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

PeerJ

Karin Verspoor¹  Antonio Jimeno Yepes¹  Geoff Macintyre¹  Cheng Soon Ong² 

[1] Department of Computing and Information Systems, The University of Melbourne, VIC, Australia;Department of Electrical and Electronic Engineering, The University of Melbourne, VIC, Australia;

关键词: Text mining;    eQTL;    HiC;    Non-coding variants;    Data integration;   

DOI  :  10.7717/peerj.639

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown