Interpreting missense variants: comparing computational methods in human disease genes CDKN2A, MLH1, MSH2, MECP2, and...

The human genome contains frequent single-basepair variants that may or may not cause genetic disease. To characterize benign vs. pathogenic missense variants, numerous computational algorithms have been developed based on comparative sequence and/or protein structure analysis. We compared computational methods that use evolutionary conservation alone, amino acid (AA) change alone, and a combination of conservation and AA change in predicting the consequences of 254 missense variants in the CDKN2A (n = 92), MLH1 (n = 28), MSH2 (n = 14), MECP2 (n = 30), and tyrosinase (TYR) (n = 90) genes. Variants were validated as either neutral or deleterious by curated locus-specific mutation databases and published functional data. All methods that use evolutionary sequence analysis have comparable overall prediction accuracy (72.9-82.0%). Mutations at codons where the AA is absolutely conserved over a sufficient evolutionary distance (about one-third of variants) had a 91.6 to 96.8% likelihood of being deleterious. Three algorithms (SIFT, PolyPhen, and A-GVGD) that differentiate one variant from another at a given codon did not significantly improve predictive value over conservation score alone using the BLOSUM62 matrix. However, when all four methods were in agreement (62.7% of variants), predictive value improved to 88.1%. These results confirm a high predictive value for methods that use evolutionary sequence conservation, with or without considering protein structural change, to predict the clinical consequences of missense variants. The methods can be generalized across genes that cause different types of genetic disease. The results support the clinical use of computational methods as one tool to help interpret missense variants in genes associated with human genetic disease.