Reanalysing genomic data by normalized coverage values uncovers CNVs in bone marrow failure gene panels

Area of Science:

• Genetics
• Molecular Biology
• Hematology

Background:

• Inherited bone marrow failure syndromes (IBMFSs) are genetic disorders causing cytopenia, physical malformations, and increased cancer risk.
• While point mutations are found in about half of IBMFS patients, the frequency and spectrum of copy number variations (CNVs) remain largely unknown.
• Current genome-wide methods for CNV detection have limitations, including missing small CNVs or low sensitivity due to insufficient read depth.

Purpose of the Study:

• To determine if reanalysis of next-generation sequencing (NGS) panel data using normalized coverage values can identify and characterize CNVs in IBMFS patients.
• To establish CNV detection as a standard practice in the diagnostic workup of IBMFS.

Main Methods:

• DNA from 258 IBMFS patients was analyzed using an NGS panel assay targeting known IBMFS genes.
• After initial analysis for point mutations, NGS data from 165 patients without pathogenic point mutations were re-analyzed for heterozygous and homozygous CNVs using normalized read coverage ratios.
• All detected deletions were validated using orthogonal methods.

Main Results:

• Pathogenic point variants were identified in 91 out of 258 patients.
• Reanalysis for CNVs revealed deletions in 10 patients.
• Heterozygous deletions in Diamond-Blackfan anemia genes (RPS19, RPL11, RPL5) were most common. A GATA2 deletion was found in a patient with myelodysplastic syndrome. Homozygous FANCA deletion was detected in an undiagnosed patient. Compound heterozygousity for deletions and point variants was identified in RBM8A and PARN.

Conclusions:

• Careful analysis of normalized coverage values in NGS panel data is a viable method for detecting CNVs in IBMFS.
• This approach should be considered a standard diagnostic practice for IBMFS patients, especially those without identified point mutations.
• CNV detection via NGS panel reanalysis improves diagnostic yield for genetically heterogeneous IBMFS.