Using SIFT and PolyPhen to predict loss-of-function and gain-of-function mutations

Area of Science:

• Genetics
• Bioinformatics
• Molecular Biology

Background:

• Interpreting novel missense variants poses a significant challenge in genetic diagnostics.
• In silico bioinformatic tools are increasingly used to predict variant pathogenicity, but their diagnostic utility requires validation.

Purpose of the Study:

• To evaluate the predictive performance of SIFT (Sorting Intolerant from Tolerant) and PolyPhen (Polymorphism Phenotyping) tools.
• To assess these tools on 141 missense variants (131 pathogenic, 8 benign) within the ABCC8, GCK, and KCNJ11 genes.

Main Methods:

• Tested 141 missense variants, including 66 gain-of-function and 67 loss-of-function mutations.
• Assessed evolutionary conservation at affected residues using UCSC genome browser multiple sequence alignments.

Main Results:

• SIFT and PolyPhen demonstrated high sensitivity (69% and 68%) but low specificity (13% and 16%).
• Both tools were more effective in predicting loss-of-function than gain-of-function mutations.
• Residue conservation was a more reliable indicator of pathogenicity; conserved residues were associated with mutations, while polymorphic residues indicated benign variants.

Conclusions:

• SIFT and PolyPhen can aid in prioritizing variants likely causing loss of protein function.
• Due to low specificity, predictions from these tools should be interpreted cautiously.
• Further evidence is crucial to confirm or refute the pathogenicity of novel missense variants before clinical reporting.