Analyzing Secondary Findings in hEDS Population: A Substudy

Abstract

Hypermobile Ehlers-Danlos syndrome (hEDS) is a rare hereditary connective tissue disorder affecting approximately 1 in 5,000 individuals worldwide. This thesis presents a substudy conducted as part of a larger effort to establish a comprehensive repository for hEDS patients. The specific focus of our substudy was to identify and report secondary findings in the American College of Medical Genetics and Genomics (ACMG) recommended genes. We performed variant interpretation using whole-exome and whole-genome sequencing data from 126 individuals diagnosed with hEDS and Hypermobility Spectrum disorders (HSD), as well as their affected and unaffected family members. The analysis targeted 73 actionable genes. Our analysis revealed that nine out of the 126 (7.1%) hEDS participants carried one of six distinct pathogenic or likely pathogenic variants in at least one ACMG gene, based on the ACMG v3.0 gene-specific reporting criteria (Miller et al. 2021). Notably, RYR1, MSH2, and HNF1A were the most frequently affected genes identified in this cohort. To initiate the identification of pathogenic (P) and likely pathogenic (LP) variants, we employed InterVar, an automated variant classification tool. However, our analysis demonstrated that InterVar overestimated variant pathogenicity in nine out of 12 instances. Moreover, InterVar failed to report P/LP variants in six instances overall. These findings underscore the importance of manual curation in accurately identifying P/LP variants. By utilizing a combination of computational prediction tools and manual review of experimental data, researchers and clinicians can ensure that variant classifications are as accurate and informative as possible. Informed by our findings, genetic counseling services will be offered to individuals who received positive results and consented to receiving secondary findings. We will emphasize the significance of clinical confirmation testing and appropriate medical management for positive cases. Through these measures, we strive to provide optimal care for our participants and enhance the accuracy of our research outcomes.