Federated Generalized Linear Mixed Models for Collaborative Genome-Wide Association Studies

Authors

Wentao Li
Han Chen
Xiaoqian Jiang
Arif Harmanci

Publication Date

7-28-2023

Journal

iScience

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 population-stratification and utilizes efficient local-gradient updates among sites, incorporating both fixed and random effects. The accuracy and efficiency of dMEGA are demonstrated through simulated and real datasets. dMEGA is publicly available at https://github.com/Li-Wentao/dMEGA.

Keywords

Health sciences, Clinical genetics, Human genetics, Genomics

Comments

Supplementary Materials

PMID: 37529100