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

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

Author information

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

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