{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T20:06:03Z","timestamp":1770840363781,"version":"3.50.1"},"reference-count":48,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T00:00:00Z","timestamp":1769472000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"},{"start":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T00:00:00Z","timestamp":1769472000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Genetic Epidemiology"],"published-print":{"date-parts":[[2026,2]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>\n                    Harmonizing osteoporosis\u2010related data across multiple data sets is essential for improving the accuracy and generalizability of bone mineral density (BMD) assessments. This study developed a harmonization framework to standardize phenotypic and genomic variables across three major US osteoporosis data sets: GDBF, GWAS, and NHANES. We standardized key phenotypic variables (BMD, body mass index (BMI), age, sex, and race\/ethnicity) using cohort\u2010specific data dictionaries and applied multiple imputations by chained equations (MICEs) to manage missing data. Genomic data were harmonized using principal component analysis (PCA)\u2010based batch effect corrections. Residual regression methods were applied to standardize BMD values. The effectiveness of harmonization on BMD prediction was evaluated using generalized estimating equations (GEEs) and mixed\u2010effects models. Post\u2010harmonization, inter\u2010study variability in BMI was significantly reduced (\n                    <jats:italic>\u03a9<\/jats:italic>\n                    <jats:sup>2<\/jats:sup>\n                    \u2009=\u20090.0028), and BMD associations with covariates remained consistent across data sets. Harmonized models showed improved predictive performance, with explained variance in BMD increasing (\n                    <jats:italic>R<\/jats:italic>\n                    <jats:sup>2<\/jats:sup>\n                    \u2009=\u20090.14%). PCA confirmed the effective alignment of genetic data, reducing batch effects and improving cross\u2010study compatibility. This study demonstrates the feasibility and effectiveness of harmonizing phenotypic and genomic data for osteoporosis research. The harmonization framework enhances BMD prediction accuracy, supports more inclusive osteoporosis risk assessment, and improves the integration of multi\u2010cohort data sets for future research. These findings highlight the potential of data harmonization in advancing precision medicine for osteoporosis prevention and management.\n                  <\/jats:p>","DOI":"10.1002\/gepi.70028","type":"journal-article","created":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T11:19:56Z","timestamp":1769512796000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Integrative Harmonization of Phenotypic and Genomic Data Improves Bone Mineral Density Prediction in Multi\u2010Study Osteoporosis Research"],"prefix":"10.1002","volume":"50","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-6807-3693","authenticated-orcid":false,"given":"Anqi","family":"Liu","sequence":"first","affiliation":[{"name":"Department of Biomedical Informatics College of Medicine The Ohio State University Columbus Ohio USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianing","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics College of Medicine The Ohio State University Columbus Ohio USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lang","family":"Wu","sequence":"additional","affiliation":[{"name":"Department of Interdisciplinary Oncology LSU\u2010LCMC Health Cancer Center, School of Medicine, Louisiana State University Health Sciences Center New Orleans Louisiana USA"},{"name":"Department of Genetics LSU\u2010LCMC Health Cancer Center, School of Medicine, Louisiana State University Health Sciences Center New Orleans Louisiana USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4679-8903","authenticated-orcid":false,"given":"Qing","family":"Wu","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics College of Medicine The Ohio State University Columbus Ohio USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2026,1,27]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1002\/wics.101"},{"key":"e_1_2_9_3_1","volume-title":"National Health and Nutrition Examination Survey, 2017\u2013March 2020 Prepandemic File: Sample Design, Estimation, and Analytic Guidelines","author":"Akinbam L.","year":"2022"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1093\/molbev\/msq148"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1002\/mpr.329"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1038\/nrg3932"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(02)08657-9"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.1186\/1742-7622-10-12"},{"key":"e_1_2_9_9_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btu014"},{"key":"e_1_2_9_10_1","doi-asserted-by":"publisher","DOI":"10.1038\/ng.2249"},{"key":"e_1_2_9_11_1","doi-asserted-by":"publisher","DOI":"10.9778\/cmajo.20180062"},{"key":"e_1_2_9_12_1","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9781139165495"},{"key":"e_1_2_9_13_1","doi-asserted-by":"publisher","DOI":"10.1183\/09031936.50130408"},{"key":"e_1_2_9_14_1","doi-asserted-by":"publisher","DOI":"10.1186\/1753-6561-8-S1-S17"},{"key":"e_1_2_9_15_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00198-018-4704-5"},{"key":"e_1_2_9_16_1","doi-asserted-by":"publisher","DOI":"10.1002\/jbmr.38"},{"key":"e_1_2_9_17_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.bone.2006.09.029"},{"key":"e_1_2_9_18_1","doi-asserted-by":"publisher","DOI":"10.2307\/2529876"},{"key":"e_1_2_9_19_1","doi-asserted-by":"publisher","DOI":"10.3389\/fpsyg.2013.00863"},{"key":"e_1_2_9_20_1","doi-asserted-by":"publisher","DOI":"10.1377\/hlthaff.2017.1595"},{"key":"e_1_2_9_21_1","volume-title":"Data on the Relationship Between Chloroform and Bone Mineral Density","author":"Liang J.","year":"2023"},{"key":"e_1_2_9_22_1","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/73.1.13"},{"key":"e_1_2_9_23_1","doi-asserted-by":"publisher","DOI":"10.1002\/9781119482260"},{"key":"e_1_2_9_24_1","doi-asserted-by":"publisher","DOI":"10.3389\/fgene.2019.01227"},{"key":"e_1_2_9_25_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41588-019-0379-x"},{"key":"e_1_2_9_26_1","doi-asserted-by":"publisher","DOI":"10.1186\/s13059-020-01997-2"},{"key":"e_1_2_9_27_1","unstructured":"National Center for Biotechnology Information. n.d\u2010a. 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