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

Abdelaziz Belkadi, Alexandre Bolze, Yuval Itan, Aurélie Cobat, Quentin B. Vincent, Alexander Antipenko, Lei Shang, Bertrand Boisson, Jean Laurent Casanova, Laurent Abel

Research output: Contribution to journalArticle

159 Citations (Scopus)

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 was 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 variants, ∼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 variants). 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.

Original languageEnglish
Pages (from-to)5473-5478
Number of pages6
JournalProceedings of the National Academy of Sciences of the United States of America
Volume112
Issue number17
DOIs
Publication statusPublished - 28 Apr 2015
Externally publishedYes

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Exome
Genome
Nucleotides

Keywords

  • Exome
  • Genetic variants
  • Genome
  • Mendelian disorders
  • Next-generation sequencing

ASJC Scopus subject areas

  • General

Cite this

Whole-genome sequencing is more powerful than whole-exome sequencing for detecting exome variants. / Belkadi, Abdelaziz; Bolze, Alexandre; Itan, Yuval; Cobat, Aurélie; Vincent, Quentin B.; Antipenko, Alexander; Shang, Lei; Boisson, Bertrand; Casanova, Jean Laurent; Abel, Laurent.

In: Proceedings of the National Academy of Sciences of the United States of America, Vol. 112, No. 17, 28.04.2015, p. 5473-5478.

Research output: Contribution to journalArticle

Belkadi, Abdelaziz ; Bolze, Alexandre ; Itan, Yuval ; Cobat, Aurélie ; Vincent, Quentin B. ; Antipenko, Alexander ; Shang, Lei ; Boisson, Bertrand ; Casanova, Jean Laurent ; Abel, Laurent. / Whole-genome sequencing is more powerful than whole-exome sequencing for detecting exome variants. In: Proceedings of the National Academy of Sciences of the United States of America. 2015 ; Vol. 112, No. 17. pp. 5473-5478.
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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 was 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 variants, ∼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 variants). 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.",
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AU - Belkadi, Abdelaziz

AU - Bolze, Alexandre

AU - Itan, Yuval

AU - Cobat, Aurélie

AU - Vincent, Quentin B.

AU - Antipenko, Alexander

AU - Shang, Lei

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AU - Casanova, Jean Laurent

AU - Abel, Laurent

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AB - 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 was 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 variants, ∼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 variants). 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.

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