Combining the interactome and deleterious SNP predictions to improve disease gene identification.
A method has been developed for the prediction of proteins involved in genetic disorders. This involved combining deleterious SNP prediction with a system based on protein interactions and phenotype distances; this is the first time that deleterious SNP prediction has been used to make predictions across linkage-intervals. At each step we tested and selected the best procedure, revealing that the computationally expensive method of assigning medical meta-terms to create a phenotype distance matrix was outperformed by a simple word counting technique. We carried out in-depth benchmarking with increasingly stringent data sets, reaching precision values of up to 75% (19% recall) for 10-Mb linkage-intervals (averaging 100 genes). For the most stringent (worst-case) data we attained an overall recall of 6%, yet still achieved precision values of up to 90% (4% recall). At all levels of stringency and precision the addition of predicted deleterious SNPs was shown to increase recall.
Care
M A
MA
Institute of Molecular and Cellular Biology, University of Leeds, Leeds, West Yorkshire, United Kingdom.
Bradford
J R
JR
Needham
C J
CJ
Bulpitt
A J
AJ
Westhead
D R
DR
D000465:Algorithms; D019295:Computational Biology / Q000379:methods; D020022:Genetic Predisposition to Disease / Q000235:genetics*; D006801:Humans; D020641:Polymorphism, Single Nucleotide; D011485:Protein Binding; D025941:Protein Interaction Mapping / Q000379:methods*; D011506:Proteins / Q000235:genetics* / Q000378:metabolism*; D015203:Reproducibility of Results
D011506:Proteins