Copy-number-variation and copy-number-alteration region detection by cumulative plots

TL;DR

This paper introduces a cumulative plot method for detecting copy number variations and alterations in genomic data, demonstrating its effectiveness in identifying both large and small regions in cancer and germline studies.

Contribution

The paper presents a novel, intuitive, and scale-free graphical approach using cumulative plots for identifying copy number variation regions in genomic data.

Findings

Successfully detected a 9Mb hemizygous deletion

Identified a 1Mb homozygous deletion on chromosome 13

Potential to detect regions below 100kb in size

Abstract

Background: Regions with copy number variations (in germline cells) or copy number alteration (in somatic cells) are of great interest for human disease gene mapping and cancer studies. They represent a new type of mutation and are larger-scaled than the single nucleotide polymorphisms. Using genotyping microarray for copy number variation detection has become standard, and there is a need for improving analysis methods. Results: We apply the cumulative plot to the detection of regions with copy number variation/alteration, on samples taken from a chronic lymphocytic leukemia patient. Two sets of whole-genome genotyping of 317k single nucleotide polymorphisms, one from the normal cell and another from the cancer cell, are analyzed. We demonstrate the utility of cumulative plot in detecting a 9Mb (9 x 10^6 bases) hemizygous deletion and 1Mb homozygous deletion on chromosome 13. We also show the possibility to detect smaller copy number variation/alteration regions below the 100kb range. Conclusions: As a graphic tool, the cumulative plot is an intuitive and a scale-free (window-less) way for detecting copy number variation/alteration regions, especially when such regions are small.