Assessing the forensic value of DNA evidence from Y chromosomes and mitogenomes

Y-chromosomal and mitochondrial DNA profiles have been used as evidence in courts for decades, yet the problem of evaluating the weight of evidence has not been adequately resolved. Both are lineage markers (inherited from just one parent), which presents different interpretation challenges compared with standard autosomal DNA profiles (inherited from both parents), for which recombination increases profile diversity and weakens the effects of relatedness. We review approaches to the evaluation of lineage marker profiles for forensic identification, focussing on the key roles of profile mutation rate and relatedness. Higher mutation rates imply fewer individuals matching the profile of an alleged contributor, but they will be more closely related. This makes it challenging to evaluate the possibility that one of these matching individuals could be the true source, because relatedness may make them more plausible alternative contributors than less-related individuals, and they may not be well mixed in the population. These issues reduce the usefulness of profile databases drawn from a broad population: the larger the population, the lower the profile relative frequency because of lower relatedness with the alleged contributor. Many evaluation methods do not adequately take account of relatedness, but its effects have become more pronounced with the latest generation of high-mutation-rate Y profiles

Statistical methods for match probabilities with applications to Y chromosome data

Accurate estimates of Y-STR haplotype frequencies is an interesting problem in itself, but is especially important in forensic genetics, where the frequencies are used to calculate the likelihood ratio (LR) for the evidential weight of a DNA profile found at a crime scene. In this thesis, four methods for Y-STR haplotype frequency estimation are compared with respect to accuracy and bias. This is performed on data from simulated from Wright-Fisher populations with empirical mutation rates and different sampling factors like sample size and the number of markers comprising the haplotypes. Three of the four methods are count based methods (CBMs) and the last method is an allele based method (ABM), defined by if the method represent Y-STR haplotypes as indecomposable objects or not. The first method, named the Count Method (CM) is derived from the empirical frequency of the haplotype. The next two CBMs, the Kappa Model (KM) and the Good-Turing estimator (GTE) are based on the proportion of haplotypes observed a particular number of times in the sample. Last, the Discrete Laplace Method (DLM) identifies subpopulation centers by clustering and models allele frequencies at each loci with discrete Laplace distributions. Hapltotype frequency estimates are then obtained by multiplying estimated allele frequencies across loci. The CBMs underestimated the LR in all scenarios. The DLM have the highest mean accuracy in general, but also have more variance and a tendency to overestimate the LR slightly when haplotypes are composed of more markers. More research into which factors significantly affect haplotype frequency estimates is encouraged.Nøyaktige estimater for Y-STR haplotype frekvenser er et interessant problem i seg selv, men er spesielt viktig innen rettsgenetikk, hvor frekvensene brukes til°a beregne ’likelihood ratio’ (LR) for bevisstyrken til en DNA-profil funnet ved ett °asted. I denne avhandlingen presenterer fire metoder for beregning av Y-STR haplotype frekvenser, disse sammenlignes med hensyn p°a nøyaktighet og bias. Dette gjennomføres p°a data fra simulerte Wright-Fisher populasjoner med empiriske mutasjonsrater og under forskjellige utvalgsfaktorer som utvalgsstørrelse og antall markører haplotypene best°ar av. Tre av de fire metodene er ’tellebaserte’ og den siste er ’allelbasert’, definert som om metodene representerer Y-STR haplotypene som udekomponerbare enheter eller ikke. Den første metoden, kalt ’the Count Method’ (CM) er utledet basert p°a den empiriske frekvensen til haplotypen som undersøkes. De neste to metodene (tellebaserte), ’the Kappa Model’ (KM) og ’the Good-Turing estimator’ (GTE) er basert p°a andelen haplotyper som er observert ett gitt antall ganger i utvalget. Den siste metoden, ’the Discrete Laplace Model’ (DLM) identifiserer subpopulasjonssentere ved samle lignende haplotyper i klynger og deretter modellere allelfrekvenser ved hver loci med discrete Laplace fordelinger. Haplotypefrekvens estimatene beregnes deretter ved°a multiplisere sammen estimerte allelfrekvenser over alle loci. De tellebaserte metodene underestimerte LR i alle scenarioene. DLM har høyest gjennomsnittlig nøyaktighet genrerelt, men hadde ogs°a mer varians og en tendens til °a overestimere LR i en liten grad, n°ar haplotypene best°ar av flere markører. Mer forskning p°a hvilke faktorer som signifikant p°avirker haplotypefrekvens estimatene oppfordres.M-BIA

Current challenges in statistical DNA evidence evaluation

This thesis covers different problems concerning the evaluation of DNA evidence. It is mainly divided into two parts: the first regards the DIP-STR genotyping techniques. It addresses the imperative need of developing a model to assign the likelihood ratio for DIP-STR results, and compares, from a statistical and forensic perspective, the advantages of this novel marker system compared to traditional marker systems, such as STR and Y-STR. The second part deals with several more general statistical aspects involved in the evaluation of DNA evidence. It aims at defining the differences between full Bayesian methods and ad hoc plug-in approximations, and at solving the rare type match problem for Y-STR data. The issues of the different reductions of data and of the levels of uncertainty involved in frequentist solutions are also discussed. These two parts are connected in the final project, by developing a Bayesian solution for the rare type match problem for DIP-STR marker system. Moreover, the initial model for DIP-STR data is improved in the light of the statistical discussion of the second part: any ad hoc solution is avoided to obtain a full Bayesian approach.Analysis and Stochastic

Y-Chromosome DNA Extraction from Post-Cranial Skeletal Elements

The use of DNA in forensic science has become an integral tool for victim and perpetrator identifications, missing person’s cases, paternity testing, etc. A major use of DNA is in the identification of unknown deceased individuals. With a reported number of individuals well over 8,000 in the United States, improved methods to accurately collect and analyze DNA from modern human bone are needed. This project took the preliminary steps to improve DNA sampling and extraction methods by analyzing the Y-chromosome DNA yield from the two bone types. While both types are composed of the same materials, cortical bone is the tightly packed bone on the outer layer, and trabecular is the sponge-like bone located inside. The yields observed in cortical and trabecular bone samples in a set of human remains could help determine what type of samples need to be taken for successful DNA analysis. Samples were collected from various locations throughout the skeleton from each of the two bone types and subjected to quantitative polymerase chain reaction (qPCR) analysis. qPCR determines the amount of DNA in each sample. The averages from each bone type were compared to determine if one type preserves DNA better. The preliminary data collected from this project has provided a stepping stone in the right direction to improve how DNA sampling from modern human remains is done. Further research on this topic must be done to increase the validity of the results and affect the current methods used