{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T09:15:26Z","timestamp":1764580526826,"version":"3.46.0"},"reference-count":45,"publisher":"Wiley","issue":"8","license":[{"start":{"date-parts":[[2025,10,30]],"date-time":"2025-10-30T00:00:00Z","timestamp":1761782400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Genetic Epidemiology"],"published-print":{"date-parts":[[2025,12]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>Genotype imputation is a widely\u2010used data augmentation approach that is applied to samples of related and\/or unrelated individuals. Association testing may then be carried out on the complete data with commonly\u2010used methods. This approach has typically not accounted for the mix of observed and imputed data, although recent work has noted the potential for introduction of confounding in case\u2010control studies. In the Alzheimer's Disease Sequencing Project family sample we found severe inflation of the test statistics in logistic regression analysis following genotype imputation, even after standard covariate adjustments. Here we dissect sources of this inflation, which is driven by three factors: frequency\u2010dependent bias in imputation\u2010induced allele frequencies, differential measurement error, and differential genotyping rates in cases versus controls that introduces confounding. To address the problem, we propose a statistic, imputation deviance (), which can be easily computed from the observed and imputed genotype probabilities. We show that , as an additional fixed\u2010effect covariate, controls the genome\u2010wide inflation in analysis of this family\u2010based sample, and we speculate that use of imputation deviance may also provide a practical approach to correct for genotype imputation effects in other settings, particularly when a data set is unbalanced and includes related individuals.<\/jats:p>","DOI":"10.1002\/gepi.70021","type":"journal-article","created":{"date-parts":[[2025,10,30]],"date-time":"2025-10-30T12:03:01Z","timestamp":1761825781000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Adjustment for Genotype Imputation Uncertainty Corrects for Inflated Type I Error in Family\u2010Based Association Testing"],"prefix":"10.1002","volume":"49","author":[{"given":"Tyler R. C.","family":"Day","sequence":"first","affiliation":[{"name":"Department of Medicine, Division of Medical Genetics University of Washington Seattle Washington USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Joshua C.","family":"Bis","sequence":"additional","affiliation":[{"name":"Cardiovascular Health Research Unit, Department of Medicine University of Washington Seattle Washington USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nicola","family":"Chapman","sequence":"additional","affiliation":[{"name":"Department of Medicine, Division of Medical Genetics University of Washington Seattle Washington USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"suffix":"Jr.","given":"Alejandro Q.","family":"Nato","sequence":"additional","affiliation":[{"name":"Department of Medicine, Division of Medical Genetics University of Washington Seattle Washington USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andrea R. V. 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