regSNPs-ASB: A Computational Framework for Identifying Allele-Specific Transcription Factor Binding From ATAC-seq...

Area of Science:

• Genomics and Bioinformatics
• Molecular Biology
• Epigenetics

Background:

• Expression quantitative trait loci (eQTL) analysis identifies genetic variants linked to gene expression but cannot pinpoint causal variants.
• Disease-associated single-nucleotide polymorphisms (SNPs) often reside in regulatory regions, influencing transcription factor binding and allele-specific gene expression.
• Distinguishing functional regulatory SNPs is crucial for understanding disease etiology and developing targeted therapies.

Purpose of the Study:

• To identify functional single-nucleotide polymorphisms (SNPs) that alter transcriptional regulation and potentially impact cellular function.
• To present regSNPs-ASB, a novel computational approach for identifying regulatory SNPs within transcription factor binding sites.
• To validate the identified regulatory SNPs by integrating with gene expression and chromatin interaction data.

Main Methods:

• Developed regSNPs-ASB, a generalized linear model-based approach utilizing Assay for Transposase-Accessible Chromatin with high-throughput sequencing (ATAC-seq) raw read counts from heterozygous loci.
• Analyzed differential transposase-cleavage patterns between alleles to infer preferential transcription factor binding.
• Integrated regSNPs-ASB output with RNA-sequencing and chromatin interaction data for functional validation.

Main Results:

• Identified 53 regulatory SNPs in MCF-7 breast cancer cells and 125 in mesenchymal stem cells (MSCs).
• In MCF-7 cells, 32 out of 74 associated genes (43%) exhibited significant allele-specific expression.
• A significant overlap was observed between identified regulatory SNPs and known eQTLs in both cell types, supporting their role in allelic gene expression differences.

Conclusions:

• regSNPs-ASB is an effective tool for identifying causal variants from ATAC-seq data, enabling prioritization of eQTLs for functional validation.
• This method aids in understanding the molecular mechanisms underlying diseases by pinpointing functional genetic variants.
• The identification of causal variants facilitates the development of potential therapeutic targets.