Abstract
Mendelian randomization is the use of genetic variants to assess the effect of intervening on a risk factor using observational data. We consider the scenario in which there is a pharmacomimetic (i.e., treatment-mimicking) genetic variant that can be used as a proxy for a particular pharmacological treatment that changes the level of the risk factor. If the association of the pharmacomimetic genetic variant with the risk factor is stronger in one subgroup of the population, then we may expect the effect of the treatment to be stronger in that subgroup. We test for gene-gene interactions in the associations of variants with a modifiable risk factor, where one genetic variant is treated as pharmacomimetic and the other as an effect modifier, to find genetic subgroups of the population with different predicted response to treatment. If individual genetic variants that are strong effect modifiers cannot be found, moderating variants can be combined using a random forest of interaction trees method into a polygenic response score, analogous to a polygenic risk score for risk prediction. We illustrate the application of the method to investigate effect heterogeneity in the effect of statins on low-density lipoprotein cholesterol.
Highlights
HMGCR inhibitors on disease outcomes (Khera & Rader, 2009)
Associations between the HMGCR variants and Genetic variants can be treated as proxies for treatments to coronary artery disease risk suggest that statins should assess the effect of intervening on a particular biological reduce coronary artery disease risk (Ference, Majeed, pathway using observational data (Plenge, Scolnick, & Penumetcha, Flack, & Brook, 2015), as has been observed
We have introduced an agnostic approach to combine genetic variants into a composite effect modifier that divides the population into genetic subgroups which are predicted to respond differently to a particular treatment
Summary
HMGCR inhibitors on disease outcomes (Khera & Rader, 2009). Associations between the HMGCR variants and Genetic variants can be treated as proxies for treatments to coronary artery disease risk suggest that statins should assess the effect of intervening on a particular biological reduce coronary artery disease risk (Ference, Majeed, pathway using observational data (Plenge, Scolnick, & Penumetcha, Flack, & Brook, 2015), as has been observed. Genetic variants in the HMGCR gene region, representing proxies for statins, and genetic variants in the proprotein convertase subtilisin‐kexin type 9 (PCSK9) gene region, representing proxies for PCSK9 inhibitors, showed no interaction in their associations with either LDL‐cholesterol or coronary artery disease (Ference et al, 2016). By summing the contributions of large numbers of variants across the whole genome into a single univariable score, prediction is improved compared to approaches that take information on a small number of variants (Dudbridge, 2013; Inouye et al, 2018) This suggests the possibility of using a similar approach to construct genetic subgroups of the population which differ in their predicted response to pharmacological treatment, even if no individual variants can be found that have a strong gene–gene interaction. Software for implementing the method is available from https://github.com/zmx21/polyresponse
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