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迟发性阿尔茨海默病遗传风险因素的验证

validation of late-onset Alzheimer's disease genetic risk factors.

作者信息

Sasner Michael, Preuss Christoph, Pandey Ravi S, Uyar Asli, Garceau Dylan, Kotredes Kevin P, Williams Harriet, Oblak Adrian L, Lin Peter Bor-Chian, Perkins Bridget, Soni Disha, Ingraham Cindy, Lee-Gosselin Audrey, Lamb Bruce T, Howell Gareth R, Carter Gregory W

机构信息

The Jackson Laboratory, 600 Main St, Bar Harbor, ME, 04609 USA.

The Jackson Laboratory for Genomic Medicine, 10 Discovery Drive, Farmington, CT, 06032 USA.

出版信息

bioRxiv. 2023 Dec 24:2023.12.21.572849. doi: 10.1101/2023.12.21.572849.

Abstract

INTRODUCTION

Genome-wide association studies have identified over 70 genetic loci associated with late-onset Alzheimer's disease (LOAD), but few candidate polymorphisms have been functionally assessed for disease relevance and mechanism of action.

METHODS

Candidate genetic risk variants were informatically prioritized and individually engineered into a LOAD-sensitized mouse model that carries the AD risk variants APOE4 and Trem2*R47H. Potential disease relevance of each model was assessed by comparing brain transcriptomes measured with the Nanostring Mouse AD Panel at 4 and 12 months of age with human study cohorts.

RESULTS

We created new models for 11 coding and loss-of-function risk variants. Transcriptomic effects from multiple genetic variants recapitulated a variety of human gene expression patterns observed in LOAD study cohorts. Specific models matched to emerging molecular LOAD subtypes.

DISCUSSION

These results provide an initial functionalization of 11 candidate risk variants and identify potential preclinical models for testing targeted therapeutics.

摘要

引言

全基因组关联研究已鉴定出70多个与晚发性阿尔茨海默病(LOAD)相关的基因位点,但很少有候选多态性被从功能上评估其与疾病的相关性及作用机制。

方法

通过信息学方法对候选遗传风险变异进行优先级排序,并将其分别构建到一个携带AD风险变异APOE4和Trem2*R47H的LOAD敏感小鼠模型中。通过将4个月和12个月大时用Nanostring小鼠AD检测板测量的脑转录组与人类研究队列进行比较,评估每个模型与疾病的潜在相关性。

结果

我们为11个编码和功能丧失风险变异创建了新模型。多个遗传变异的转录组效应重现了LOAD研究队列中观察到的多种人类基因表达模式。特定模型与新出现的分子LOAD亚型相匹配。

讨论

这些结果为11个候选风险变异提供了初步的功能验证,并确定了用于测试靶向治疗药物的潜在临床前模型。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/886f/10769393/5480c6f6e925/nihpp-2023.12.21.572849v1-f0001.jpg

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