{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T23:37:55Z","timestamp":1772321875094,"version":"3.50.1"},"reference-count":27,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,4,5]],"date-time":"2025-04-05T00:00:00Z","timestamp":1743811200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,4,5]],"date-time":"2025-04-05T00:00:00Z","timestamp":1743811200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Bioinformatics"],"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>The primary goal of predictive modeling for compositional microbiome data is to better understand and predict disease susceptibility based on the relative abundance of microbial species. Current approaches in this area often assume a high-dimensional sparse setting, where only a small subset of microbiome features is considered relevant to the outcome. However, in real-world data, both large and small effects frequently coexist, and acknowledging the contribution of smaller effects can significantly enhance predictive performance. To address this challenge, we developed Bayesian Compositional Generalized Linear Mixed Models for Analyzing Microbiome Data (BCGLMM). BCGLMM is capable of identifying both moderate taxa effects and the cumulative impact of numerous minor taxa, which are often overlooked in conventional models. With a sparsity-inducing prior, the structured regularized horseshoe prior, BCGLMM effectively collaborates phylogenetically related moderate effects. The random effect term efficiently captures sample-related minor effects by incorporating sample similarities within its variance-covariance matrix. We fitted the proposed models using Markov Chain Monte Carlo (MCMC) algorithms with rstan. The performance of the proposed method was evaluated through extensive simulation studies, demonstrating its superiority with higher prediction accuracy compared to existing methods. We then applied the proposed method on American Gut Data to predict inflammatory bowel disease (IBD). To ensure reproducibility, the code and data used in this paper are available at <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/github.com\/Li-Zhang28\/BCGLMM\" ext-link-type=\"uri\">https:\/\/github.com\/Li-Zhang28\/BCGLMM<\/jats:ext-link>.<\/jats:p>","DOI":"10.1186\/s12859-025-06114-3","type":"journal-article","created":{"date-parts":[[2025,4,5]],"date-time":"2025-04-05T10:48:46Z","timestamp":1743850126000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Bayesian compositional generalized linear mixed models for disease prediction using microbiome data"],"prefix":"10.1186","volume":"26","author":[{"given":"Li","family":"Zhang","sequence":"first","affiliation":[]},{"given":"Xinyan","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Justin M.","family":"Leach","sequence":"additional","affiliation":[]},{"given":"A. K. M. F.","family":"Rahman","sequence":"additional","affiliation":[]},{"given":"Carrie R.","family":"Howell","sequence":"additional","affiliation":[]},{"given":"Nengjun","family":"Yi","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,4,5]]},"reference":[{"issue":"1","key":"6114_CR1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1144\/GSL.SP.2006.264.01.01","volume":"264","author":"V Pawlowsky-Glahn","year":"2006","unstructured":"Pawlowsky-Glahn V, Egozcue JJ. Compositional data and their analysis: an introduction. Geol Soc. 2006;264(1):1\u201310.","journal-title":"Geol Soc"},{"key":"6114_CR2","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1007\/978-3-642-36809-7","volume-title":"Analyzing compositional data with R vol","author":"KG Boogaart","year":"2013","unstructured":"Boogaart KG, Tolosana-Delgado R. Analyzing compositional data with R vol. New York: Springer; 2013. p. 122."},{"issue":"2","key":"6114_CR3","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1111\/j.2517-6161.1982.tb01195.x","volume":"44","author":"J Aitchison","year":"1982","unstructured":"Aitchison J. The statistical analysis of compositional data. J Roy Stat Soc: Ser B (Methodol). 1982;44(2):139\u201360.","journal-title":"J Roy Stat Soc: Ser B (Methodol)"},{"issue":"4","key":"6114_CR4","doi-asserted-by":"crossref","first-page":"785","DOI":"10.1093\/biomet\/asu031","volume":"101","author":"W Lin","year":"2014","unstructured":"Lin W, Shi P, Feng R, Li H. Variable selection in regression with compositional covariates. Biometrika. 