|YOO AH KIM|| at 11:00
Identifying Causal Genes and Dysregulated Pathways in Complex Diseases
In complex diseases various combinations of genomic perturbations often lead to the same phenotype. On a molecular level, combinations of genomic perturbations potentially dys-regulate the same important cellular pathways. Such pathway-centric perspective is fundamental to understanding the mechanisms of complex diseases and the identification of potential drug targets. While previous methods have given valuable insight into the modular nature of diseases, they did not provide a genome-wide view on possible effectors of such dys-regulation.In order to provide an integrated perspective on complex disease mechanisms, we developed a novel computational method to simultaneously identify causal genes and dys-regulated pathways. We first identified a representative set of genes that are differentially expressed in cancer and control and assuming that diseases associated gene expression changes are in large extend caused by genomic alterations, we determined potential paths from such genomic causes to target genes through a network of molecular interactions. Applying our method to sets of genomic alterations and gene expression profiles of 158 Glioblastoma multiforme (GBM) patients we uncovered candidate causal genes and causal paths that are potentially responsible for the altered expression of disease genes. As expected, we found a number of causal genes in the large areas of genomic alteration on chromosomes 7 and 10, coinciding with the genomic locations of EGFR and PTEN. In addition, we discovered other putative causal genes that potentially play a role in the disease. Our method combines Quantitative Trait Loci (eQTL) analysis with pathway information resulting in a very powerful approach which allows to identify potential causal genes as well as intermediate nodes on molecular pathways that mediate information flow between causal and target genes. While copy number variation and gene expression data of glioblastoma patients provided opportunities to test our approach, our method can be applied to any disease system where genetic variations play a fundamental causal role.