Statistical Methods for the Analysis of Forensic DNA Mixtures

dc.contributor.advisorSujit Ghosh, Committee Memberen_US
dc.contributor.advisorDahlia Nielsen, Committee Memberen_US
dc.contributor.advisorBruce S. Weir, Committee Chairen_US
dc.contributor.advisorGene Eisen, Committee Memberen_US
dc.contributor.authorBeecham, Gary Wayne Jr.en_US
dc.date.accessioned2010-04-02T18:26:47Z
dc.date.available2010-04-02T18:26:47Z
dc.date.issued2006-07-11en_US
dc.degree.disciplineBioinformaticsen_US
dc.degree.leveldissertationen_US
dc.degree.namePhDen_US
dc.description.abstractForensic DNA mixtures are often interpreted statistically using a likelihood ratio. These ratios are of the form, "The evidence is LR times more likely when assuming the prosecution's hypothesis than when assuming the defense hypothesis." The likelihood ratio calculations rest on the allelic frequencies, yet these frequencies are estimated from only a small portion of the population. Therefore, because of sampling error, the likelihood ratio is an estimate, a random variable. In Chapter 2 the use of a confidence interval to report the variation of likelihood ratios is proposed. The formula for the confidence interval is herein explained and a computer program has been made available. In Chapter 3, a maximum likelihood method is given for the inclusion of peak intensities in forensic DNA mixture likelihood ratio calculations. Observed peak intensities are the result of the underlying composition of the mixture: the amount contributed, and the genotypes of the contributors. This chapter proposes the use of the maximum likelihood method to weight each possible genotype combination by the likelihood of the genotype given the peak intensities. Models based on the Normal and Dirichlet distributions are described. Both models tend to weight more correct genotypes higher, though the Normal model puts much more emphasis on the best model(s) than the Dirichlet. This method can also be applied to certain cases of allele drop out. In the final chapter, several different situations are explored. Four standard cases are considered: single-contributor evidence, two-contributor evidence, the paternity index, and the consideration of relationship by pedigree. These four standard cases are used as an introduction to basic concepts, which are in turn used to discuss more complicated cases later in the chapter. The more complicated cases discussed include analysis of a paternity index from a mixture, relatives and mixtures, consideration of relatives in the presence of population substructure, and a case of canine parentage under varying degrees of relatedness.en_US
dc.identifier.otheretd-07052006-154821en_US
dc.identifier.urihttp://www.lib.ncsu.edu/resolver/1840.16/3109
dc.rightsI hereby certify that, if appropriate, I have obtained and attached hereto a written permission statement from the owner(s) of each third party copyrighted matter to be included in my thesis, dissertation, or project report, allowing distribution as specified below. I certify that the version I submitted is the same as that approved by my advisory committee. I hereby grant to NC State University or its agents the non-exclusive license to archive and make accessible, under the conditions specified below, my thesis, dissertation, or project report in whole or in part in all forms of media, now or hereafter known. I retain all other ownership rights to the copyright of the thesis, dissertation or project report. I also retain the right to use in future works (such as articles or books) all or part of this thesis, dissertation, or project report.en_US
dc.subjectlikelihood ratioen_US
dc.subjectstatisticsen_US
dc.subjectforensicsen_US
dc.subjectmixturesen_US
dc.subjectDNAen_US
dc.titleStatistical Methods for the Analysis of Forensic DNA Mixturesen_US

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