A Panel of rSNPs Demonstrating Allelic Asymmetry in Both ChIP-seq and RNA-seq Data and the Search for Their Phenotypic Outcomes through Analysis of DEGs

• Published in: International journal of molecular sciences 2021-07, Vol.22 (14), p.7240
• Main Authors: Korbolina, Elena E., Bryzgalov, Leonid O., Ustrokhanova, Diana Z., Postovalov, Sergey N., Poverin, Dmitry V., Damarov, Igor S., Merkulova, Tatiana I.
• Format: Article
• Language: English
• Subjects: Alleles; Asymmetry; Binding; Biological activity; Cancer; Data search; Datasets; Encyclopedias; Endothelial cells; Experiments; Gene amplification; Gene expression; Genetic diversity; Genomes; Genomics; Phenotypes; Probability; Proteins; Single-nucleotide polymorphism; Statistical methods
• ISSN: 1422-0067; 1661-6596; 1422-0067
• DOI: 10.3390/ijms22147240

Currently, the detection of the allele asymmetry of gene expression from RNA-seq data or the transcription factor binding from ChIP-seq data is one of the approaches used to identify the functional genetic variants that can affect gene expression (regulatory SNPs or rSNPs). In this study, we searched for rSNPs using the data for human pulmonary arterial endothelial cells (PAECs) available from the Sequence Read Archive (SRA). Allele-asymmetric binding and expression events are analyzed in paired ChIP-seq data for H3K4me3 mark and RNA-seq data obtained for 19 individuals. Two statistical approaches, weighted z-scores and predicted probabilities, were used to improve the efficiency of finding rSNPs. In total, we identified 14,266 rSNPs associated with both allele-specific binding and expression. Among them, 645 rSNPs were associated with GWAS phenotypes; 4746 rSNPs were reported as eQTLs by GTEx, and 11,536 rSNPs were located in 374 candidate transcription factor binding motifs. Additionally, we searched for the rSNPs associated with gene expression using an SRA RNA-seq dataset for 281 clinically annotated human postmortem brain samples and detected eQTLs for 2505 rSNPs. Based on these results, we conducted Gene Ontology (GO), Disease Ontology (DO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses and constructed the protein–protein interaction networks to represent the top-ranked biological processes with a possible contribution to the phenotypic outcome.