Single gene mutations are increasingly being adopted as clinical biomarkers for the optimal application of various therapeutic areas (such as cancer and cardiovascular disease). A single nucleotide polymorphism (SNP), the most common type of genetic variation in human populations, can affect the abundance and function of gene products at the molecular level. In practice, a single-predictor model is usually built using the best single-SNP (or single-gene) biomarker with the most significant drug-SNP (or drug-gene) association in randomized clinical trials. Current statistical methods for the best single-SNP biomarker selection rely on a variety of ranking procedures. However, these existing approaches (i) cannot accurately distinguish drug-SNP interaction effects (i.e., predictive effects) from SNP main effects (i.e., prognostic effects); (ii) do not necessarily yield the true ordering or provide a confidence interval for ranking. In this paper, we propose a novel method called Multiple comparisons with the best using Marginal Means (3M). Specifically, 3M first calculates unbiased estimates of SNPs' predictive effects using marginal means (i.e., least squares means). To select the best predictive SNP (i.e., SNP with the largest unbiased drug-SNP interaction effect), 3M uses a nonparametric bootstrap method to construct constrained simultaneous confidence intervals under the framework of Multiple Comparisons with the Best. Simulation studies demonstrate that our proposed method is more reliable in distinguishing predictive effects from prognostic effects, and thus more powerful to identify the true best predictive SNP than existing methods. Finally, we applied our method to the IMPROVE-IT (IMProved Reduction of Outcomes: Vytroin Efficacy International Trial) pharmacogenomics genome-wide association study (GWAS) data to search for the best predictive SNP across the genome. One single best predictive SNP rs114462013 (STAG1) on chromosome 3 was detected, with the association previously identified in the literature.
Zhai et al. (Wed,) studied this question.