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长期阅读的基因组测序和老龄化和神经退行症中的多omics

Tanner D Jensen¹, Yann Le Guen², Lia Talozzi^3,4

¹Department of Genetics, Stanford University School of Medicine, Stanford, CA.

medRxiv : the preprint server for health sciences

|November 24, 2025

概括

长期阅读的基因组测序揭示了影响健康衰老和神经退行性疾病的广泛结构变异 (SVs). 这些以前未被发现的SVs为阿尔茨海默氏症和帕金森病等疾病的遗传原因提供了新的见解.

科学领域:

基因组学就是基因组学.
神经科学是一个神经科学.
衰老研究研究衰老研究

背景情况:

结构变异 (SVs) 是遗传变异的重要来源,但在衰老和神经退行性疾病中未得到充分研究.
短读全基因组测序 (WGS) 在检测SVs.全谱方面存在局限性.

研究的目的:

在健康老龄化和神经退行性疾病患者队列中,使用长读基因组测序 (lrGS) 综合地绘制结构变异 (SV).
将 SV 数据与多原子数据 (甲基化,转录组学,蛋白质组学) 集成,以了解它们的功能影响.
研究SVs在衰老和神经退行性疾病的遗传结构中的作用.

主要方法:

在551个深度表型个体上进行纳米孔长读基因组测序 (lrGS).
将SV数据与匹配的甲基化,转录和蛋白质组数据集成.
全基因组关联研究 (GWAS) 局部化和SV定量特征位点 (SV-QTL) 分析.
贝叶斯模型用于优先考虑罕见的功能性SVs.

主要成果:

lrGS比短读WGS识别了60%以上的SV,包括许多单核酸变体 (SNV) 错过的SV.
发现了超过6万个SV-QTLs,证明了SVs在分子特征上的调节潜力.
与SNV相比,SV更频繁地被精确地绘制为因果变异.
与阿尔茨海默氏症和帕金森病的局部化GWAS突出显示了关键位置的SV (例如TMEM106B,BIN3,NBEAL1).
在已知的风险基因附近优先考虑罕见的功能性SVs,使用多原子异常值丰富和贝叶斯模型.

结论:

在健康的衰老和神经退行中存在广泛的调节性SV.
lrGS对于全面了解复杂的遗传架构至关重要,特别是对于SVs.
SVs在衰老和神经退行性疾病的遗传基础中发挥着重要作用,需要进一步研究.

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