Abstract
BackgroundNumerous 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.ResultsIn 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.ConclusionsOur 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.
Highlights
Numerous genome-wide association studies (GWAS) conducted to date revealed genetic variants associated with various diseases, including breast and prostate cancers
We present a large-scale analysis of the high-resolution chromosome conformation capture (Hi-C) data in the context of relationships among high-risk single-nucleotide polymorphisms (SNPs) identified by GWAS for breast and prostate cancers, regulatory elements including transcription factors (TFs) and enhancers, and genes associated with both diseases
Mapping genetic variants to regulatory elements and target genes In this study, we compare two distinct approaches to link genetic variants highly associated with disease, with a p-value of ≤5 × 10− 8 according to the GWAS data, to regulatory elements and their target genes (Fig. 1)
Summary
Numerous genome-wide association studies (GWAS) conducted to date revealed genetic variants associated with various diseases, including breast and prostate cancers. GWAS conducted on many different tumor types, including pancreatic [22], ovarian [23], lung [24], prostate [25], and breast cancer [26], identified numerous risk alleles, most of which are common and individually confer only a modest increase in disease risk. The vast majority of genetic variants identified through GWAS (> 90%) are located in the non-coding regions of the genome [29] These variants can provide useful insights into mechanisms responsible for the development and progression of various diseases through the alteration of regulatory elements, such as transcription factors (TFs) and active enhancers, affecting the expression of certain disease-related genes [30, 31]
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