{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T19:31:47Z","timestamp":1773084707413,"version":"3.50.1"},"reference-count":14,"publisher":"Oxford University Press (OUP)","issue":"19","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2007,10,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Motivation: The identification and characterization of susceptibility genes that influence the risk of common and complex diseases remains a statistical and computational challenge in genetic association studies. This is partly because the effect of any single genetic variant for a common and complex disease may be dependent on other genetic variants (gene\u2013gene interaction) and environmental factors (gene\u2013environment interaction). To address this problem, the multifactor dimensionality reduction (MDR) method has been proposed by Ritchie et al. to detect gene\u2013gene interactions or gene\u2013environment interactions. The MDR method identifies polymorphism combinations associated with the common and complex multifactorial diseases by collapsing high-dimensional genetic factors into a single dimension. That is, the MDR method classifies the combination of multilocus genotypes into high-risk and low-risk groups based on a comparison of the ratios of the numbers of cases and controls. When a high-order interaction model is considered with multi-dimensional factors, however, there may be many sparse or empty cells in the contingency tables. The MDR method cannot classify an empty cell as high risk or low risk and leaves it as undetermined.<\/jats:p><jats:p>Results: In this article, we propose the log-linear model-based multifactor dimensionality reduction (LM MDR) method to improve the MDR in classifying sparse or empty cells. The LM MDR method estimates frequencies for empty cells from a parsimonious log-linear model so that they can be assigned to high-and low-risk groups. In addition, LM MDR includes MDR as a special case when the saturated log-linear model is fitted. Simulation studies show that the LM MDR method has greater power and smaller error rates than the MDR method. The LM MDR method is also compared with the MDR method using as an example sporadic Alzheimer's disease.<\/jats:p><jats:p>Contact: \u00a0tspark@stats.snu.ac.kr<\/jats:p>","DOI":"10.1093\/bioinformatics\/btm396","type":"journal-article","created":{"date-parts":[[2007,9,15]],"date-time":"2007-09-15T00:14:51Z","timestamp":1189815291000},"page":"2589-2595","source":"Crossref","is-referenced-by-count":72,"title":["Log-linear model-based multifactor dimensionality reduction method to detect gene\u2013gene interactions"],"prefix":"10.1093","volume":"23","author":[{"given":"Seung","family":"Yeoun Lee","sequence":"first","affiliation":[{"name":"1 Department of Applied Mathematics, Sejong University, 98 Gunja-Dong Kwangjin-Gu, Seoul 143-747, Korea, 2Department of Statistics, University of Wisconsin, 1300 University Avenue Madison, WI 53706, 3Department of Epidemiology and Biostatistics, Case Western Reserve University, 10900 Euclid Avenue Cleveland, Ohio 44106-7281, USA and 4Department of Statistics, Seoul National University, San 56-1 Shillim-Dong, Kwanak-Gu, Seoul 151-747, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yujin","family":"Chung","sequence":"additional","affiliation":[{"name":"1 Department of Applied Mathematics, Sejong University, 98 Gunja-Dong Kwangjin-Gu, Seoul 143-747, Korea, 2Department of Statistics, University of Wisconsin, 1300 University Avenue Madison, WI 53706, 3Department of Epidemiology and Biostatistics, Case Western Reserve University, 10900 Euclid Avenue Cleveland, Ohio 44106-7281, USA and 4Department of Statistics, Seoul National University, San 56-1 Shillim-Dong, Kwanak-Gu, Seoul 151-747, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Robert C.","family":"Elston","sequence":"additional","affiliation":[{"name":"1 Department of Applied Mathematics, Sejong University, 98 Gunja-Dong Kwangjin-Gu, Seoul 143-747, Korea, 2Department of Statistics, University of Wisconsin, 1300 University Avenue Madison, WI 53706, 3Department of Epidemiology and Biostatistics, Case Western Reserve University, 10900 Euclid Avenue Cleveland, Ohio 44106-7281, USA and 4Department of Statistics, Seoul National University, San 56-1 Shillim-Dong, Kwanak-Gu, Seoul 151-747, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Youngchul","family":"Kim","sequence":"additional","affiliation":[{"name":"1 Department of Applied Mathematics, Sejong University, 98 Gunja-Dong Kwangjin-Gu, Seoul 143-747, Korea, 2Department of Statistics, University of Wisconsin, 1300 University Avenue Madison, WI 53706, 3Department of Epidemiology and Biostatistics, Case Western Reserve University, 10900 Euclid Avenue Cleveland, Ohio 44106-7281, USA and 4Department of Statistics, Seoul National University, San 56-1 Shillim-Dong, Kwanak-Gu, Seoul 151-747, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Taesung","family":"Park","sequence":"additional","affiliation":[{"name":"1 Department of Applied Mathematics, Sejong University, 98 Gunja-Dong Kwangjin-Gu, Seoul 143-747, Korea, 2Department of Statistics, University of Wisconsin, 1300 University Avenue Madison, WI 53706, 3Department of Epidemiology and Biostatistics, Case Western Reserve University, 10900 Euclid Avenue Cleveland, Ohio 44106-7281, USA and 4Department of Statistics, Seoul National University, San 56-1 Shillim-Dong, Kwanak-Gu, Seoul 151-747, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2007,10,1]]},"reference":[{"key":"2023041208435815000_","first-page":"85","author":"Agresti","year":"2002","journal-title":"Categorical Data Analysis"},{"key":"2023041208435815000_","first-page":"651","article-title":"Possible increased risk for Alzheimer's disease associated with neprilysin gene. 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