改进的多祖先精细映射识别了底层分子特征和疾病风险的cis调控变异
Zeyun Lu1, Xinran Wang1, Matthew Carr1
1Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
概括
新的SuShiE模型提高了在多种祖先中识别因果性cis-molecular定量特征位点 (cis-molQTLs) 的精度. 它通过更好地反映共享的遗传架构来改善复杂特征的基因发现.
科学领域:
- 遗传学 是一个遗传学.
- 统计遗传学 统计遗传学
- 生物信息学是一种生物信息学.
背景情况:
- 对cis-molecular定量特征位点 (cis-molQTLs) 的统计精细映射旨在准确识别因果变异.
- 现有的方法无法充分考虑祖先之间共享的遗传架构.
- 杆链接不平衡 (LD) 异质性对于提高精细映射精度至关重要.
研究的目的:
- 引入共享单一效应总和 (SuShiE) 模型,用于多祖先的cis-molQTL精细映射.
- 为了提高精细映射精度,推断跨祖先效应大小相关性,并估计祖先特定的表达式预测重量.
- 识别与分子特征和复杂疾病风险相关的遗传变异.
主要方法:
- 将SuShiE模型应用于来自不同祖先的mRNA表达和血蛋白水平 (TOPMed MESA和GENOA研究).
- 与基线方法相比,SuShiE性能在基因发现和变异优先级方面进行了比较.
- 利用估计的cis-molQTL效应大小来进行全转录组 (TWAS) 和全蛋白质组 (PWAS) 关联研究.
主要成果:
- 与基线相比,SuShiE精制了16%更多的cis-molQTL,并优先考虑了较少的变体,功能丰富度更高.
- 平均而言,在祖先之间推断出一致的cis-molQTL架构,在预测功能丧失不容忍的基因中观察到显著的异质性.
- 使用SuShiE的TWAS和PWAS在一个大型生物银行队列中确定了44个与白细胞特征相关的额外基因.
结论:
- SuShiE显著提高了多祖先 cis-molQTL 精细映射的精度和功率.
- 该模型为跨祖先分子特征的共享和异质遗传架构提供了新的见解.
- SuShiE增强了与复杂疾病风险相关的基因的识别,证明了其在遗传关联研究中的实用性.
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