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NPJ Genom Med. 2016;1. pii: 16006. Epub 2016 Apr 27.

Integrative genomics analyses unveil downstream biological effectors of disease-specific polymorphisms buried in intergenic regions.

Author information

1
BIO5 institute, University of Arizona, Tucson, AZ, USA; Department of Medicine, University of Arizona, Tucson, AZ, USA; Department of Medicine, University of Illinois at Chicago, IL, USA; Section of Genetic Medicine, Department of Medicine, University of Chicago, IL, USA; Center for Biomedical Informatics, Department of Medicine, University of Chicago, IL, USA.
2
BIO5 institute, University of Arizona, Tucson, AZ, USA; Department of Medicine, University of Arizona, Tucson, AZ, USA; Department of Medicine, University of Illinois at Chicago, IL, USA.
3
Departments of Biomedical Informatics and Medicine, Vanderbilt University, TN, USA.
4
Section of Genetic Medicine, Department of Medicine, University of Chicago, IL, USA; Center for Biomedical Informatics, Department of Medicine, University of Chicago, IL, USA.
5
Computation Institute, Argonne National Laboratory and University of Chicago, IL, USA.
6
Section of Genetic Medicine, Department of Medicine, University of Chicago, IL, USA; Center for Biomedical Informatics, Department of Medicine, University of Chicago, IL, USA; Department of Pediatrics, University of Chicago, USA.
7
Department of Medicine, University of Arizona, Tucson, AZ, USA.
8
Computation Institute, Argonne National Laboratory and University of Chicago, IL, USA; Mathematics and Computer Science Division, Argonne National Laboratory, Chicago, IL, USA; Department of Computer Science, University of Chicago, Chicago, IL, USA.
9
Department of Genetics, Institute for Quantitative Biomedical Sciences, Geisel School of Medicine, Dartmouth College, Hanover, NH, USA; Penn Institute for Biomedical Informatics, Perelman School of Medicine, the University of Pennsylvania, PA, USA.
10
BIO5 institute, University of Arizona, Tucson, AZ, USA; Department of Medicine, University of Arizona, Tucson, AZ, USA; Department of Medicine, University of Illinois at Chicago, IL, USA; Section of Genetic Medicine, Department of Medicine, University of Chicago, IL, USA; Center for Biomedical Informatics, Department of Medicine, University of Chicago, IL, USA; Computation Institute, Argonne National Laboratory and University of Chicago, IL, USA; Institute for Genomics and Systems Biology, Argonne National Laboratory & University of Chicago, IL, USA; University of Arizona Cancer Center, University of Arizona, Tucson, AZ, USA; Section for Bioinformatics, Department of Bioengineering, University of Illinois at Chicago, IL, USA; Department of Biopharmaceutical Sciences, University of Illinois at Chicago, IL, USA.

Abstract

Functionally altered biological mechanisms arising from disease-associated polymorphisms, remain difficult to characterize when those variants are intergenic, or, fall between genes. We sought to identify shared downstream mechanisms by which inter- and intragenic single nucleotide polymorphisms (SNPs) contribute to a specific physiopathology. Using computational modeling of 2 million pairs of disease-associated SNPs drawn from genome wide association studies (GWAS), integrated with expression Quantitative Trait Loci (eQTL) and Gene Ontology functional annotations, we predicted 3,870 inter-intra and inter-intra SNP pairs with convergent biological mechanisms (FDR<0.05). These prioritized SNP pairs with overlapping mRNA targets or similar functional annotations were more likely to be associated with the same disease than unrelated pathologies (OR>12). We additionally confirmed synergistic and antagonistic genetic interactions for a subset of prioritized SNP pairs in independent studies of Alzheimer's disease (entropy p=0.046), bladder cancer (entropy p=0.039), and rheumatoid arthritis (PheWAS case-control p<10-4). Using ENCODE datasets, we further statistically validated that the biological mechanisms shared within prioritized SNP pairs are frequently governed by matching transcription factor binding sites and long-range chromatin interactions. These results provide a "roadmap" of disease mechanisms emerging from GWAS and further identify candidate therapeutic targets among downstream effectors of intergenic SNPs.

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