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Updated: Sep 29, 2025

In Vivo Functional Study of Disease-associated Rare Human Variants Using Drosophila
Published on: August 20, 2019
Genomic answers for children: Dynamic analyses of >1000 pediatric rare disease genomes
Ana S A Cohen1, Emily G Farrow2, Ahmed T Abdelmoity3
1Genomic Medicine Center, Children's Mercy Kansas City, Kansas City, MO; Department of Pathology and Laboratory Medicine, Children's Mercy Kansas City, Kansas City, MO; UKMC School of Medicine, University of Missouri Kansas City, Kansas City, MO.
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.

