Available On Demand

Forensic genetic genealogy (FGG), also known as forensic investigative genetic genealogy (FIGG) is a powerful tool for investigative lead generation for the identification of unknown remains, solving cold cases and innocence projects. The traditional FIGG workflow combines a DNA profile generated using microarrays with a database to identify genetic relatives. Though this method has successfully resolved over 200 cases, it fails to address the challenges of forensic samples and operational laboratories, introducing consumer privacy concerns, broken chain of custody and limited implementation utility. An improved FIGG workflow addresses these limitations by combining targeted sequencing designed for poor quality samples with a new algorithm for calculating long-range kinship, on a platform that can be readily integrated into an operational setting.

This webinar will walk through the typical FIGG workflow and compare how the improved process performs against the traditional and alternative approaches, both in assay sensitivity and analysis performance. There will be a mock case study, demonstrating sample confirmation out to a second cousin. This will include a detailed look at the interpretation and statistical results of the targeted assay compared to array-based direct-to-consumer genotyping, including strengths and limitations. Lastly, several practical considerations for implementation into an operational laboratory, as well as best practices and tips for success, will be shared.


In this webinar, you will learn: 

  • How the basic genetic genealogy process works
  • A comparison of microarray, whole-genome sequencing and targeted sequencing technologies for workflow, outcome success and utility in an operational lab
  • A case study on long-range kinship determination using a targeted sequencing approach
  • Best practices and tips for success during implementation


Daniela Cuenca

Senior Criminalist

California Department of Justice, Jan Bashinski DNA Laboratory

Register now to watch on demand