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可改变的阿尔茨海默病风险的共享遗传结构:基因组结构方程建模研究。

The shared genetic architecture of modifiable risk for Alzheimer's disease: a genomic structural equation modelling study.

机构信息

Centre for Psychiatry and Mental Health, Wolfson Institute of Population Health, Queen Mary University of London, London, UK; Preventive Neurology Unit, Wolfson Institute of Population Health, Queen Mary University of London, London, UK; Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, CO, USA.

Preventive Neurology Unit, Wolfson Institute of Population Health, Queen Mary University of London, London, UK.

出版信息

Neurobiol Aging. 2022 Sep;117:222-235. doi: 10.1016/j.neurobiolaging.2022.02.016. Epub 2022 Jun 11.

Abstract

Targeting modifiable risk factors may help to prevent Alzheimer's disease (AD), but the pathways by which these risk factors influence AD risk remain incompletely understood. We identified genome-wide association studies for AD and its major modifiable risk factors. We calculated the genetic correlation among these traits and modelled this using genomic structural equation modelling. We identified complex networks of genetic overlap among AD risk factors, but AD itself was largely genetically distinct. The data were best explained by a bi-factor model, incorporating a Common Factor for AD risk, and 3 orthogonal sub-clusters of risk factors. Taken together, our findings suggest that there is extensive shared genetic architecture between AD modifiable risk factors, but this is largely independent of AD genetic pathways. Extensive genetic pleiotropy between risk factors may influence AD indirectly by decreasing cognitive reserve or increasing risk of multimorbidity, leading to poorer brain health. Further work to understand the biology reflected by this communality may provide novel mechanistic insights that could help to prioritise targets for dementia prevention.

摘要

针对可改变的风险因素可能有助于预防阿尔茨海默病(AD),但这些风险因素影响 AD 风险的途径仍不完全清楚。我们确定了 AD 及其主要可改变风险因素的全基因组关联研究。我们计算了这些特征之间的遗传相关性,并使用基因组结构方程模型对其进行建模。我们在 AD 风险因素之间发现了复杂的遗传重叠网络,但 AD 本身在很大程度上是遗传上不同的。数据最好用双因素模型来解释,该模型包含 AD 风险的共同因素和 3 个正交的风险因素子群。总的来说,我们的研究结果表明,AD 可改变的风险因素之间存在广泛的共同遗传结构,但这在很大程度上独立于 AD 的遗传途径。风险因素之间广泛的遗传多效性可能通过降低认知储备或增加多种疾病的风险,从而导致较差的大脑健康,间接地影响 AD。进一步研究反映这种共性的生物学可能会提供新的机制见解,有助于确定预防痴呆症的目标。

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