{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,10]],"date-time":"2026-10-10T13:01:43Z","timestamp":1791637303435,"version":"4.3.4"},"reference-count":20,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2003,8,6]],"date-time":"2003-08-06T00:00:00Z","timestamp":1060128000000},"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":[[2003,9]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    We propose a method to analyze haplotype effects using ideas derived from Bayesian spatial statistics. We assume that two haplotypes that are similar to one another in structure are likely to have similar risks, and define a distance metric to specify the appropriate level of closeness between the two haplotypes. Through the choice of distance metric, varying levels of population genetics theory can be incorporated into the modeling process, including some that allow estimation of the location of the disease causing mutation(s). This location can be estimated, along with the other parameters of the model, using Markov chain Monte Carlo (MCMC) estimation methods. We demonstrate the effectiveness of the model on two real datasets, a well\u2010known dataset used to fine\u2010map the gene for cystic fibrosis, and one used to localize the gene for Friedreich's ataxia.\n                    <jats:italic>Genet Epidemiol<\/jats:italic>\n                    25:95\u2013105, 2003. \u00a9 2003 Wiley\u2010Liss, Inc.\n                  <\/jats:p>","DOI":"10.1002\/gepi.10251","type":"journal-article","created":{"date-parts":[[2003,8,8]],"date-time":"2003-08-08T09:20:22Z","timestamp":1060334422000},"page":"95-105","source":"Crossref","is-referenced-by-count":32,"title":["Application of Bayesian spatial statistical methods to analysis of haplotypes effects and gene mapping"],"prefix":"10.1002","volume":"25","author":[{"given":"John","family":"Molitor","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Paul","family":"Marjoram","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Duncan","family":"Thomas","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2003,8,6]]},"reference":[{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1007\/BF00116466"},{"key":"e_1_2_1_3_1","first-page":"29","volume-title":"Bayesian statistics 7","author":"Carlin BP","year":"2002"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1002\/9781119115151"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1006\/geno.1996.0419"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.1990.10476213"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4899-4485-6"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/82.4.711"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.2307\/2684423"},{"key":"e_1_2_1_10_1","first-page":"733","article-title":"On conditional and intrinsic autoregressions","volume":"82","author":"Besag J","year":"1995","journal-title":"Biometrika"},{"key":"e_1_2_1_11_1","first-page":"151","volume-title":"Bayesian models for spatially correlated disease and exposure data","author":"Kelsall J","year":"1999"},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1126\/science.2570460"},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1101\/gr.194801"},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1086\/302537"},{"key":"e_1_2_1_15_1","first-page":"359","volume-title":"Markov chain Monte Carlo in practice. Chap 20, Bayesian Mapping of Disease","author":"Molli\u00e9 A","year":"1996"},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1086\/302956"},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1086\/339271"},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1002\/1097-0258(20000815)19:15<2015::AID-SIM422>3.0.CO;2-E"},{"key":"e_1_2_1_19_1","first-page":"777","article-title":"A powerful likelihood method for the analysis of linkage disequilibrium between trait loci and one or more polymorphic marker loci","volume":"56","author":"Terwilliger J","year":"1995","journal-title":"Am J Hum Genet"},{"issue":"1","key":"e_1_2_1_20_1","first-page":"S712","article-title":"Bayes estimates of haplotype effects","volume":"21","author":"Thomas DC","year":"2001","journal-title":"Genet Epidemiol"},{"key":"e_1_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1086\/515475"}],"container-title":["Genetic Epidemiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.wiley.com\/onlinelibrary\/tdm\/v1\/articles\/10.1002%2Fgepi.10251","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/gepi.10251","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,17]],"date-time":"2023-11-17T07:54:20Z","timestamp":1700207660000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1002\/gepi.10251"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2003,8,6]]},"references-count":20,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2003,9]]}},"alternative-id":["10.1002\/gepi.10251"],"URL":"https:\/\/doi.org\/10.1002\/gepi.10251","archive":["Portico"],"relation":{"has-review":[{"id-type":"doi","id":"10.3410\/f.1015738.198059","asserted-by":"object"}]},"ISSN":["0741-0395","1098-2272"],"issn-type":[{"value":"0741-0395","type":"print"},{"value":"1098-2272","type":"electronic"}],"subject":[],"published":{"date-parts":[[2003,8,6]]}}}