DBLR™

DBLR™ is a highly valuable investigative tool which allows STRmix™ users to:

• Optionally include Amelogenin in the calculations.

• Apply population stratification in the Kinship, Search Database and Explore Deconvolution modules.

  Leverage probabilistic links within the Kinship module to probabilistically condition on the presence of a sample donor.

• Utilise sequence based data from STRmix™ NGS or a UAS Sample Details Report within the Kinship, Search Database and Explore Deconvolution modules.

• Undertake direct comparison of one or many components of a forensic DNA mixture to a database of known individuals (i.e. “Who contributed to the profile?”).

• Carry out familial searching for a range of different relationships including siblings, half-siblings, parents, and children (i.e. “Is there a relative of the donor in the database?”).

• Search for common contributors between mixed DNA profiles (mixture to mixture comparisons). These LRs can be visualised using a heat map, and include cluster graphs.

• Determine the genotypes of the most likely contributors to a profile.

• Visualise the value of evidence by calculating expected LRs for one or many components of forensic DNA profiles for true and non-contributors using randomly generated individuals.

• Manage automated searches for one or many DNA profiles against one or many databases of known individuals. Includes direct matching between databases.

• Manage databases of known contributors and STRmix™ deconvolutions from unsolved casework for easy matching.

• Combine multiple evidence profiles under the assumption that there is a common contributor within the different samples (Common Donor).

• Build any pedigree imaginable and calculate likelihood ratios given the different propositions (Kinship). Kinship pedigrees can now take sex into account.

• Model linkage, mutation and FST in the Kinship module.

• Carry out pre-checks within the Kinship module to check for inconsistencies between the pedigree and the samples. (New in DBLR™ v1.5)

• Protect your settings with a user defined password.

DBLR™ uses efficient algorithms for the fast calculation of LRs. With DBLR™ the user can import STRmix™ deconvolutions or single source-profiles and visualise the value of the evidence or carry out fast database searches. The DBLR™ Kinship function is both powerful and flexible. The user can load STRmix deconvolutions or single-source profiles from known individuals and link these with one or more pedigrees. The Common Donor function can better resolve the genotypes of queried contributors and search these against a database to identify possible donors.

Validation

DBLR™ has been extensively validated by the STRmix team based at PHF Science (formerly ESR), New Zealand.

Visualise the value of your evidence

Using DBLR™ we can explore the interpretation results from a DNA profile given different hypotheses. We calculate thousands of likelihood ratios and by plotting these we can see the expected range for the different hypotheses. This can quickly help inform whether the profile is suitable for comparison with a person of interest or if it is suitable for entry onto a database for matching.

Papers describing the mathematics, validation and application of DBLR™:

[1] K. Slooten, Identifying common donors in DNA mixtures, with applications to database searches. Forensic Science International: Genetics. 2017; 26:40-7.

[2] M. Kruijver, J.-A. Bright, H. Kelly, J. Buckleton, Exploring the probative value of mixed DNA profiles. Forensic Science International: Genetics. 2019; 41: 1-10.

[3] J.-A. Bright, D. Taylor, Z. Kerr, J. Buckleton, M. Kruijver, The efficacy of DNA mixture to mixture matching. Forensic Science International: Genetics. 2019; 41: 64-71.

[4] D. Taylor, E. Rowe, M. Kruijver, D. Abarno, J.-A. Bright, J. Buckleton, Inter-sample contamination detection using mixture deconvolution comparison. Forensic Science International: Genetics. 2019; 160-167.

[5] J.-A. Bright, M. Jones Dukes, S.N. Pugh, I.W. Evett, J.S. Buckleton, Applying calibration to LRs produced by a DNA interpretation software. Australian Journal of Forensic Sciences. 2019; 1-7 https://doi.org/10.1080/00450618.2019.1682668(external link)

[6] Taylor D, Kruijver M. Combining evidence across multiple mixed DNA profiles for improved resolution of a donor when a common contributor can be assumed. Forensic Science International: Genetics 2020; 49.

[7] H. Kelly, Z. Kerr, K. Cheng, M. Kruijver, J.-A. Bright, Developmental validation of a software implementation of a flexible framework for the assignment of likelihood ratios for forensic investigations. Forensic Science International: Reports. 2021; Volume 4, 100231 https://www.sciencedirect.com/science/article/pii/S2665910721000621?via%3Dihub(external link)

[8] M. Kruijver, D. Taylor, J.-A. Bright, Evaluating DNA evidence possibly involving multiple (mixed) samples, common donors and related contributors, Forensic Science International: Genetics. 2021; 54 https://doi.org/10.1016/j.fsigen.2021.102532(external link)

[9] Kruijver M, Kelly H, Bright J-A, Buckleton J. Evaluating DNA Mixtures with Contributors from Different Populations Using Probabilistic Genotyping. Genes. 2023; 14(1):40. https://www.mdpi.com/2073-4425/14/1/40(external link)

[10] Bright, J. Buckleton, D. Taylor. Probabilistic interpretation of the Amelogenin locus. Forensic Science International: Genetics. 2021;52:102462

[11] M. Kruijver, H. Kelly, D. Taylor, J Buckleton. Addressing uncertain assumptions in DNA evidence evaluation. Forensic Science International: Genetics. 2023; 66:102913