When drug treatments bias genetic studies: Mediation and interaction

• Published in: PloS one Vol. 14; no. 8; p. e0221209
• Main Authors: Schmidt, Amand F., Heerspink, Hiddo J. L., Denig, Petra, Finan, Chris, Groenwold, Rolf H. H.
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 28.08.2019; Public Library of Science (PLoS)
• Subjects: Accounting; Bias; Biology and Life Sciences; Blood pressure; Cardiovascular disease; Computer Simulation; Confidence intervals; Consortia; Diabetes; Drug interactions; Drug Prescriptions; Drug therapy; Engineering and Technology; Estimates; Genes; Genetic analysis; Genetic aspects; Genetic Diseases, Inborn - drug therapy; Genetic Diseases, Inborn - epidemiology; Genetic Diseases, Inborn - genetics; Genetic diversity; Genetic effects; Genetic research; Genetic variance; Genetics; Genome-Wide Association Study; Genomes; Genotype & phenotype; Graph theory; Heart; Humans; Longitude; Mediation; Medical treatment; Medicine and Health Sciences; Negotiating; Pharmacogenomic Variants - genetics; Pharmacy; Phenotype; Phenotypes; Physiological aspects; Quantitative trait loci; Quantitative Trait Loci - genetics; Regression analysis; Rejection rate; Research and Analysis Methods; Root-mean-square errors; Studies; Trajectory analysis; Type 2 diabetes; United Kingdom; United Kingdom > UK; England; Netherlands
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0221209

Increasingly, genetic analyses are conducted using information from subjects with established disease, who often receive concomitant treatment. We determined when treatment may bias genetic associations with a quantitative trait. Graph theory and simulated data were used to explore the impact of drug prescriptions on (longitudinal) genetic effect estimates. Analytic derivations of longitudinal genetic effects are presented, accounting for the following scenarios: 1) treatment allocated independently of a genetic variant, 2) treatment that mediates the genetic effect, 3) treatment that modifies the genetic effect. We additionally evaluate treatment modelling strategies on bias, the root mean squared error (RMSE), coverage, and rejection rate. We show that in the absence of treatment by gene effect modification or mediation, genetic effect estimates will be unbiased. In simulated data we found that conditional models accounting for treatment, confounding, and effect modification were generally unbiased with appropriate levels of confidence interval coverage. Ignoring the longitudinal nature of treatment prescription, however (e.g. because of incomplete records in longitudinal data), biased these conditional models to a similar degree (or worse) as simply ignoring treatment. The mere presence of (drug) treatment affecting a GWAS phenotype is insufficient to bias genetic associations with quantitative traits. While treatment may bias associations through effect modification and mediation, this might not occur frequently enough to warrant general concern at the presence of treated subjects in GWAS. Should treatment by gene effect modification or mediation be present however, current GWAS approaches attempting to adjust for treatment insufficiently account for the multivariable and longitudinal nature of treatment trajectories and hence genetic estimates may still be biased.