Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, 35 Convent Drive, Bethesda, MD, 20892, USA.
Department of Molecular and Clinical Pharmacology, Institute of Translational Medicine, University of Liverpool, L69 3BX, Liverpool, UK.
Cell Tissue Res. 2018 Jul;373(1):9-20. doi: 10.1007/s00441-018-2817-y. Epub 2018 Mar 13.
Over the last two decades, we have witnessed a revolution in the field of Parkinson's disease (PD) genetics. Great advances have been made in identifying many loci that confer a risk for PD, which has subsequently led to an improved understanding of the molecular pathways involved in disease pathogenesis. Despite this success, it is predicted that only a relatively small proportion of the phenotypic variability has been explained by genetics. Therefore, it is clear that common heritable components of disease are still to be identified. Dissecting the genetic architecture of PD constitutes a critical effort in identifying therapeutic targets and although such substantial progress has helped us to better understand disease mechanism, the route to PD disease-modifying drugs is a lengthy one. In this review, we give an overview of the known genetic risk factors in PD, focusing not on individual variants but the larger networks that have been implicated following comprehensive pathway analysis. We outline the challenges faced in the translation of risk loci to pathobiological relevance and illustrate the need for integrating big-data by noting success in recent work which adopts a broad-scale screening approach. Lastly, with PD genetics now progressing from identifying risk to predicting disease, we review how these models will likely have a significant impact in the future.
在过去的二十年中,我们见证了帕金森病 (PD) 遗传学领域的一场革命。在确定许多导致 PD 风险的基因座方面取得了重大进展,这随后使我们对疾病发病机制中涉及的分子途径有了更好的理解。尽管取得了这一成功,但预计遗传因素只能解释 PD 表型变异性的相对较小比例。因此,很明显,仍需要确定常见的可遗传疾病成分。解析 PD 的遗传结构是确定治疗靶点的关键努力,尽管如此巨大的进展帮助我们更好地了解疾病机制,但 PD 疾病修饰药物的研发道路仍然漫长。在这篇综述中,我们概述了 PD 中的已知遗传风险因素,重点不是个别变体,而是在进行综合途径分析后涉及的更大网络。我们概述了将风险基因座转化为病理生物学相关性所面临的挑战,并通过指出最近采用广泛筛选方法的工作中的成功,说明了整合大数据的必要性。最后,随着 PD 遗传学从识别风险发展到预测疾病,我们回顾了这些模型在未来可能产生的重大影响。
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