한빛사논문
- 조회1,101
Seonggyun Han1, Emily DiBlasi2, Eric T. Monson2, Andrey Shabalin2, Elliott Ferris3, Danli Chen2, Alison Fraser4, Zhe Yu4, Michael Staley5, W. Brandon Callor5, Erik D. Christensen5, David K. Crockett6, Qingqin S. Li7, Virginia Willour8, Amanda V. Bakian2, Brooks Keeshin2,9, Anna R. Docherty2, Karen Eilbeck1 and Hilary Coon2
1Department of Biomedical Informatics, University of Utah School of Medicine, Salt Lake City, UT, USA.
2Department of Psychiatry & Huntsman Mental Health Institute, University of Utah School of Medicine, Salt Lake City, UT, USA.
3Department of Neurobiology, University of Utah School of Medicine, Salt Lake City, UT, USA.
4Pedigree & Population Resource, Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA.
5Office of the Medical Examiner, Utah Department of Health and Human Services, Salt Lake City, UT, USA.
6Clinical Analytics, Intermountain Health, Salt Lake City, UT, USA.
7Neuroscience Therapeutic Area, Janssen Research & Development, LLC, Titusville, NJ, USA.
8Department of Psychiatry, University of Iowa, Iowa City, IA, USA.
9Department of Pediatrics, University of Utah, Salt Lake City, UT, USA.
Corresponding author : Correspondence to Seonggyun Han.
Abstract
Recent large-scale genome-wide association studies (GWAS) have started to identify potential genetic risk loci associated with risk of suicide; however, a large portion of suicide-associated genetic factors affecting gene expression remain elusive. Dysregulated gene expression, not assessed by GWAS, may play a significant role in increasing the risk of suicide death. We performed the first comprehensive genomic association analysis prioritizing brain expression quantitative trait loci (eQTLs) within regulatory regions in suicide deaths from the Utah Suicide Genetic Risk Study (USGRS). 440,324 brain-regulatory eQTLs were obtained by integrating brain eQTLs, histone modification ChIP-seq, ATAC-seq, DNase-seq, and Hi-C results from publicly available data. Subsequent genomic analyses were conducted in whole-genome sequencing (WGS) data from 986 suicide deaths of non-Finnish European (NFE) ancestry and 415 ancestrally matched controls. Additional independent USGRS suicide deaths with genotyping array data (n = 4657) and controls from the Genome Aggregation Database were explored for WGS result replication. One significant eQTL locus, rs926308 (p = 3.24e−06), was identified. The rs926308-T is associated with lower expression of RFPL3S, a gene important for neocortex development and implicated in arousal. Gene-based analyses performed using Sherlock Bayesian statistical integrative analysis also detected 20 genes with expression changes that may contribute to suicide risk. From analyzing publicly available transcriptomic data, ten of these genes have previous evidence of differential expression in suicide death or in psychiatric disorders that may be associated with suicide, including schizophrenia and autism (ZNF501, ZNF502, CNN3, IGF1R, KLHL36, NBL1, PDCD6IP, SNX19, BCAP29, and ARSA). Electronic health records (EHR) data was further merged to evaluate if there were clinically relevant subsets of suicide deaths associated with genetic variants. In summary, our study identified one risk locus and ten genes associated with suicide risk via gene expression, providing new insight into possible genetic and molecular mechanisms leading to suicide.
논문정보
- Research article
- 2023년 10월 (BRIC 등록일 2023-10-05)
- 국외 연구진
- 바이오·의료융합 > 뇌인지과학
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