2014;101(4):785\u201397.","journal-title":"Biometrika"},{"issue":"10","key":"6114_CR5","doi-asserted-by":"crossref","first-page":"3596","DOI":"10.1021\/acs.jproteome.7b00325","volume":"16","author":"HU Zacharias","year":"2017","unstructured":"Zacharias HU, Rehberg T, Mehrl S, Richtmann D, Wettig T, Oefner PJ, Spang R, Gronwald W, Altenbuchinger M. Scale-invariant biomarker discovery in urine and plasma metabolite fingerprints. J Proteome Res. 2017;16(10):3596\u2013605.","journal-title":"J Proteome Res"},{"issue":"1","key":"6114_CR6","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1111\/biom.12956","volume":"75","author":"J Lu","year":"2019","unstructured":"Lu J, Shi P, Li H. Generalized linear models with linear constraints for microbiome compositional data. Biometrics. 2019;75(1):235\u201344.","journal-title":"Biometrics"},{"key":"6114_CR7","doi-asserted-by":"crossref","unstructured":"Calle ML, Susin A. coda4microbiome: compositional data analysis for microbiome studies. bioRxiv, 2022: 2022\u201306","DOI":"10.1101\/2022.06.09.495511"},{"issue":"3","key":"6114_CR8","doi-asserted-by":"crossref","first-page":"824","DOI":"10.1111\/biom.13335","volume":"77","author":"L Zhang","year":"2021","unstructured":"Zhang L, Shi Y, Jenq RR, Do K-A, Peterson CB. Bayesian compositional regression with structured priors for microbiome feature selection. Biometrics. 2021;77(3):824\u201338.","journal-title":"Biometrics"},{"issue":"1","key":"6114_CR9","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1002\/sim.9946","volume":"43","author":"L Zhang","year":"2024","unstructured":"Zhang L, Zhang X, Yi N. Bayesian compositional generalized linear models for analyzing microbiome data. Stat Med. 2024;43(1):141\u201355.","journal-title":"Stat Med"},{"issue":"2","key":"6114_CR10","doi-asserted-by":"crossref","first-page":"1003264","DOI":"10.1371\/journal.pgen.1003264","volume":"9","author":"X Zhou","year":"2013","unstructured":"Zhou X, Carbonetto P, Stephens M. Polygenic modeling with bayesian sparse linear mixed models. PLoS Genet. 2013;9(2):1003264.","journal-title":"PLoS Genet"},{"key":"6114_CR11","volume-title":"Bayesian data analysis","author":"A Gelman","year":"2014","unstructured":"Gelman A, Carlin JB, Stern HS, Rubin DB. Bayesian data analysis. Boca Raton: Taylor & Francis; 2014."},{"key":"6114_CR12","doi-asserted-by":"crossref","DOI":"10.1016\/j.sste.2019.100301","volume":"31","author":"M Morris","year":"2019","unstructured":"Morris M, Wheeler-Martin K, Simpson D, Mooney SJ, Gelman A, DiMaggio C. Bayesian hierarchical spatial models: Implementing the besag york molli\u00e9 model in stan. Spatial Spatio-temporal Epidemiol. 2019;31: 100301.","journal-title":"Spatial Spatio-temporal Epidemiol"},{"key":"6114_CR13","doi-asserted-by":"crossref","unstructured":"Gelman A, Jakulin A, Pittau MG, Su Y-S. A weakly informative default prior distribution for logistic and other regression models 2008","DOI":"10.2139\/ssrn.1010421"},{"key":"6114_CR14","doi-asserted-by":"crossref","unstructured":"Piironen J, Vehtari A. Sparsity information and regularization in the horseshoe and other shrinkage priors 2017","DOI":"10.1214\/17-EJS1337SI"},{"key":"6114_CR15","doi-asserted-by":"crossref","unstructured":"Zhang L, Zhang X, Leach J, Rahman A, Yi N. Bayesian compositional models for ordinal response. Statistical Methods in Medical Research, 2024","DOI":"10.1177\/09622802241247730"},{"issue":"2","key":"6114_CR16","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1111\/j.2517-6161.1974.tb00999.x","volume":"36","author":"J Besag","year":"1974","unstructured":"Besag J. Spatial interaction and the statistical analysis of lattice systems. J Roy Stat Soc: Ser B (Methodol). 1974;36(2):192\u2013225.","journal-title":"J Roy Stat Soc: Ser B (Methodol)"},{"key":"6114_CR17","doi-asserted-by":"crossref","DOI":"10.1201\/9780203487808","volume-title":"Hierarchical modeling and analysis for spatial data","author":"S Banerjee","year":"2003","unstructured":"Banerjee S, Carlin BP, Gelfand AE. Hierarchical modeling and analysis for spatial data. Boca Raton: Chapman and Hall\/CRC; 2003."},{"issue":"4","key":"6114_CR18","first-page":"733","volume":"82","author":"J Besag","year":"1995","unstructured":"Besag J, Kooperberg C. On conditional and intrinsic autoregressions. Biometrika. 