Best practices for the interpretation and reporting of clinical whole genome sequencing

Christina A Austin-Tse^#^{1

2

3}, Vaidehi Jobanputra^#^{4

5}, Denise L Perry⁶, David Bick⁷, Ryan J Taft⁶, Eric Venner⁸, Richard A Gibbs⁸, Ted Young⁹, Sarah Barnett¹⁰, John W Belmont⁶, Nicole Boczek^{10

11}, Shimul Chowdhury¹², Katarzyna A Ellsworth¹², Saurav Guha⁴, Shashikant Kulkarni^{13

14}, Cherisse Marcou¹⁰, Linyan Meng^{13

14}, David R Murdock^{8

14}, Atteeq U Rehman⁴, Elizabeth Spiteri¹⁵, Amanda Thomas-Wilson⁴, Hutton M Kearney^#¹⁰, Heidi L Rehm^#^{16

17}; Medical Genome Initiative*

Affiliations

¹ Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA. ctse@mgh.harvard.edu.
² Laboratory for Molecular Medicine, Mass General Brigham Personalized Medicine, Cambridge, MA, USA. ctse@mgh.harvard.edu.
³ Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA. ctse@mgh.harvard.edu.
⁴ Molecular Diagnostics Laboratory, New York Genome Center, New York, NY, USA.
⁵ Department of Pathology and Cell Biology, Columbia University Irving Medical Center, New York, NY, USA.
⁶ Illumina Inc., San Diego, CA, USA.
⁷ HudsonAlpha Institute for Biotechnology, Huntsville, AL, USA.
⁸ Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA.
⁹ Genome Diagnostics, Department of Paediatric Laboratory Medicine, The Hospital for Sick Children, Toronto, ON, Canada.
¹⁰ Division of Laboratory Genetics and Genomics, Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA.
¹¹ Center for Individualized Medicine, College of Medicine, Mayo Clinic, Rochester, MN, USA.
¹² Rady Children's Institute for Genomic Medicine, San Diego, CA, USA.
¹³ Baylor Genetics and Baylor College of Medicine, Houston, TX, USA.
¹⁴ Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA.
¹⁵ Department of Pathology, Stanford Medicine, Stanford University, Stanford, CA, USA.
¹⁶ Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.
¹⁷ Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.

^# Contributed equally.

PMID: 35395838
PMCID: PMC8993917
DOI: 10.1038/s41525-022-00295-z

Review

Best practices for the interpretation and reporting of clinical whole genome sequencing

Christina A Austin-Tse et al. NPJ Genom Med. 2022.

. 2022 Apr 8;7(1):27.

doi: 10.1038/s41525-022-00295-z.

Authors

Affiliations

¹ Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA. ctse@mgh.harvard.edu.
² Laboratory for Molecular Medicine, Mass General Brigham Personalized Medicine, Cambridge, MA, USA. ctse@mgh.harvard.edu.
³ Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA. ctse@mgh.harvard.edu.
⁴ Molecular Diagnostics Laboratory, New York Genome Center, New York, NY, USA.
⁵ Department of Pathology and Cell Biology, Columbia University Irving Medical Center, New York, NY, USA.
⁶ Illumina Inc., San Diego, CA, USA.
⁷ HudsonAlpha Institute for Biotechnology, Huntsville, AL, USA.
⁸ Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA.
⁹ Genome Diagnostics, Department of Paediatric Laboratory Medicine, The Hospital for Sick Children, Toronto, ON, Canada.
¹⁰ Division of Laboratory Genetics and Genomics, Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA.
¹¹ Center for Individualized Medicine, College of Medicine, Mayo Clinic, Rochester, MN, USA.
¹² Rady Children's Institute for Genomic Medicine, San Diego, CA, USA.
¹³ Baylor Genetics and Baylor College of Medicine, Houston, TX, USA.
¹⁴ Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA.
¹⁵ Department of Pathology, Stanford Medicine, Stanford University, Stanford, CA, USA.
¹⁶ Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.
¹⁷ Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.

^# Contributed equally.

PMID: 35395838
PMCID: PMC8993917
DOI: 10.1038/s41525-022-00295-z

Abstract

Whole genome sequencing (WGS) shows promise as a first-tier diagnostic test for patients with rare genetic disorders. However, standards addressing the definition and deployment practice of a best-in-class test are lacking. To address these gaps, the Medical Genome Initiative, a consortium of leading health care and research organizations in the US and Canada, was formed to expand access to high quality clinical WGS by convening experts and publishing best practices. Here, we present best practice recommendations for the interpretation and reporting of clinical diagnostic WGS, including discussion of challenges and emerging approaches that will be critical to harness the full potential of this comprehensive test.

PubMed Disclaimer

Conflict of interest statement

D.L.P. and R.J.T. are current employees and shareholders of Illumina Inc. The remaining authors declare no competing interests.

Figures

**Fig. 1. Clinical Whole Genome Sequencing Workflow.**
Primary WGS analysis (blue) refers to the technical production of DNA sequence data from biological samples through the process of converting raw sequencing instrument signals into nucleotides and sequence reads; secondary analysis (green) refers to the identification of DNA variants through read alignment and variant calling; and tertiary analysis (yellow) refers to adding context through variant annotation and the subsequent informatics-driven filtering, triaging, and classification of variants. Tertiary analysis also includes case interpretation, variant confirmation, segregation analysis, and reporting. While case interpretation is integrated into the laboratory process, it is important to note that clinical correlation on the part of the ordering provider is a key final step in the process and may inform additional tertiary analysis steps. Figure originally published in Marshall et al. 2020.

**Fig. 2. WGS analysis process, including genotype-driven and phenotype-driven approaches.**
Minimum necessary data filtering and prioritization approaches are shown.

**Fig. 3. Triage review decision-making process.**
Variant, gene, and phenotype information must be simultaneously integrated to determine which variants should be nominated as potentially reportable.

See this image and copyright information in PMC

References

1. Scocchia A, et al. Clinical whole genome sequencing as a first-tier test at a resource-limited dysmorphology clinic in Mexico. NPJ Genom. Med. 2019;4:5. - PMC - PubMed
1. Lionel AC, et al. Improved diagnostic yield compared with targeted gene sequencing panels suggests a role for whole-genome sequencing as a first-tier genetic test. Genet. Med. 2018;20:435–443. - PMC - PubMed
1. Meienberg J, Bruggmann R, Oexle K, Matyas G. Clinical sequencing: is WGS the better WES? Hum. Genet. 2016;135:359–362. - PMC - PubMed
1. Belkadi A, et al. Whole-genome sequencing is more powerful than whole-exome sequencing for detecting exome variants. Proc. Natl Acad. Sci. USA. 2015;112:5473–5478. - PMC - PubMed
1. Lelieveld SH, Spielmann M, Mundlos S, Veltman JA, Gilissen C. Comparison of exome and genome sequencing technologies for the complete capture of protein‐coding regions. Hum. Mutat. 2015;36:815–822. - PMC - PubMed

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Best practices for the interpretation and reporting of clinical whole genome sequencing

Affiliations

Best practices for the interpretation and reporting of clinical whole genome sequencing

Authors

Affiliations

Abstract

Conflict of interest statement

Figures

References

Publication types

LinkOut - more resources

Full Text Sources