Detection of Significant Copy Number Variations From Multiple Samples in Next-Generation Sequencing Data

Analyzing copy number variations (CNVs) from next-generation sequencing (NGS) data has become a common approach to detect disease susceptibility genes. The main challenge is how to utilize the NGS data with limited coverage depth to detect significant CNVs. Here, we introduce a new statistical method, the derivative of correlation coefficient (DCC), to detect significant CNVs that recurrently occur in multiple samples using read depth signals. We use a sliding window to calculate a correlation coefficient for each genome bin, and compute corresponding derivatives by fitting curves to the correlation coefficient. Then, the detection of significant CNVs was transformed into a problem of detecting significant derivatives reflecting genome breakpoints that can be solved using statistical hypothesis testing. We tested and compared the performance of DCC against several peer methods using a large number of simulation data sets, and validated DCC using several real sequencing data sets derived from the European Genome-Phenome archive, DNA Data Bank of Japan, and the 1000 Genomes Project. Experimental results suggest that DCC is an effective approach for identifying CNVs, outperforming peer methods in the terms of detection power and accuracy. DCC can be used to detect significant or recurrent CNVs in various NGS data sets, thus providing useful information to study genomic mutations and find disease susceptibility genes.