估计由当地祖先解释的遗传性,并从总结统计数据中评估混合物映射中的分层偏差
Tsz Fung Chan1, Xinyue Rui1, David V Conti1
1Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
American journal of human genetics
|October 24, 2023
概括
从混合映射总结统计 (HAMSTA) 来估计遗传性,可以准确地推断混合种群中的本地祖先遗传性 (hγ2). 这种方法调整了祖先分层,为遗传研究提供了公正的估计和校准的错误率.
科学领域:
- 人口遗传学 人口遗传学
- 统计遗传学 统计遗传学
- 基因组学就是基因组学.
背景情况:
- 由当地祖先 (hγ2) 解释的遗传性是理解混合种群中复杂特征的关键.
- 估计hγ2可能会受到祖先人口结构的偏见.
- 现有的方法很难纠正祖先分层.
研究的目的:
- 从混合物映射总结统计 (HAMSTA) 引入遗传性估计.
- 开发一种推断hγ2的方法,同时调整祖先分层偏差.
- 为添加剂映射提供校准的家庭智能错误率 (FWER).
主要方法:
- 使用混合物映射的总结统计数据.
- 采用HAMSTA衍生抽样方案进行FWER估计.
- 将HAMSTA方法应用于非洲裔美国人的定量表型.
主要成果:
- 哈姆斯塔对hγ2的估计大约是不偏见的,并且对祖先分层有很强的依据.
- 在分层下进行混合物映射时,已证明校准的FWER为5%~.
- 观察到的hγ2范围从0.0025到0.033跨越20个表型.
- 由于祖先分层,发现了最小的通货膨胀 (平均通货膨胀系数~0.99).
结论:
- 汉斯塔 (HAMSTA) 为全基因组遗传性估计提供了一种快速而强大的方法.
- 该方法有效地评估了添加剂映射测试统计中的偏差.
- 汉斯塔 (HAMSTA) 提高了混合群体基因架构研究的准确性.
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