regSNPs: a strategy for prioritizing regulatory single nucleotide substitutions

Teng, Mingxiang, Ichikawa, Shoji, Padgett, Leah R., Wang, Yadong, Mort, Matthew Edwin, Cooper, David Neil ORCID: https://orcid.org/0000-0002-8943-8484, Koller, Daniel L., Foroud, Tatiana, Edenberg, Howard J., Econs, Michael J. and Liu, Yunlong 2012. regSNPs: a strategy for prioritizing regulatory single nucleotide substitutions. Bioinformatics 28 (14) , pp. 1879-1886. 10.1093/bioinformatics/bts275

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Abstract

Motivation: One of the fundamental questions in genetics study is to identify functional DNA variants that are responsible to a disease or phenotype of interest. Results from large-scale genetics studies, such as genome-wise associations studies (GWAS), and the availability of high throughput sequencing technologies provide opportunities in identifying causal variants. Despite the technical advances, informatics methodologies need to be developed to prioritize thousands of variants for potential causative effects. Results: We present regSNPs, an informatics strategy that integrates several established bioinformatics tools, for prioritizing regulatory SNPs, i.e. the SNPs in the promoter regions that potentially affect phenotype through changing transcription of downstream genes. Comparing to existing tools, regSNPs has two distinct features. It considers degenerative features of binding motifs by calculating the differences on the binding affinity caused by the candidate variants and integrates potential phenotypic effects of various transcription factors. When tested by using the disease-causing variants documented in the Human Gene Mutation Database, regSNPs showed mixed performance on various diseases. regSNPs predicted 3 SNPs that can potentially affect bone density in a region detected in an earlier linkage study. Potential effects of one of the variants were validated using luciferase reporter assay.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Medicine
Subjects:	R Medicine > R Medicine (General)
Publisher:	Oxford University Press
ISSN:	1367-4803
Last Modified:	09 Nov 2022 08:07
URI:	https://orca.cardiff.ac.uk/id/eprint/42048

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