Faculty, Staff and Student Publications
Language
English
Publication Date
7-1-2024
Journal
PLOS Computational Biology
DOI
10.1371/journal.pcbi.1012142
PMID
39047024
PMCID
PMC11299833
PubMedCentral® Posted Date
7-24-2024
PubMedCentral® Full Text Version
Post-print
Abstract
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.
Keywords
Humans, Linear Models, Information Dissemination, Computational Biology, Software, Genome-Wide Association Study, Genetic Association Studies
Published Open-Access
yes
Recommended Citation
, "FedGMMAT: Federated Generalized Linear Mixed Model Association Tests" (2024). Faculty, Staff and Student Publications. 446.
https://digitalcommons.library.tmc.edu/uthshis_docs/446