Variability in pathogenicity prediction programs: impact on clinical diagnostics

The performance of 17 publicly available pathogenicity prediction programs was assayed using a dataset consisting of 122 credibly pathogenic and benign variants in genes associated with the RASopathy family of disorders and limb ‐girdle muscular dystrophy. Performance metrics were compared between the programs to determine the most accurate program for loss of function and gain‐of‐function mechanisms. The best performer was MutPred, which had a weighted accuracy of 82.6% in the full dataset. AbstractCurrent practice by clinical diagnostic laboratories is to utilize online prediction programs to help determine the significance of novel variants in a given gene sequence. However, these programs vary widely in their methods and ability to correctly predict the pathogenicity of a given sequence change. The performance of 17 publicly available pathogenicity prediction programs was assayed using a dataset consisting of 122 credibly pathogenic and benign variants in genes associated with the RASopathy family of disorders and limb ‐girdle muscular dystrophy. Performance metrics were compared between the programs to determine the most accurate program for loss‐of‐function and gain‐of‐function mechanisms. No one program correctly predicted the pathogenicity of all variants analyzed. A major hindrance to the analysis w as the lack of output from a significant portion of the programs. The best performer was MutPred, which had a weighted accuracy of 82.6% in the full datas...
Source: Molecular Genetics & Genomic Medicine - Category: Genetics & Stem Cells Authors: Tags: Method Source Type: research