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Mendelian randomisation approaches to the study of prenatal exposures: A systematic review

Abstract Background Mendelian randomisation (MR) designs apply instrumental variable techniques using genetic variants to study causal effects. MR is increasingly used to evaluate the role of maternal exposures during pregnancy on offspring health. Objectives We review the application of MR to prenatal exposures and describe reporting of methodologic challenges in this area. Data sources We searched PubMed, EMBASE, Medline Ovid, Cochrane Central, Web of Science, and Google Scholar. Study selection and data extraction Eligible studies met the following criteria: (a) a maternal pregnancy exposure; (b) an outcome assessed in offspring of the pregnancy; and (c) a genetic variant or score proposed as an instrument or proxy for an exposure. Synthesis We quantified the frequency of reporting of MR conditions stated, techniques used to examine assumption plausibility, and reported limitations. Results Forty‐three eligible studies were identified. When discussing challenges or limitations, the most common issues described were known potential biases in the broader MR literature, including population stratification (n = 29), weak instrument bias (n = 18), and certain types of pleiotropy (n = 30). Of 22 studies presenting point estimates for the effect of exposure, four defined their causal estimand. Twenty‐four studies discussed issues unique to prenatal MR, including selection on pregnancy (n = 1) and pleiotropy via postnatal exposure (n = 10) or offspring genotype (n = 20). Conclusions Prenatal MR studies frequently discuss issues that affect all MR studies, but rarely discuss problems specific to the prenatal context, including selection on pregnancy and effects of postnatal exposure. Future prenatal MR studies should report and attempt to falsify their assumptions, with particular attention to issues specific to prenatal MR. Further research is needed to evaluate the impacts of biases unique to prenatal MR in practice.


II. Details of extraction procedure
Data points were extracted by the first author (ED); to ensure accuracy in extraction, 5 included studies were randomly chosen for independent extraction by a coauthor (JL). Data points on discussion of MR assumptions were considered in agreement when both authors agreed on the presence/absence of any discussion of violations of the assumption in question.
Sensitivity analyses and limitations were excluded from the comparison checking due to variability in how specific secondary analyses and limitations were categorized. This is because, rather than prespecifying sets of possible limitations and sensitivity analyses of interest, extraction of data points related to both sensitivity analyses and limitations discussed were open-ended to allow for unexpected or unknown analyses and perspectives. This approach meant that each independent extractor could generate an arbitrarily large number of reported limitations and sensitivity analyses based on the same article. Thus, it would be difficult to measure the degree to which independent extractors agreed on datapoints related to sensitivity analyses and limitations discussed, as it is not possible to measure the number of datapoints the two authors agreed were not present in the dataset.

III.
Supplementary To estimate the average causal effect of an exposure X on an outcome Y, using an instrument Z in the total study population, investigators must assume one of the following conditions hold [2].
4a. The effect of X on Y is identical (constant) for all individuals in the population: 4b. No effect modification by the instrument Z in all levels of the exposure X: Or equivalently Recent research has found that the average causal effect can also be identified, even in the presence of violations of the second and third assumption, under one of two alternative assumptions by an additional variable J, as can be seen in Supplemental Figure 1 [3]. In this case, the usual 3 IV assumptions are replaced by the following: 4d. No additive U-X interaction on the average causal effect of X on Y: If the above assumptions are not plausible for a particular analysis, researchers can estimate the average causal effect within the compliers, those individuals for whom = > = > [2]. This value is also known as the local average treatment effect, or LATE. In order to estimate this quantity, researchers must assume: 4e. The causal effect of Z on X is monotonic, that is, it only works in one direction for every individual in the study population. Formally, is a nondecreasing function of z on the support of Z .

V. Interpretation of certain additional point-estimating assumptions in pregnancy Mendelian randomization
Four studies in this review reported additional point-estimating assumptions and their targeted estimand. Of these, 3 assumed monotonicity (assumption 4e) in order to estimate the average causal effect among the compliers, and 1 assumed no effect modification by the instrument in all levels of the exposure (assumption 4b) to estimate the average causal effect in the total study population.
In the context of certain pregnancy exposures, there is evidence that conditions 4a, 4b, 4c, and 4d are unreasonable. When genes related to alcohol metabolism are used as instruments for maternal drinking during pregnancy, fetal exposure to alcohol and alcohol metabolites will depend on maternal intake and the speed at which the mother can metabolize alcohol, as well as other environmental factors. For the same level of maternal alcohol intake, offspring of slow metabolizers will have a longer exposure to alcohol, and would be at greater risk of negative health outcomes [5]. This means that the average causal effect of alcohol exposure on offspring outcomes will be modified by the level of the maternal genetic variant proposed as an instrument, violating conditions 4a, 4b, 4c, and 4d. For this reason, most studies of alcohol use during pregnancy in this review focused on a testing approach, rather than point estimation. The same logic applies to other metabolism-related genetic variants proposed as instruments for substance use behaviors, like smoking and caffeine use. In these cases, studies may choose to focus on approaches with weaker assumptions, such as the complier average treatment effect, testing approaches, or bounds.
It is important to note that, in prenatal MR proposing maternal genetic factors as instruments, the interpretation of "compliers" and the complier average causal effect (described in Appendix III above) are different than the usual interpretation in MR studies or most studies using instrumental variable analyses [4]. This is because a mother-child pair's compliance status is determined by the relationship between a mother's genetics and exposure, while the average causal effect of interest occurs in the offspring of those mothers. In typical MR and instrumental variable studies, the proposed instrument, exposure, and outcome are all measured within the same individual. In those cases, under condition 4e, researchers can estimate the average causal effect among the compliers. In contrast, in pregnancy MR designs, under condition 4e, researchers can estimate the average causal effect among the offspring of mothers who are compliers, although the offspring themselves would not necessarily be compliers.
Supplemental Figure 1: DAG representing an instrumental variable model with violation of assumption 3 by J. Under this model, valid estimation of ( = − =0 ) is possible using the alternative assumptions presented by Wang and Tchetgen Tchetgen [3].