Abstract
High-throughput sequencing of DNA coding regions has become a common way of assaying genomic variation in the study of human diseases. Copy number variation (CNV) is an important type of genomic variation, but detecting and characterizing CNV from exome sequencing is challenging due to the high level of biases and artifacts. We propose CODEX, a normalization and CNV calling procedure for whole exome sequencing data. The Poisson latent factor model in CODEX includes terms that specifically remove biases due to GC content, exon capture and amplification efficiency, and latent systemic artifacts. CODEX also includes a Poisson likelihood-based recursive segmentation procedure that explicitly models the count-based exome sequencing data. CODEX is compared to existing methods on a population analysis of HapMap samples from the 1000 Genomes Project, and shown to be more accurate on three microarray-based validation data sets. We further evaluate performance on 222 neuroblastoma samples with matched normals and focus on a well-studied rare somatic CNV within the ATRX gene. We show that the cross-sample normalization procedure of CODEX removes more noise than normalizing the tumor against the matched normal and that the segmentation procedure performs well in detecting CNVs with nested structures.
Highlights
Copy number variants (CNVs) are large insertions and deletions that lead to gains and losses of segments of chromosomes
We found that some samples have estimates with multiple peaks in fj (GC), which suggests that a parametric functional form assuming unimodality may be too simplistic
We show through several data sets that CODEX’s multisample normalization procedure offers higher sensitivity and specificity for detection and genotyping of both common and rare CNVs
Summary
Copy number variants (CNVs) are large insertions and deletions that lead to gains and losses of segments of chromosomes. CNVs are an important and abundant source of variation in the human genome [1,2,3,4]. Like other types of genetic variation, some CNVs have been associated with diseases, such as neuroblastoma [5], autism [6] and Crohn’s disease [7]. Better understanding of the genetics of CNVassociated diseases requires accurate CNV detection. Traditional genome-wide approaches to detect CNVs make use of array comparative genome hybridization (CGH) or single nucleotide polymorphism (SNP) array data [8,9,10]. Paired-end Sanger sequencing, which is often used as the gold standard platform for CNV detection, has better resolution and accuracy but requires significant time and budget investment
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