Cargando…

Characterizing sensitivity and coverage of clinical WGS as a diagnostic test for genetic disorders

BACKGROUND: Due to its reduced cost and incomparable advantages, WGS is likely to lead to changes in clinical diagnosis of rare and undiagnosed diseases. However, the sensitivity and breadth of coverage of clinical WGS as a diagnostic test for genetic disorders has not been fully evaluated. METHODS:...

Descripción completa

Detalles Bibliográficos
Autores principales: Sun, Yan, Liu, Fengxia, Fan, Chunna, Wang, Yaoshen, Song, Lijie, Fang, Zhonghai, Han, Rui, Wang, Zhonghua, Wang, Xiaodan, Yang, Ziying, Xu, Zhenpeng, Peng, Jiguang, Shi, Chaonan, Zhang, Hongyun, Dong, Wei, Huang, Hui, Li, Yun, Le, Yanqun, Sun, Jun, Peng, Zhiyu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8045368/
https://www.ncbi.nlm.nih.gov/pubmed/33849535
http://dx.doi.org/10.1186/s12920-021-00948-5
_version_ 1783678670043348992
author Sun, Yan
Liu, Fengxia
Fan, Chunna
Wang, Yaoshen
Song, Lijie
Fang, Zhonghai
Han, Rui
Wang, Zhonghua
Wang, Xiaodan
Yang, Ziying
Xu, Zhenpeng
Peng, Jiguang
Shi, Chaonan
Zhang, Hongyun
Dong, Wei
Huang, Hui
Li, Yun
Le, Yanqun
Sun, Jun
Peng, Zhiyu
author_facet Sun, Yan
Liu, Fengxia
Fan, Chunna
Wang, Yaoshen
Song, Lijie
Fang, Zhonghai
Han, Rui
Wang, Zhonghua
Wang, Xiaodan
Yang, Ziying
Xu, Zhenpeng
Peng, Jiguang
Shi, Chaonan
Zhang, Hongyun
Dong, Wei
Huang, Hui
Li, Yun
Le, Yanqun
Sun, Jun
Peng, Zhiyu
author_sort Sun, Yan
collection PubMed
description BACKGROUND: Due to its reduced cost and incomparable advantages, WGS is likely to lead to changes in clinical diagnosis of rare and undiagnosed diseases. However, the sensitivity and breadth of coverage of clinical WGS as a diagnostic test for genetic disorders has not been fully evaluated. METHODS: Here, the performance of WGS in NA12878, the YH cell line, and the Chinese trios were measured by assessing their sensitivity, PPV, depth and breadth of coverage using MGISEQ-2000. We also compared the performance of WES and WGS using NA12878. The sensitivity and PPV were tested using the family-based trio design for the Chinese trios. We further developed a systematic WGS pipeline for the analysis of 8 clinical cases. RESULTS: In general, the sensitivity and PPV for SNV/indel detection increased with mean depth and reached a plateau at an ~ 40X mean depth using down-sampling samples of NA12878. With a mean depth of 40X, the sensitivity of homozygous and heterozygous SNPs of NA12878 was > 99.25% and > 99.50%, respectively, and the PPV was 99.97% and 98.96%. Homozygous and heterozygous indels showed lower sensitivity and PPV. The sensitivity and PPV were still not 100% even with a mean depth of ~ 150X. We also observed a substantial variation in the sensitivity of CNV detection across different tools, especially in CNVs with a size less than 1 kb. In general, the breadth of coverage for disease-associated genes and CNVs increased with mean depth. The sensitivity and coverage of WGS (~ 40X) was better than WES (~ 120X). Among the Chinese trios with an ~ 40X mean depth, the sensitivity among offspring was > 99.48% and > 96.36% for SNP and indel detection, and the PPVs were 99.86% and 97.93%. All 12 previously validated variants in the 8 clinical cases were successfully detected using our WGS pipeline. CONCLUSIONS: The current standard of a mean depth of 40X may be sufficient for SNV/indel detection and identification of most CNVs. It would be advisable for clinical scientists to determine the range of sensitivity and PPV for different classes of variants for a particular WGS pipeline, which would be useful when interpreting and delivering clinical reports. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12920-021-00948-5.
format Online
Article
Text
id pubmed-8045368
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-80453682021-04-14 Characterizing sensitivity and coverage of clinical WGS as a diagnostic test for genetic disorders Sun, Yan Liu, Fengxia Fan, Chunna Wang, Yaoshen Song, Lijie Fang, Zhonghai Han, Rui Wang, Zhonghua Wang, Xiaodan Yang, Ziying Xu, Zhenpeng Peng, Jiguang Shi, Chaonan Zhang, Hongyun Dong, Wei Huang, Hui Li, Yun Le, Yanqun Sun, Jun Peng, Zhiyu BMC Med Genomics Research Article BACKGROUND: Due to its reduced cost and incomparable advantages, WGS is likely to lead to changes in clinical diagnosis of rare and undiagnosed diseases. However, the sensitivity and breadth of coverage of clinical WGS as a diagnostic test for genetic disorders has not been fully evaluated. METHODS: Here, the performance of WGS in NA12878, the YH cell line, and the Chinese trios were measured by assessing their sensitivity, PPV, depth and breadth of coverage using MGISEQ-2000. We also compared the performance of WES and WGS using NA12878. The sensitivity and PPV were tested using the family-based trio design for the Chinese trios. We further developed a systematic WGS pipeline