Setting Standards and Developing Technology

to Aid the Human Identity Testing Community

John M. Butler,

P.M. Vallone, M.D. Coble, A.E. Decker, C.R. Hill, J.W. Redman, D.L. Duewer, and M.C. Kline

National Institute of Standards and Technology,

100 Bureau Drive, Mail Stop 8311, Gaithersburg, MD 20899 USA

Our project team at the U.S. National Institute of Standards and Technology (NIST) is funded by National Institute of Justice (NIJ) to conduct research that benefits the human identity testing community and to create tools that enable forensic DNA laboratories to be more effective in analyzing DNA. We conduct interlaboratory studies, produce new assays to enable improved recovery of information from degraded DNA, evaluate new loci for potential future use in human identity applications, and generate standard information and training materials that are made available on the NIST STRBase website: http://www.cstl.nist.gov/biotech/strbase/. New genetic markers and assays involving STR and SNP loci are examined in a U.S. reference population data set involving approximately 650 samples that are of Caucasian, Hispanic, and African American origin. A portion of this presentation will also be devoted to discussing the results from the mixture interpretation interlaboratory study (MIX05) conducted in early 2005 where over 50 different laboratories returned interpretation results on the same DNA samples. Our efforts to improve STR and SNP typing resources and assays for the community will also be described.

John M. Butler, National Institute of Standards and Technology, 100 Bureau Drive MS 8311, Building 227, Room B250, Gaithersburg, MD 20899 USA; Tel: 301-975-4049; Fax: 301-975-8505; email: john.butler@nist.gov

Evolution of microsatellite sequences

Christian Schlötterer

Forensic Interpretation of Haploid DNA Mixtures

Michael Krawczak

Institut für Medizinische Informatik und Statistik

Christian-Albrechts-Universität Kiel

Brunswiker Strasse 10

24105 Kiel

The mathematical concept previously introduced for the forensic interpretation of DNA mixtures using non-associated genetic markers has been adapted to the assessment of haplotypes. Such calculus is required, for example, when mitochondrial or Y-chromosomal markers are used in forensics. In addition to outlining the general mathematical framework, we devise two approaches to its practical computational implementation, involving either the inclusion-exclusion principle of probability theory or a recursion in the number of unknown contributors invoked. The two approaches scale differently, depending upon the complexity of the case and the diversity of the markers used. The performance of Y-chromosomal microsatellites (Y-STRs) as a means of trace donor discrimination has been assessed, using the derived formulas. Dased upon data from the Y-chromosomal Haplotype Reference Database (YHRD), the exclusion chance of a non-contributor is shown to vary between 95% in the case of two contributors to the trace, and 70% for five contributors. It must be emphasised that these estimates are likely to be conservative since the calculations involved only haplotypes known to occur in YHRD. Along the same line, the correct and unbiased interpretation of haploid DNA mixtures may still be hampered by the fact that the respective evidence is impossible to quantify if haplotypes necessary to explain the trace have not been observed before.

Contact:krawczak@medinfo.uni-kiel.de
Forensic molecular pathology and pharmacogenetics

Antti Sajantila

Representing and solving complex DNA identification cases using Bayesian networks

Philip Dawid

Department of Statistical Science, University College London, Gower Street, London WC1E 6BT, United Kingdom.

with Julia Mortera and Paola Vicard, Universita Roma Tre

Problems of forensic identification from DNA profile evidence can become extremely challenging, both logically and computationally, in the presence of such complicating features as missing data on individuals, mixed trace evidence, mutation, silent alleles, laboratory and handling errors, etc. etc. In recent years it has been shown how Bayesian networks can be used to represent and solve such problems.

"Object-oriented" Bayesian network systems, such as Hugin version 6, allow a network to contain repeated instances of other networks.  This architecture proves particularly natural and useful for genetic problems, where there is repetition of such basic structures as Mendelian inheritance or mutation processes.

I will describe a "construction set" of fundamental networks, that can be pieced together, as required, to represent and solve a wide variety of problems arising in forensic genetics.  Some examples of their use will be provided.

Contact: dawid@stats.ucl.ac.uk