Ruth Heller. Inference following aggregate level hypothesis testing in large scale genomic data

In many genomic applications, it is common to perform tests using aggregate-level statistics within naturally defined classes for powerful identification of signals. Following aggregate-level testing, it is naturally of interest to infer on the individual units that are within classes that contain signal. Failing to account for class selection will produce biased inference. We develop multiple testing procedures that allow rejection of individual level null hypotheses while controlling for conditional (familywise or false discovery) error rates. We use simulation studies to illustrate validity and power of the proposed procedures in comparison to several possible alternatives. We illustrate the usefulness of our procedures in a natural application involving whole-genome expression quantitative trait loci (eQTL) analysis across 17 tissue types using data from The Cancer Genome Atlas (TCGA) Project.

Joint work with Nilanjan Chatterjee, Abba Krieger, and Jianxin Shi.