| Title: | Polygenic modelling of treatment effect heterogeneity |
| Journal: | Genetic Epidemiology |
| Published: | 10 Aug 2020 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/32779269/ |
| DOI: | https://doi.org/10.1002/gepi.22347 |
| URL: | https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/gepi.22347 |
| Citations: | 7 (4 in last 2 years) as of 8 Aug 2024 |
Publication 3694
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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.</p>
14 Keywords
- Biological Specimen Banks
- Cholesterol, LDL
- Computer Simulation
- Epistasis, Genetic
- Female
- Genetic Heterogeneity
- Genetic Variation
- Humans
- Male
- Mendelian Randomization Analysis
- Middle Aged
- Multifactorial Inheritance
- Risk Factors
- United Kingdom
2 Authors
- Zhi Ming Xu
- Stephen Burgess
1 Application
| Application ID | Title |
|---|---|
| 50063 | Identifying genetic predictors of response to pharmaceutical drugs using machine learning |
Enabling scientific discoveries that improve human health