Gatk Acnv: Allelic Copy-Number Variation Discovery From Snps And Coverage Data

We propose and evaluate a novel algorithm for inferring germline and somatic copy number variation from whole exome sequencing (WES) and whole genome sequencing (WGS) data. Starting with the depth of aligned short reads from a cohort of samples, we use a Bayesian model for learning sequencing bias and simultaneously detecting CNV events using a hidden Markov model for change-point detection. A unified framework is used to call both germline and somatic CNVs. Denoising and event discovery are performed self-consistently to achieve maximum accuracy. In contrast to previous methods, our model naturally accounts for mixed sex cohorts and can detect events on sex chromosomes. Furthermore, we can detect excessively noisy samples and extract useful information within a probabilistic framework. Our implementation can also utilize Spark clusters, enabling the processing of larger cohorts and allowing for improved runtime performance. We benchmark the new method for precision, recall, and reproducibility of both germline and somatic variants. Evaluations are performed on a cohort of WES samples from The Cancer Genome Atlas with matching WGS data. For germline variants, we use blood normal samples and compare our calls on WES data against Genome STRiP calls on WGS data. We find that GATK CNV yields remarkably higher precision and recall compared to XHMM and CODEX software packages. For somatic variants, we compare our calls against TITAN and find a remarkably high concordance. Citation Format: Mehrtash Babadi, David I. Benjamin, Samuel K. Lee, Andrey Smirnov, Aaron Chevalier, Lee Lichtenstein, Valentin Ruano Rubio. GATK CNV: copy-number variation discovery from coverage data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 3580. doi:10.1158/1538-7445.AM2017-3580