{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,21]],"date-time":"2025-11-21T05:27:32Z","timestamp":1763702852798,"version":"3.45.0"},"reference-count":65,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2025,11,19]],"date-time":"2025-11-19T00:00:00Z","timestamp":1763510400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Analysis of longitudinal data in high-dimensional gene\u2013environment interaction studies have been extensively conducted using variable selection methods. Despite their success, these studies have been consistently challenged by the lack of uncertainty quantification procedures to identify main and interaction effects under longitudinal phenotypes that follow heavy-tailed distributions due to disease heterogeneity. In this article, to improve statistical rigor of variable selection-based G \u00d7 E analysis, we propose to apply the robust Bayesian linear mixed-effect model with a false discovery rate (FDR) control procedure to tackle these challenges. The Bayesian mixed model adopts a robust likelihood function to account for skewness in longitudinal phenotypic measurements, and it imposes spike-and-slab priors to detect important main and interaction effects. Leveraging the parallelism between spike-and-slab priors and the Bayesian approach to hypothesis testing, we perform variable selection and uncertainty quantification through a Bayesian false discovery rate (FDR)-assisted procedure. Numerical analyses have demonstrated the advantage of our proposal over alternative approaches. A case study of a longitudinal cancer prevention study with high-dimensional lipid measures yields main and interaction effects with important biological implications.<\/jats:p>","DOI":"10.3390\/a18110728","type":"journal-article","created":{"date-parts":[[2025,11,19]],"date-time":"2025-11-19T15:03:15Z","timestamp":1763564595000},"page":"728","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Prioritizing Longitudinal Gene\u2013Environment Interactions Using an FDR-Assisted Robust Bayesian Linear Mixed Model"],"prefix":"10.3390","volume":"18","author":[{"given":"Xiaoxi","family":"Li","sequence":"first","affiliation":[{"name":"Department of Statistics, Kansas State University, Manhattan, KS 66506, USA"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-0084-8738","authenticated-orcid":false,"given":"Kun","family":"Fan","sequence":"additional","affiliation":[{"name":"Department of Statistics, Kansas State University, Manhattan, KS 66506, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5172-141X","authenticated-orcid":false,"given":"Cen","family":"Wu","sequence":"additional","affiliation":[{"name":"Department of Statistics, Kansas State University, Manhattan, KS 66506, USA"}]}],"member":"1968","published-online":{"date-parts":[[2025,11,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1038\/nrg1578","article-title":"Gene\u2013environment interactions in human diseases","volume":"6","author":"Hunter","year":"2005","journal-title":"Nat. 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