Detecting Copy Number Variations via a Bayesian Approach Adapting to Both Whole Genome and Targeted Exome Sequencing

• Activity Number: 336 - Next- Generation Sequencing
• Type: Contributed
• Date/Time: Tuesday, August 1, 2017 : 10:30 AM to 12:20 PM
• Sponsor: Biometrics Section
• Author(s): Yu-Chung Wei* and Guan-Hua Huang
• Companies: Department of Statistics, Feng Chia University and Institute of Statistics, National Chiao Tung University
• Keywords: Bayesian inference ; Copy number variations ; Exome sequencing ; Next generation sequencing ; Reversible jump Markov chain Monte Carlo ; Whole genome sequencing

Abstract

Copy number variations (CNVs) are genomic structural mutations with abnormal gene fragment copies. Current CNV detection algorithms for next generation sequencing (NGS) are developed for specific genome targets, including whole genome sequencing and targeted exome sequencing based on the differently data types and corresponding assumptions. Many whole genome tools assume the continuity of search space and reads uniform coverage across the genome. However, these assumptions break down in the exome capture because of discontinuous segments and exome specific functional biases. In order to develop a method adapting to both data types, we specify the large unconsidered genomic fragments as gaps to preserve the truly location information. A Bayesian hierarchical model was built and an efficient reversible jump Markov chain Monte Carlo inference algorithm was utilized to incorporate the gap information. The performance of gap settings for the Bayesian procedure was evaluated and compared with competing approaches using both simulations and real data.