ReviewPost screenA conceptual framework for pharmacodynamic genome-wide association studies in pharmacogenomics
Introduction
It has been recognized that there exists a great variability among different individuals in response to a particular drug, with a large proportion determined by genes 1, 2. The term pharmacogenetics or pharmacogenomics was proposed to investigate the effects of individual genes on drug disposition and drug response. With the rapid development of high-throughput genotyping techniques, genome-wide association studies (GWAS) using multiple single nucleotide polymorphisms (SNPs) to test association with a phenotype have revolutionized the study of the comprehensive genetic architecture of complex traits. In the last three years, this approach has been increasingly applied to search for genetic influences on drug response 3, 4, 5, 6, 7, 8, 9 or adverse drug reactions 10, 11, 12. Many GWAS studies identified significant genetic associations that display potential clinical implications in practice [13].
Despite benefits in identifying genetic variants for complex traits, GWAS also raises questions. One of the most important is why the identified variants account for so little heritability 14, 15. When applied to pharmacogenomics, GWAS can raise additional challenges [2]. First, since it is challenging to obtain adequate numbers of cases for pharmacogenomics GWAS, there is insufficient power to detect small or moderately sized genetic effects. This sharply contrasts with complex-disease GWAS studies in which a sample size with a large population is common [16]. Second, in order to determine an optimal dose for individual patients, phenotypic response is often measured at a range of drug doses, leading to the longitudinal feature of drug response. Third, longitudinal trajectories of drug response in clinical trials may be sampled at sparsely distributed doses, with dose levels varying from subject to subject; the measurements may be affected by noise and are dependent within the same subject. To better study the genetic etiology of drug response, therefore, a special analytical model should be derived by considering these characteristics.
The purpose of this report is to introduce a dynamic model for pharmacogenomics GWAS through incorporating repeated measures of drug response. While we will show that statistical modeling of multiple measurements increases the power of gene detection, this treatment also exhibits a significant biological relevance. Normally, when a drug is administered to a patient, it must be absorbed, distributed to its site of action, interact with its targets, undergo metabolism, and finally be excreted [17]. This process, called pharmacokinetics (PKs), influences the concentration of a drug reaching its target, and it interacts with another process associated with the drug target, called pharmacodynamics (PD), to determine drug response. By modeling the effects of a drug over a range of doses, the PD response can be quantified. The incorporation of PK and PD models to map quantitative trait loci for drug response has proven to be powerful for gene identification 18, 19, 20. More recently, new insight has been provided for the mechanistic basis of interactions between genes and drug reactions by dissecting drug response as a dynamic system in which the coordination of various biochemical components is modeled by a series of differential equations [21]. We propose that integrating PD processes into GWAS will facilitate the mechanistic elucidation of the genetic architecture of drug response.
Section snippets
Dynamic pattern of pharmacogenetic control
It has been recognized that specific genes are involved in regulating drug response via PK and PD processes of medications 22, 23. In Fig. 1, a gene is assumed to determine inherited differences in drug disposition (e.g. metabolizing enzymes and transporters), expressed as PK reactions (top, Fig. 1), and in regulating drug targets (e.g. receptors), expressed as PD reactions (middle, Fig. 1) [22]. Patients are different in drug clearance (or the area under the plasma concentration–time curve)
Study design
Suppose there is a group of patients who differ in age, race, sex, body mass index (BMI), and other demographics, recruited from a natural population to study their response to a particular drug. If the drug is administered with a series of doses, each patient is measured for some phenotypes that reflect drug effect. Let Ci = (Ci1, …, CiTi) denote dose levels administered to subject i and yi = [yi(Ci1), …, yi(CiTi)] denote the vector of effect phenotypes at Ti at different doses for this subject.
Model validation
To investigate the statistical behavior of the PD model for GWAS, we performed simulation studies for a genomic region derived from a GWAS dataset and calculated the mean genotypic curves under an additive genetic model. The simulation studies mimicked a pharmacogenomic trial of asthma [26], reflecting a general design used for practical pharmacogenomic studies in terms of sample size, dose level, and demographical attributes of participants. The phenotypic longitudinal data of drug response
Discussion
Drug reactions can be better described as a coordinated network of genes, proteins and biochemical reactions [27]. However, traditional pharmacogenetic or pharmacogenomics studies merely consider the associations between genes and final outcomes of pharmacological parameters. By incorporating the PD and PK mechanisms through mathematical equations, the statistical method proposed for GWAS will provide a computational tool for identifying genetic variants associated with drug response and,
Acknowledgment
Funding support: NIH/NHLBI-1U10HL098115 to DM.
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