Location: Animal Parasitic Diseases Laboratory
Project Number: 8042-32000-098-07-A
Project Type: General Cooperative Agreement
Start Date: Apr 19, 2010
End Date: Nov 30, 2014
The specific objectives of this agreement are to Identify differentially expressed (DE) genes in blood in response to PRRSV infection; Determine putative gene sets and pathways that predict a pig's ability to clear PRRSV infection and maintain weight gain; and Validate utility of gene sets and pathways for prediction of responsiveness to PRRSV infections in multiple populations. Predictive blood tests of pigs with improved PRRS disease resistance and growth maintenance; increased understanding of mechanisms involved in pig responses to PRRSV infection; scientific publications.
The university's Data storage will be centralized at ISU via the PRRS Host Genetics Consortium (PHGC) database and for this project the microarray data through ANEXdb. A tool will be developed to accept two-color array data as well as updated annotations for the Pigoligoarray oligonucleotides. For the first objective the microarray data analysis, lists of important DE genes will be annotated using DAVID tools, clustered and drawn into networks to generate informative networks that describe the common response to PRRSV over time in high viral load and low viral load animals. These networks will be further reinforced and expanded using orthogonal data such as cytokine expression and the second objective data. Will overlay the first and second objective networks to potentially clarify modules and hubs responding in vivo, and attempt to understand regulatory networks across datasets. For Classifier development will use all the first and second objective expression data (using gene sets clustered by expression pattern within and across Objectives., separately or in combination with QPCR, cytokine or other phenotypic data) to create and evaluate several classifiers using machine learning algorithms implemented in Weka. Initially, 24-48 genes will be used from each sample using sets of RNAs from PHGC samples representing the 4 different virus/weight categories to construct the classifiers. The performance of the resulting classifiers (as measured by classification accuracy, sensitivity, specificity, and area under ROC curve), will be evaluated using rigorous statistical cross-validation. Genes predicted to be good classifiers will be tested by coPIs using QPCR on additional samples for the iterative refinement process to improve classifier predictions.