Abstract
Copy number variation (CNV) is a form of structural variation which has abnormal alterations of the copy number in the genome. It is considered to be related to diverse diseases so CNV plays an important role for the study of genetic diseases and cancer. Following the advances in the technology of next-generation sequencing (NGS), the difficulty of analyzing CNV is a decrease and some analysis tools have been developed to detect CNVs. However, the majority of current researches are focused on the data which are of low coverage, long interested regions, and paired case-control samples. There is lack of discussions with high coverage, short targeted regions and unpaired case-control samples. Therefore in this study, we propose a two-step ANOVA model to detect CNVs. This model can be applied on different types of data. The main idea of this model is to apply two different ANOVA models to discover CNVs. We first estimate the base effects from control samples, and then the case samples are effectively adjusted with the base effects in order to detecting CNVs. The results have been summarized by three parts. In the simulation study, different CNVs incidence rates are designed to observe the change of results by the proposed model. We also have a comparison with the other tool, ExomeCNV, in the simulated study. In real data analysis, we show the result of what our model has found in the oral cancer data. In association study, we indicate an analysis process for some clinical traits of oral cancer. In conclusion, the two-step ANOVA model has lower false positive rate. Our model also indicates that the conventional ANOVA model has great performance over the high coverage data compared to sophisticate schemes. The association study detected several important CNVs that are very likely to play an important role in the oral cancer etiology.