Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA.
Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA; Data Tecnica International, Glen Echo, MD, USA.
Lancet Neurol. 2020 Feb;19(2):170-178. doi: 10.1016/S1474-4422(19)30287-X. Epub 2019 Sep 11.
Parkinson's disease is a complex neurodegenerative disorder for which both rare and common genetic variants contribute to disease risk, onset, and progression. Mutations in more than 20 genes have been associated with the disease, most of which are highly penetrant and often cause early onset or atypical symptoms. Although our understanding of the genetic basis of Parkinson's disease has advanced considerably, much remains to be done. Further disease-related common genetic variability remains to be identified and the work in identifying rare risk alleles has only just begun. To date, genome-wide association studies have identified 90 independent risk-associated variants. However, most of them have been identified in patients of European ancestry and we know relatively little of the genetics of Parkinson's disease in other populations. We have a limited understanding of the biological functions of the risk alleles that have been identified, although Parkinson's disease risk variants appear to be in close proximity to known Parkinson's disease genes and lysosomal-related genes. In the past decade, multiple efforts have been made to investigate the genetic architecture of Parkinson's disease, and emerging technologies, such as machine learning, single-cell RNA sequencing, and high-throughput screens, will improve our understanding of genetic risk.
帕金森病是一种复杂的神经退行性疾病,罕见和常见的遗传变异都与疾病风险、发病和进展有关。已经有 20 多个基因突变与该疾病相关,其中大多数具有高度外显性,并且常常导致早发性或非典型症状。尽管我们对帕金森病的遗传基础有了相当大的了解,但仍有许多工作要做。还需要进一步确定与疾病相关的常见遗传变异性,并且识别罕见风险等位基因的工作才刚刚开始。迄今为止,全基因组关联研究已经确定了 90 个独立的风险相关变异。然而,其中大多数是在欧洲血统的患者中发现的,我们对其他人群中的帕金森病遗传学了解相对较少。尽管帕金森病风险变异似乎与已知的帕金森病基因和溶酶体相关基因密切相关,但我们对已确定的风险等位基因的生物学功能的了解有限。在过去的十年中,已经做出了多项努力来研究帕金森病的遗传结构,而新兴技术,如机器学习、单细胞 RNA 测序和高通量筛选,将提高我们对遗传风险的理解。