title: Function of cancer associated genes revealed by modern univariate and multivariate association tests.
abstract: Copy number variation (CNV) plays a role in pathogenesis of many human diseases, especially cancer. Several whole genome CNV association studies have been performed for the purpose of identifying cancer associated CNVs. Here we undertook a novel approach to whole genome CNV analysis, with the goal being identification of associations between CNV of different genes (CNV-CNV) across 60 human cancer cell lines. We hypothesize that these associations point to the roles of the associated genes in cancer, and can be indicators of their position in gene networks of cancer-driving processes. Recent studies show that gene associations are often non-linear and non-monotone. In order to obtain a more complete picture of all CNV associations, we performed omnibus univariate analysis by utilizing dCov, MIC, and HHG association tests, which are capable of detecting any type of association, including non-monotone relationships. For comparison we used Spearman and Pearson association tests, which detect only linear or monotone relationships. Application of dCov, MIC and HHG tests resulted in identification of twice as many associations compared to those found by Spearman and Pearson alone. Interestingly, most of the new associations were detected by the HHG test. Next, we utilized dCov's and HHG's ability to perform multivariate analysis. We tested for association between genes of unknown function and known cancer-related pathways. Our results indicate that multivariate analysis is much more effective than univariate analysis for the purpose of ascribing biological roles to genes of unknown function. We conclude that a combination of multivariate and univariate omnibus association tests can reveal significant information about gene networks of disease-driving processes. These methods can be applied to any large gene or pathway dataset, allowing more comprehensive analysis of biological processes.
authors.affiliation: Faculty of Industrial Engineering and Management, Technion- Israel Institute of Technology, Technion City, Haifa 3200003, Israel.
authors.forename: Malka
authors.initials: M
authors.lastname: Gorfine
authors.affiliation: Faculty of Biology, Technion- Israel Institute of Technology, Technion City, Haifa 3200003, Israel.
authors.forename: Boaz
authors.initials: B
authors.lastname: Goldstein
authors.affiliation: Faculty of Biology, Technion- Israel Institute of Technology, Technion City, Haifa 3200003, Israel.
authors.forename: Alla
authors.initials: A
authors.lastname: Fishman
authors.affiliation: Department of Statistics and Operations Research, Tel Aviv University, Ramat Aviv, Tel Aviv 6997801, Israel.
authors.forename: Ruth
authors.initials: R
authors.lastname: Heller
authors.affiliation: Tel Aviv, Israel.
authors.forename: Yair
authors.initials: Y
authors.lastname: Heller
authors.affiliation: Faculty of Biology, Technion- Israel Institute of Technology, Technion City, Haifa 3200003, Israel.
authors.forename: Ayelet T
authors.initials: AT
authors.lastname: Lamm
mesh_terms: D045744:Cell Line, Tumor; D055028:Comparative Genomic Hybridization; D056915:DNA Copy Number Variations / Q000235:genetics*; D030541:Databases, Genetic; D015894:Genome, Human; D055106:Genome-Wide Association Study; D006801:Humans; D009363:Neoplasm Proteins / Q000235:genetics*; D009369:Neoplasms / Q000235:genetics* / Q000473:pathology; D015398:Signal Transduction / Q000235:genetics
chemical_list: D009363:Neoplasm Proteins