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

Authors

Wentao Li
Han Chen
Xiaoqian Jiang
Arif Harmanci

Language

English

Publication Date

8-18-2023

Journal

iScience

DOI

10.1016/j.isci.2023.107227

PMID

10.1016/j.isci.2023.107227

PMCID

PMC10387571

PubMedCentral® Posted Date

6-28-2023

PubMedCentral® Full Text Version

Post-print

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 (

Published Open-Access

yes

Recommended Citation

Li, Wentao; Chen, Han; Jiang, Xiaoqian; et al., "Federated Generalized Linear Mixed Models for Collaborative Genome-Wide Association Studies" (2023). Faculty, Staff and Student Publications. 450.
https://digitalcommons.library.tmc.edu/uthshis_docs/450