FedGMMAT: Federated generalized linear mixed model association tests

• Published in: PLoS computational biology 2024-07, Vol.20 (7), p.e1012142
• Main Authors: Li, Wentao, Chen, Han, Jiang, Xiaoqian, Harmanci, Arif
• Format: Article
• Language: English
• Subjects: Analysis; Biology and Life Sciences; Computer and Information Sciences; Electronic data processing; Genetic research; Genetic susceptibility; Information management; Laws, regulations and rules; Medical records; Methods; Physical Sciences; Privacy, Right of; Research and Analysis Methods
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1012142

Increasing genetic and phenotypic data size is critical for understanding the genetic determinants of diseases. Evidently, establishing practical means for collaboration and data sharing among institutions is a fundamental methodological barrier for performing high-powered studies. As the sample sizes become more heterogeneous, complex statistical approaches, such as generalized linear mixed effects models, must be used to correct for the confounders that may bias results. On another front, due to the privacy concerns around Protected Health Information (PHI), genetic information is restrictively protected by sharing according to regulations such as Health Insurance Portability and Accountability Act (HIPAA). This limits data sharing among institutions and hampers efforts around executing high-powered collaborative studies. Federated approaches are promising to alleviate the issues around privacy and performance, since sensitive data never leaves the local sites. Motivated by these, we developed FedGMMAT, a federated genetic association testing tool that utilizes a federated statistical testing approach for efficient association tests that can correct for confounding fixed and additive polygenic random effects among different collaborating sites. Genetic data is never shared among collaborating sites, and the intermediate statistics are protected by encryption. Using simulated and real datasets, we demonstrate FedGMMAT can achieve the virtually same results as pooled analysis under a privacy-preserving framework with practical resource requirements.