An Evaluation of Copy Number Variation Detection Tools from Whole-Exome Sequencing Data

Abstract

Copy number variation (CNV) has been found to play an important role in human disease. Next-generation sequencing technology, including whole-genome sequencing (WGS) and whole-exome sequencing (WES), has become a primary strategy for studying the genetic basis of human disease. Several CNV calling tools have recently been developed on the basis of WES data. However, the comparative performance of these tools using real data remains unclear. An objective evaluation study of these tools in practical research situations would be beneficial. Here, we evaluated four well-known WES-based CNV detection tools (XHMM, CoNIFER, ExomeDepth, and CONTRA) using real data generated in house. After evaluation using six metrics, we found that the sensitive and accurate detection of CNVs in WES data remains challenging despite the many algorithms available. Each algorithm has its own strengths and weaknesses. None of the exome-based CNV calling methods performed well in all situations; in particular, compared with CNVs identified from high coverage WGS data from the same samples, all tools suffered from limited power. Our evaluation provides a comprehensive and objective comparison of several well-known detection tools designed for WES data, which will assist researchers in choosing the most suitable tools for their research needs.

Identification of Copy Number Variants Through Whole-Exome Sequencing in Autosomal Recessive Nonsyndromic Hearing Loss

“…While XHMM utilizes the principal component analysis followed by the Hidden Markov model to identify CNVs, CoNIFER uses a singular value decomposition technique to correct systematic biases and identifies a CNV call if the corrected signal reaches a predefined threshold at no less than three subsequent exons (Fromer et al, 2012;Krumm et al, 2012). Breakpoint detection is an advantage of the XHMM (Fromer et al, 2012;Tan et al, 2014). According to our data, XHMM showed more reliable results for the size of the OTOA deletion.…”

“…We also noted that CoNIFER CNV analysis missed two heterozygous deletions used as positive controls. Recent CNV comparison studies in WES showed that by using the previously described heterozygosity check method (Zhu et al, 2012), CoNIFER detects less (40%) heterozygous false-positive deletions (for regions > 1 kb) compared to XHMM (64%) (Tan et al, 2014), suggesting that it might be missing some true positives to increase specificity. Conservative predefined thresholds in default settings of the CoNIFER might be the reason for missing heterozygous deletions in positive controls in our data set.…”

“…The main applications of WES in most laboratories are identifying single-nucleotide variants (SNVs) and small deletion/ insertions (INDELs). While various practical algorithms for the detection of copy number variants (CNVs) have recently been developed (Fromer et al, 2012;Krumm et al, 2012;Tan et al, 2014), their usage in general is not common and their application in deafness has not been reported.…”

Whole-genome sequencing is more powerful than whole-exome sequencing for detecting exome variants

“…Diagnostic and research laboratories, whether public or private, therefore tend to search for coding variants, most of which can be detected by WES, first. Such variants can also be detected by WGS, and several studies previously compared WES and WGS for different types of variations and/or in different contexts (9,(11)(12)(13)(14)(15)(16), but none of them in a really comprehensive manner. Here, we compared WES and WGS, in terms of detection rates and quality, for single-nucleotide variants (SNVs), small insertions/ deletions (indels), and copy-number variants (CNVs) within the regions of the human genome covered by WES, using the most recent next-generation sequencing (NGS) technologies.…”

Lessons learned from gene identification studies in Mendelian epilepsy disorders

“…Identification of somatic and germline mosaic events or copy number variants remain complicated despite the progress made in the field. 31,32 Copy number variants have an important contribution to many Mendelian disorders [33][34][35] and are often overlooked or disregarded in NGS studies. The pathogenic variant can also be unsequenced due to lack of targeting, capturing, or bad mapping quality.…”