Mount Sinai Researchers on Importance of Understanding Genetic Variants and Disease Risk

Ron Do, PhD, associate professor of the Charles Bronfman Institute for Personalized Medicine at the Icahn School of Medicine at Mount Sinai, and Iain Forrest, MD-PhD candidate in Dr Do’s lab, explain the importance of their recent study, which measured penetrance of pathogenic and loss-of-function clinical variants. The study, "Population-Based Penetrance of Deleterious Clinical Variants," was published in JAMA.

Transcript

Why did you do this study?

Forrest: What really motivated us was to try to find a different way and, what some might argue, a better way to actually assess the disease risk that's associated with genetic variants. Currently, the way that we determine if a variant is so-called pathogenic—meaning it's implicated in disease—is we assign these rather broad labels of pathogenicity to them. What this means is we can call a variant pathogenic, we can call it benign, we can call it uncertain. And the problem with this is that they're very broad and coarse, and they're not very good at giving precise estimates of what the actual disease risk is. That was the major hole in the field that we want to fill, and we were able to fill this hole with a study because we had a unique resource of really large electronic health record linked biobanks with tens of thousands of people that really enabled us to assess what is the disease risk—aka the penetrance—of variants at a very, very large scale.

Why is understanding the penetrance of pathogenic and loss-of-function variants important?

Do: Penetrance is defined as the probability of disease for individuals who have the variant allele. Therefore there's value in stratifying individuals by their disease risk. Despite this value, the penetrance is actually unknown for most pathogenic variants, and for the few variants where penetrance has been reported in prior studies, they aren't necessarily accurate. Inflation of penetrance has been reported by prior studies, and these studies have used family-based or disease-focused cohorts which can cause upward bias in penetrance estimates.