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Prediction of functional regulatory SNPs in monogenic and complex disease
Author(s) -
Zhao Yiqiang,
Clark Wyatt T.,
Mort Matthew,
Cooper David N.,
Radivojac Predrag,
Mooney Sean D.
Publication year - 2011
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.21559
Subject(s) - biology , single nucleotide polymorphism , genetics , computational biology , disease , gene , genotype , medicine
Next‐generation sequencing (NGS) technologies are yielding ever higher volumes of human genome sequence data. Given this large amount of data, it has become both a possibility and a priority to determine how disease‐causing single nucleotide polymorphisms (SNPs) detected within gene regulatory regions (rSNPs) exert their effects on gene expression. Recently, several studies have explored whether disease‐causing polymorphisms have attributes that can distinguish them from those that are neutral, attaining moderate success at discriminating between functional and putatively neutral regulatory SNPs. Here, we have extended this work by assessing the utility of both SNP‐based features (those associated only with the polymorphism site and the surrounding DNA) and gene‐based features (those derived from the associated gene in whose regulatory region the SNP lies) in the identification of functional regulatory polymorphisms involved in either monogenic or complex disease. Gene‐based features were found to be capable of both augmenting and enhancing the utility of SNP‐based features in the prediction of known regulatory mutations. Adopting this approach, we achieved an AUC of 0.903 for predicting regulatory SNPs. Finally, our tool predicted 225 new regulatory SNPs with a high degree of confidence, with 105 of the 225 falling into linkage disequilibrium blocks of reported disease‐associated genome‐wide association studies SNPs. Hum Mutat 32:1183–1190, 2011. ©2011 Wiley‐Liss, Inc.