NEW YORK (GenomeWeb) – A Chinese Academy of Sciences-led team has started digging into the regulatory features that interact with genetic variants implicated in schizophrenia risk for a study published online today in Nature Communications.
Using results from nearly three-dozen chromatin immunoprecipitation sequencing experiments, along with transcription factor position weight matrices, the researchers looked for regulatory interactions with nearly 200 functionally annotated schizophrenia-associated loci found through prior genome-wide association studies.
Their search unearthed 132 schizophrenia risk SNPs at 81 loci that appear to alter binding patterns for 21 different transcription factors, including 97 SNPs with apparent ties to human brain tissue gene expression.
Dozens of the "transcription factor binding-disrupting" SNPs turned up at sites bound by either the RNA polymerase II subunit-coding gene POLR2A or by the transcription factor and transcriptional repressor CTCF. For the team's follow-up experiments, it used reporter gene assays in cell lines to shore up the proposed links between transcription factor binding shifts and nine of the SNPs, and to detect allele-specific expression for 10 of the schizophrenia risk SNPs.
"Our study provides new insights into the genetic mechanisms of schizophrenia," senior author Xiong-Jian Luo, an animal models and human disease mechanisms researcher at the Chinese Academy of Sciences, and his colleagues wrote. "Further mechanistic investigation and functional characterization of the identified causal variants and genes will help us understand the pathogenesis of schizophrenia."
Prior GWAS have led to more than 180 loci with strong ties to schizophrenia risk, the team explained. The causal variants at these sites are yet to be identified, for the most part, although many of the risk variants are known to fall in in non-coding parts of the genome.
Based on those patterns, the researchers set out to explore regulatory features and potential gene expression effects for suspected causal SNPs from three published GWAS papers.
Starting from 23,400 non-overlapping SNPs, the team used bioinformatics — in combination with expression quantitative trait locus (eQTL) data for hundreds of individuals with or without schizophrenia from three brain databases, reporter assay results, and other information — to functionally annotate and prioritize the risk SNP set.
To that, the researchers added functional genomic insights gleaned from ChIP-seq experiments focused on 34 transcription factors in brain tissue or neuronal cell samples, which highlighted transcription factor binding motifs that were subsequently classified further with position weight matrix data for the transcription factors.
Along with analyses of the genes, pathways, and cell types affected by risk SNP-related regulatory changes, the team profiled the transcription factors upended by the variants. Some 40 of the 132 schizophrenia-related SNPs that disrupted transcription factor binding turned up at sites with POLR2A binding motifs, for example, while 38 risk loci overlapped with CTCF binding sites. Still other SNPs appeared to upend binding by USF1, MAX1, and other transcription factors.
"Our study reveals gene regulatory mechanisms affected by schizophrenia risk SNPs (including widespread disruption of POLR2A and CTCF binding) and identifies target genes for mechanistic studies and drug development," the authors wrote.