MR Dictionary

Pleiotropy

Refers to the potential for genetic variants (including single nucleotide polymorphisms (SNPs)) to associate with multiple phenotypes. 

The relevance of a SNP being pleiotropic to an MR study is context-specific. If the SNP is pleiotropic because it influences the outcome only through the exposure of interest (trait A), this is known as vertical pleiotropy and is the exact phenomenon that MR seeks to detect. If the same variant is associated with another exposure (trait B) and the variant only influences the outcome through trait B, then the variant would be exhibiting horizontal pleiotropy in an MR analysis of trait A on the outcome. This would result in violation of the exclusion restriction and, hence, bias of the MR effect estimate.  A further complication is the distinction between correlated and uncorrelated pleiotropy. Correlated pleiotropy is most likely to occur if the genetic variants being used as an instrumental variable (IV) influence both the exposure and outcome through a heritable shared factor (e.g., a confounder of the exposure and outcome association). Uncorrelated pleiotropy occurs when the genetic variants being used as an IV influences the outcome in a manner that is independent from the exposure. There are nuanced ways in which correlated and uncorrelated horizontal pleiotropy can bias causal estimates derived from MR analyses and, in the context of two-sample MR, the correlated mechanism by which genetic variants influence the outcome can lead to violation of other assumptions (e.g., the INstrument Strength Independent of Direct Effect (InSIDE) assumption).

Vertical and Horizontal Pleiotropy. Adapted from Hemani et al. and Holmes et al. (A) Classic horizontal pleiotropy, whereby the instrument (Z) for the exposure of interest (X) is independently associated with the outcome (Y) either directly or indirectly through other trait(s) – denoted “?”. Here, this would violate the third assumption of MR and would bias results from an MR study. (B) Indirect horizontal pleiotropy, whereby another SNP (Z2) in linkage disequilibrium (LD) with the instrument (Z1) for the exposure of interest (X) is associated with the outcome (Y) and, due to this correlation between SNPs, the instrument is therefore not independent of the outcome of interest. This is another reason to use independent genetic variants as instruments in an MR analysis and to have some biological knowledge about the mechanisms by which the SNPs are associated with the exposure. (C) A depiction of vertical pleiotropy, whereby the genetic instrument (Z) for the exposure (X) is associated with other trait(s) – denoted “?” – however, this reflects the downstream effects of the exposure that is likely on the causal pathway linking the exposure to the outcome (Y). This is the very essence of MR and is not something that needs to be accounted for in analyses. Measured and unmeasured confounders in all diagrams as represented by “U”, “U1” and “U2”.
Figure 4.2 - Vertical and Horizontal Pleiotropy. Adapted from Hemani et al. and Holmes et al. (A) Classic horizontal pleiotropy, whereby the instrument (Z) for the exposure of interest (X) is independently associated with the outcome (Y) either directly or indirectly through other trait(s) – denoted “?”. Here, this would violate the third assumption of MR and would bias results from an MR study. (B) Indirect horizontal pleiotropy, whereby another SNP (Z2) in linkage disequilibrium (LD) with the instrument (Z1) for the exposure of interest (X) is associated with the outcome (Y) and, due to this correlation between SNPs, the instrument is therefore not independent of the outcome of interest. This is another reason to use independent genetic variants as instruments in an MR analysis and to have some biological knowledge about the mechanisms by which the SNPs are associated with the exposure. (C) A depiction of vertical pleiotropy, whereby the genetic instrument (Z) for the exposure (X) is associated with other trait(s) – denoted “?” – however, this reflects the downstream effects of the exposure that is likely on the causal pathway linking the exposure to the outcome (Y). This is the very essence of MR and is not something that needs to be accounted for in analyses. Measured and unmeasured confounders in all diagrams as represented by “U”, “U1” and “U2”.

References

Other terms in 'Sources of bias and limitations in MR':