Exploring and mitigating potential bias when genetic instrumental variables are associated with multiple non-exposure traits in Mendelian randomization - PubMed (original) (raw)

Exploring and mitigating potential bias when genetic instrumental variables are associated with multiple non-exposure traits in Mendelian randomization

Qian Yang et al. Eur J Epidemiol. 2022 Jul.

Abstract

With the increasing size and number of genome-wide association studies, individual single nucleotide polymorphisms are increasingly found to associate with multiple traits. Many different mechanisms could result in proposed genetic IVs for an exposure of interest being associated with multiple non-exposure traits, some of which could bias MR results. We describe and illustrate, through causal diagrams, a range of scenarios that could result in proposed IVs being related to non-exposure traits in MR studies. These associations could occur due to five scenarios: (i) confounding, (ii) vertical pleiotropy, (iii) horizontal pleiotropy, (iv) reverse causation and (v) selection bias. For each of these scenarios we outline steps that could be taken to explore the underlying mechanism and mitigate any resulting bias in the MR estimation. We recommend MR studies explore possible IV-non-exposure associations across a wider range of traits than is usually the case. We highlight the pros and cons of relying on sensitivity analyses without considering particular pleiotropic paths versus systematically exploring and controlling for potential pleiotropic or other biasing paths via known traits. We apply our recommendations to an illustrative example of the effect of maternal insomnia on offspring birthweight in UK Biobank.

Keywords: Causal diagram; Confounding; Mendelian randomization; Pleiotropy; Selection bias; UK Biobank.

© 2022. The Author(s).

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Conflict of interest statement

D.A.L. reports receiving research support from Medtronic and Roche Diagnostics for research outside the submitted work. All other authors declare no competing interests.

Figures

Fig. 1

Fig. 1

Directed acyclic graphs illustrating scenarios when an unexpected genetic instrumental variable-non-exposure trait association could be observed. Z: genetic instrumental variable; X: exposure of interest; Y: outcome of interest; U: unmeasured confounders; W: non-exposure traits; C: confounding factors, e.g. population stratification, cryptic relatedness and assortative mating; S, selection. For simplicity, we use single nodes even when there may be multiple variables, and these scenarios do not consider time-varying exposures and critical/sensitive-period exposure effects [2, 17]. Scenarios illustrated by 1.1, 1.2, 3.1–3.3, 5.1–5.4 would be expected to bias the MR estimate of X–Y effect; 1.3, 1.4, 2.1, 2.2 and 3.4 would not; 5.2 and 5.4 would be unbiased under the null

Fig. 2

Fig. 2

Associations of unweighted polygenetic risk score (PRS) for insomnia with six non-exposure traits before and after adjustment for population stratification. Supplementary Table 1 summarizes how education, frequency of alcohol intake and ever smoking are coded in this study

Fig. 3

Fig. 3

Mendelian randomization estimates for a non-exposure traits-birthweight (W–Y) effects, b non-exposure traits-insomnia (W–X) effects, and c insomnia-non-exposure traits (X–W) effects. “Usually” having insomnia is coded as 1, while “sometimes/rarely/never” having insomnia is coded as 0 (Supplementary Table 1)

Fig. 3

Fig. 3

Mendelian randomization estimates for a non-exposure traits-birthweight (W–Y) effects, b non-exposure traits-insomnia (W–X) effects, and c insomnia-non-exposure traits (X–W) effects. “Usually” having insomnia is coded as 1, while “sometimes/rarely/never” having insomnia is coded as 0 (Supplementary Table 1)

Fig. 3

Fig. 3

Mendelian randomization estimates for a non-exposure traits-birthweight (W–Y) effects, b non-exposure traits-insomnia (W–X) effects, and c insomnia-non-exposure traits (X–W) effects. “Usually” having insomnia is coded as 1, while “sometimes/rarely/never” having insomnia is coded as 0 (Supplementary Table 1)

Fig. 4

Fig. 4

Multivariable Mendelian randomization (MVMR) estimates for the effect of maternal insomnia on offspring birthweight. Estimates are differences in mean birthweight when comparing reporting usually experiencing insomnia to never, rarely or sometimes experiencing it with and without adjustment for potential horizontal pleiotropy via maternal age at first birth, education and ever smoking

Fig. 5

Fig. 5

Sensitivity analyses for the effect of maternal insomnia on offspring birthweight using two-sample Mendelian randomization (MR)

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