与多基因背景的相互作用影响英国生物银行中的定量特征
Lino A F Ferreira1,2, Sile Hu1,2, Robert W Davies2,3
1Centre for Human Genetics, University of Oxford.
medRxiv : the preprint server for health sciences
|December 3, 2025
概括
我们开发了一种强大的新方法来检测基因相互作用,在英国生物库数据中发现了52个特征的144个独立相互作用. 这种方法揭示了影响人类表型的复杂生物网络.
科学领域:
- 人类遗传学 人类遗传学
- 统计基因组学 统计基因组学
- 生物信息学是一种生物信息学.
背景情况:
- 关联研究确定与表型相关的遗传变异,但生物学机制仍然难以阐明.
- 基因在复杂的网络中起作用,这表明基因相互作用 (epistasis) 是预期的,但由于广大的搜索空间和小效果大小,很难检测到.
研究的目的:
- 开发一种强大的统计方法,用于检测单核酸多态 (SNP) 和聚基因分数 (PGS) 中聚合的变异组之间的相互作用.
- 确定影响人类定量特征的新型遗传相互作用网络.
- 探索基于转录因子结合位点的PGS的功能分区,以发现监管相互作用.
主要方法:
- 开发了一种新的SNP-by-PGS相互作用的统计测试,适用于任何定量特征.
- 将该方法应用于英国生物银行欧洲样本中的97种定量表型.
- 开发了用于精制信号和检测双 SNP-SNP 相互作用的方法,包括使用 HOCOMOCO 数据库对 PGS 的功能分区.
主要成果:
- 确定了144个影响52个特征的独立相互作用,包括APOE,FTO和TCF7L2.2.等基因中已知的疾病风险变异.
- 检测到38个双对SNP相互作用,包括已知的性酸酶水平的相互作用和对乙氨基酸水平的新型相互作用.
- 确定了12种涉及功能分区PGS的相互作用,包括TCF7L2和KDM2A之间的调节相互作用,影响糖化血红蛋白.
结论:
- 开发的方法显著增加了检测遗传相互作用网络的功率.
- 这项研究大大扩大了已知的人类表型的表观效应的范围.
- 统计相互作用有效地反映了基因和调节元素之间的潜在生物相互依赖.
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