Delafrouz, P. | Search bioRxiv

bioRxiv2024

Structural Basis of Differential Gene Expression at eQTLs Loci from High-Resolution Ensemble Models of 3D Single-Cell Chromatin Conformations

MotivationTechniques such as high-throughput chromosome conformation capture (Hi-C) have provided a wealth of information on the organization of the nucleus and the genome important for understanding gene expression regulation. Additionally, Genome-Wide Association Studies (GWASs) have uncovered thousands of loci related to complex traits. Expression quantitative trait loci (eQTL) studies have further linked the genetic variants to alteration in expression levels of associated target genes across individuals. However, the functional roles of many eQTLs located in non-coding regions are unclear. Current joint analyses of Hi-C and eQTLs data lack advanced computational tools, limiting what can be learned from these data. ResultIn this work, we developed a computational method for simultaneous analysis of Hi-C and eQTL data. Our method can identify a small set of non-random interactions from all Hi-C interactions. Using these non-random interactions, we reconstruct large ensemble (x105) of high-resolution single-cell 3D chromatin conformations with thorough sampling, which accurately replicate Hi-C measurements. Our results revealed the presence of many-body interactions in chromatin conformation at single-cell level in eQTL locus, offering detailed view into how three-dimensional structures of chromatin form the physical foundation for gene regulation, including how genetic variants of eQTLs affect the expression level of their associated eGenes. Furthermore, our method can deconvolve chromatin heterogeneity and investigate the spatial associations of eQTLs and eGenes at subpopulation level to reveal their regulatory impacts on gene expression. Together, ensemble modeling of thoroughly sampled single cell chromatin conformations from Hi-C, along with eQTL data, helps to decipher how chromatin 3D structures provide the physical basis for gene regulation, expression control, and aid in understanding of the overall structure-function relationships of genome organization. Availability and implementation: It is available at https://github.com/uic-liang-lab/3DChromFolding-eQTL-Loci

bioinformatics↗

bioRxiv2024

A New Approach for Discovering Functional Links Connecting Non-Coding Regulatory Variants to Gene Targets

Genome-wide association studies (GWAS) have linked thousands of genetic variants to various complex traits or diseases. However, most identified variants have weak individual effects, are correlated with nearby polymorphisms due to linkage disequilibrium (LD), and are located in non-coding cis-regulatory elements (CREs). These characteristics complicate the assessment of the direct impact of each variant on tissue specific gene expression and phenotype. To address this challenge, we have developed a novel algorithm that leverages polymer folding and 3D chromatin interactions to prioritize and identify putative causal variants and their target genes. From the millions of eQTL-Gene pairs identified by GTEx in human somatic tissues, we classify only [~]10-20% as putative functional eQTL-Gene pairs supported by phenotypic associations confirmed through CRISPR deletion experiments. Our findings show that unlike most variants, functional eQTL-Gene pairs predominantly reside within the same topologically associating domain (TAD) and have strong associations with cell-type specific cis-regulatory elements (CREs), enriched for binding sites of tissue-specific transcription factors. Unlike most approaches that rely on linear distance or other chromatin features (histone code, accessibility), our algorithm emphasizes the importance of physical interactions and 3D chromatin folding in gene regulation, as the identified eQTL-Gene pairs are all among the small fraction of physical chromatin interactions sufficient for chromatin locus folding. Overall, our algorithm reduces false positive associations between DNA variants and genes identified by eQTL analysis and uncovers novel variant-gene pair associations. These findings suggest a mechanism where a small number of regulatory variants control tissue specific gene expression via their physical association with target genes confined within the same TAD. Our approach provides new insights into the molecular mechanisms driving GWAS phenotypes.

genomics↗