Systematic dissection of biases in whole-exome and whole-genome sequencing reveals major determinants of coding sequence coverage

Abstract

Advantages and diagnostic effectiveness of the two most widely used resequencing approaches, whole exome (WES) and whole genome (WGS) sequencing, are often debated. WES dominated largescale resequencing projects because of lower cost and easier data storage and processing. Rapid development of 3 rd generation sequencing methods and novel exome sequencing kits predicate the need for a robust statistical framework allowing informative and easy performance comparison of the emerging methods. In our study we developed a set of statistical tools to systematically assess coverage of coding regions provided by several modern WES platforms, as well as PCR-free WGS. We identified a substantial problem in most previously published comparisons which did not account for mappability limitations of short reads. Using regression analysis and simple machine learning, as well as several novel metrics of coverage evenness, we analyzed the contribution from the major determinants of CDS coverage. Contrary to a common view, most of the observed bias in modern WES stems from mappability limitations of short reads and exome probe design rather than sequence composition. We also identified the ~ 500 kb region of human exome that could not be effectively characterized using short read technology and should receive special attention during variant analysis. Using our novel metrics of sequencing coverage, we identified main determinants of WES and WGS performance. Overall, our study points out avenues for improvement of enrichment-based methods and development of novel approaches that would maximize variant discovery at optimal cost. Next-generation sequencing (NGS) is rapidly becoming an invaluable tool in human genetics research and clinical diagnostics 1-3. Practical use of NGS methods has dramatically increased with the development of targeted sequencing approaches, such as whole-exome sequencing (WES) or targeted sequencing of gene panels. WES emerged as an efficient alternative to whole-genome sequencing (WGS) due to both lower sequencing cost and simplification of variant analysis and data storage 4. More than 80% of all variants reported in ClinVar, and more than 89% of variants reported to be pathogenic, come from the protein-coding part of the genome; this number increases to 99% when immediate CDS vicinity is included. Even allowing for the sampling bias, there is an overall agreement that most heritable diseases appear to be caused by alterations in the protein-coding regions of the

A comparative study of single nucleotide variant detection performance using three massively parallel sequencing methods

“…Previous studies have suggested that the qualities of DNA sequencing with WGS and WES are similar [34], and that WES is an efficient alternative to WGS [31,35]. In support of these findings, we did not observe much difference in the median number of HQFE variants for WES and WGS in the pairwise comparison (Table 1).…”

Next-Generation Sequencing in the Field of Primary Immunodeficiencies: Current Yield, Challenges, and Future Perspectives

“…These shortcomings are caused by mapping errors, which are inherent to short read sequencing approaches when applied to repetitive regions and because of the lack of targeting of regions that are outside of the exons. Because these sources of genetic variation might play a role in human disease, there is a possibility that diagnoses in PIDs are missed based on WES data [25,46]. Ultimately, it is expected in the future that WGS will offer a more complete test to assess both the coding and non-coding part of the genome, outweighing the extra costs posed by more complex analysis of the data and data storage.…”

Utility of Circulating Tumor DNA in Different Clinical Scenarios of Breast Cancer

“…According to the study published by Barbitoff, Y.A. et al most of the observed bias in WES stems from mappability limitations of short reads, as well as exome probe design [29]. Due to the high complexity of the analysis, the lack of biological material, and the huge cost associated, neither WGS nor WES will be possible as routine molecular strategies used to analyze the ctDNA.…”