Abstract
High-throughput DNA sequencing enables detection of copy number variations (CNVs) on the genome-wide scale with finer resolution compared to array-based methods but suffers from biases and artifacts that lead to false discoveries and low sensitivity. We describe CODEX2, as a statistical framework for full-spectrum CNV profiling that is sensitive for variants with both common and rare population frequencies and that is applicable to study designs with and without negative control samples. We demonstrate and evaluate CODEX2 on whole-exome and targeted sequencing data, where biases are the most prominent. CODEX2 outperforms existing methods and, in particular, significantly improves sensitivity for common CNVs.
Highlights
Copy number variations (CNVs) are large deletions and duplications of segments of the chromosome
In the study of disease, CNVs usually appear in two contexts: germline CNVs refer to inherited variants, many of which are polymorphic at the population level [2]; in contrast, somatic CNVs, referred to as copy number aberrations (CNAs), are the copy number changes resulting from somatic mutations, such as those commonly observed in cancer
Our results demonstrate that CODEX2 significantly improves both sensitivity and specificity over existing methods, especially for common CNVs
Summary
Copy number variations (CNVs) are large deletions and duplications of segments of the chromosome. CNVs are pervasive in the human genome and play a causal role in diseases such as cancer [1]. In the study of disease, CNVs usually appear in two contexts: germline CNVs refer to inherited variants, many of which are polymorphic at the population level [2]; in contrast, somatic CNVs, referred to as copy number aberrations (CNAs), are the copy number changes resulting from somatic mutations, such as those commonly observed in cancer. Germline CNVs can be described as common or rare based on their population frequencies. This paper addresses the problem of detection of both germline and somatic CNVs and, in particular, of improving detection sensitivity for common CNVs in both categories.
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