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

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.

Replication of associations between cytokine and cytokine receptor single nucleotide polymorphisms and measles-specific adaptive immunophenotypic extremes

“…The use of selected samples with extreme immunophenotypes versus random population samples maximized variability and statistical power for differentiating true from false positives [12], while being economically parsimonious. A limitation of our study is an increased signal-to-noise ratio, since statistical power may be affected by the use of different humoral and cell-mediated immune response measures in the 1 st and 2 nd cohorts [27]. Also, the modest sample size of the 1 st and 2 nd cohorts may have limited our ability to detect smaller genetic effects.…”

Promises and challenges of pharmacogenetics: an overview of study design, methodological and statistical issues

“…Under these conditions, it is assumed that the genetic variant is causal; however, it is possible that the variant allele is in LD with the actual causal variant, which may require a larger sample size. 79,80 If a rare genetic variant is anticipated with a small or modest effect, a sample size of more than 900,000 participants would be required. However, if a common variant with a large effect was expected, then a sample size of approximately 900 participants is needed.…”

ADME pharmacogenetics: current practices and future outlook

“…Many of these areas have been highlighted in Section 4 and at the beginning of the current section. It should be stressed that a PGX analysis should also account for genetic and non-genetic effects associated with the underlying disease, its progression, severity and impact of placebo effect in the target population [69]. Bias introduced by each of these potential confounders may drive much of the controversy in the field today.…”