Prioritization of Candidate Disease Genes using Microarray Data and Functional Relations

Motivation: Copy number alterations (CNAs) are an important driving force in tumor formation. Given tumor material, such CNAs can be measured using array Comparative Genomic Hybridization (aCGH). By analyzing a collection of aCGH measurements derived from a set of tumors, regions on the genome where recurrent CNAs occur more frequently than one would expect under a chance process can be identified. These regions are potentially causal to cancer development. These regions are typically large, containing thousands of genes, and by combining these findings with gene expression data, the number of putative candidate cancer genes can be further narrowed down. However, these lists of putative cancer genes are still very long, requiring expensive and time-consuming laboratory validation to identify the true causal genes. In addition, this approach does not take relationships between genes on different loci into account and does not exploit the vast amount of knowledge contained in functional databases. Here, we propose a novel method that prioritizes candidate disease genes not only using experiment specific data (gene expression and copy number alteration data) but also using the assumption that disease genes are functionally related. We identify functional gene relationships using Gene Ontology, TRANSFAC, paralogy data and InterPro domains. Results: Our approach inputs putative disease loci that were determined by a separate algorithm based on, for example, copy number data. Here a locus is a contiguous stretch of the genome containing at least one gene. Given these disease loci, we determine the genes that reside on the loci. Then, we create, for a given gene on a locus, multiple prioritizations based on (1) the correlation of the mRNA expression levels and the DNA copy-number variations of that gene and based on the degree to which that gene is related to genes on the other loci according to (2) Gene Ontology, (3) TRANSFAC, (4) paralogy data and (5) InterPro domains. Finally, we merge all the prioritizations using the Q order statistic, resulting in one gene prioritization per locus. This approach differs from existing approaches in the sense that not all relationships datasets are merged into a single network, but that we first prioritize genes using the separate datasets and combine the prioritizations using the Q Rank Order Statistic. This clearly reveals which dataset supports the high ranking of a candidate gene and it is straightforward, due to this modular approach, to incorporate additional datasets. to whom correspondence should be addressed In addition, existing approaches are improved in several regards. First, we have created a Gene Ontology similarity measure that reduces the bias towards well-known, and thus better annotated genes, and that shows good separation of OMIM-gene-pairs. We have also shown that null distributions for the summed similarities derived by permuting the chromosomal locations of complete loci and permuting the genes separately and thus releasing the constraint that they should be adjacent, are different. As a result, an analytical approximation to the null distribution based on the Central Limit Theorem (CLT) cannot be used to calculate the null hypothesis for permuting loci, as it assumes uncorrelated genes. We extended the computation of the null-distribution based the CLT in such a way that the variance of the sum is not a linear function of the number of genes, but is the sum of power functions (a · x b ) of the sizes of the loci. This allows a major reduction in computation time since time consuming permutations need not be performed any more. Our evaluation shows that different data sources are bestperforming for different experiments, but that the combination of all data sources is constantly well-performing.