Familial searching on DNA mixtures with dropout

CSYseq: The first Y-chromosome sequencing tool typing a large number of Y-SNPs and Y-STRs to unravel worldwide human population genetics

Male-specific Y-chromosome (chrY) polymorphisms are interesting components of the DNA for population genetics. While single nucleotide polymorphisms (Y-SNPs) indicate distant evolutionary ancestry, short tandem repeats (Y-STRs) are able to identify close familial kinships. Detailed chrY analysis provides thus both biogeographical background information as paternal lineage identification. The rapid advancement of high-throughput massive parallel sequencing (MPS) technology in the past decade has revolutionized genetic research. Using MPS, single-base information of both Y-SNPs as Y-STRs can be analyzed in a single assay typing multiple samples at once. In this study, we present the first extensive chrY-specific targeted resequencing panel, the 'CSYseq', which simultaneously identifies slow mutating Y-SNPs as evolution markers and rapid mutating Y-STRs as patrilineage markers. The panel was validated by paired-end sequencing of 130 males, distributed over 65 deep-rooted pedigrees covering 1,279 generations. The CSYseq successfully targets 15,611 Y-SNPs including 9,014 phylogenetic informative Y-SNPs to identify 1,443 human evolutionary Y-subhaplogroup lineages worldwide. In addition, the CSYseq properly targets 202 Y-STRs, including 81 slow, 68 moderate, 27 fast and 26 rapid mutating Y-STRs to individualize close paternal relatives. The targeted chrY markers cover a high average number of reads (Y-SNP = 717, Y-STR = 150), easy interpretation, powerful discrimination capacity and chrY specificity. The CSYseq is interesting for research on different time scales: to identify evolutionary ancestry, to find distant family and to discriminate closely related males. Therefore, this panel serves as a unique tool valuable for a wide range of genetic-genealogical applications in interdisciplinary research within evolutionary, population, molecular, medical and forensic genetics.

Evaluating DNA evidence possibly involving multiple (mixed) samples, common donors and related contributors

Forensic DNA profiling is used in various circumstances to evaluate support for two competing propositions with the assignment of a likelihood ratio. Many software implementations exist that tackle a range of inference problems spanning identification and relationship testing. We propose a flexible likelihood ratio framework that caters to inference problems in forensic genetics. The framework allows for investigation of the degree of support for the contribution of multiple persons to multiple samples allowing for persons to be related according to a pedigree, including inbred relationships. We explain how a number of routine as well as more complex problems can be treated within this framework.

Investigative genetic genealogy: Current methods, knowledge and practice

Investigative genetic genealogy (IGG) has emerged as a new, rapidly growing field of forensic science. We describe the process whereby dense SNP data, commonly comprising more than half a million markers, are employed to infer distant relationships. By distant we refer to degrees of relatedness exceeding that of first cousins. We review how methods of relationship matching and SNP analysis on an enlarged scale are used in a forensic setting to identify a suspect in a criminal investigation or a missing person. There is currently a strong need in forensic genetics not only to understand the underlying models to infer relatedness but also to fully explore the DNA technologies and data used in IGG. This review brings together many of the topics and examines their effectiveness and operational limits, while suggesting future directions for their forensic validation. We further investigated the methods used by the major direct-to-consumer (DTC) genetic ancestry testing companies as well as submitting a questionnaire where providers of forensic genetic genealogy summarized their operation/services. Although most of the DTC market, and genetic genealogy in general, has undisclosed, proprietary algorithms we review the current knowledge where information has been discussed and published more openly.

Accurate assessment of the weight of evidence for DNA mixtures by integrating the likelihood ratio

Several methods exist for weight of evidence calculations on DNA mixtures. Especially if dropout is a possibility, it may be difficult to estimate mixture specific parameters needed for the evaluation. For semi-continuous models, the LR for a person to have contributed to a mixture depends on the specified number of contributors and the probability of dropout for each. We show here that, for the semi-continuous model that we consider, the weight of evidence can be accurately obtained by applying the standard statistical technique of integrating the likelihood ratio against the parameter likelihoods obtained from the mixture data. This method takes into account all likelihood ratios belonging to every choice of parameters, but LR's belonging to parameters that provide a better explanation to the mixture data put in more weight into the final result. We therefore avoid having to estimate the number of contributors or their probabilities of dropout, and let the whole evaluation depend on the mixture data and the allele frequencies, which is a practical advantage as well as a gain in objectivity. Using simulated mixtures, we compare the LR obtained in this way with the best informed LR, i.e., the LR using the parameters that were used to generate the data, and show that results obtained by integration of the LR approximate closely these ideal values. We investigate both contributors and non-contributors for mixtures with various numbers of contributors. For contributors we always obtain a result close to the best informed LR whereas non-contributors are excluded more strongly if a smaller dropout probability is imposed for them. The results therefore naturally lead us to reconsider what we mean by a contributor, or by the number of contributors.