PolyGEE: a generalized estimating equation approach to the efficient and robust estimation of polygenic effects in large-scale association studies.
PolyGEE: a generalized estimating equation approach to the efficient and robust estimation of polygenic effects in large-scale association studies.
复制标题
PolyGEE：一种广义估计方程方法，用于在大规模关联研究中有效且稳健地估计多基因效应。
DOI：
10.1093/biostatistics/kxx040
复制
发表时间：
2018
期刊：
影响因子：
--
通讯作者：
Fier,HeideLoehlein
中科院分区：
文献类型：
--
作者：
Hecker,Julian;Prokopenko,Dmitry;Lange,Christoph;Fier,HeideLoehlein
为了量化大规模关联研究中的多基因效应，即未检测到的遗传效应，我们提出了一个基于广义估计方程(GEE)的估计框架。我们开发了一个用于复杂疾病的单变量关联检验统计的边际模型，该模型推广了现有的方法，如LD分数回归，并适用于基于总体的设计、基于家庭的设计或两者的任意组合。我们扩展了标准的GEE方法，使得所提出的边际模型的参数可以基于外部参考面板的工作相关/链接不平衡(LD)矩阵来估计。与标准方法相比，我们的方法实现了显著的效率提升，同时它对LD结构的错误指定具有鲁棒性，即参照板的LD结构可能与研究人群中的真实LD结构有很大不同。在模拟研究以及在基于人口和基于家庭的研究中的应用中，我们说明了所提出的GEE框架的特点。我们的结果表明，我们的方法可以比现有方法高出100%的效率。
To quantify polygenic effects, i.e. undetected genetic effects, in large-scale association studies, we propose a generalized estimating equation (GEE) based estimation framework. We develop a marginal model for single-variant association test statistics of complex diseases that generalizes existing approaches such as LD Score regression and that is applicable to population-based designs, to family-based designs or to arbitrary combinations of both. We extend the standard GEE approach so that the parameters of the proposed marginal model can be estimated based on working-correlation/linkage-disequilibrium (LD) matrices from external reference panels. Our method achieves substantial efficiency gains over standard approaches, while it is robust against misspecification of the LD structure, i.e. the LD structure of the reference panel can differ substantially from the true LD structure in the study population. In simulation studies and in applications to population-based and family-based studies, we illustrate the features of the proposed GEE framework. Our results suggest that our approach can be up to 100% more efficient than existing methodology.