Federated generalized linear mixed models for collaborative genome-wide association studies.

ABSTRACT: 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 (dMEGA), a method that enables federated generalized linear mixed model-based association testing across multiple sites without explicitly sharing genotype and phenotype data. dMEGA employs a reference projection to correct for populatio

SUBMITTER: Li W 

PROVIDER: S-EPMC10387571 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature