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Combining the interactome and deleterious SNP predictions to improve disease gene identification
Author(s) -
Care M.A.,
Bradford J.R.,
Needham C.J.,
Bulpitt A.J.,
Westhead D.R.
Publication year - 2009
Publication title -
human mutation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.981
H-Index - 162
eISSN - 1098-1004
pISSN - 1059-7794
DOI - 10.1002/humu.20917
Subject(s) - biology , snp , computational biology , genetics , linkage (software) , single nucleotide polymorphism , identification (biology) , phenotype , interactome , precision and recall , benchmarking , gene , computer science , artificial intelligence , genotype , botany , marketing , business
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. Hum Mutat 0, 1–9, 2009. © 2009 Wiley‐Liss, Inc.