贝叶斯混合模型推断为相关样本下的遗传关联与大脑网络表型的遗传关联
Xinyuan Tian1, Yiting Wang1, Selena Wang1
1Department of Biostatistics, Yale University, 60 College St, New Haven, CT 06520, United States.
Biostatistics (Oxford, England)
|March 18, 2024
概括
这项研究引入了一种新的贝叶斯模型来分析大脑连接遗传学,考虑复杂的家庭结构. 新方法有助于识别影响大脑网络的遗传变异,提供可解释的结果.
科学领域:
- 神经科学是一个神经科学.
- 量化遗传学 量化遗传学
- 统计遗传学 统计遗传学
背景情况:
- 对大脑连接的遗传关联研究正在通过新的成像和遗传学技术取得进展.
- 由于其网络结构和生物复杂性,大脑连接带来了独特的挑战.
- 现有的模型在神经成像遗传学中常见的样本相关性方面扎.
研究的目的:
- 提出一个贝叶斯网络-响应混合效应模型,用于大脑连接现象型.
- 纳入人口结构,如血统和未知的样本关系.
- 在成像遗传学中解决当前网络响应建模的局限性.
主要方法:
- 开发了一个贝叶斯网络响应混合效应模型,用于网络变异的表型.
- 使用效应网络配置模拟的遗传效应,具有网络间的稀疏性和网络内部的收缩.
- 采用马尔科夫链蒙特卡洛 (MCMC) 算法来量化不确定性.
主要成果:
- 广泛的模拟证明了该模型的有效性.
- 将模型应用于人类连接组项目关于大脑结构连接的数据.
- 对大脑连接的遗传基础取得了可信和可解释的结果.
结论:
- 拟议的模型有效地分析了对大脑连接的遗传影响,处理复杂的人口结构.
- 它提供了一个强大的框架来剖析对网络变异表型的遗传贡献.
- 该模型提供了一个一般的线性混合效应回归框架,适用于超出大脑连接研究.
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