Identification of copy number variants from exome sequence data

Abstract

Background: With advances in next generation sequencing technologies and genomic capture techniques, exome sequencing has become a cost-effective approach for mutation detection in genetic diseases. However, computational prediction of copy number variants (CNVs) from exome sequence data is a challenging task. Whilst numerous programs are available, they have different sensitivities, and have low sensitivity to detect smaller CNVs (1-4 exons). Additionally, exonic CNV discovery using standard aCGH has limitations due to the low probe density over exonic regions. The goal of our study was to develop a protocol to detect exonic CNVs (including shorter CNVs that cover 1-4 exons), combining computational prediction algorithms and a high-resolution custom CGH array.

Results: We used six published CNV prediction programs (ExomeCNV, CONTRA, ExomeCopy, ExomeDepth, CoNIFER, XHMM) and an in-house modification to ExomeCopy and ExomeDepth (ExCopyDepth) for computational CNV prediction on 30 exomes from the 1000 genomes project and 9 exomes from primary immunodeficiency patients. CNV predictions were tested using a custom CGH array designed to capture all exons (exaCGH). After this validation, we next evaluated the computational prediction of shorter CNVs. ExomeCopy and the in-house modified algorithm, ExCopyDepth, showed the highest capability in detecting shorter CNVs. Finally, the performance of each computational program was assessed by calculating the sensitivity and false positive rate.

Conclusions: In this paper, we assessed the ability of 6 computational programs to predict CNVs, focussing on short (1-4 exon) CNVs. We also tested these predictions using a custom array targeting exons. Based on these results, we propose a protocol to identify and confirm shorter exonic CNVs combining computational prediction algorithms and custom aCGH experiments.

Figure 1

Count and length distributions of CNVs predicted by the programs in the study. (a) Length distribution of CNVs predicted by each program. (b) Count (number of CNVs) distribution of CNVs predicted by each program.

Figure 2

exCNVs predicted by programs and overlapping exCNVs predicted by program combinations. Each dot represents an individual exome. (a) Number of exonic duplications predicted by each program. (b) Number of exonic deletions predicted by each program. (c) Number of overlapping duplications predicted by each program combination. (d) Number of overlapping deletions predicted by each program combination. Program combinations (c and d): 1, ExomeCopy/ExCopyDepth; 2, ExomeCopy/ExCopyDepth/CONTRA; 3, ExomeCopy/ExCopyDepth/CoNIFER; 4, ExomeCopy/ExCopyDepth/XHMM; 5, ExomeCopy/ExCopyDepth/ExomeDepth; 6, ExomeCopy/ExCopyDepth/ExomeDepth/XHMM; 7, ExomeCopy/ExCopyDepth/CONTRA/XHMM; 8, ExomeCopy/ExCopyDepth/CoNIFER/XHMM.

Figure 3

Relative cumulative frequency for true positive CNVs. In order to clearly highlight the proportion of short CNVs (with 1–4 exons) predicted by each program, relative cumulative frequency distributions were presented using only the TP CNVs with 0 to 20 exons.

Figure 4

Sensitivity versus false positive rate for CNV prediction. (a) ExomeCopy. (b) ExCopyDepth. (c) Intersection of ExomeCopy and ExCopyDepth (overlapping CNVs predicted by ExomeCopy and ExCopydepth). (d) ExomeDepth. (e) CoNIFER. (f) XHMM. Sensitivity = true positive CNVs/(true positive CNVs + false negative CNVs). False positive rate = false positive CNVs/(false positive CNVs + true positive CNVs).