{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,21]],"date-time":"2025-11-21T06:31:44Z","timestamp":1763706704037,"version":"build-2065373602"},"reference-count":32,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2024,1,3]],"date-time":"2024-01-03T00:00:00Z","timestamp":1704240000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Genes"],"abstract":"<jats:p>Genome-wide association studies (GWAS) have successfully revealed many disease-associated genetic variants. For a case-control study, the adequate power of an association test can be achieved with a large sample size, although genotyping large samples is expensive. A cost-effective strategy to boost power is to integrate external control samples with publicly available genotyped data. However, the naive integration of external controls may inflate the type I error rates if ignoring the systematic differences (batch effect) between studies, such as the differences in sequencing platforms, genotype-calling procedures, population stratification, and so forth. To account for the batch effect, we propose an approach by integrating External Controls into the Association Test by Regression Calibration (iECAT-RC) in case-control association studies. Extensive simulation studies show that iECAT-RC not only can control type I error rates but also can boost statistical power in all models. We also apply iECAT-RC to the UK Biobank data for M72 Fibroblastic disorders by considering genotype calling as the batch effect. Four SNPs associated with fibroblastic disorders have been detected by iECAT-RC and the other two comparison methods, iECAT-Score and Internal. However, our method has a higher probability of identifying these significant SNPs in the scenario of an unbalanced case-control association study.<\/jats:p>","DOI":"10.3390\/genes15010067","type":"journal-article","created":{"date-parts":[[2024,1,3]],"date-time":"2024-01-03T03:18:07Z","timestamp":1704251887000},"page":"67","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Integrating External Controls by Regression Calibration for Genome-Wide Association Study"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2409-5564","authenticated-orcid":false,"given":"Lirong","family":"Zhu","sequence":"first","affiliation":[{"name":"Department of Mathematical Sciences, Michigan Technological University, Houghton, MI 49931, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shijia","family":"Yan","sequence":"additional","affiliation":[{"name":"Department of Mathematical Sciences, Michigan Technological University, Houghton, MI 49931, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xuewei","family":"Cao","sequence":"additional","affiliation":[{"name":"Department of Mathematical Sciences, Michigan Technological University, Houghton, MI 49931, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuanglin","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Mathematical Sciences, Michigan Technological University, Houghton, MI 49931, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9342-3269","authenticated-orcid":false,"given":"Qiuying","family":"Sha","sequence":"additional","affiliation":[{"name":"Department of Mathematical Sciences, Michigan Technological University, Houghton, MI 49931, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,1,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Price, A.L., Spencer, C.C., and Donnelly, P. 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