Associating disease-related genetic variants in intergenic regions to the genes they impact

PeerJ. 2014;2:e639 DOI 10.7717/peerj.639

 

Journal Homepage

Journal Title: PeerJ

ISSN: 2167-8359 (Online)

Publisher: PeerJ Inc.

LCC Subject Category: Medicine

Country of publisher: United States

Language of fulltext: English

Full-text formats available: PDF, HTML, XML

 

AUTHORS

Geoff Macintyre (Department of Computing and Information Systems, The University of Melbourne, VIC, Australia)
Antonio Jimeno Yepes (Department of Computing and Information Systems, The University of Melbourne, VIC, Australia)
Cheng Soon Ong (Department of Electrical and Electronic Engineering, The University of Melbourne, VIC, Australia)
Karin Verspoor (Department of Computing and Information Systems, The University of Melbourne, VIC, Australia)

EDITORIAL INFORMATION

Blind peer review

Editorial Board

Instructions for authors

Time From Submission to Publication: 10 weeks

 

Abstract | Full Text | Full Text

We present a method to assist in interpretation of the functional impact of intergenic disease-associated SNPs that is not limited to search strategies proximal to the SNP. The method builds on two sources of external knowledge: the growing understanding of three-dimensional spatial relationships in the genome, and the substantial repository of information about relationships among genetic variants, genes, and diseases captured in the published biomedical literature. We integrate chromatin conformation capture data (HiC) with literature support to rank putative target genes of intergenic disease-associated SNPs. We demonstrate that this hybrid method outperforms a genomic distance baseline on a small test set of expression quantitative trait loci, as well as either method individually. In addition, we show the potential for this method to uncover relationships between intergenic SNPs and target genes across chromosomes. With more extensive chromatin conformation capture data becoming readily available, this method provides a way forward towards functional interpretation of SNPs in the context of the three dimensional structure of the genome in the nucleus.