Genetics in MedicineGenetic & Genomic MedicinePeer-reviewed journal article
Genomic answers for children: Dynamic analyses of >1000 pediatric rare disease genomes
Ana S.A. CoheniD, Emily FarrowiD, Ahmed T. AbdelmoityiD, Joseph T. AlaimoiD, Shivarajan Manickavasagam Amudhavalli, John T. AndersoniD, Lalit R. BansaliD, Lauren E. BartikiD, Primo A. Baybayan, Bradley Belden, Courtney BerriosiD, Rebecca L. BiswelliD, Pawel BuczkowicziD, Orion J. BuskeiD, Shreyasee Chakraborty, Warren CheungiD, Keith A. CoffmaniD, Ashley M. CooperiD, Laura CrossiD, Tom CurraniD, Thuy Tien T. Dang, Mary Elfrink, Kendra L. Engleman, Erin FecskeiD, Cynthia Fieser, , Tomi PastineniD
Abstract
PURPOSETo provide comprehensive diagnostic and candidate analyses in a pediatric rare disease cohort through the Genomic Answers for Kids (GA4K) program. METHODSExtensive analyses of 960 families with suspected genetic disorders including short-read exome (ES) and genome sequencing (srGS); PacBio HiFi long-read GS (HiFi-GS); variant calling for small-nucleotide (SNV), structural (SV) and repeat variants; and machine-learning variant prioritization. Structured phenotypes, prioritized variants and pedigrees are stored in PhenoTips database, with data sharing through controlled access (dbGAP). RESULTSDiagnostic rates ranged from 11% for cases with prior negative genetic tests to 34.5% in naive patients. Incorporating SVs from GS added up to 13% of new diagnoses in previously unsolved cases. HiFi-GS yielded increased discovery rate with >4-fold more rare coding SVs than srGS. Variants and genes of unknown significance (VUS/GUS) remain the most common finding (58% of non-diagnostic cases). CONCLUSIONComputational prioritization is efficient for diagnostic SNVs. Thorough identification of non-SNVs remains challenging and is partly mitigated by 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 >1,000 human alleles to facilitate implementation of new sequencing platforms for rare disease diagnoses.
- organism: human
- assay: whole-exome sequencing
- assay: whole-genome sequencing
- assay: long-read sequencing
- research focus: variant calling
- research focus: variant effect prediction
- approach: machine learning (classical)
- Citations
- 96
- FWCI
- 11.46
- Field percentile
- top 1%
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