Forensic Genetics
This module covers the basic statistical and genetic methods and principles central to forensic analysis. These include the derivation of likelihood ratios (LRs) for the presentation and evaluation of genetic evidence based on autosomal, mitochondrial, and Y-chromosome short tandem repeat markers. The course consists of ten interactive 90-minute lectures.
The module starts with an introduction to human identification and an outline of the framework required for modern DNA evidence interpretation. Following lectures will discuss different DNA modeling techniques; the importance of accurately reporting and presenting evidence; the use of lineage markers; and address the complications of mixture interpretation and relatedness testing. Concepts are demonstrated with in-class exercises.
The module is suitable for graduate students in population genetics, forensic science practitioners, and lawyers facing DNA evidence.
Learning Objectives: After attending this module, participants will be able to:
1. Calculate single-locus likelihood ratios for a binary model with specified propositions under simplified settings.
2. Identify and describe the three main likelihood ratio modeling approaches, including their strengths and limitations, and understand the workings of probabilistic genotyping software.
3. Describe the hierarchy of propositions and identify and formulate them in a case setting.
4. Understand and detect bias in forensic settings, including recognizing the prosecution fallacy.
5. Predict kinship levels for pairs of individuals in pedigrees and interpret the forensic implications.
6. Understand emerging new molecular profiling techniques for human identification.
Course Dates
- Mon June 2, 8:30 p.m. – 5:00 p.m. EST
- Tue June 3, 8:30 a.m. – 5:00 p.m. EST
- Wed June 4, 8:30 a.m. – 12:00 p.m. EST
Instructors
- Sanne Aalbers
- Michael Coble
- Bruce Weir
Suggested Course Pairings
Statistical Methods Stream
- Module INT2: Introduction to Programming in R and Python
- Module ST2: Bayesian Statistics
- Module HE3: Artifical Intelligence/ML for Genetics
Course Materials
Please email sisg@biosci.gatech.edu for free access.
About the Instructors
Sanne Aalbers is a Post-Doctoral Researcher in the Applied Genetics Group at the National Institute of Standards and Technology in Gaithersburg Maryland. She obtained her PhD with Bruce Weir at the University of Washington and is an Adjunct Professor in the Department of Forensic Sciences at the George Washington University. Sanne’s research specializes in the analysis of statistical methods for forensic and genetic applications, focusing mainly on forensic DNA sequence data. Learn more about Sanne’s work here.
Michael Coble is Associate Professor in the Center for Human Identification at the University of Texas Health Sciences Center, Fort Worth. His main research focus is on issues associated with interpretation of DNA mixtures and the use of software for analyses of complex types of genetic markers such as mtDNA, X and Y STRs. Learn more about Mike’s work here.
Bruce Weir is Professor Emeritus of Biostatistics at the University of Washington, Seattle, and holds adjunct appointments at Massey, Otago and Auckland Universities in New Zealand. He was the founding Director of the Summer Institutes in Statistical Genetics for 28 years, and is a Fellow of the Royal Society, in recognition of his contributions to forensic genetic inference and statistical measurement of population structure. Learn more about Bruce’s work here.