Investigating genotype‐to‐phenotype correlation in CHARGE syndrome by deep phenotyping and multiparametric clustering

Abstract

AbstractCHARGE syndrome, due to CHD7 pathogenic variations, is an autosomal dominant disorder characterized by a large spectrum of severity. Despite the great number of variations reported, no clear genotype‐to‐phenotype correlation has been reported. Unsupervised machine learning and clustering was undertaken using a retrospective cohort of 42 patients, after deep radiologic and clinical phenotyping, to establish genotype–phenotype correlation for CHD7‐related CHARGE syndrome. It resulted in three clusters showing phenotypes of different severities. While no clear genotype–phenotype correlation appeared within the first two clusters, a single patient was outlying the cohort data (cluster 3) with the most atypical phenotype and the most distal frameshift variant in the gene. We added two other patients with similar distal pathogenic variants and observed a tendency toward mild and/or atypical phenotypes. We hypothesized that this finding could potentially be related to escaping nonsense mediated RNA decay, but found no evidence of such decay in vivo for any of the CHD7 pathogenic variation tested. This indicates that this milder phenotype may rather result from the production of a protein retaining all functional domains.