Abstract: This patent application relates generally to mixture deconvolution systems and methods for identifying DNA profiles. Various embodiments of the present invention concern the deconvolution of unknown DNA profiles in a two-person DNA mixture into two DNA profiles. Deconvolution methods isolate distinct DNA profiles from a DNA mixture without the need to match against DNA reference profiles. Various embodiments include a mixture deconvolution pipeline that involves a series of mathematical steps and machine learning algorithms to achieve the desired performance and decision-support outputs. Various embodiments enable distant familial matching to existing investigative genetic genealogy (IGG; also known as forensic genetic genealogy (FGG)) databases. This capability enables the generation of investigative leads from unresolved casework samples (i.e., DNA mixtures) by identifying possible genealogical relationships to one or more person(s) of interest.

Abstract: The disclosure provides various systems and methods for identifying individuals from one or more samples. In particular, improved systems and methods of analysis are provided for handling multiple contributors, as well as systems and methods that model not only individual error rates per locus but factor in amplification of errors induced through PCR cycles. In some embodiments, modeling of error rates can be applied in multi-contributor settings to more accurately establish real alleles from artifacts. Other aspects involve application of sequencing in error modeling. Further, methods are provided for determining the presence of common individual DNA profiles in one or more complex DNA mixtures and for deconvolution of multiple complex DNA mixtures into shared individual components. The methods of the disclosure do not require any prior knowledge of individual DNA profiles or contributors to the complex DNA mixtures.

Abstract: The disclosure provides various systems and methods for identifying individuals from one or more samples. In particular, improved systems and methods of analysis are provided for handling multiple contributors, as well as systems and methods that model not only individual error rates per locus but factor in amplification of errors induced through PCR cycles. In some embodiments, modeling of error rates can be applied in multi-contributor settings to more accurately establish real alleles from artifacts. Other aspects involve application of sequencing in error modeling. Further, methods are provided for determining the presence of common individual DNA profiles in one or more complex DNA mixtures and for deconvolution of multiple complex DNA mixtures into shared individual components.

Abstract: The disclosure provides various systems and methods for identifying individuals from one or more samples. In particular, improved systems and methods of analysis are provided for handling multiple contributors, as well as systems and methods that model not only individual error rates per locus but factor in amplification of errors induced through PCR cycles. In some embodiments, modeling of error rates can be applied in multi-contributor settings to more accurately establish real alleles from artifacts. Other aspects involve application of sequencing in error modeling. Further, methods are provided for determining the presence of common individual DNA profiles in one or more complex DNA mixtures and for deconvolution of multiple complex DNA mixtures into shared individual components.