Trick or treat: The effect of placebo on the power of pharmacogenetic association studies

Clara Singer,Iris Grossman,Jacques S Beckmann,Nili Avidan,Itsik Pe'Er

doi:10.1186/1479-7364-2-1-28

Abstract

The genetic mapping of drug-response traits is often characterised by a poor signal-to-noise ratio that is placebo related and which distinguishes pharmacogenetic association studies from classical case-control studies for disease susceptibility. The goal of this study was to evaluate the statistical power of candidate gene association studies under different pharmacogenetic scenarios, with special emphasis on the placebo effect. Genotype/phenotype data were simulated, mimicking samples from clinical trials, and response to the drug was modelled as a binary trait. Association was evaluated by a logistic regression model. Statistical power was estimated as a function of the number of single nucleotide polymorphisms (SNPs) genotyped, the frequency of the placebo 'response', the genotype relative risk (GRR) of the response polymorphism, the strategy for selecting SNPs for genotyping, the number of individuals in the trial and the ratio of placebo-treated to drugtreated patients. We show that: (i) the placebo 'response' strongly affects the statistical power of association studies -- even a highly penetrant drug-response allele requires at least a 500-patient trial in order to reach 80 per cent power, several-fold more than the value estimated by standard tools that are not calibrated to pharmacogenetics; (ii) the power of a pharmacogenetic association study depends primarily on the penetrance of the response genotype and, when this penetrance is fixed, power decreases for larger placebo effects; (iii) power is dramatically increased when adding markers; (iv) an optimal study design includes a similar number of placebo- and drugtreated patients; and (v) in this setting, straightforward haplotype analysis does not seem to have an advantage over single marker analysis.

Highlights

Pharmacogenetics (PGx) — the study of how genetic differences influence the variability in patients’ response to drugs1 — investigates genes ideally covering all of the drug’s interactions in the course of its passage through the body.[2]
Even for the best penetrance-scenario examined (GRR 1⁄4 3 and placebo effect f0 1⁄4 26.6 per cent), more than 500 individuals are required to be included in the clinical trial to reach the standard level of 80 per cent power
We have shown that the attributes characteristic of a clinical trial, the magnitude of the placebo effect, have unexpected implications on the statistical power of PGx association studies

Summary

Introduction

Pharmacogenetics (PGx) — the study of how genetic differences influence the variability in patients’ response to drugs1 — investigates genes ideally covering all of the drug’s interactions in the course of its passage through the body.[2]. There are examples where a single gene may exert a dominant effect on treatment efficacy, as in the case of cytochrome P4502D6 (CYP2D6), where deficient patients need to be identified before treatment initiation by codeine and its derivatives due to efficacy loss.[3] More commonly, the phenotype of drug response is classified as multifactorial, as it generally results from the interaction of a number of different genetic, as well as environmental, factors. An example of this is the efficacy of clozapine therapy in the treatment of schizophrenia.[4]

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