Module dates/times: Monday, July 27; Tuesday, July 28, and Wednesday, July 29. Live sessions will start no earlier than 8 a.m. Pacific and end no later than 2:30 p.m. Pacific, except for Wednesdays. For modules that end on Wednesday, live sessions will end by 11 a.m. Pacific. For modules that start on Wednesday, live sessions will begin no earlier than 11:30 a.m.
This model covers the basic statistical and genetic methods leading to likelihood ratios (LRs) for the presentation of genetic evidence. It provides the background necessary for using analysis results from packages such as CODIS Popstats.
This module also:
- Describes forensic STR markers: mutation process, genotyping technology, and electropherogram artifacts particularly new considerations for back, forward, double back stutter and exotics.
- Reviews principles of population genetics, and measurement of relatedness.
- Covers general principles of evidence evaluation using LRs, computing LRs for identification using presence/absence of autosomal STR genotypes and for mitochondrial and Y-chromosome markers.
- Addresses the complications of mixture interpretation when the queried contributor is a relative of true contributor.
- Describes the consequences of database searches.
- Discusses briefly probabilistic interpretation of STR profiles.
- Provides information about new molecular techniques for human identification.
The module is suitable for graduate students in population genetics, forensic science practitioners, and lawyers facing DNA evidence.
Access 2019 course materials.
Learning Objectives: After attending this module, participants will be able to:
- Calculate single-locus likelihood ratios for a binary model with specified propositions under simplified settings (i.e. assuming no population structure and STR typing anomalies).
- Identify and describe the three main likelihood ratio modeling approaches, including strengths and limitations.
- Describe the hierarchy of propositions, identify the level of propositions in a case setting, and formulate propositions following the principles for setting hypotheses.
- Understand and recognize bias in a forensic setting, including cases where bias leads to potential fallacies such as the prosecution's fallacy and the association fallacy.
- Predict kinship values for pairs of individuals in simple pedigrees.