{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,26]],"date-time":"2025-10-26T15:03:37Z","timestamp":1761491017804,"version":"3.41.2"},"reference-count":29,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2020,7,7]],"date-time":"2020-07-07T00:00:00Z","timestamp":1594080000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Science Foundation of China","doi-asserted-by":"publisher","award":["11671375"],"award-info":[{"award-number":["11671375"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Natural Science Foundation of Anhui Provincial Education Department","award":["KJ2017A171"],"award-info":[{"award-number":["KJ2017A171"]}]},{"DOI":"10.13039\/501100002947","name":"Anhui Medical University","doi-asserted-by":"publisher","award":["XJ201710"],"award-info":[{"award-number":["XJ201710"]}],"id":[{"id":"10.13039\/501100002947","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Translational and Applied Research","award":["201711160015"],"award-info":[{"award-number":["201711160015"]}]},{"name":"Zhejiang Institute of Research and Innovation Seed Fund, and General Research Fund","award":["17308018"],"award-info":[{"award-number":["17308018"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,5,20]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Genome-wide association studies (GWAS) using longitudinal phenotypes collected over time is appealing due to the improvement of power. However, computation burden has been a challenge because of the complex algorithms for modeling the longitudinal data. Approximation methods based on empirical Bayesian estimates (EBEs) from mixed-effects modeling have been developed to expedite the analysis. However, our analysis demonstrated that bias in both association test and estimation for the existing EBE-based methods remains an issue. We propose an incredibly fast and unbiased method (simultaneous correction for EBE, SCEBE) that can correct the bias in the naive EBE approach and provide unbiased P-values and estimates of effect size. Through application to Alzheimer\u2019s Disease Neuroimaging Initiative data with 6\u00a0414\u00a0695 single nucleotide polymorphisms, we demonstrated that SCEBE can efficiently perform large-scale GWAS with longitudinal outcomes, providing nearly 10\u00a0000 times improvement of computational efficiency and shortening the computation time from months to minutes. The SCEBE package and the example datasets are available at https:\/\/github.com\/Myuan2019\/SCEBE.<\/jats:p>","DOI":"10.1093\/bib\/bbaa130","type":"journal-article","created":{"date-parts":[[2020,5,28]],"date-time":"2020-05-28T11:08:19Z","timestamp":1590664099000},"source":"Crossref","is-referenced-by-count":7,"title":["SCEBE: an efficient and scalable algorithm for genome-wide association studies on longitudinal outcomes with mixed-effects modeling"],"prefix":"10.1093","volume":"22","author":[{"given":"Min","family":"Yuan","sequence":"first","affiliation":[{"name":"Anhui Medical University, Anhui, China"}]},{"given":"Xu Steven","family":"Xu","sequence":"additional","affiliation":[{"name":"Genmab US, Inc., Princeton, NJ, USA"}]},{"given":"Yaning","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Statistics and Finance, University of Science and Technology of China, Heifei, China"}]},{"given":"Yinsheng","family":"Zhou","sequence":"additional","affiliation":[{"name":"Department of Statistics and Finance, University of Science and Technology of China, Heifei, China"}]},{"given":"Yi","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Statistics and Finance, University of Science and Technology of China, Heifei, China"}]},{"given":"Jinfeng","family":"Xu","sequence":"additional","affiliation":[{"name":"Department of Statistics and Actuarial Science, University of Hong Kong, Pok Fu Lam, Hong Kong"}]},{"given":"Jose","family":"Pinheiro","sequence":"additional","affiliation":[{"name":"Janssen Research and Development LLC, Raritan, NJ, USA"}]},{"name":"for the Alzheimer\u2019s Disease Neuroimaging 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