Abstract
Copy number variation (CNV) detection has become an integral part many of genetic studies and new technologies promise to revolutionize our ability to detect and link them to disease. However, recent studies highlight discrepancies in the genome wide CNV profile when measured by different technologies and even by the same technology. Furthermore, the change point algorithms used to call CNVs can have substantial disagreement on the same data set. We focus this article on comparative genomic hybridization (CGH) arrays because this platform lends itself well to accurate statistical modeling. We describe some newer methodological developments in local statistics that are well suited for CNV detection and calling on CGH arrays. Then we use both simulation studies and public data to compare these new local methods with the global methods that currently dominate literature. These results offer suggestions for choosing a particular method and provide insight to the lack of reproducibility that has been seen in the field so far.
Highlights
The identification of copy number variations (CNV) has been integral in improving our understanding of the molecular basis for many diseases
It is clear that the sparse signal methods are substantially more powerful than Circular Binary Segmentation (CBS) (p < 0.001) and the difference in power is even more dramatic for the t-error distribution than for the normal distribution
Sara appears to outperform the other algorithms for the smallest 3 CNV widths, but the power stops increasing for larger width aberrations
Summary
The identification of copy number variations (CNV) has been integral in improving our understanding of the molecular basis for many diseases. While sequencing platforms hold promise for CNV detection, array based platforms are the primary technology used to identify CNVs useful for diagnostics. These platforms have developed rapidly to provide increased genome resolution that should provide increased power to detect smaller CNV. An alarming number of studies have reported discrepancies when comparing calls from a replicate sample measured on different platforms and even on the same platform (Baumbusch et al, 2008; Curtis et al, 2009; Pinto et al, 2011). It has been noted that many of the removed regions detected only by one method can be validated (Conrad et al, 2010; Pinto et al, 2011)
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