Convergent downstream candidate mechanisms of independent intergenic polymorphisms between co-classified diseases implicate epistasis among noncoding elements

Jiali Han^1,2,†, Jianrong Li^1,2,†, Ikbel Achour^1,2,†, Lorenzo Pesce³, Ian Foster³, Haiquan Li^1,4,*, Yves A. Lussier^1,4,*

¹Center for Biomedical Informatics and Biostatistics (CB2)
²Departments of Medicine and of Systems and Industrial Engineering, The University of Arizona
³Computation Institute, Argonne National Laboratory and University of Chicago
⁴BIO5 Institute, UACC, and Dept of Medicine, The University of Arizona
^†Authors contributed equally to this work
^*Corresponding author
Email: jialih@email.arizona.edu, jianrong@email.arizona.edu, ikachour@gmail.com, lpesce@cs.uchicago.edu, foster@cs.uchicago.edu, haiquan@email.arizona.edu, yves@email.arizona.edu

Pacific Symposium on Biocomputing 23:524-535(2018)

© 2018 World Scientific
Open Access chapter published by World Scientific Publishing Company and distributed under the terms of the Creative Commons Attribution (CC BY) 4.0 License.

Abstract

Eighty percent of DNA outside protein coding regions was shown biochemically functional by the ENCODE project, enabling studies of their interactions. Studies have since explored how convergent downstream mechanisms arise from independent genetic risks of one complex disease. However, the cross-talk and epistasis between intergenic risks associated with distinct complex diseases have not been comprehensively characterized. Our recent integrative genomic analysis unveiled downstream biological effectors of disease-specific polymorphisms buried in intergenic regions, and we then validated their genetic synergy and antagonism in distinct GWAS. We extend this approach to characterize convergent downstream candidate mechanisms of distinct intergenic SNPs across distinct diseases within the same clinical classification. We construct a multipartite network consisting of 467 diseases organized in 15 classes, 2,358 disease-associated SNPs, 6,301 SNPassociated mRNAs by eQTL, and mRNA annotations to 4,538 Gene Ontology mechanisms. Functional similarity between two SNPs (similar SNP pairs) is imputed using a nested information theoretic distance model for which p-values are assigned by conservative scale-free permutation of network edges without replacement (node degrees constant). At FDR≤5%, we prioritized 3,870 intergenic SNP pairs associated, among which 755 are associated with distinct diseases sharing the same disease class, implicating 167 intergenic SNPs, 14 classes, 230 mRNAs, and 134 GO terms. Co-classified SNP pairs were more likely to be prioritized as compared to those of distinct classes confirming a noncoding genetic underpinning to clinical classification (odds ratio ~3.8; p≤10^-25). The prioritized pairs were also enriched in regions bound to the same/interacting transcription factors and/or interacting in long-range chromatin interactions suggestive of epistasis (odds ratio ~ 2,500; p≤10^-25). This prioritized network implicates complex epistasis between intergenic polymorphisms of co-classified diseases and offers a roadmap for a novel therapeutic paradigm: repositioning medications that target proteins within downstream mechanisms of intergenic disease-associated SNPs. Supplementary information and software: http://lussiergroup.org/publications/disease_class