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

Geoff Macintyre; Antonio Jimeno Yepes; Cheng Soon Ong; Karin Verspoor

doi:10.7717/peerj.639

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

PeerJ. 2014 Oct 23:2:e639. doi: 10.7717/peerj.639. eCollection 2014.

Authors

Geoff Macintyre^#^{1

2}, Antonio Jimeno Yepes^#¹, Cheng Soon Ong^{3

4

5}, Karin Verspoor^{1

6}

Affiliations

¹ Department of Computing and Information Systems, The University of Melbourne, VIC, Australia.
² Centre for Neural Engineering, The University of Melbourne, VIC, Australia.
³ Department of Electrical and Electronic Engineering, The University of Melbourne, VIC, Australia.
⁴ Machine Learning Group, NICTA Canberra Research Laboratory, Australia.
⁵ Research School of Computer Science, Australian National University, Australia.
⁶ Health and Biomedical Informatics Centre, The University of Melbourne, VIC, Australia.

^# Contributed equally.

Abstract

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.

Keywords: Data integration; HiC; Non-coding variants; Text mining; eQTL.

Grants and funding

We have received funding from National ICT Australia (NICTA) and the University of Melbourne, Australia. NICTA is funded by the Australian Government through the Department of Communications and the Australian Research Council through the ICT Centre of Excellence Program. NICTA is also funded and supported by the Australian Capital Territory, the New South Wales, Queensland and Victorian Governments, the Australian National University, the University of New South Wales, the University of Melbourne, the University of Queensland, the University of Sydney, Griffith University, Queensland University of Technology, Monash University and other university partners. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.