多效性
遗传建筑学
疾病
结构方程建模
痴呆
风险因素
生物
阿尔茨海默病
全基因组关联研究
遗传关联
计算生物学
遗传学
进化生物学
心理学
生物信息学
基因
医学
单核苷酸多态性
计算机科学
数量性状位点
表型
基因型
机器学习
内科学
作者
Isabelle F. Foote,Benjamin Meir Jacobs,Georgina Mathlin,Cameron Watson,Phazha LK Bothongo,Sheena Waters,Ruth Dobson,Alastair Noyce,Kamaldeep Bhui,Ania Korszun,Charles R. Marshall
标识
DOI:10.1016/j.neurobiolaging.2022.02.016
摘要
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.
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