Autosomal STR interpretation moved to probabilistic genotyping years ago. It’s now the standard in most U.S. labs. Y-STR interpretation didn’t make the same jump. Most labs still rely on database comparison and manual counting, even in the sexual assault cases where Y-STR is often the only way to pull a male profile out of a heavy female background.
Complex mixtures with multiple male contributors complicate it further. Stochastic effects. Peak height imbalance. Multi-copy loci. No consensus framework for handling any of it from lab to lab. That’s where the inconsistency and the subjectivity creep in.
Jo-Anne Bright, Ph.D., of the New Zealand Institute for Public Health and Forensic Science, will present the work at ISHI 37 in a talk called “It’s Ys: Probabilistic Genotyping of Y-STR Profiles.”
Below, Bright talks about the sensitivity and specificity testing behind the method, the validation questions still to work through, and what a lab might need to have in place before trying this on its own casework.
We asked Jo-Anne a few questions ahead of her session. Autosomal interpretation moved on years ago. Y-STR is still catching up.
What first drew you to this case, question, or problem?
I have been working on probabilistic genotyping methods for many years now. The interpretation of Y-STRs using probabilistic genotyping methods was an obvious extension of the science.
What's the single most important idea you want people to walk away with?
That Y-STR profiles can be more informative when interpreted using more robust methods.
What are common misconceptions this talk addresses?
That implementing new interpretation methods is only a software coding problem and not scientific problem. There have been some really interesting scientific challenges in this development.
What's the most interesting or unexpected thing you found doing this work?
That a large number of models developed for the interpretation of autosomal profiles are just as suitable for Y-STR profiles.
Who would benefit most from hearing this talk?
Forensic biologists who analyse Y-STR profiles
What's one thing from this talk people can actually use once they're back at work, whether that's a technique, a case example, or a new way of thinking about the evidence?
It’s perhaps a little too early for STRmix Y but I think people can start thinking about how probabilistic genotyping of Y-STR profiles might be implemented in their own labs. What their data needs might be, and what types of cases and profiles might be suitable.
What are you most looking forward to at ISHI this year, besides your own session?
Catching up with friends and meeting new ones
What's your best advice for someone attending ISHI for the first time?
Find new people to talk to. Informal conversations can be as informative and interesting as the science being discussed inside the sessions.
What do you enjoy doing when you're away from the office/lab?
A few years ago, I moved to the beach to be closer to family. I like spending time outdoors exploring the local countryside and walks.