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.
Highlights
Copy number variations (CNV) are a part of normal Human genetic variability
The second aim of this work was demonstrating the effectiveness of association tests which are capable of detecting non-monotone relationships, such as distance covariance (dCov), maximal information coefficient (MIC) and HHG for analyzing whole genome association data
In addition to the traditional association tests, Spearman and Pearson, we applied three tests, dCov, MIC and HHG, which are capable of detecting non-monotone relationships
Summary
Copy number variations (CNV) are a part of normal Human genetic variability. Tens of thousands of CNVs have been reported in the Database of Genomic Variants (DGV) based on healthy control samples [1,2]. CNVs are a significant component of variation in disease risk and occurrence of many diseases and disorders, including cancer, HIV infection, autism, and psychiatric diseases [3,4,5]. CNV is one of the most important somatic aberrations found [6]. Nowadays CNV analysis has become a central part of cancer research and many studies concentrate on detecting CNVs in the human genome in normal and diseased tissues and cells. In clinics a growing number of CNV are used for diagnostics and personalized therapy
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