New Identification Perspectives with Forensic Genetic Genealogy

For more than two decades forensic science took a targeted approach of typing relatively small panels of short tandem repeat (STR) markers coupled with capillary electrophoresis for human identification purposes. This approach generally has been highly effective and has been adopted worldwide. However, it has limitations such as sensitivity of detection, particularly with highly degraded DNA samples, resolution power only for direct comparisons and kinship analyses typically with first degree relationships. Additionally, many investigative leads cannot be developed if the source of forensic biological evidence or a first degree relative of unidentified human remains is not in current government-maintained DNA databases. The advent of massively parallel sequencing (MPS) and dense single nucleotide polymorphisms (SNPs) analyses greatly extends human identification capabilities.

Indeed, MPS coupled with forensic genetic genealogy (FGG) overcomes many of the limitations of STR typing, such as generation of usable DNA profiles from highly degraded samples and kinship associations as distant as 7th to 9th degree relatives. To establish potential kinship relationships, dense SNP data are searched against a database(s) of reference samples from consented volunteers. Associations are made primarily on identity-by-descent (IBD) segment analysis in which homologous chromosomal regions are measured in centimorgans (cMs), with the amount and total size of shared segments serving as indicators of genetic relationships. Larger shared segments typically signify closer kinship, while smaller shared segments indicate more distant relationships. Thus, FGG by searching for near and distant relatives and the increased sensitivity of detection offered by MPS greatly expands the range of cases in which DNA evidence can generate investigative leads.

With these capabilities there is a need to go beyond predominantly human-centered workflows and limited hypothesis testing and instead embrace automation and capabilities to reason consistently, transparently, and at scale over increasingly complex genetic, genealogical, and contextual information. Artificial intelligence (AI) will be an enabling layer which is particularly suited for FGG as a computational decision-support system(s) that structures, prioritizes, and documents reasoning over genetic associations, genealogical structures, and investigative context during identity hypothesis development. Properly designed AI-enabled systems offer a path to sustainably scaling FGG while supporting scientific rigor. Lastly, the incorporation of FGG and AI into operational laboratories and investigative agencies requires governance mechanisms that ensure transparency, accountability, privacy protection, and human oversight.

Featured Speaker: Professor Bruce Budowle

Dr. Budowle worked at the FBI’s Laboratory Division for 26 years and at Center for Human Identification at the University of North Texas Health Science Center for 13 years. He has published more than 750 articles and testified in well over 300 criminal cases in the areas of molecular biology, population genetics, statistics, quality assurance, and forensic biology. He continues research and work in the areas of forensic genomics (particularly in forensic genetic genealogy) and contributes to supporting humanitarian efforts via human identification. He currently is a visiting professor at the University of Helsinki and a consultant with Othram, Inc.