Simultaneous identification of causal genes and dys-regulated pathways in complex diseases Yoo-Ah Kim, Stefan Wuchty and Teresa M Przytycka Paper to be presented at RECOMB 2010 in Lisbon, August 2010

We developed a novel multistep algorithm to identify causal genes and associated dysregulated pathways by integrating several levels of analysis and data, including gene expression, genomic alterations and molecular interactions. Specifically, we aim to identify pathways, starting from genes that are located in areas of genomic alterations in human gliomas to potential target genes by following molecular interactions such as protein-protein interactions, phosphorylation events and protein-transcription factor interactions. Target genes Disease cases Causal genes pathways Outline

‘Target genes’ are genes differentially expressed in disease samples. The set of target genes is chosen so that to minimize the total number of target genes, while ensuring that every disease case is associated with a chosen minimal number of DE genes. Selecting target genes

eQTL mapping Search for a cause of observed DE by associating target genes with alterations in genetic loci. Loci are locally clustered into sets of consecutive loci (tag loci) with correlated copy number values, to reduce computational costs and alleviate multiple hypothesis testing problem. Linear regression between expression value of a gene and copy number of tag locus. Tag loci with significant p-values are chosen for subsequent steps.

Identifying candidate causal genes For each target gene and associated tag locus find candidate causal genes in the genomic region of that tag locus. Gene is more likely to be causal if there exists a path in the underlying interaction network connecting it with corresponding target gene. Problem of finding a path through a network is modeled as current flow in electric circuit. Compute the current flow from the target gene to its potential causal genes as a function of gene expression correlation at edges and target gene. Current flow is obtained by solving a system of linear equations. Candidate causal genes within loci are selected based on empirical p-values.

Identifying final causal genes We want to explain all disease cases with a minimal number of causal genes. The set of causal genes is chosen so that to minimize their total number, while ensuring minimal number of targets covered by causal genes in every disease case.

Dysregulated pathways Using current flow solution, determine path with maximum current from causal gene to target gene.

Algorithm refinements 1.Allow the target genes to only interact with transcription factors 2.Account for directions of protein-DNA interactions and phosphorylation events. Heuristic solution: solve undirected version, remove edges used in opposite direction, solve the linear system again, etc., until only a small number of directed edges are used incorrrectly.

Application to glioma data Data: -mRNA expression data for 321 glioma patients and 32 controls -Copy number alteration data for the same patients and controls -interaction network – pooled data from databases of protein-protein interactions, phosphorylation (networKIN, phosphoELM) and protein-DNA interactions (TRED)

Application to glioma data

PTEN, a tumor supressor is a causal gene, subnetwork with paths to all its targets. Enriched in genes involved in cell cycle and ER overload response. CDK4, a target gene connected to its causal genes. Enriched in genes involved in cell cycle processes and positive regulation of cell proliferation.