Testing multiple hypotheses through IMP weighted FDR based on a genetic functional network with application to a new zebrafish transcriptome study

In genome-wide studies, thousands of hypothesis tests are carried out at the same time. Bonferroni correction and False Discovery Rate (FDR) can effectively control type I error but often yield a high false negative rate. We aim to develop a more powerful method to detect differential expressed genes. We present an Weighted False Discovery Rate (WFDR) method that incorporate biological knowledge from genetic networks. We first identify weights using Integrative Multi-species Prediction (IMP) and then apply the weights in WFDR to identify differentially expressed genes through a IMP-WFDR algorithm. We conducted a simulation study to characterize the performance of this method. We performed genomic characterization to identify potential synergistic and antagonist interactions between the highly-conserved zebrafish cftr gene and the environmental toxicant arsenic, particularly in the context of a systemic infection with Pseudomonas aeruginosa. Zebrafish were exposed to arsenic at 10 parts per billion and/or infected with P. aeruginosa. Appropriate controls were included. We then applied IMP-WFDR during the analysis of differentially expressed genes. We compared the mRNA expression for each group and found over 200 differentially expressed genes and several enriched pathways including defense response pathways, arsenic response pathways, and the Notch signaling pathway.