When drug treatments bias genetic studies: Mediation and interaction
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 dru...
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| Published in | PloS one Vol. 14; no. 8; p. e0221209 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
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United States
Public Library of Science
28.08.2019
Public Library of Science (PLoS) |
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| Abstract | 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. |
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| AbstractList | 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.BACKGROUNDIncreasingly, 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.METHODSGraph 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.RESULTSWe 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.CONCLUSIONThe 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. 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. 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. Background 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. Methods 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. Results 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. Conclusion 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. Background 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. Methods 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. Results 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. Conclusion 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. BackgroundIncreasingly, 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.MethodsGraph 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.ResultsWe 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.ConclusionThe 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. |
| Audience | Academic |
| Author | Denig, Petra Schmidt, Amand F. Heerspink, Hiddo J. L. Groenwold, Rolf H. H. Finan, Chris |
| AuthorAffiliation | 4 Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, the Netherlands 1 Institute of Cardiovascular Science, Faculty of Population Health, University College London, London, England, United Kingdom 3 Department of Clinical Pharmacy and Pharmacology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands London School of Hygiene and Tropical Medicine, UNITED KINGDOM 2 Department of Cardiology, Division Heart and Lungs, University Medical Center Utrecht, Utrecht, the Netherlands |
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| Author_xml | – sequence: 1 givenname: Amand F. orcidid: 0000-0003-1327-0424 surname: Schmidt fullname: Schmidt, Amand F. – sequence: 2 givenname: Hiddo J. L. surname: Heerspink fullname: Heerspink, Hiddo J. L. – sequence: 3 givenname: Petra surname: Denig fullname: Denig, Petra – sequence: 4 givenname: Chris surname: Finan fullname: Finan, Chris – sequence: 5 givenname: Rolf H. H. surname: Groenwold fullname: Groenwold, Rolf H. H. |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/31461463$$D View this record in MEDLINE/PubMed |
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| CitedBy_id | crossref_primary_10_1002_pds_5437 crossref_primary_10_1186_s12920_023_01444_8 |
| Cites_doi | 10.1002/pds.3965 10.1007/978-0-387-21706-2 10.1002/we.1666 10.1038/nrg1521 10.1002/sim.2165 10.1197/jamia.M2128 10.1038/ng.3667 10.2337/dc11-1465 |
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| Copyright | COPYRIGHT 2019 Public Library of Science 2019 Schmidt et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2019 Schmidt et al 2019 Schmidt et al |
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| References_xml | – volume: 25 start-page: 355 year: 2016 ident: pone.0221209.ref007 article-title: Tailoring treatments using treatment effect modification publication-title: Pharmacoepidemiology and Drug Safety doi: 10.1002/pds.3965 – volume-title: Modern Applied Statistics With STechnometrics year: 2002 ident: pone.0221209.ref011 doi: 10.1007/978-0-387-21706-2 – year: 2019 ident: pone.0221209.ref004 article-title: Association of Chromosome 9p21 with Subsequent Coronary Heart Disease Events: A GENIUS-CHD Study of Individual Participant Data publication-title: Circ Genom Precis Med – volume: 17 start-page: 1753 year: 2014 ident: pone.0221209.ref010 article-title: Probabilistic wind power forecasts with an inverse power curve transformation and censored regression publication-title: Wind Energy doi: 10.1002/we.1666 – volume: 8 start-page: 173 year: 2016 ident: pone.0221209.ref009 article-title: Heteroscedastic Censored and Truncated Regression with crch publication-title: R-Journal – year: 2016 ident: pone.0221209.ref013 article-title: Predictors of HbA1c levels in patients initiating metformin publication-title: Current Medical Research and Opinion – volume: 45 start-page: 1 year: 2011 ident: pone.0221209.ref014 article-title: mice: Multivariate Imputation by Chained Equations in R publication-title: Journal of Statistical Software – volume: 36 start-page: 2674 year: 2015 ident: pone.0221209.ref002 article-title: The GENIUS-CHD consortium publication-title: European Heart Journal – ident: pone.0221209.ref003 article-title: Subsequent Event Risk in Individuals with Established Coronary Heart Disease: Design and Rationale of the GENIUS-CHD Consortium publication-title: Circulation: Genomic and Precision Medicine – volume: 6 start-page: 95 year: 2005 ident: pone.0221209.ref001 article-title: Genome-wide association studies for common diseases and complex traits publication-title: Nature Reviews Genetics doi: 10.1038/nrg1521 – volume: 24 start-page: 2911 year: 2005 ident: pone.0221209.ref006 article-title: Adjusting for treatment effects in studies of quantitative traits: Antihypertensive therapy and systolic blood pressure publication-title: Statistics in Medicine doi: 10.1002/sim.2165 – volume: 14 start-page: 349 year: 2007 ident: pone.0221209.ref012 article-title: Computerized Extraction of Information on the Quality of Diabetes Care from Free Text in Electronic Patient Records of General Practitioners publication-title: Journal of the American Medical Informatics Association doi: 10.1197/jamia.M2128 – volume: 48 start-page: 1171 year: 2016 ident: pone.0221209.ref005 article-title: The genetics of blood pressure regulation and its target organs from association studies in 342,415 individuals publication-title: Nat Genet doi: 10.1038/ng.3667 – year: 2017 ident: pone.0221209.ref008 – volume: 35 start-page: 446 year: 2012 ident: pone.0221209.ref015 article-title: Quantifying the effect of metformin treatment and dose on glycemic control publication-title: Diabetes Care doi: 10.2337/dc11-1465 |
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| SubjectTerms | 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 |
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| Title | When drug treatments bias genetic studies: Mediation and interaction |
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