Abstract
We present CANOES, an algorithm for the detection of rare copy number variants from exome sequencing data. CANOES models read counts using a negative binomial distribution and estimates variance of the read counts using a regression-based approach based on selected reference samples in a given dataset. We test CANOES on a family-based exome sequencing dataset, and show that its sensitivity and specificity is comparable to that of XHMM. Moreover, the method is complementary to Gaussian approximation-based methods (e.g. XHMM or CoNIFER). When CANOES is used in combination with these methods, it will be possible to produce high accuracy calls, as demonstrated by a much reduced and more realistic de novo rate in results from trio data.
Highlights
Copy number variants (CNVs) play a key role in human disease
We have presented a computational method (CANOES) to detect rare CNVs in exome sequencing studies that has high sensitivity for small events
We demonstrated that it can effectively be used in conjunction with XHMM to filter for high-quality CNV calls
Summary
Copy number variants (CNVs) play a key role in human disease. Rare CNVs may account for ∼15% of cases of pediatric neurodevelopmental disease [1]. A recent study found that severe obesity is often associated with a significant burden of large rare CNVs [2]. The DNA in targeted regions (targets) consisting of the exons and other selected genomic regions is captured and sequenced, producing sequence data for non-contiguous regions spread across the genome [11]. These sequence data can be used to detect CNVs, because the depth of sequence coverage at any target is generally correlated with the copy number at that target. This correlation can be used to construct a model, for any particular sample, of what the sequence depth at any particular target should be in the absence of a CNV [12]
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