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

Abstract

We compared whole-exome sequencing (WES) and whole-genome sequencing (WGS) in six unrelated individuals. In the regions targeted by WES capture (81.5% of the consensus coding genome), the mean numbers of single-nucleotide variants (SNVs) and small insertions/deletions (indels) detected per sample were 84,192 and 13,325, respectively, for WES, and 84,968 and 12,702, respectively, for WGS. For both SNVs and indels, the distributions of coverage depth, genotype quality, and minor read ratio were more uniform for WGS than for WES. After filtering, a mean of 74,398 (95.3%) high-quality (HQ) SNVs and 9,033 (70.6%) HQ indels were called by both platforms. A mean of 105 coding HQ SNVs and 32 indels were identified exclusively by WES, whereas 692 HQ SNVs and 105 indels were identified exclusively by WGS. We Sanger sequenced a random selection of these exclusive variants. For SNVs, the proportion of false-positive variants was higher for WES (78%) than for WGS (17%). The estimated mean number of real coding SNVs (656, ~3% of all coding HQ SNVs) identified by WGS and missed by WES was greater than the number of SNVs identified by WES and missed by WGS (26). For indels, the proportions of false-positive variants were similar for WES (44%) and WGS (46%). Finally, WES was not reliable for the detection of copy number variations, almost all of which extended beyond the targeted regions. Although currently more expensive, WGS is more powerful than WES for detecting potential disease-causing mutations within WES regions, particularly those due to SNVs. SignificanceWhole-exome sequencing (WES) is gradually being optimized to identify mutations in increasing proportions of the protein-coding exome, but whole-genome sequencing (WGS) is becoming an attractive alternative. WGS is currently more expensive than WES, but its cost should decrease more rapidly than that of WES. We compared WES and WGS on six unrelated individuals. The distribution of quality parameters for single-nucleotide variants (SNVs) and insertions/deletions (indels) was more uniform for WGS than for WES. The vast majority of SNVs and indels were identified by both techniques, but an estimated 650 high-quality coding SNVs (~3% of coding variants) were detected by WGS and missed by WES. WGS is therefore slightly more efficient than WES for detecting mutations in the targeted exome.

Deep sequencing of 10,000 human genomes

“…S3). The ECR encompasses 84% of the human genome, and includes 91.5% of the human exome sequence (GENCODE; 96 Mb), which is consistent with recent reports on coverage of the human exome in whole-genome analyses (11). We also examined the relevance for clinical variant calls: 28,831 of 30,288 (95.2%) unique ClinVar (https://www.ncbi.nlm.nih.gov/clinvar/) and HGMD (www.hgmd.…”

The human gene damage index as a gene-level approach to prioritizing exome variants

“…The advent of next-generation sequencing (NGS)-based approaches, including whole-exome sequencing (WES), whole-genome sequencing (WGS), and RNASeq, has facilitated the large-scale detection of gene variants at both the individual and population levels (2)(3)(4)(5)(6). In patients suffering from a monogenic disease, at most two variants are disease causing [true positives (TP)], and the other 20,000 or so proteincoding exome variants are false positives (FP; type I error).…”

Next Generation Sequencing and Its Clinical Applications: The Growing Pains

“…For example, when different library preparations are involved, even starting with same DNA or RNA stock solution, the reproducibility of potential somatic mutation calls (whose variant frequencies usually fall below 25%) is in general poor [5]. For WES this seems particularly concerning, considering the work of Belkadi et al who showed that more than 50% of high quality variant calls were false positives by using Sanger sequencing to confirm randomly selected single nucleotide variant calls [6]. Our un-published studies are in agreement with this finding as mutation calls from three library preparations from the same batch of FFPE tissue DNA showed less than 50% of all variant calls generated from WES were shared by the three replicates even though high quality filters were applied (Chang et al manuscript in preparation).…”