GIANT Tutorials

Tutorials for analyzing your genes in the context of predicted genome-wide tissue networks

# Tissue-specific functional relationships

Getting started: single tissue query

Predicted tissue-specific network

Biological process, disease, and pathway

Example: IL1B response to inflammation in blood vessels

The above steps produced the predicted functional relationship and related genes of IL1B in the blood vessel. These genes serve as excellent canddiates for a role in mediating IL1B inflammatory response and additional experiments. For example, in Greene, CS, et al., we experimentally verified the tissue specific molecular response of blood vessel cells to stimulation by IL1B, a proinflammatory ctytokine.

We profiled the gene expression of human aortic smooth muscle cells (HASMCs; the predominant cell type in blood vessels) stimulated with IL-1β. Examination of the genes whose expression was significantly upregulated at 2 h after stimulation showed that 18 of the 20 IL1B network neighbors were among the top 500 most upregulated genes in the experiment (P = 2.07 × 10−23). The blood vessel network was the most accurate tissue network in predicting this experimental outcome; none of the other tissue-specific networks or the tissue-naive network performed as well when evaluated by each network's ability to predict the result of IL-1β stimulation on the cells.

#Translational example: studying the role of PARK7 in two tissue-specific networks

Multi-network gene set enrichment

# NetWAS: Network-guided GWAS Analysis

NetWAS integrates tissue-specific networks and nominally significant p-values in genome-wide association studies to identify biogloically important disease-gene associations. The method has potential to discover novel candidate genes, and does not depend on known disease-associations, thus retaining the unbiased nature of GWAS.

Example: Analyzing a hypertension GWAS with the kidney network