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Characterizing Sensitivity and Coverage of Clinical WGS As a Diagnostic Test for Genetic Disorders

Overview
Publisher Biomed Central
Specialty Genetics
Date 2021 Apr 14
PMID 33849535
Citations 17
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Abstract

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.

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References
1.
Thiffault I, Farrow E, Zellmer L, Berrios C, Miller N, Gibson M . Clinical genome sequencing in an unbiased pediatric cohort. Genet Med. 2018; 21(2):303-310. PMC: 6752301. DOI: 10.1038/s41436-018-0075-8. View

2.
Chen K, Wallis J, McLellan M, Larson D, Kalicki J, Pohl C . BreakDancer: an algorithm for high-resolution mapping of genomic structural variation. Nat Methods. 2009; 6(9):677-81. PMC: 3661775. DOI: 10.1038/nmeth.1363. View

3.
Kohler S, Schulz M, Krawitz P, Bauer S, Dolken S, Ott C . Clinical diagnostics in human genetics with semantic similarity searches in ontologies. Am J Hum Genet. 2009; 85(4):457-64. PMC: 2756558. DOI: 10.1016/j.ajhg.2009.09.003. View

4.
Lelieveld S, Spielmann M, Mundlos S, Veltman J, Gilissen C . Comparison of Exome and Genome Sequencing Technologies for the Complete Capture of Protein-Coding Regions. Hum Mutat. 2015; 36(8):815-22. PMC: 4755152. DOI: 10.1002/humu.22813. View

5.
de Vries B, Pfundt R, Leisink M, Koolen D, Vissers L, Janssen I . Diagnostic genome profiling in mental retardation. Am J Hum Genet. 2005; 77(4):606-16. PMC: 1275609. DOI: 10.1086/491719. View