University of Washington, Seattle
A geneticist discusses a way to assess the effects of disease-causing gene mutations.
Although thousands of rare inherited disorders are clearly monogenic — caused by single-gene mutations — the overall picture is usually more complex. Genetic and environmental modifiers, as well as differences in the gene variants themselves, can affect how disease genes are expressed and how a disease manifests itself (the phenotype).
Marc Vidal of the Dana-Farber Cancer Institute in Boston, Massachusetts, and his team reveal that disease-causing mutations may fit into two groups on the basis of the type of perturbation they cause. ‘Edgetic’ mutations affect specific interactions in a network of genes, whereas ‘nodal’ ones remove proteins from the network altogether (Q. Zhong et al. Mol. Sys. Biol. 5, 321; 2009). The researchers did computational analyses, using the tendency of disease-associated mutations to be in-frame — producing full-length mutated proteins — or truncating, producing truncated proteins, as a proxy for edgetic or nodal perturbations, respectively. They found that, for many genes underlying multiple diseases, different phenotypes were associated with different ratios of in-frame versus truncating mutations.
The authors then did experiments evaluating whether disease-associated mutations tend to disrupt known protein–protein interactions in a way that is consistent with edgetic versus nodal perturbation. They suggest that at least some of the phenotypic variability in monogenic diseases might correlate with specific patterns of network perturbation.
The experiments are limited, but the approach of cloning mutations and serially evaluating their impact is appealing. Various genome-sequencing projects will soon catalogue hundreds of thousands of coding variants of uncertain significance. Generalized, scalable methods to evaluate the functional relevance of these variants and to place them into a broader biological context will be crucial.