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

机译:用多数函数函数基因组数据集成对精神病基因组关联研究的解释

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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.
机译:基因组关联研究和其他发现遗传学方法提供了一种方法,以确定可能指向新治疗途径,增强诊断工具,增强诊断工具的行为障碍的先前未知的生物机制,并产生更深入地了解精神病条件的生物学。通过大规模的合作努力,精神遗传学的最新进展。这些研究已经开始出土,许多与人群中的精神疾病和行为性状相关的新型遗传变异。表征产生的疾病相关的遗传变异性并优先考虑功能后续的重大挑战,使它们有助于机械理解和治疗方法的发展。模型生物研究产生了广泛的基因组数据,可以深入了解变形作用的神经生物学机制,但必须制定一个有凝聚力的努力,以确定行为性状的生物学调制的哪些方面在物种中被进化地保守。本综述中列出的可扩展计算,新的数据集成策略和高级分析方法提供了一种框架,可以有效地利用模型生物数据以支持临床相关的精神表型。

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