SBIR-STTR Award

Lynntech Inc. proposes a Phase I SBIR effort to develop Quick-Single Nucleotide Polymorphism (Quick-SNP), a forensic software tool for pedigree reconstruction. Quick-SNP will analyze naïve DNA profiles, and in the process, establish sample identities, predict familial relationships (kinship and ancestral), and ultimately provide phenotypic profiles. Lynntech will develop Quick-SNP with a computational architecture that is efficient and capable of scaling to DNA profiles created by ever-increasing deep genomic sequencing techniques. Deep-sequencing makes it possible to combine useful SNP variants, routinely used for the rapid identification of close relatedness within a database, with additional statistical information. SNPs can be used to reconstruct Identity-by-Descent (IBD) relationships in pedigree reconstructions, thus providing estimates of kinship. In discussion with the DTRA, databases will be selected that model a wide variety of regional specific SNP profiles, along with databases that can be used to model deeper IBD characteristics. In addition, detailed requirements will be established, including the criteria for ancestry-estimation, proper security procedures to be established, user interface needs, and performance, facilities and hardware requirements. At the completion of Phase I, Lynntech will provide a final report and demonstrate the preliminary architecture to the cognizant project officer. The final report will include an analysis of the architecture performance using unambiguous statistical methods, a description of the system parameterization, and a thoughtful discussion of the limitations and weaknesses of the approach. The report will also include Lynntech’s plans for the development, during Phase II, of Quick-SNP’s user interface.

A current limitation of the use of forensic genetics, in particular kinship and phenotypic prediction, is the black box nature of many of the proprietary approaches currently in the market. Lynntech Inc. proposes a Phase II effort to develop Quick Single Nucleotide Polymorphism (Quick-SNP) as a DNA forensic genomics tool following the guidelines established by the President’s Council of Advisors on Science and Technology for the Forensic Sciences (Lander, 2016). To establish the foundational validity that the techniques are scientifically sound, replicable, and accurate in a lab environment, and to ultimately demonstrate applied validity in real world applications. Quick-SNP analyzes naïve DeoxyriboNucleic Acid (DNA) profiles, and in the process, establishes sample identities, predicts familial relationships (kinship and biogeographic ancestral), and ultimately will provide phenotypic profiles. Lynntech is developing Quick-SNP with a computational architecture that is efficient and capable of scaling to DNA profiles created by ever-increasing deep genomic sequencing techniques. Quick-SNP uses a large panel of SNPs (>100k SNPs) and an Artificial Intelligence approach to reconstruct Identity-by-Descent (IBD) relationships in pedigree reconstructions, thus improving the statistical power and depth of estimates of kinship. For the Phase II efforts, Lynntech will focus on iterative improvement to the approach developed during Phase I. Lynntech will mature the computational architecture by improving the performance as compared to the preliminary architecture evaluated as part of the Phase I effort. Lynntech will develop a graphical user interface (GUI) that will promote a proper analytic workflow, and provide for an ease-of-use that provides a statistically relevant interpretation of the results. Lynntech will identify weaknesses in performance that could be improved through additional data, modified statistical approaches, and / or additional pre-processing steps and will codify / relay observations to the project officer. Lynntech’s phase II deliverables will be a demonstration of the Quick-SNP platform with the introduction of novel genetic profiles whose pedigrees have been established by other means and a report detailing (1) description of the approach, including optimization techniques and performance outcomes, (2) testing and validation methods, and (3) advantages and disadvantages / limitations of the method; and a user interface with any associated executables.