1995;82(4):733\u201346.","journal-title":"Biometrika"},{"key":"6114_CR19","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/S0065-2504(08)60168-3","volume":"14","author":"EW Beals","year":"1984","unstructured":"Beals EW. Bray-curtis ordination: an effective strategy for analysis of multivariate ecological data. Adv Ecol Res. 1984;14:1\u201355.","journal-title":"Adv Ecol Res"},{"issue":"12","key":"6114_CR20","doi-asserted-by":"crossref","first-page":"8228","DOI":"10.1128\/AEM.71.12.8228-8235.2005","volume":"71","author":"C Lozupone","year":"2005","unstructured":"Lozupone C, Knight R. Unifrac: a new phylogenetic method for comparing microbial communities. Appl Environ Microbiol. 2005;71(12):8228\u201335.","journal-title":"Appl Environ Microbiol"},{"issue":"5","key":"6114_CR21","doi-asserted-by":"crossref","first-page":"797","DOI":"10.1016\/j.ajhg.2015.04.003","volume":"96","author":"N Zhao","year":"2015","unstructured":"Zhao N, Chen J, Carroll IM, Ringel-Kulka T, Epstein MP, Zhou H, Zhou JJ, Ringel Y, Li H, Wu MC. Testing in microbiome-profiling studies with mirkat, the microbiome regression-based kernel association test. Am J Human Genet. 2015;96(5):797\u2013807.","journal-title":"Am J Human Genet"},{"issue":"1","key":"6114_CR22","doi-asserted-by":"crossref","first-page":"540","DOI":"10.1214\/17-AOAS1102","volume":"12","author":"TW Randolph","year":"2018","unstructured":"Randolph TW, Zhao S, Copeland W, Hullar M, Shojaie A. Kernel-penalized regression for analysis of microbiome data. Ann Appl Statist. 2018;12(1):540.","journal-title":"Ann Appl Statist"},{"key":"6114_CR23","first-page":"1","volume":"80","author":"P-C B\u00fcrkner","year":"2017","unstructured":"B\u00fcrkner P-C. brms: An r package for bayesian multilevel models using stan. J Stat Softw. 2017;80:1\u201328.","journal-title":"J Stat Softw"},{"key":"6114_CR24","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-77244-8","volume-title":"Clinical prediction models: a practical approach to development, validation, and updating","author":"E Steyerberg","year":"2009","unstructured":"Steyerberg E. Clinical prediction models: a practical approach to development, validation, and updating. New York: Springer; 2009. https:\/\/doi.org\/10.1007\/978-0-387-77244-8."},{"key":"6114_CR25","doi-asserted-by":"crossref","first-page":"1413","DOI":"10.1007\/s11222-016-9696-4","volume":"27","author":"A Vehtari","year":"2017","unstructured":"Vehtari A, Gelman A, Gabry J. Practical bayesian model evaluation using leave-one-out cross-validation and waic. Stat Comput. 2017;27:1413\u201332.","journal-title":"Stat Comput"},{"key":"6114_CR26","doi-asserted-by":"crossref","DOI":"10.1017\/CBO9780511790942","volume-title":"Data analysis using regression and multilevel\/hierarchical models","author":"A Gelman","year":"2006","unstructured":"Gelman A, Hill J. Data analysis using regression and multilevel\/hierarchical models. Cambridge: Cambridge University Press; 2006."},{"issue":"3","key":"6114_CR27","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1128\/mSystems.00031-18","volume":"3","author":"D McDonald","year":"2018","unstructured":"McDonald D, Hyde E, Debelius JW, Morton JT, Gonzalez A, Ackermann G, Aksenov AA, Behsaz B, Brennan C, Chen Y, et al. American gut: an open platform for citizen science microbiome research. Msystems. 2018;3(3):10\u20131128.","journal-title":"Msystems"}],"container-title":["BMC Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-025-06114-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12859-025-06114-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-025-06114-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,5]],"date-time":"2025-04-05T10:50:09Z","timestamp":1743850209000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcbioinformatics.biomedcentral.com\/articles\/10.1186\/s12859-025-06114-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,5]]},"references-count":27,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["6114"],"URL":"https:\/\/doi.org\/10.1186\/s12859-025-06114-3","relation":{},"ISSN":["1471-2105"],"issn-type":[{"value":"1471-2105","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,4,5]]},"assertion":[{"value":"25 October 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 March 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 April 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors have no Conflict of interest to declare","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"98"}}