. Mendelian randomization (MR) analysis has been established as a reliable method to investigate the causal relationship between risk factors and diseases using genetic variants to proxy modifiable exposures. Over recent years, MR analysis in OC research has received extensive attention, providing valuable insights into the etiology of OC as well as holding promise for identifying potential therapeutic interventions. This review provides a comprehensive overview of the key principles and assumptions of MR analysis. Published MR studies focusing on the causality between different risk factors and OC risk are summarized, along with comprehensive analysis of the method and its future applications. The results of MR studies on OC showed that higher BMI and height, earlier age at menarche, endometriosis, schizophrenia, and higher circulating β-carotene and circulating zinc levels are associated with an increased risk of OC. In contrast, polycystic ovary syndrome; vitiligo; higher circulating vitamin D, magnesium, and testosterone levels; and HMG-CoA reductase inhibition are associated with a reduced risk of OC. MR analysis presents a2 valuable approach to understanding the causality between different risk factors and OC after full consideration of its inherent assumptions and limitations.

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Genetic Complexities of Cerebral Small Vessel Disease, Blood Pressure, and Dementia

Sargurupremraj,

Soumaré,

Bis

et al. 2024

JAMA Netw Open

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ImportanceVascular disease is a treatable contributor to dementia risk, but the role of specific markers remains unclear, making prevention strategies uncertain.ObjectiveTo investigate the causal association between white matter hyperintensity (WMH) burden, clinical stroke, blood pressure (BP), and dementia risk, while accounting for potential epidemiologic biases.Design, Setting, and ParticipantsThis study first examined the association of genetically determined WMH burden, stroke, and BP levels with Alzheimer disease (AD) in a 2-sample mendelian randomization (2SMR) framework. Second, using population-based studies (1979-2018) with prospective dementia surveillance, the genetic association of WMH, stroke, and BP with incident all-cause dementia was examined. Data analysis was performed from July 26, 2020, through July 24, 2022.ExposuresGenetically determined WMH burden and BP levels, as well as genetic liability to stroke derived from genome-wide association studies (GWASs) in European ancestry populations.Main Outcomes and MeasuresThe association of genetic instruments for WMH, stroke, and BP with dementia was studied using GWASs of AD (defined clinically and additionally meta-analyzed including both clinically diagnosed AD and AD defined based on parental history [AD-meta]) for 2SMR and incident all-cause dementia for longitudinal analyses.ResultsIn 2SMR (summary statistics–based) analyses using AD GWASs with up to 75 024 AD cases (mean [SD] age at AD onset, 75.5 [4.4] years; 56.9% women), larger WMH burden showed evidence for a causal association with increased risk of AD (odds ratio [OR], 1.43; 95% CI, 1.10-1.86; P = .007, per unit increase in WMH risk alleles) and AD-meta (OR, 1.19; 95% CI, 1.06-1.34; P = .008), after accounting for pulse pressure for the former. Blood pressure traits showed evidence for a protective association with AD, with evidence for confounding by shared genetic instruments. In the longitudinal (individual-level data) analyses involving 10 699 incident all-cause dementia cases (mean [SD] age at dementia diagnosis, 74.4 [9.1] years; 55.4% women), no significant association was observed between larger WMH burden and incident all-cause dementia (hazard ratio [HR], 1.02; 95% CI, 1.00-1.04; P = .07). Although all exposures were associated with mortality, with the strongest association observed for systolic BP (HR, 1.04; 95% CI, 1.03-1.06; P = 1.9 × 10−14), there was no evidence for selective survival bias during follow-up using illness-death models. In secondary analyses using polygenic scores, the association of genetic liability to stroke, but not genetically determined WMH, with dementia outcomes was attenuated after adjusting for interim stroke.ConclusionsThese findings suggest that WMH is a primary vascular factor associated with dementia risk, emphasizing its significance in preventive strategies for dementia. Future studies are warranted to examine whether this finding can be generalized to non-European populations.

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Are Mendelian randomization investigations immune from bias due to reverse causation?

Cited by 72 publications

References 23 publications

Interpretation of mendelian randomization using one measure of an exposure that varies over time

Interpretation of mendelian randomization using one measure of an exposure that varies over time

Review of Mendelian Randomization Studies on Ovarian Cancer

Genetic Complexities of Cerebral Small Vessel Disease, Blood Pressure, and Dementia

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