Benchmarking Computational Methods for Estimating the Pathogenicity of Wilson’s Disease Mutations

Abstract

Genetic variations in the gene encoding the copper-transport protein ATP7B are the primary cause of Wilsons disease. Controversially, clinical prevalence seems much smaller than prevalence estimated by genetic screening tools, causing fear that many people are undiagnosed although early diagnosis and treatment is essential. To address this issue, we benchmarked 16 state-of-the-art computational disease-prediction methods against established data of missense ATP7B mutations. Our results show that the quality of the methods vary widely. We show the importance of optimizing the threshold of the methods used to distinguish pathogenic from non-pathogenic mutations against data of clinically confirmed pathogenic and non-pathogenic mutations. We find that most methods use thresholds that predict too many ATP7B mutations to be pathogenic. Thus, our findings explain the current controversy on Wilsons disease prevalence, because meta analysis and text search methods include many computational estimates that lead to higher disease prevalence than clinically observed. Since proteins differ widely, a one-size-fits-all threshold for all proteins cannot distinguish efficiently pathogenic and non-pathogenic mutations, as shown here. We also show that amino acid changes with small evolutionary substitution probability, mainly due to amino acid volume, are more associated with disease, implying a pathological effect on the conformational state of the protein, which could affect copper transport or ATP recognition and hydrolysis. These findings may be a first step towards a more quantitative genotype-phenotype relationship of Wilsons disease.