iRegNet3D: three-dimensional integrated regulatory network for the genomic analysis of coding and non-coding disease mutations

Abstract

The mechanistic details of most disease-causing mutations remain poorly explored within the context of regulatory networks. We present a high-resolution three-dimensional integrated regulatory network (iRegNet3D) in the form of a web tool, where we resolve the interfaces of all known transcription factor (TF)-TF, TF-DNA and chromatin-chromatin interactions for the analysis of both coding and non-coding disease-associated mutations to obtain mechanistic insights into their functional impact. Using iRegNet3D, we find that disease-associated mutations may perturb the regulatory network through diverse mechanisms including chromatin looping. iRegNet3D promises to be an indispensable tool in large-scale sequencing and disease association studies.Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-016-1138-2) contains supplementary material, which is available to authorized users.

Most chromatin interactions are not in linkage disequilibrium

“…Additionally, disease-associated variants are enriched in regulatory regions (Kundaje et al 2015), especially those from tissues relevant to the phenotype (Parker et al 2013). Functionally annotating noncoding variants and correctly mapping causal variants to the genes and pathways they affect is critical for understanding disease mechanisms and using genetics in precision medicine (Kim et al 2016a;Liang et al 2017;Lu et al 2017;Nishizaki and Boyle 2017).…”

Most regulatory interactions are not in linkage disequilibrium

“…Additionally, diseaseassociated variants are enriched in regulatory regions [4], especially those from tissues relevant to the phenotype [5]. Functionally annotating non-coding variants and correctly mapping causal variants to the genes and pathways they affect is critical for understanding disease mechanisms and using genetics in precision medicine [6][7][8][9].Common practice associates non-coding variants with the closest gene promoter or promoters within the same LD block. However, regulatory variants can affect phenotypes by changing the expression of target genes up to several megabases (mb) away [10][11][12][13], well beyond their LD block (median length ≈ 1-2kb, Supplementary Table 1b).…”

“…Additionally, diseaseassociated variants are enriched in regulatory regions [4], especially those from tissues relevant to the phenotype [5]. Functionally annotating non-coding variants and correctly mapping causal variants to the genes and pathways they affect is critical for understanding disease mechanisms and using genetics in precision medicine [6][7][8][9].…”

Genetic Study of Hepatocyte Nuclear Factor 1 Alpha Variants in Development of Early-Onset Diabetes Type 2 and Maturity-Onset Diabetes of the Young 3 in Iran

“…[20] The difference in age of onset between the two probands may be due to errors of diagnosis, or inheritance of different genes modifying the phenotype,[21] and even environmental factors or different lifestyle. [22]…”