Exome CNV detection and classification in rare diseases

Description

Dr. Gabrielle Lemire will discuss exome copy number variant (CNV) detection, analysis and classification on a cohort of 6,633 families with undiagnosed rare genetic disorders. With the resolution provided by exome sequencing, they identified a causative CNV in 2.6% of families and assessed CNV pathogenicity by applying an advanced classification approach. 

Overview of Presentation

• CNVs can be difficult to identify by standard exome sequencing and challenges still remain in accurate classification of CNV pathogenicity.
• CNV calling using GATK-gCNV was performed on exomes from a cohort of 6,633 families (15,759 individuals) with heterogeneous phenotypes and variable prior genetic testing collected at the Broad Institute Center for Mendelian Genomics.
• The addition of CNV detection to exome analysis identified causal CNVs for 171 families (2.6%).
• To classify CNV variant pathogenicity, we used the 2020 American College of Medical Genetics and Genomics/ClinGen CNV interpretation standards and developed additional criteria to evaluate allelic and functional data as well as variants on the X chromosome to further advance the framework.
• We interpreted 151 CNVs as likely pathogenic/pathogenic and 20 CNVs as high-interest variants of uncertain significance.
• Calling CNVs from existing exome data increases the diagnostic yield for individuals undiagnosed after standard testing approaches, providing a higher-resolution alternative to arrays at a fraction of the cost of genome sequencing.