{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,10]],"date-time":"2026-10-10T07:50:59Z","timestamp":1791618659270,"version":"4.3.4"},"reference-count":34,"publisher":"Wiley","issue":"8","license":[{"start":{"date-parts":[[2006,9,19]],"date-time":"2006-09-19T00:00:00Z","timestamp":1158624000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Genetic Epidemiology"],"published-print":{"date-parts":[[2006,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Missing single nucleotide polymorphisms (SNPs) are quite common in genetic association studies. Subjects with missing SNPs are often discarded in analyses, which may seriously undermine the inference of SNP\u2010disease association. In this article, we develop two haplotype\u2010based imputation approaches and one tree\u2010based imputation approach for association studies. The emphasis is to evaluate the impact of imputation on parameter estimation, compared to the standard practice of ignoring missing data. Haplotype\u2010based approaches build on haplotype reconstruction by the expectation\u2010maximization (EM) algorithm or a weighted EM (WEM) algorithm, depending on whether case\u2010control status is taken into account. The tree\u2010based approach uses a Gibbs sampler to iteratively sample from a full conditional distribution, which is obtained from the classification and regression tree (CART) algorithm. We employ a standard multiple imputation procedure to account for the uncertainty of imputation. We apply the methods to simulated data as well as a case\u2010control study on developmental dyslexia. Our results suggest that imputation generally improves efficiency over the standard practice of ignoring missing data. The tree\u2010based approach performs comparably well as haplotype\u2010based approaches, but the former has a computational advantage. The WEM approach yields the smallest bias at a price of increased variance. <jats:italic>Genet. Epidemiol.<\/jats:italic> \u00a9 2006 Wiley\u2010Liss, Inc.<\/jats:p>","DOI":"10.1002\/gepi.20180","type":"journal-article","created":{"date-parts":[[2006,9,19]],"date-time":"2006-09-19T20:57:13Z","timestamp":1158699433000},"page":"690-702","source":"Crossref","is-referenced-by-count":38,"title":["Imputation methods to improve inference in SNP association studies"],"prefix":"10.1002","volume":"30","author":[{"given":"James Y.","family":"Dai","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ingo","family":"Ruczinski","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael","family":"LeBlanc","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Charles","family":"Kooperberg","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2006,9,19]]},"reference":[{"key":"e_1_2_1_2_1","unstructured":"Affymetrix Corporation.2006. 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