Using SIFT and PolyPhen to predict loss-of-function and gain-of-function mutations.
BACKGROUND
The interpretation of novel missense variants is a challenge with increasing numbers of such variants being identified and a responsibility to report the findings in the context of all available scientific evidence. Various in silico bioinformatic tools have been developed that predict the likely pathogenicity of missense variants; however, their utility within the diagnostic setting requires further investigation.


OBJECTIVE
The aim of our study was to test the predictive value of two of these tools, sorting intolerant from tolerant (SIFT) and polymorphism phenotyping (PolyPhen), in a set of 141 missense variants (131 pathogenic, 8 benign) identified in the ABCC8, GCK, and KCNJ11 genes.


METHODS
Sixty-six of the mutations caused a gain of protein function, while 67 were loss-of-function mutations. The evolutionary conservation at each residue was also investigated using multiple sequence alignments from the UCSC genome browser.


RESULTS
The sensitivity of SIFT and PolyPhen was reasonably high (69% and 68%, respectively), but their specificity was low (13% and 16%). Both programs were significantly better at predicting loss-of-function mutations than gain-of-function mutations (SIFT, p = 0.001; PolyPhen, p < or = 0.0001). The most reliable method for assessing the likely pathogenicity of a missense variant was to investigate the degree of conservation at the affected residue. Eighty-eight percent of the mutations affected highly conserved residues, while all of the benign variants occurred at residues that were polymorphic across multiple species.


CONCLUSIONS
Although SIFT and PolyPhen may be useful in prioritizing changes that are likely to cause a loss of protein function, their low specificity means that their predictions should be interpreted with caution and further evidence to support/refute pathogenicity should be sought before reporting novel missense changes.
Flanagan
Sarah E
SE
Institute of Biomedical and Clinical Science, Peninsula Medical School, University of Exeter, Exeter, United Kingdom. sarah.flanagan@pms.ac.uk
Patch
Ann-Marie
AM
Ellard
Sian
S
D018528:ATP-Binding Cassette Transporters / Q000235:genetics; D019295:Computational Biology / Q000379:methods*; D030342:Genetic Diseases, Inborn / Q000175:diagnosis* / Q000235:genetics; D000081410:Germinal Center Kinases; D006801:Humans; D025202:Molecular Diagnostic Techniques / Q000379:methods; D020125:Mutation, Missense / Q000502:physiology*; D020641:Polymorphism, Single Nucleotide / Q000502:physiology; D024661:Potassium Channels, Inwardly Rectifying / Q000235:genetics; D011237:Predictive Value of Tests; D011379:Prognosis; D017346:Protein Serine-Threonine Kinases / Q000235:genetics; D011955:Receptors, Drug / Q000235:genetics; D017422:Sequence Analysis, DNA / Q000379:methods; D012984:Software; D013329:Structure-Activity Relationship; D064233:Sulfonylurea Receptors; D000099161:Kcnj11 Channel
D018528:ATP-Binding Cassette Transporters; D000081410:Germinal Center Kinases; D024661:Potassium Channels, Inwardly Rectifying; D017346:Protein Serine-Threonine Kinases; D011955:Receptors, Drug; D064233:Sulfonylurea Receptors; D000099161:Kcnj11 Channel