Testing multiple hypotheses through IMP weighted FDR based on a genetic functional network with application to a new zebrafish transcriptome study In genomewide studies, hundreds of thousands of hypothesis tests are performed simultaneously. 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 differentially expressed genes. We present a Weighted False Discovery Rate (WFDR) method that incorporate biological knowledge from genetic networks. We first identify weights using Integrative Multispecies Prediction (IMP) and then apply the weights in WFDR to identify differentially expressed genes through an IMPWFDR algorithm. We performed a gene expression experiment to identify zebrafish genes that change expression in the presence of arsenic during a systemic Pseudomonas aeruginosa infection. Zebrafish were exposed to arsenic at 10 parts per billion andor infected with P. aeruginosa. Appropriate controls were included. We then applied IMPWFDR 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.

Factors Affecting NetworkBased Gene Prediction Across Diverse Diseases There are many current efforts to integrate biological interaction data with disease information in order to predict new genes associated with complex diseases. Networkbased learning methods such as logistic regression can utilize this information to identify disease genes, and are typically applied to proteinprotein interaction networks. However, little is reported about what factors influence the performance of these networkbased methods. Here, we explore features that affect networkbased disease gene prediction performance. We devise two crossvalidation schemes to evaluate the impact of various parameters, settings and disease qualities across a wide range of diseases. We demonstrate that including gene regulatory interactions and including lowconfidence disease genes improves disease gene prediction performance. Further, network connectivity among highconfidence disease genes is a strong indicator of prediction performance. We demonstrate that network and input features can have a dramatic effect on prediction performance, and these should be carefully considered when designing networkbased algorithms to find new disease genes.