A data-driven framework for CNV pathogenicity classification
Creators: name: Wetzel, Alyssa Sue
Publisher: name: University of Iowa
Year: 2022
Subjects: public abstract

description: The phenotypic spectrum of human microdeletion and microduplication syndromes (MMS) is heterogeneous but often involves intellectual disability, autism spectrum disorders, dysmorphic features, and/or multiple congenital anomalies. While the common recurrent copy number variants (CNVs) which underlie these MMS have been well-studied (“pathogenic CNVs”), the expansion of clinical genomic testing has led to the identification of many rare non-recurrent CNVs and CNVs in unaffected control populations (“benign CNVs”). To date, hundreds of unique MMS CNVs have been reported, maps of normal copy number variation have been developed, and thousands of variants of unclear clinical significance (VUS) have been identified. VUS represent a large subset of rare and non-recurrent CNVs in which there is insufficient evidence to assign a benign or pathogenic classification.
description: The interpretation of clinical chromosomal microarrays (CMAs) has historically relied on the relevance of identified CNVs to the clinical phenotype. However, new interpretation guidelines focus on standardizing pathogenicity classifications based on genomic location, gene content, and previous publications rather than the immediate clinical relevance. Discriminating benign CNVs from those that are pathogenic for rare genetic disorders, therefore, relies on understanding what regions of the human genome are tolerant to copy number variation. Further complicating the characterization of CNV pathogenicity are discordant classifications of variants across clinical laboratories, phenotypic heterogeneity, incomplete penetrance, and the lack of understanding of the contributions of individual genes to the clinical phenotype for well-known and well-characterized pathogenic CNVs. Lastly, to account for advances in our understanding of regions of the genome which are tolerant and intolerant to copy number variation, periodic reanalysis is encouraged. Due to the volume of CNVs that are ascertained clinically, reanalysis of pathogenicity classifications is a significant undertaking which requires the aid of computational tools.
description: In this thesis work described herein, I provide a data-driven framework for improving the pathogenicity classification of clinically ascertained CNVs with the goal of decreasing the number of VUS test results which are returned clinically. The overall project was driven by two main aims: (1) delineate regions of the human genome which are tolerant and intolerant to copy number variation and (2) develop a framework to facilitate the prioritization and re-analysis of CNV pathogenicity on a large-scale. The findings from my dissertation broaden the knowledgebase of both pathogenic and benign CNVs, provide a resource for clinicians in the interpretation of non-recurrent CNVs, and provide two computational tools which can be utilized by the broader research community to continually generate updated maps of benign regions (Benign-Ex) and enable large-scale high-throughput CNV classification, reanalysis, and prioritization (DISCRIMINATOR).