Computational prediction of diseasecausing CNVs from exome sequence data

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

Primary immunodeficiency diseases: Genomic approaches delineate heterogeneous Mendelian disorders

Background Primary immunodeficiency diseases (PIDDs) are clinically and genetically heterogeneous disorders thus far associated with mutations in more than 300 genes. The clinical phenotypes derived from distinct genotypes can overlap. Genetic etiology can be a prognostic indicator of disease severity and can influence treatment decisions. Objective We sought to investigate the ability of whole-exome screening methods to detect disease-causing variants in patients with PIDDs. Methods Patients with PIDDs from 278 families from 22 countries were investigated by using whole-exome sequencing. Computational copy number variant (CNV) prediction pipelines and an exome-tiling chromosomal microarray were also applied to identify intragenic CNVs. Analytic approaches initially focused on 475 known or candidate PIDD genes but were nonexclusive and further tailored based on clinical data, family history, and immunophenotyping. Results A likely molecular diagnosis was achieved in 110 (40%) unrelated probands. Clinical diagnosis was revised in about half (60/110) and management was directly altered in nearly a quarter (26/110) of families based on molecular findings. Twelve PIDD-causing CNVs were detected, including 7 smaller than 30 Kb that would not have been detected with conventional diagnostic CNV arrays. Conclusion This high-throughput genomic approach enabled detection of disease-related variants in unexpected genes; permitted detection of low-grade constitutional, somatic, and revertant mosaicism; and provided evidence of a mutational burden in mixed PIDD immunophenotypes