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转录组学对精神障碍的多基因机制的深入了解。

Transcriptomic Insight Into the Polygenic Mechanisms Underlying Psychiatric Disorders.

机构信息

Department of Psychiatry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California; Intellectual and Developmental Disabilities Research Center, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California.

Department of Psychiatry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California; Intellectual and Developmental Disabilities Research Center, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California; Program in Neurobehavioral Genetics, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California; Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California.

出版信息

Biol Psychiatry. 2021 Jan 1;89(1):54-64. doi: 10.1016/j.biopsych.2020.06.005. Epub 2020 Jun 12.


DOI:10.1016/j.biopsych.2020.06.005
PMID:32792264
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7718368/
Abstract

Over the past decade, large-scale genetic studies have successfully identified hundreds of genetic variants robustly associated with risk for psychiatric disorders. However, mechanistic insight and clinical translation continue to lag the pace of risk variant identification, hindered by the sheer number of targets and their predominant noncoding localization, as well as pervasive pleiotropy and incomplete penetrance. Successful next steps require identification of "causal" genetic variants and their proximal biological consequences; placing variants within biologically defined functional contexts, reflecting specific molecular pathways, cell types, circuits, and developmental windows; and characterizing the downstream, convergent neurobiological impact of polygenicity within an individual. Here, we discuss opportunities and challenges of high-throughput transcriptomic profiling in the human brain, and how transcriptomic approaches can help pinpoint mechanisms underlying genetic risk for psychiatric disorders at a scale necessary to tackle daunting levels of polygenicity. These include transcriptome-wide association studies for risk gene prioritization through integration of genome-wide association studies with expression quantitative trait loci. We outline transcriptomic results that inform our understanding of the brain-level molecular pathology of psychiatric disorders, including autism spectrum disorder, bipolar disorder, major depressive disorder, and schizophrenia. Finally, we discuss systems-level approaches for integration of distinct genetic, genomic, and phenotypic levels, including combining spatially resolved gene expression and human neuroimaging maps. Results highlight the importance of understanding gene expression (dys)regulation across human brain development as a major contributor to psychiatric disease pathogenesis, from common variants acting as expression quantitative trait loci to rare variants enriched for gene expression regulatory pathways.

摘要

在过去的十年中,大规模的遗传研究成功地确定了数百个与精神疾病风险密切相关的遗传变异。然而,机制的深入了解和临床转化仍然落后于风险变异识别的步伐,这是由于靶点数量众多且主要是非编码定位,以及广泛的多效性和不完全外显率所致。下一步的成功需要确定“因果”遗传变异及其近端生物学后果;将变异置于生物学定义的功能背景中,反映特定的分子途径、细胞类型、回路和发育窗口;并描述个体中多基因遗传的下游、趋同的神经生物学影响。在这里,我们讨论了高通量转录组谱分析在人类大脑中的机遇和挑战,以及转录组方法如何帮助在必要的规模上确定精神疾病遗传风险的机制,以解决令人望而却步的多基因遗传水平。这些方法包括通过整合全基因组关联研究和表达数量性状基因座,进行风险基因优先级的全转录组关联研究。我们概述了转录组结果,这些结果为我们理解精神疾病的大脑水平分子病理学提供了信息,包括自闭症谱系障碍、双相情感障碍、重度抑郁症和精神分裂症。最后,我们讨论了整合不同遗传、基因组和表型水平的系统水平方法,包括结合空间分辨的基因表达和人类神经影像学图谱。研究结果强调了理解人类大脑发育过程中的基因表达(失调)调节作为精神疾病发病机制的主要因素的重要性,从作为表达数量性状基因座的常见变异到富集基因表达调控途径的罕见变异。

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[2]
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[3]
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Biomedicines. 2025-1-12

[4]
Transcriptomics : Approaches to Quantifying Gene Expression and Their Application to Studying the Human Brain.

Curr Top Behav Neurosci. 2024

[5]
Knowledge graphs in psychiatric research: Potential applications and future perspectives.

Acta Psychiatr Scand. 2025-3

[6]
Cross-ancestry atlas of gene, isoform, and splicing regulation in the developing human brain.

Science. 2024-5-24

[7]
Temporal changes of gene expression in health, schizophrenia, bipolar disorder, and major depressive disorder.

Schizophrenia (Heidelb). 2024-2-17

[8]
A characteristic cerebellar biosignature for bipolar disorder, identified with fully automatic machine learning.

IBRO Neurosci Rep. 2023-7-1

[9]
Investigating trait variability of gene co-expression network architecture in brain by controlling for genomic risk of schizophrenia.

PLoS Genet. 2023-10

[10]
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本文引用的文献

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