综合基因遗传性估计揭示了基础复杂特征的罕见编码变异的遗传结构
Zhengtong Liu1, Boyang Fu2, Moonseong Jeong1
1Department of Computer Science, University of California, Los Angeles, Los Angeles, CA, USA.
bioRxiv : the preprint server for biology
|November 24, 2025
概括
我们开发了FLEX,这是一种新方法,可以从整个外体序列数据中准确估计遗传性,即使是罕见的遗传变异. 通过揭示罕见编码变异对遗传性的重大贡献,FLEX提高了对复杂特征的理解.
科学领域:
- 遗传学和基因组学 遗传学和基因组学
- 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:
- 使用全外因组测序 (WES) 数据估计来自罕见编码变异的遗传性是具有挑战性的,因为链接不平衡 (LD) 和数据稀疏性.
- 现有的方法往往难以准确地捕捉罕见和超罕见变异对复杂特征变异的贡献.
研究的目的:
- 引入FLEX (eXome-wide和基因水平遗传性的快速,LD意识的估计),这是一个用于估计和从WES数据分区遗传性的新框架.
- 开发一种可扩展和灵活的方法,整合所有编码变体,从常见到超罕见,同时对LD效应进行校正.
主要方法:
- FLEX使用一个统一的模型,从WES数据中整合所有编码变体 (从常见到超罕见).
- 该框架对链接不平衡 (LD) 效应进行了校正,以提高遗传性估计的准确性.
- FLEX支持个人级别和总结统计数据,确保大规模生物银行数据集的计算效率.
主要成果:
- 模拟表明,FLEX的校准很好,并提供准确的遗传概率估计.
- 应用到英国生物库 WES 数据 (N=153,351) 确定了64个显著的基因特征对,其中罕见的编码变异平均贡献了基因水平遗传性的38%.
- 结合罕见和超罕见的编码变体,与归算的SNP相比,遗传率估计增加了24.8%,效果大小大约是18倍.
结论:
- FLEX提供了一种适应性和准确的方法来量化基因水平的遗传性,从而推进对复杂特征遗传结构的理解.
- 这项研究强调了罕见和超罕见的编码变体在复杂特征中的重要作用,这些特征通常被传统方法遗漏.
- 通过在基因层面提供精确的遗传性估计,FLEX促进了与特征相关的基因的发现.
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