{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T16:08:32Z","timestamp":1777565312331,"version":"3.51.4"},"reference-count":22,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2026,1,15]],"date-time":"2026-01-15T00:00:00Z","timestamp":1768435200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,1,15]],"date-time":"2026-01-15T00:00:00Z","timestamp":1768435200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["U01 AG073079"],"award-info":[{"award-number":["U01 AG073079"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01 AG065636"],"award-info":[{"award-number":["R01 AG065636"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["RF1 AG067924"],"award-info":[{"award-number":["RF1 AG067924"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"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>Genome\u2010wide association studies (GWAS) have been instrumental in identifying genetic variants associated with complex traits and diseases, including Alzheimer's disease (AD). However, traditional GWAS approaches often focus on European populations, which may lead to loss of power and limit the generalizability of findings across diverse ancestries. On the other hand, LS\u2010Imputation, a nonparametric trait imputation method, leverages GWAS summary statistics and genotype data to impute missing traits, which can then be used for GWAS and other downstream analyses. Although LS\u2010Imputation has been applied successfully to European populations, its performance in non\u2010European populations would be hindered by smaller sample sizes, leading to reduced imputation accuracy. To address these limitations, we propose two novel variants of LS\u2010Imputation\u2010LS\u2010Imputation\u2010Combined and LS\u2010Imputation\u2010Transfer\u2014designed to integrate multi\u2010ancestry GWAS data and enhance imputation performance. LS\u2010Imputation\u2010Combined optimally combines GWAS summary statistics from multiple ancestries, while LS\u2010Imputation\u2010Transfer sequentially refines imputed trait values across ancestries using stochastic gradient descent. We evaluate these methods using data from the UK Biobank and the Alzheimer's Disease Sequencing Project (ADSP), first applying them to high\u2010density lipoprotein (HDL) cholesterol levels as a proof\u2010of\u2010concept before focusing on imputing AD status in Black individuals for genetic association analysis. Our results demonstrate that integrating multi\u2010ancestry GWAS data improves trait imputation accuracy, with LS\u2010Imputation\u2010Transfer achieving the highest performance.<\/jats:p>","DOI":"10.1002\/gepi.70030","type":"journal-article","created":{"date-parts":[[2026,1,15]],"date-time":"2026-01-15T12:01:57Z","timestamp":1768478517000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Large\u2010Scale Genotype\u2010Based Trait Imputation With Multi\u2010Ancestry GWAS Data"],"prefix":"10.1002","volume":"50","author":[{"given":"Jingchen","family":"Ren","sequence":"first","affiliation":[{"name":"School of Statistics University of Minnesota Minneapolis Minnesota USA"},{"name":"Division of Biostatistics and Health Data Science, School of Public Health University of Minnesota Minneapolis Minnesota USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1159-0582","authenticated-orcid":false,"given":"Wei","family":"Pan","sequence":"additional","affiliation":[{"name":"Division of Biostatistics and Health Data Science, School of Public Health University of Minnesota Minneapolis Minnesota USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2026,1,15]]},"reference":[{"key":"e_1_2_7_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ajhg.2022.12.011"},{"key":"e_1_2_7_3_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41588-022-01024-z"},{"key":"e_1_2_7_4_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-7908-2604-3_16"},{"key":"e_1_2_7_5_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-018-0579-z"},{"key":"e_1_2_7_6_1","doi-asserted-by":"publisher","DOI":"10.1186\/s13024-019-0333-5"},{"key":"e_1_2_7_7_1","doi-asserted-by":"publisher","DOI":"10.1001\/archpsyc.63.2.168"},{"key":"e_1_2_7_8_1","doi-asserted-by":"publisher","DOI":"10.1126\/science.1072994"},{"key":"e_1_2_7_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jalz.2016.12.012"},{"key":"e_1_2_7_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2020.117347"},{"key":"e_1_2_7_11_1","doi-asserted-by":"publisher","DOI":"10.1001\/jamaneurol.2020.3536"},{"key":"e_1_2_7_12_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41588-019-0358-2"},{"key":"e_1_2_7_13_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41380-023-02089-w"},{"key":"e_1_2_7_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2009.191"},{"key":"e_1_2_7_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jalz.2012.11.007"},{"issue":"3","key":"e_1_2_7_16_1","article-title":"Using GWAS Summary Data to Impute Traits for Genotyped Individuals","volume":"4","author":"Ren J.","year":"2023","journal-title":"HGG Advances"},{"key":"e_1_2_7_17_1","doi-asserted-by":"publisher","DOI":"10.1093\/hmg\/ddad097"},{"key":"e_1_2_7_18_1","doi-asserted-by":"publisher","DOI":"10.1002\/sim.9975"},{"key":"e_1_2_7_19_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41588-020-00776-w"},{"key":"e_1_2_7_20_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41588-021-00921-z"},{"key":"e_1_2_7_21_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-019-1310-4"},{"issue":"2","key":"e_1_2_7_22_1","article-title":"Evaluating and Improving Health Equity and Fairness of Polygenic Scores","volume":"5","author":"Zhang T.","year":"2024","journal-title":"HGG Advances"},{"key":"e_1_2_7_23_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ajhg.2022.09.010"}],"container-title":["Genetic 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