is-rSNP: a novel technique for in silico regulatory SNP detection.
BACKGROUND
Determining the functional impact of non-coding disease-associated single nucleotide polymorphisms (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), it can be determined which SNPs are likely rSNPs. However, predictions in this manner are noisy and no method exists that determines the statistical significance of a nucleotide variation on a PWM score.


RESULTS
We have designed an algorithm for in silico rSNP detection called is-rSNP. 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 have tested our method on 41 experimentally verified rSNPs, correctly predicting the disrupted TF in 28 cases. We also analysed 146 disease-associated SNPs with no known functional impact in an attempt to identify candidate rSNPs. Of the 11 significantly predicted disrupted TFs, 9 had previous evidence of being associated with the disease in the literature. These results demonstrate that is-rSNP is suitable for high-throughput screening of SNPs for potential regulatory function. This is a useful and important tool in the interpretation of GWAS.


BACKGROUND
is-rSNP software is available for use at: www.genomics.csse.unimelb.edu.au/is-rSNP.
Macintyre
Geoff
G
Department of Computer Science and Software Engineering, University of Melbourne, Victoria, Australia. gmaci@csse.unimelb.edu.au
Bailey
James
J
Haviv
Izhak
I
Kowalczyk
Adam
A
D000465:Algorithms; D000483:Alleles; D004247:DNA / Q000032:analysis; D030541:Databases, Genetic; D004194:Disease / Q000235:genetics; D055106:Genome-Wide Association Study; D006801:Humans; D020641:Polymorphism, Single Nucleotide; D056510:Position-Specific Scoring Matrices; D012045:Regulatory Sequences, Nucleic Acid; D012984:Software; D014157:Transcription Factors / Q000378:metabolism
D014157:Transcription Factors; D004247:DNA
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