Interpretation of psychiatric genome-wide association studies with multispecies heterogeneous functional genomic data integration.

Interpretation of psychiatric genome-wide association studies with multispecies heterogeneous functional genomic data integration.
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DOI:
10.1038/s41386-020-00795-5
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发表时间:
2021-01
期刊:
Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology
影响因子:
--
通讯作者:
Chesler EJ
Chesler EJ
中科院分区:
其他
文献类型:
--
作者:
Reynolds T;Johnson EC;Huggett SB;Bubier JA;Palmer RHC;Agrawal A;Baker EJ;Chesler EJ

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全基因组关联研究和其他发现遗传学方法提供了一种手段,以确定以前未知的生物学机制的行为障碍,可能指向新的治疗途径,增强诊断工具,并产生更深入的了解生物学的精神疾病。精神病遗传学的最新进展是通过大规模的合作努力取得的。这些研究已经开始发掘许多与人类群体中的精神疾病和行为特征相关的新的遗传变异。在表征所产生的疾病相关遗传变异和优先功能随访方面仍然存在重大挑战,以使其有助于对治疗方法的机制理解和开发。模式生物的研究已经产生了广泛的基因组数据,可以提供深入了解变异作用的神经生物学机制,但必须作出一致的努力,以确定哪些方面的行为特征的生物调制是进化上保守的物种。可扩展的计算,新的数据集成策略和先进的分析方法在这篇评论中概述了提供了一个框架,有效地利用模式生物数据,支持临床相关的精神病表型。
Genome-wide association studies and other discovery genetics methods provide a means to identify previously unknown biological mechanisms underlying behavioral disorders that may point to new therapeutic avenues, augment diagnostic tools, and yield a deeper understanding of the biology of psychiatric conditions. Recent advances in psychiatric genetics have been made possible through large-scale collaborative efforts. These studies have begun to unearth many novel genetic variants associated with psychiatric disorders and behavioral traits in human populations. Significant challenges remain in characterizing the resulting disease-associated genetic variants and prioritizing functional follow-up to make them useful for mechanistic understanding and development of therapeutics. Model organism research has generated extensive genomic data that can provide insight into the neurobiological mechanisms of variant action, but a cohesive effort must be made to establish which aspects of the biological modulation of behavioral traits are evolutionarily conserved across species. Scalable computing, new data integration strategies, and advanced analysis methods outlined in this review provide a framework to efficiently harness model organism data in support of clinically relevant psychiatric phenotypes.
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