Control Charting Genomic Data.


Journal

The journal of applied laboratory medicine
ISSN: 2576-9456
Titre abrégé: J Appl Lab Med
Pays: England
ID NLM: 101693884

Informations de publication

Date de publication:
07 07 2021
Historique:
received: 05 08 2020
accepted: 12 10 2020
pubmed: 16 12 2020
medline: 16 10 2021
entrez: 15 12 2020
Statut: ppublish

Résumé

Control charting is routine in the quality assurance of traditional clinical laboratory testing. Genomic tests are not typically managed by control charting. We examined control charting to monitor the performance of a clinical next-generation sequencing (NGS) assay. We retrospectively examined 3 years of control material (NA12878) data from clinical genomic epilepsy testing. Levey-Jennings plots were used to visualize changes in control material depth of sequencing coverage in genomic regions of an epilepsy genomic panel. Changes in depth of coverage were correlated with changes in the manufactured lot of capture probe reagent. Depth of coverage was also correlated between quality control material and clinical samples. Fifty-seven sequencing runs of NA12878 were analyzed for 1811 genomic regions targeting 108 genes. Manufactured probe lot changes were associated with significant changes in the average coverage of 537 genomic regions and the lowest coverage of 173 regions (using a critical cut-off of P < 5.52 x 10-6). Genomic regions with the highest sensitivity to lot-to-lot variation by average sequencing depth of coverage were not the same regions with the highest sensitivity by lowest sequencing depth of coverage. Levey-Jennings plots displayed differences in genomic depth of coverage across capture probe reagent lot changes. There was moderate correlation between the changes in depth of sequencing across lot changes for control material and clinical cases (r2 = 0.45). Genomic control charting can be used routinely by clinical laboratories to monitor assay performance and ensure the quality of testing.

Sections du résumé

BACKGROUND
Control charting is routine in the quality assurance of traditional clinical laboratory testing. Genomic tests are not typically managed by control charting. We examined control charting to monitor the performance of a clinical next-generation sequencing (NGS) assay.
METHODS
We retrospectively examined 3 years of control material (NA12878) data from clinical genomic epilepsy testing. Levey-Jennings plots were used to visualize changes in control material depth of sequencing coverage in genomic regions of an epilepsy genomic panel. Changes in depth of coverage were correlated with changes in the manufactured lot of capture probe reagent. Depth of coverage was also correlated between quality control material and clinical samples.
RESULTS
Fifty-seven sequencing runs of NA12878 were analyzed for 1811 genomic regions targeting 108 genes. Manufactured probe lot changes were associated with significant changes in the average coverage of 537 genomic regions and the lowest coverage of 173 regions (using a critical cut-off of P < 5.52 x 10-6). Genomic regions with the highest sensitivity to lot-to-lot variation by average sequencing depth of coverage were not the same regions with the highest sensitivity by lowest sequencing depth of coverage. Levey-Jennings plots displayed differences in genomic depth of coverage across capture probe reagent lot changes. There was moderate correlation between the changes in depth of sequencing across lot changes for control material and clinical cases (r2 = 0.45).
CONCLUSIONS
Genomic control charting can be used routinely by clinical laboratories to monitor assay performance and ensure the quality of testing.

Identifiants

pubmed: 33319223
pii: 6034828
doi: 10.1093/jalm/jfaa201
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

892-901

Informations de copyright

© American Association for Clinical Chemistry 2020. All rights reserved. For permissions, please email: journals.permissions@oup.com.

Auteurs

Jing Xu (J)

Department of Pathology, University of Texas Southwestern Medical Center, Dallas, TX.

Eric Crossley (E)

Department of Pathology, Children's Health System of Texas, Dallas, TX.

Jennifer Wagenfuehr (J)

Department of Pathology, Children's Health System of Texas, Dallas, TX.

Midori Mitui (M)

Department of Pathology, Children's Health System of Texas, Dallas, TX.

Eric Londin (E)

Computational Medicine Center, Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA.

Khushbu Patel (K)

Department of Pathology, University of Texas Southwestern Medical Center, Dallas, TX.
Department of Pathology, Children's Health System of Texas, Dallas, TX.

Jason Y Park (JY)

Department of Pathology, University of Texas Southwestern Medical Center, Dallas, TX.
Department of Pathology, Children's Health System of Texas, Dallas, TX.
McDermott Center for Human Growth and Development, University of Texas Southwestern Medical Center, Dallas, TX.

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Classifications MeSH