Predicting Penetrance of SCN5A Rare Variants
Peering Beyond the Black and White and Into the Shades of Grey
This article requires a subscription to view the full text. If you have a subscription you may use the login form below to view the article. Access to this article can also be purchased.
See Article by Kroncke and Glazer et al
Evaluating the impact of rare variants on disease development is often challenging in the context of presumed monogenic Mendelian disorders. Although multiple strategies have been developed to attempt to classify rare variants as pathogenic or benign, clinical genetic testing laboratories currently use guidelines developed by the American College of Medical Genetics (ACMG).1–5 Despite considerable insight being gleaned over the past decade, a definitive classification for many rare variants remains elusive, complicating care for patients and their family members. Our incomplete understanding of the impact of rare variants on disease development has been highlighted by a greater than anticipated burden of rare genetic variation in large population-based whole exome and genome registries.6 Beyond the surprising degree of variation that seems to be tolerated, the allele frequencies of many functionally relevant rare variants are incompatible for monogenic culprits of very rare syndromes. This has led to the realization that many of these variants may be more aptly viewed as conferring disease susceptibility, rather than reflecting disease-causing variants in isolation.
Recognition that a large proportion of functionally relevant rare variants are likely insufficient to cause disease in isolation has alluded to the notion that variant pathogenicity may be more appropriately characterized on a continuous, rather than binary scale. Contemporary methods for assessing genetic variants, including in silico tools and the ACMG guidelines, attempt to dichotomize rare variants as pathogenic or benign.5 This binary classification system likely fails to capture the spectrum of risk that genetic variants impart on the likelihood of disease development, which may limit the ability of the treating clinician to optimally leverage genotype for patient care.
In contrast to the binary output provided by in silico predictive models and the ACMG guidelines, functional studies can potentially provide …