Evaluating the potential role of pleiotropy in Mendelian randomization studies

Abstract

Pleiotropy, the phenomenon of a single genetic variant influencing multiple traits, is likely widespread in the human genome. If pleiotropy arises because the single nucleotide polymorphism (SNP) influences one trait, which in turn influences another ('vertical pleiotropy'), then Mendelian randomization (MR) can be used to estimate the causal influence between the traits. Of prime focus among the many limitations to MR is the unprovable assumption that apparent pleiotropic associations are mediated by the exposure (i.e. reflect vertical pleiotropy), and do not arise due to SNPs influencing the two traits through independent pathways ('horizontal pleiotropy'). The burgeoning treasure trove of genetic associations yielded through genome wide association studies makes for a tantalizing prospect of phenome-wide causal inference. Recent years have seen substantial attention devoted to the problem of horizontal pleiotropy, and in this review we outline how newly developed methods can be used together to improve the reliability of MR.

Figure 1.

 (A) The same SNP can associate with multiple traits due to vertical pleiotropy, horizontal pleiotropy and linkage disequilibrium with distinct causal variants depending on the analytical context. To estimate the causal influence of gene expression level (Gene) 1 on Trait 1, SNP 1 is a valid instrument that acts in a vertical pleiotropic manner. But SNP 1 has a horizontal pleiotropic effect when using it to estimate the causal influence of Gene 1 on Trait 2. If SNP 1 was used to instrument Gene 1 to test its effect on Trait 3, it would exhibit a pleiotropic association through linkage disequilibrium with SNP 2. (B) A directed acyclic graph (DAG) in which four SNPs instrument an exposure. The fourth SNP has a horizontal pleiotropic effect of magnitude $\alpha$. The impact of the horizontal pleiotropic effect is shown in the scatter plot in (C), where the grey slope represents the true causal effect obtained from the three valid instruments, and the red slope represents the IVW estimate when all SNPs are used as instruments.

Appendix figure 1.

(A) Unobserved confounding (U) makes it impossible to be fully confident that an association between risk factor X and outcome Y represents a measure of causal effect of X on Y. (B) In a perfect RCT, randomization to treatment (T) removes the possibility of confounding, enabling the causal effect of T on Y to be estimated. (C) MR uses genetic variants (G) that explain some variation in the exposure X to estimate the causal effect of X on Y. G must satisfy the instrumental variable assumptions, encoded by the solid arrows (and the strict absence of the dotted arrows) in (C). (D) Instrumental variable methods can also be used in clinical trials when randomization is imperfect because some patients do not receive the treatment they were originally assigned. This is referred to as `non-compliance’. An MR analysis is conceptually and mathematically equivalent to the analysis of RCT data in the presence of non-compliance, where the SNP (G) and exposure (X) proxy for randomization (R) and treatment (T), respectively.