bioRxivGenomicsPreprint
Interrogation of noncoding schizophrenia risk variants using CRISPR-based functional genomics
Marisa C. HamiltoniD, Julia W. RileyiD, Alexander C. Nelson, Boxun LiiD, Sophie F. Dornbaum, Alexias SafiiD, Xiekui Cui, Ian Richard JonesiD, Aaron A Coley, Kevin T. Hagy, Ruhi Rai, Alejandro BarreraiD, Andrew S. AlleniD, Yin Shen, Richard I. SherwoodiD, Michael I LoveiD, Patrick F. SullivaniD, Charles A. GersbachiD, Gregory E. CrawfordiD
Abstract
Schizophrenia (SCZ) is a highly heritable complex disorder influenced by coding and noncoding genetic variation. Its genetic causes, particularly those involving noncoding variation, are largely unknown. High-throughput CRISPR screens enable dissection of disease-associated loci and identification of noncoding regulatory elements and variants that modulate gene expression. We screened SCZ GWAS loci linked to genes that are also associated in whole-exome sequencing studies to identify regulatory elements and variants impacting expression of disease-relevant genes. We used CRISPRi paired with HCR-FlowFISH to epigenetically silence 333 putative regulatory elements and measure the downstream effects on gene expression of causal SCZ genes, FAM120A, SV2A, and STAG1, in iPSCs and iPSC-derived neurons (iNeurons). We identified 78 regulatory elements that significantly alter expression of a SCZ gene, including noncoding enhancers/silencers as well as promoters of genes and lncRNAs. Pooled prime editing screens interrogated noncoding variant influence on gene expression for SCZ-associated variants and uncharacterized common variants from diverse population studies. We find that a common variant in the promoter of SV2A, rs112851681:A>G (MAF = 3.56%, 1000 Genomes) enhances transcriptional activity in iPSCs and iNeurons. These findings show distinct noncoding mechanisms that map within GWAS signals, and provide a path forward for interrogating noncoding regulatory elements and variants in disease loci.
- organism: human
- assay: CRISPR screen
- research focus: gene regulatory network inference
- approach: experimental assay
- Citations
- 0
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