A network-based approach to eQTL interpretation and SNP functional characterization

Abstract

1 Summary Expression quantitative trait locus (eQTL) analysis associates genotype with gene expression, but most eQTL studies only include cis-acting variants and generally examine a single tissue. We used data from 13 tissues obtained by the Genotype-Tissue Expression (GTEx) project v6.0 and, in each tissue, identified both cis-and trans-eQTLs. For each tissue, we represented significant associations between single nucleotide polymorphisms (SNPs) and genes as edges in a bipartite network. These networks are organized into dense, highly modular communities often representing coherent biological processes. Global network hubs are enriched in distal gene regulatory regions such as enhancers, but are devoid of disease-associated SNPs from genome wide association studies. In contrast, local, community-specific network hubs (core SNPs) are preferentially located in regulatory regions such as promoters and enhancers and highly enriched for trait and disease associations. These results provide help explain how many weak-effect SNPs might together influence cellular function and phenotype.2

Druggable Transcriptional Networks in the Human Neurogenic Epigenome

“…This study demonstrated that eQTL communities are comprised of functionally related sets of variants, including GWAS SNPs, in a tissue-specific manner. In addition, for diseases such as chronic obstructive pulmonary disease, SNPs significantly associated with chronic obstructive pulmonary disease risk were enriched within eQTL intermediate nodes (Fagny et al, 2016). This is consistent with results suggesting that the genetic component of many disease phenotypes are combinations of GWAS SNPs of small to intermediate effect sizes, in contrast to causal pharmacogenomic SNPs identified from GWAS that exhibit moderate to large effects sizes (Higgins et al, 2015a).…”

“…Tissue-specific, regulatory cis-and trans-interacting community-based networks have been characterized by combining disease SNPs from GWAS with eQTL data (Fagny et al, 2016). To detect regulatory communities, cis-and trans-eQTLs from 13 human tissues were mapped using bipartite graphs, in which significant eQTLs were revealed as edges of a bipartite network linking SNPs and gene nodes.…”

“…Although these are considered negative consequences of medication use and may be attributable to changes in drug target receptor physiology, closer examination suggests that this is an emergent property of drug-disease networks. Drug networks are characterized by the simultaneous presence of different subnetworks organized within separate layers, with links and nodes of qualitatively different types and quantitatively different features (Fagny et al, 2016;Hu et al, 2016). The concept of hardwired networks is inherently inaccurate, as adaptive rewiring of biologic networks is part of healthy physiology, disease progression and drug use, drug-drug interactions, and drug-gene interactions.…”

Using single‐cell multiple omics approaches to resolve tumor heterogeneity

“…This suggests that organization of paternal compartmentalization is likely inherited from the sperm [75]. The organization of loops, TADs, and compartments is significant because it infers which regions are more active and perhaps more prone to mutations [7]. On a more localized level, a separate study used a modified sciATAC-SEQ approach called SCI-seq was demonstrated on 16,000 single cells from different cancer types.…”

“…However, as mutations accumulate over time, they increase genetic variations and the likelihood of developing a neoplasm. Communities of mutations, or alterations to driver genes, can lead to increases in proliferation, a higher frequency of errors in transcription and replication, and/or the enabling of apoptotic evasion [6,7]. Finally, recent studies indicate that metastases may also derive from early disseminated cancer cells [8].…”