Computational prediction of diseasecausing CNVs from exome sequence data

Abstract

Copy number variants (CNVs) are a class of structural variants containing deletions and duplications, and contribute to a broad range of human diseases. Therefore, disease-causing CNV detection has become an important aspect of genetic disease diagnosis. With the widespread utility of exome sequencing as a genetic diagnostic test, a range of prediction programs was developed to detect clinically relevant CNVs. The objective of this study is to evaluate strengths and weaknesses of exome-based CNV prediction programs and introduce methods to overcome the challenges of disease-causing CNV detection . This thesis presents a systematic approach to identify clinically relevant CNVs. Here, a detailed study on commonly used exome-based CNV prediction programs is provided while introducing a custom prediction algorithm (ExCopyDepth), custom aCGH (exaCGH) and a new software package (cnvScan). Clinical importance of these tools are demonstrated by identifying disease-causing CNVs in a large patient cohort. In conclusion , software products and array platform developed in this study provide necessary resources to improve the diagnosis of patients with genetic diseases