for the analysis of 8 clinical cases. RESULTS: In general, the sensitivity and PPV for SNV/indel detection increased with mean depth and reached a plateau at an ~ 40X mean depth using down-sampling samples of NA12878. With a mean depth of 40X, the sensitivity of homozygous and heterozygous SNPs of NA12878 was > 99.25% and > 99.50%, respectively, and the PPV was 99.97% and 98.96%. Homozygous and heterozygous indels showed lower sensitivity and PPV. The sensitivity and PPV were still not 100% even with a mean depth of ~ 150X. We also observed a substantial variation in the sensitivity of CNV detection across different tools, especially in CNVs with a size less than 1 kb. In general, the breadth of coverage for disease-associated genes and CNVs increased with mean depth. The sensitivity and coverage of WGS (~ 40X) was better than WES (~ 120X). Among the Chinese trios with an ~ 40X mean depth, the sensitivity among offspring was > 99.48% and > 96.36% for SNP and indel detection, and the PPVs were 99.86% and 97.93%. All 12 previously validated variants in the 8 clinical cases were successfully detected using our WGS pipeline. CONCLUSIONS: The current standard of a mean depth of 40X may be sufficient for SNV/indel detection and identification of most CNVs. It would be advisable for clinical scientists to determine the range of sensitivity and PPV for different classes of variants for a particular WGS pipeline, which would be useful when interpreting and delivering clinical reports. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12920-021-00948-5. BioMed Central 2021-04-13 /pmc/articles/PMC8045368/ /pubmed/33849535 http://dx.doi.org/10.1186/s12920-021-00948-5 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research Article
Sun, Yan
Liu, Fengxia
Fan, Chunna
Wang, Yaoshen
Song, Lijie
Fang, Zhonghai
Han, Rui
Wang, Zhonghua
Wang, Xiaodan
Yang, Ziying
Xu, Zhenpeng
Peng, Jiguang
Shi, Chaonan
Zhang, Hongyun
Dong, Wei
Huang, Hui
Li, Yun
Le, Yanqun
Sun, Jun
Peng, Zhiyu
Characterizing sensitivity and coverage of clinical WGS as a diagnostic test for genetic disorders
title Characterizing sensitivity and coverage of clinical WGS as a diagnostic test for genetic disorders
title_full Characterizing sensitivity and coverage of clinical WGS as a diagnostic test for genetic disorders
title_fullStr Characterizing sensitivity and coverage of clinical WGS as a diagnostic test for genetic disorders
title_full_unstemmed Characterizing sensitivity and coverage of clinical WGS as a diagnostic test for genetic disorders
title_short Characterizing sensitivity and coverage of clinical WGS as a diagnostic test for genetic disorders
title_sort characterizing sensitivity and coverage of clinical wgs as a diagnostic test for genetic disorders
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8045368/
https://www.ncbi.nlm.nih.gov/pubmed/33849535
http://dx.doi.org/10.1186/s12920-021-00948-5
work_keys_str_mv AT sunyan characterizingsensitivityandcoverageofclinicalwgsasadiagnostictestforgeneticdisorders
AT liufengxia characterizingsensitivityandcoverageofclinicalwgsasadiagnostictestforgeneticdisorders
AT fanchunna characterizingsensitivityandcoverageofclinicalwgsasadiagnostictestforgeneticdisorders
AT wangyaoshen characterizingsensitivityandcoverageofclinicalwgsasadiagnostictestforgeneticdisorders
AT songlijie characterizingsensitivityandcoverageofclinicalwgsasadiagnostictestforgeneticdisorders
AT fangzhonghai characterizingsensitivityandcoverageofclinicalwgsasadiagnostictestforgeneticdisorders
AT hanrui characterizingsensitivityandcoverageofclinicalwgsasadiagnostictestforgeneticdisorders
AT wangzhonghua characterizingsensitivityandcoverageofclinicalwgsasadiagnostictestforgeneticdisorders
AT wangxiaodan characterizingsensitivityandcoverageofclinicalwgsasadiagnostictestforgeneticdisorders
AT yangziying characterizingsensitivityandcoverageofclinicalwgsasadiagnostictestforgeneticdisorders
AT xuzhenpeng characterizingsensitivityandcoverageofclinicalwgsasadiagnostictestforgeneticdisorders
AT pengjiguang characterizingsensitivityandcoverageofclinicalwgsasadiagnostictestforgeneticdisorders
AT shichaonan characterizingsensitivityandcoverageofclinicalwgsasadiagnostictestforgeneticdisorders
AT zhanghongyun characterizingsensitivityandcoverageofclinicalwgsasadiagnostictestforgeneticdisorders
AT dongwei characterizingsensitivityandcoverageofclinicalwgsasadiagnostictestforgeneticdisorders
AT huanghui characterizingsensitivityandcoverageofclinicalwgsasadiagnostictestforgeneticdisorders
AT liyun characterizingsensitivityandcoverageofclinicalwgsasadiagnostictestforgeneticdisorders
AT leyanqun characterizingsensitivityandcoverageofclinicalwgsasadiagnostictestforgeneticdisorders
AT sunjun characterizingsensitivityandcoverageofclinicalwgsasadiagnostictestforgeneticdisorders
AT pengzhiyu characterizingsensitivityandcoverageofclinicalwgsasadiagnostictestforgeneticdisorders