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Prediction of functional regulatory SNPs in monogenic and complex disease

Zhao, Yiqiang, Clark, Wyatt T., Mort, Matthew Edwin, Cooper, David Neil, Radivojac, Predrag and Mooney, Sean D. 2011. Prediction of functional regulatory SNPs in monogenic and complex disease. Human Mutation 32 (10) , pp. 1183-1190. 10.1002/humu.21559

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Abstract

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.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Medicine
Subjects: Q Science > QH Natural history > QH426 Genetics
Uncontrolled Keywords: Regulatory mutations ; Machine learning ; Monogenic disease ; Complex disease ; Single nucleotide polymorphisms ; SNP
Publisher: John Wiley & Sons
ISSN: 1059-7794
Last Modified: 07 Apr 2019 00:20
URI: http://orca.cf.ac.uk/id/eprint/22849

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