Multi-subgroup Gene Screening Using Semi-parametric Hierarchical Mixture Models and the Optimal Discovery Procedure: Application to a Randomized Clinical Trial in Multiple Myeloma

Abstract

This article proposes an efficient approach to screening genes associated with a phenotypic variable of interest in genomic studies with subgroups. In order to capture and detect various association profiles across subgroups, we flexibly estimate the underlying effect size distribution across subgroups using a semi-parametric hierarchical mixture model for subgroup-specific summary statistics from independent subgroups. We then perform gene ranking and selection using an optimal discovery procedure based on the fitted model with control of false discovery rate. Efficiency of the proposed approach, compared with that based on standard regression models with covariates representing subgroups, is demonstrated through application to a randomized clinical trial with microarray gene expression data in multiple myeloma, and through a simulation experiment.

Exploring predictive biomarkers from clinical genome-wide association studies via multidimensional hierarchical mixture models

“…Basically, SNPs that have different effect sizes between control and treatment groups would be categorized as predictive markers, while others would be categorized as prognostic markers. However, a specific criterion to categorize SNPs will be subjectively determined (see Matsui et al [6]., for example). In addition, we assessed the performance of the ODP through a simulation study based on the two clinical trials (see Section F in the Supplementary Notes).…”

“…In this analysis, we used the efficient multi-subgroup gene screening method [6] (Fig. 1) developed to overcome the problem of insufficient power to detect interaction effects (see Section D in the Supplementary Notes for details).…”

“…To overcome the lack of statistical power, an alternative effective multi-subgroup gene screening method [6] using a multidimensional semi-parametric hierarchical mixture model [7,8] has been developed by Matsui et al (Fig. 1).…”

Two‐stage analysis for selecting fixed numbers of features in omics association studies

“…When setting for feature j as a cut-off point for feature selection, we select a set Θ of features whose values are equal or greater than ( ). We evaluate a FDR based on the fitted model [20,23],…”

Efficient screening of predictive biomarkers for individual treatment selection

“…To develop the molecular diagnostic tools, a key task is the identification of predictive biomarkers associated with subgroups of individuals who might receive beneficial or harmful effects from different available treatments (Matsui et al ., 2015; Lipkovich et al ., 2017). It is typically explained by interactions between the treatment and candidate biomarkers, but the conventional interaction tests have substantial difficulties for these analyses because of their serious limitations of statistical powers (Matsui et al ., 2018).…”