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http://eprints.org/relation/isVersionOf https://pure.iiasa.ac.at/id/document/51485 http://eprints.org/relation/isVolatileVersionOf https://pure.iiasa.ac.at/id/document/51485 http://eprints.org/relation/isIndexCodesVersionOf https://pure.iiasa.ac.at/id/document/51485 archive 5 disk0/00/01/29/11 2016-04-26 10:46:45 2021-08-27 17:40:50 2016-04-26 10:46:45 article show Moltchanova E.V. 7915 Pitkäniemi J. Haapala L. pub prog_esm Bayesian spatial modeling has become important in disease mapping and has also been suggested as a useful tool in genetic fine mapping. We have implemented the Potts model and applied it to the Genetic Analysis Workshop 14 (GAW14) simulated data. Because the "answers" were known we have analyzed latent phenotype P 1-related observed phenotypes affection status (genetically determined) and i (random) in the Danacaa population replicate 2. Analysis of the microsatellite/single-nucleotide polymorphism-based haplotypes at chromosomes 1 and 3 failed to identify multiple clusters of haplotype effects. However, the analysis of separately simulated data with postulated differences in the effects of the two clusters has yielded clear estimated division into the two clusters, demonstrating the correctness of the algorithm. Although we could not clearly identify the disease-related and the non-associated groups of haplotypes, results of both GAW14 and our own simulation encourage us to improve the efficiency and sensitivity of the estimation algorithm and to further compare the proposed method with more traditional methods. 2005 published Springer 10.1186/1471-2156-6-S1-S64 1460 public BMC Genetics 6 Sup.1 S64 TRUE 14712156 FALSE info:eu-repo/semantics/article