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

ABSTRACT: Although the detection of predictive biomarkers is of particular importance for the development of accurate molecular diagnostics, conventional statistical analyses based on gene-by-treatment interaction tests lack sufficient statistical power for this purpose, especially in large-scale clinical genome-wide studies that require an adjustment for multiplicity of a huge number of tests. Here we demonstrate an alternative efficient multi-subgroup screening method using multidimensional hierarchical mixture models developed to overcome this issue, with application to stroke and breast cancer randomized clinical trials with genomic data. We show that estimated effect size distributions of single nucleotide polymorphisms (SNPs) associated with outcomes, which could provide clues for exploring pr

SUBMITTER: Otani T 

PROVIDER: S-EPMC6303260 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature