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关于分析遗传关联与长期读取的测序数据的分析

Gengming He^1,2, Stephen W Scherer^2,3,4, Lisa J Strug^1,2,3,5

¹Biostatistics Division, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada.

|September 29, 2025

概括

长读数测序 (LRS) 通过揭示变异阶段关系来推进遗传关联研究. 我们的新阶段回归 (RoP) 方法揭示了传统分析遗漏的复杂遗传效应.

科学领域:

基因组学就是基因组学.
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:

全基因组关联研究 (GWAS) 识别了与特征相关的变异,但往往错过了复杂的相位依赖效应.
短读测序数据限制了分辨变异阶段的能力,掩盖了 cis/trans 相互作用和基异质性.
在遗传关联研究中利用阶段信息的统计方法尚不发达.

研究的目的:

引入阶段回归 (RoP) 方法,以模拟变体之间的 cis 和 trans 阶段效应.
评估RoP的性能与模拟中的现有方法相比.
应用RoP来识别囊性纤维化 (CF) 修饰器位点的基于阶段的遗传机制.

主要方法:

开发了回归阶段 (RoP) 统计框架.
进行模拟来比较RoP与基因型相互作用测试.
应用RoP来分析7q35和X染色体上CF相关位点的相位关系.

主要成果:

在模拟中,RoP准确地区分了in-cis和in-trans相效应,并且在模拟中优于基因型相互作用测试.
在7q35试原位点,RoP证实了两个变体 (基异质) 的独立贡献.
在SLC6A14位点,RoP确定了一种涉及促剂变体和肺特异增强剂的cis作用调节机制,功能研究证实了这一点.

结论:

利用长读测序 (LRS) 的相位信息,利用像RoP这样的方法,可以更深入地了解复杂的遗传架构.
RoP 分析可以揭示传统 GWAS 错过的协调监管机制.
这种方法增强了功能性调查,并可能为复杂疾病确定新的治疗点.

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