Volume 11 Supplement 10

Highlights from the Sixth International Society for Computational Biology (ISCB) Student Council Symposium

Open Access

is-rSNP: a novel technique for in silico regulatory SNP detection

BMC Bioinformatics201011(Suppl 10):O7


Published: 07 December 2010

Determining the functional impact of non-coding disease associated SNPs identified by genome-wide association studies (GWAS) is challenging. Many of these SNPs are likely to be regulatory SNPs (rSNPs): variations which affect the ability of a transcription factor (TF) to bind to DNA. However, experimental procedures for identifying rSNPs are expensive and labour intensive. Therefore, in silico methods are required for rSNP prediction. By scoring two alleles with a TF position weight-matrix (PWM), and observing the change in PWM score, it can be determined which SNPs are likely rSNPs. Predicting in this manner, however, yields large numbers of false positive predictions. In addition, no method exists that determines the statistical significance of a nucleotide variation on a PWM score.

We have designed an algorithm for in silico regulatory SNP detection called is-rSNP [1]. We employ novel convolution methods to determine the complete distributions of PWM scores and ratios between allele scores, facilitating assignment of statistical significance to rSNP effects. We tested our method on 41 experimentally verified rSNPs, correctly predicting the disrupted TF in 28 cases. We also analysed 146 disease associated SNPs from The Catalog of Published Genome-Wide Association Studies [2] with no known functional impact in an attempt to identify candidate rSNPs. We predicted 11 SNPs to be significantly disrupting the binding of a TF. Of these 11, 8 had previous evidence of the TF being associated with the disease in the literature. In addition, genes associated with each of the diseases were enriched for binding sites of the predicted disrupted TF in 5 cases. These results demonstrate that is-rSNP is suitable for high-throughput screening of SNPs for candidate rSNPs. This is a useful and important tool in the interpretation of GWAS.

Authors’ Affiliations

Department of Computer Science and Software Engineering, The University of Melbourne
NICTA, Victoria Research Lab, The University of Melbourne
Department of Biochemistry and Molecular Biology, The University of Melbourne
Ian Potter Centre for Cancer Genomics and Predictive Medicine, Peter MacCallum Cancer Centre
Bioinformatics and Systems Integration, The Blood and DNA Profiling Facility, Baker IDI heart and diabetes Institute


  1. Geoff Macintyre, James Bailey, Izhak Haviv, Adam Kowalczyk: is-rSNP: a novel technique for in silico regulatory SNP detection. Bioinformatics 2010, 26(18):i524-i530. 10.1093/bioinformatics/btq378View ArticleGoogle Scholar
  2. Hindorff LA, Sethupathy P, Junkins HA, Ramos EM, Mehta JP, Collins FS, Manolio TA: A Catalog of Published Genome-Wide Association Studies.2010. [http://www.genome.gov/gwastudies]Google Scholar


© Macintyre et al; licensee BioMed Central Ltd. 2010

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.