Given recent studies on the potential impacts of ignoring different genetic properties in subpopulations that mate within their subpopulation, this study examines how to account for population substructure in both Bayesian Network and relatedness estimation.
"Population substructure" refers to any population that does not randomly mate. In most species, this deviation from random mating is due to the emergence of subpopulations that mate within their subpopulation, leading to different genetic properties. Bayesian Networks are gaining popularity as a graphical tool that communicates complex probabilistic reasoning required in the evaluation of DNA evidence. This report describes a study that extends the current use of Bayesian Networks by incorporating the potential effects of population substructure on paternity calculations. It demonstrates features of HUGIN (a software package used to create Bayesian Networks) that have not yet been explored. Three paternity examples are considered: a simple case with two alleles, a simple case with multiple alleles, and a missing-father case. Population substructure also has an impact on pairwise relatedness estimation. Many estimators have been proposed over the years; however, few appropriately account for population substructure. This report presents new maximum likelihood estimators of pairwise relatedness. In addition, novel methods for relationship classification are derived. Simulation studies compare these estimators to those that do not account for population substructure. The report's final chapter presents real-data examples that demonstrate the advantages of these new methodologies. 26 tables, 44 figures, 51 references, and appended supplementary data and information
Date Published: August 1, 2005
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