Exploring the effects of genetic variation on gene regulation in cancer in the context of 3D genome structure

Abstract

Background Numerous genome-wide association studies (GWAS) conducted to date revealed genetic variants associated with various diseases, including breast and prostate cancers. Despite the availability of these large-scale data, relatively few variants have been functionally characterized, mainly because the majority of single-nucleotide polymorphisms (SNPs) map to the non-coding regions of the human genome. The functional characterization of these non-coding variants and the identification of their target genes remain challenging. Results In this communication, we explore the potential functional mechanisms of non-coding SNPs by integrating GWAS with the high-resolution chromosome conformation capture (Hi-C) data for breast and prostate cancers. We show that more genetic variants map to regulatory elements through the 3D genome structure than the 1D linear genome lacking physical chromatin interactions. Importantly, the association of enhancers, transcription factors, and their target genes with breast and prostate cancers tends to be higher when these regulatory elements are mapped to high-risk SNPs through spatial interactions compared to simply using a linear proximity. Finally, we demonstrate that topologically associating domains (TADs) carrying high-risk SNPs also contain gene regulatory elements whose association with cancer is generally higher than those belonging to control TADs containing no high-risk variants. Conclusions Our results suggest that many SNPs may contribute to the cancer development by affecting the expression of certain tumor-related genes through long-range chromatin interactions with gene regulatory elements. Integrating large-scale genetic datasets with the 3D genome structure offers an attractive and unique approach to systematically investigate the functional mechanisms of genetic variants in disease risk and progression.

Building Haplotype‐Resolved 3D Genome Maps of Chicken Skeletal Muscle

“…Nonetheless, we found that only a few specific genetic variations (such as in enhancer regions) associated with allelic PEI rewiring likely contribute to differences in SMT physiology and phenotype between the specialized broiler (meat‐producing) and layer (egg‐producing) chickens. In contrast to the large chromosome rearrangements leading to cancers [ 51 ] or severe developmental disorders [ 52 ] through dramatic 3D genome reorganizations (e.g., TAD shifts), most breed‐specific variations, although probably causing allelic PEI rewiring, had a subtle phenotypic effect. We speculate that this is most likely due to remarkable redundancy in contacts among rewired enhancers carrying high‐ F ST variants (Figure 6C–E ).…”

Identification of non-coding silencer elements and their regulation of gene expression

“…interacting regions led to the upregulation of target genes 58,64 . It is also conceivable that disruption of the mechanisms underlying this threedimensional chromatin conformation by disease-associated SNPs may affect such physical interactions and lead to disrupted gene expression and disease [137][138][139] . MPRAs that simultaneously screen diseaseassociated SNPs for their effect on potential regulatory activity have been performed [140][141][142][143] .…”

Wavelet scattering networks in deep learning for discovering protein markers in a cohort of Swedish rectal cancer patients

“…Colon cancer and rectal cancer are often grouped together known as CRC because these two cancers share many similar features, including symptoms, risk factors, and basic cell biology. Because cancer is a condition of genetic variations, 1 , 2 where the mutations can cause genes to function abnormally and result in alternative expression. Proteins, which are final products of gene expression, have influence on phenotypes and various biological processes.…”