Bayesian mixed model inference for genetic association under related samples with brain network phenotype

dc.contributor.authorTian, Xinyuan
dc.contributor.authorWang, Yiting
dc.contributor.authorWang, Selena
dc.contributor.authorZhao, Yi
dc.contributor.authorZhao, Yize
dc.contributor.departmentBiostatistics and Health Data Science, Richard M. Fairbanks School of Public Health
dc.date.accessioned2025-04-18T10:22:19Z
dc.date.available2025-04-18T10:22:19Z
dc.date.issued2024
dc.description.abstractGenetic association studies for brain connectivity phenotypes have gained prominence due to advances in noninvasive imaging techniques and quantitative genetics. Brain connectivity traits, characterized by network configurations and unique biological structures, present distinct challenges compared to other quantitative phenotypes. Furthermore, the presence of sample relatedness in the most imaging genetics studies limits the feasibility of adopting existing network-response modeling. In this article, we fill this gap by proposing a Bayesian network-response mixed-effect model that considers a network-variate phenotype and incorporates population structures including pedigrees and unknown sample relatedness. To accommodate the inherent topological architecture associated with the genetic contributions to the phenotype, we model the effect components via a set of effect network configurations and impose an inter-network sparsity and intra-network shrinkage to dissect the phenotypic network configurations affected by the risk genetic variant. A Markov chain Monte Carlo (MCMC) algorithm is further developed to facilitate uncertainty quantification. We evaluate the performance of our model through extensive simulations. By further applying the method to study, the genetic bases for brain structural connectivity using data from the Human Connectome Project with excessive family structures, we obtain plausible and interpretable results. Beyond brain connectivity genetic studies, our proposed model also provides a general linear mixed-effect regression framework for network-variate outcomes.
dc.eprint.versionFinal published version
dc.identifier.citationTian X, Wang Y, Wang S, Zhao Y, Zhao Y. Bayesian mixed model inference for genetic association under related samples with brain network phenotype [published correction appears in Biostatistics. 2024 Dec 31;26(1):kxae029. doi: 10.1093/biostatistics/kxae029.]. Biostatistics. 2024;25(4):1195-1209. doi:10.1093/biostatistics/kxae008
dc.identifier.urihttps://hdl.handle.net/1805/47162
dc.language.isoen_US
dc.publisherOxford University Press
dc.relation.isversionof10.1093/biostatistics/kxae008
dc.relation.journalBiostatistics
dc.rightsPublisher Policy
dc.sourcePMC
dc.subjectBrain connectivity
dc.subjectGenome-wide association studies
dc.subjectImaging genetics
dc.subjectMixed effects
dc.subjectNetwork-response model
dc.subjectSample relatedness
dc.titleBayesian mixed model inference for genetic association under related samples with brain network phenotype
dc.typeArticle
ul.alternative.fulltexthttps://pmc.ncbi.nlm.nih.gov/articles/PMC11639157/
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