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Polygenic modelling of treatment effect heterogeneity.

Authors
  • Xu, Zhi Ming1, 2
  • Burgess, Stephen1, 3
  • 1 MRC Biostatistics Unit, School of Clinical Medicine, University of Cambridge, Cambridge, UK.
  • 2 School of Life Sciences, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland. , (Switzerland)
  • 3 Department of Public Health and Primary Care, University of Cambridge, UK.
Type
Published Article
Journal
Genetic Epidemiology
Publisher
Wiley (John Wiley & Sons)
Publication Date
Nov 01, 2020
Volume
44
Issue
8
Pages
868–879
Identifiers
DOI: 10.1002/gepi.22347
PMID: 32779269
Source
Medline
Keywords
Language
English
License
Unknown

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. © 2020 The Authors. Genetic Epidemiology published by Wiley Periodicals LLC.

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