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Probabilistic Genotyping of Evidentiary DNA Typing Results - Module 2: Statistical Genetics and the Mechanisms of Probabalistic Genotyping

Event Dates
Event Duration
4 hours

Probabilistic genotyping is a tool that uses computing power to aid in the identification of possible genotype sets within DNA typing results and to calculate likelihood ratios to estimate evidentiary weight. In this installment of Probabilistic Genotyping of Evidentiary DNA Typing Results, we will detail the background and principles of biostatistical analysis, to include match probabilities, likelihood ratios and other specific topics aimed at furthering understanding of the statistical basis of probabilistic genotyping.

To begin, we will introduce Fst (sometimes called theta), the population genetics parameter that measures remote relatedness between apparently unrelated individuals.  We will then derive single-locus match probability formulas based on Fst in a simple model of population genetics.  Next, we consider appropriate values for Fst, and estimates of allele sampling probabilities based on database counts.  The validity of multiplying match probabilities across loci, sometimes called the “product rule,” will be discussed.

We’ll touch on some more complex issues: relatedness, mixed and low-template profiles, and the connection between match probabilities and likelihood ratios. To calculate a likelihood ratio, the analyst must develop two propositions; in simplest form – for a DNA result originating from one individual – one would consider that “the DNA is from the person of interest” and “the DNA is from an unknown, unrelated individual.” Using a variety of case scenarios ranging from simple to complex, the strategy of devising propositions and dealing with uncertainty in the number of contributors to DNA mixtures will be detailed, along with the resulting impact on the likelihood ratio. Participants will be guided through practical exercises in determining the number of contributors, developing propositions and calculating the likelihood ratio.

Dr. David Balding – University of Melbourne, Melbourne, Australia
Dr. Michael Coble – University of North Texas Health Science Center, Fort Worth, Texas
Dr. John Buckleton – Institute of Environmental Science and Research, Auckland, New Zealand
Steven Myers – California Department of Justice, Richmond, California

Date Created: May 7, 2019