A functional variant on 20q13.33 related to glioma risk alters enhancer activity and modulates expression of multiple genes

Abstract

Genome-wide association studies (GWAS) have identified single-nucleotide polymorphisms (SNPs) associated with glioma risk on 20q13.33, but the biological mechanisms underlying this association are unknown. We tested the hypothesis that a functional SNP on 20q13.33 impacted the activity of an enhancer, leading to an altered expression of nearby genes. To identify candidate functional SNPs, we identified all SNPs in linkage disequilibrium with the risk-associated SNP rs2297440 that mapped to putative enhancers. Putative enhancers containing candidate functional SNPs were tested for allele-specific effects in luciferase enhancer activity assays against glioblastoma multiforme (GBM) cell lines. An enhancer containing SNP rs3761124 exhibited allele-specific effects on activity. Deletion of this enhancer by CRISPR-Cas9 editing in GBM cell lines correlated with an altered expression of multiple genes, including STMN3, RTEL1, RTEL1-TNFRSF6B, GMEB2, and SRMS. Expression quantitative trait loci (eQTL) analyses using nondiseased brain samples, isocitrate dehydrogenase 1 (IDH1) wild-type glioma, and neurodevelopmental tissues showed STMN3 to be a consistent significant eQTL with rs3761124. RTEL1 and GMEB2 were also significant eQTLs in the context of early CNS development and/or in IDH1 wild-type glioma. We provide evidence that rs3761124 is a functional variant on 20q13.33 related to glioma/GBM risk that modulates the expression of STMN3 and potentially other genes across diverse cellular contexts.

Regulatory SNPs: Altered Transcription Factor Binding Sites Implicated in Complex Traits and Diseases

“…In order to clarify whether the intergenic region carrying the target rSNP is potentially regulatory, the data of ENCODE projects [ 76 ] are usually assayed for the presence of DNase I hypersensitive sites (DHSs) and ChIP-seq peaks for active histone marks and transcription factors. If ChIP-seq peaks are numerous, this suggests a potential enhancer function, which is further verified by an increase in the luciferase reporter activity with recording of allele-specific effects [ 23 , 26 , 77 , 78 ]. To find out which particular genes are influenced by the studied region, it is inactivated using siRNA-mediated transcriptional gene silencing [ 23 ] or CRISPR interference (CRISPRi) involving recruitment of a KRAB repressor domain fused to catalytically dead Cas9 [ 18 , 79 ] or just via deletion of this region with CRISPR/Cas9 technology [ 28 , 78 , 80 ].…”

“…If ChIP-seq peaks are numerous, this suggests a potential enhancer function, which is further verified by an increase in the luciferase reporter activity with recording of allele-specific effects [ 23 , 26 , 77 , 78 ]. To find out which particular genes are influenced by the studied region, it is inactivated using siRNA-mediated transcriptional gene silencing [ 23 ] or CRISPR interference (CRISPRi) involving recruitment of a KRAB repressor domain fused to catalytically dead Cas9 [ 18 , 79 ] or just via deletion of this region with CRISPR/Cas9 technology [ 28 , 78 , 80 ]. Then the transcription levels of the selected genes are assayed [ 23 , 28 , 79 , 80 ] or a whole transcriptome analysis is performed [ 78 , 80 ].…”

“…To find out which particular genes are influenced by the studied region, it is inactivated using siRNA-mediated transcriptional gene silencing [ 23 ] or CRISPR interference (CRISPRi) involving recruitment of a KRAB repressor domain fused to catalytically dead Cas9 [ 18 , 79 ] or just via deletion of this region with CRISPR/Cas9 technology [ 28 , 78 , 80 ]. Then the transcription levels of the selected genes are assayed [ 23 , 28 , 79 , 80 ] or a whole transcriptome analysis is performed [ 78 , 80 ]. A physical interaction between the region carrying an SNP and the potential target genes is usually confirmed with the help of Hi-C methods [ 26 , 28 , 59 , 80 ], including allele-specific chromosome conformation capture assays [ 74 ], or using available Hi-C data [ 59 , 79 , 81 ].…”

“…Thus, considerable efforts have been recently focused on the subsequent functional analysis of the SNPs with disease/trait associations revealed with the help of GWAS. Both individual SNPs (mapped by GWAS and according to LD) [ 16 , 18 , 20 , 23 , 26 , 53 , 74 , 78 , 82 , 104 ] and others and large arrays of polymorphisms are analyzed in this way; manifold methods of the state-of-the-art functional genomics are used for this purpose.…”

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

“…To make molecular sense of GWAS, many recent studies are focused on functional analysis of the SNPs with a known GWAS disease/trait associations, including both (i) individual variants [ 9 , 10 , 11 , 12 , 13 , 14 , 15 ] and (ii) large SNP arrays, with the help of state-of-the-art approaches of functional genomics. These methods comprise various functional annotations, including transcription factor (TF) binding motifs, histone modifications, promoters, enhancers, chromatin accessibility landscapes, and three-dimensional chromatin interactions [ 16 , 17 , 18 , 19 , 20 ]; expression quantitative trait loci (eQTLs) mapping [ 21 , 22 ], and several other approaches, such as massively parallel reporter assay (MPRA) [ 23 ], SNPs-seq [ 24 ], and SNPs-SELEX [ 25 ].…”