Application of full-genome analysis to diagnose rare monogenic disorders

Current genetic tests for rare diseases provide a diagnosis in only a modest proportion of cases. The Full-Genome Analysis method, FGA, combines long-range assembly and whole-genome sequencing to detect small variants, structural variants with breakpoint resolution, and phasing. We built a variant p...

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Bibliographic Details
Published in:Npj genomic medicine 2021-09, Vol.6 (1), p.77-77, Article 77
Main Authors: Shieh, Joseph T., Penon-Portmann, Monica, Wong, Karen H. Y., Levy-Sakin, Michal, Verghese, Michelle, Slavotinek, Anne, Gallagher, Renata C., Mendelsohn, Bryce A., Tenney, Jessica, Beleford, Daniah, Perry, Hazel, Chow, Stephen K., Sharo, Andrew G., Brenner, Steven E., Qi, Zhongxia, Yu, Jingwei, Klein, Ophir D., Martin, David, Kwok, Pui-Yan, Boffelli, Dario
Format: Article
Language:English
Subjects:
631/208/1516
692/700/1720
Bioinformatics
Biomedical and Life Sciences
Biomedicine
Diagnosis
Gene Function
Gene mapping
Gene Therapy
Genetic screening
Genomes
Human Genetics
Internal Medicine
Rare diseases
Whole genome sequencing
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Summary:Current genetic tests for rare diseases provide a diagnosis in only a modest proportion of cases. The Full-Genome Analysis method, FGA, combines long-range assembly and whole-genome sequencing to detect small variants, structural variants with breakpoint resolution, and phasing. We built a variant prioritization pipeline and tested FGA’s utility for diagnosis of rare diseases in a clinical setting. FGA identified structural variants and small variants with an overall diagnostic yield of 40% (20 of 50 cases) and 35% in exome-negative cases (8 of 23 cases), 4 of these were structural variants. FGA detected and mapped structural variants that are missed by short reads, including non-coding duplication, and phased variants across long distances of more than 180 kb. With the prioritization algorithm, longer DNA technologies could replace multiple tests for monogenic disorders and expand the range of variants detected. Our study suggests that genomes produced from technologies like FGA can improve variant detection and provide higher resolution genome maps for future application.
ISSN:2056-7944
2056-7944
DOI:10.1038/s41525-021-00241-5