Document Type
Article
Publication Date
6-2022
Identifier
DOI: 10.1016/j.gim.2022.02.007
Abstract
PURPOSE: This study aimed to provide comprehensive diagnostic and candidate analyses in a pediatric rare disease cohort through the Genomic Answers for Kids program.
METHODS: Extensive analyses of 960 families with suspected genetic disorders included short-read exome sequencing and short-read genome sequencing (srGS); PacBio HiFi long-read genome sequencing (HiFi-GS); variant calling for single nucleotide variants (SNV), structural variant (SV), and repeat variants; and machine-learning variant prioritization. Structured phenotypes, prioritized variants, and pedigrees were stored in PhenoTips database, with data sharing through controlled access the database of Genotypes and Phenotypes.
RESULTS: Diagnostic rates ranged from 11% in patients with prior negative genetic testing to 34.5% in naive patients. Incorporating SVs from genome sequencing added up to 13% of new diagnoses in previously unsolved cases. HiFi-GS yielded increased discovery rate with >4-fold more rare coding SVs compared with srGS. Variants and genes of unknown significance remain the most common finding (58% of nondiagnostic cases).
CONCLUSION: Computational prioritization is efficient for diagnostic SNVs. Thorough identification of non-SNVs remains challenging and is partly mitigated using HiFi-GS sequencing. Importantly, community research is supported by sharing real-time data to accelerate gene validation and by providing HiFi variant (SNV/SV) resources from >1000 human alleles to facilitate implementation of new sequencing platforms for rare disease diagnoses.
Journal Title
Genetics in medicine : official journal of the American College of Medical Genetics
Volume
24
Issue
6
First Page
1336
Last Page
1348
Keywords
AI; Genome sequencing; Long read sequencing; NGS; Rare disease
Recommended Citation
Cohen ASA, Farrow EG, Abdelmoity AT, et al. Genomic answers for children: Dynamic analyses of >1000 pediatric rare disease genomes. Genet Med. 2022;24(6):1336-1348. doi:10.1016/j.gim.2022.02.007
Comments
This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Publisher's Link: https://www.gimjournal.org/article/S1098-3600(22)00653-0/fulltext