Li Wentao, Chen Han, Jiang Xiaoqian, Harmanci Arif
School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX 77030, USA.
School of Public Health, University of Texas Health Science Center, Houston, TX 77030, USA.
iScience. 2023 Jun 28;26(8):107227. doi: 10.1016/j.isci.2023.107227. eCollection 2023 Aug 18.
Federated association testing is a powerful approach to conduct large-scale association studies where sites share intermediate statistics through a central server. There are, however, several standing challenges. Confounding factors like population stratification should be carefully modeled across sites. In addition, it is crucial to consider disease etiology using flexible models to prevent biases. Privacy protections for participants pose another significant challenge. Here, we propose distributed Mixed Effects Genome-wide Association study (), a method that enables federated generalized linear mixed model-based association testing across multiple sites without explicitly sharing genotype and phenotype data. employs a reference projection to correct for population-stratification and utilizes efficient local-gradient updates among sites, incorporating both fixed and random effects. The accuracy and efficiency of are demonstrated through simulated and real datasets. is publicly available at https://github.com/Li-Wentao/dMEGA.
联合关联测试是一种强大的方法，用于在各站点通过中央服务器共享中间统计数据的情况下进行大规模关联研究。然而，存在几个长期挑战。像群体分层这样的混杂因素应在各站点间仔细建模。此外，使用灵活模型考虑疾病病因以防止偏差至关重要。对参与者的隐私保护构成了另一个重大挑战。在此，我们提出分布式混合效应全基因组关联研究（dMEGA），这是一种方法，能够在多个站点间进行基于联合广义线性混合模型的关联测试，而无需明确共享基因型和表型数据。dMEGA采用参考投影来校正群体分层，并利用各站点间高效的局部梯度更新，纳入固定效应和随机效应。通过模拟和真实数据集证明了dMEGA的准确性和效率。dMEGA可在https://github.com/Li-Wentao/dMEGA上公开获取。
