Polygenic Modelling of Treatment Effect Heterogeneity

Overview

Journal Genet Epidemiol

Specialties Genetics
Public Health

Date 2020 Aug 12

PMID 32779269

Citations 4

Authors

Zhi Ming Xu

Stephen Burgess

Affiliations

Soon will be listed here.

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.

Citing Articles

A data-adaptive method for investigating effect heterogeneity with high-dimensional covariates in Mendelian randomization.

Tian H, Tom B, Burgess S BMC Med Res Methodol. 2024; 24(1):34.

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Using genetic association data to guide drug discovery and development: Review of methods and applications.

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Integrating genomics with biomarkers and therapeutic targets to invigorate cardiovascular drug development.

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