Identifying kinship relations using incomplete DNA: A Bayesian approach to determine the maximum likelihood pedigree using MCMC

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dc.contributor.advisorGerven, M.A.J. van
dc.contributor.advisorWiegerinck, W.A.J.J.
dc.contributor.advisorBurgers, W.G.
dc.contributor.authorAhrendt, J.
dc.date.issued2013-11-28
dc.description.abstractA method for pedigree reconstruction is proposed using Markov Chain Monte Carlo and Bayesian inference, which can reconstruct the family relations of several individuals in question, based on DNA profiles. Kinship relations are reconstructed using genetic microsatellite (STR) data from samples of related individuals. In particular, this research extends methods for pedigree reconstruction to incorporate mutations and to handle incomplete genotype samples, in which genetic profiles were either not observed for all individuals in the pedigree, or genetic profiles contain missing observations on some genetic markers. This extends pedigree reconstruction to take account for distant family relations. The algorithm is demonstrated using generated datasets and a single human dataset. The proposed method can be applied in forensic science, criminology, legal decisions, archeology, and medicine.en_US
dc.identifier.urihttp://theses.ubn.ru.nl/handle/123456789/202
dc.language.isoenen_US
dc.thesis.facultyFaculteit der Sociale Wetenschappenen_US
dc.thesis.specialisationMaster Artificial Intelligenceen_US
dc.thesis.studyprogrammeArtificial Intelligenceen_US
dc.thesis.typeMasteren_US
dc.titleIdentifying kinship relations using incomplete DNA: A Bayesian approach to determine the maximum likelihood pedigree using MCMCen_US
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