Abstract

Copy number variation (CNV) is a major type of structural genomic variation that is increasingly studied across different species for association with diseases and production traits. Established protocols for experimental detection and computational inference of CNVs from SNP array and next-generation sequencing data are available. We present the CNVRanger R/Bioconductor package which implements a comprehensive toolbox for structured downstream analysis of CNVs. This includes functionality for summarizing individual CNV calls across a population, assessing overlap with functional genomic regions, and genome-wide association analysis with gene expression and quantitative phenotypes. http://bioconductor.org/packages/CNVRanger.

Highlights

  • Copy number variation (CNV) is a frequently observed deviation from the diploid state due to duplication or deletion of genomic regions (Conrad et al, 2010)

  • We developed, described, and applied functionality for analyzing CNVs across a population, including association analysis with gene expression and quantitative phenotypes

  • Once recurrent CNV regions have been defined, CNVRanger allows to assess whether and to which extent these regions overlap with functional genomic regions (Fig. 1C)

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Summary

Introduction

Copy number variation (CNV) is a frequently observed deviation from the diploid state due to duplication or deletion of genomic regions (Conrad et al, 2010). CNVs can be experimentally detected based on comparative genomic hybridization, and computationally inferred from SNP-arrays or next-generation sequencing data (Geistlinger et al, 2018). These technologies for CNV detection report, for each sample under study, genomic regions that are duplicated or deleted with respect to a reference genome. Such regions are denoted as CNV calls and are the starting point for subsequent downstream analysis. To allow straightforward application to similar datasets, we generalize these concepts and provide refined implementations in the CNVRanger R/Bioconductor package

Reading and accessing CNV data
Summarizing individual CNV calls across a population
Overlap analysis with functional genomic regions
CNV-expression association analysis
Findings
CNV-phenotype association analysis

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