Sensitivity of inferences in forensic genetics to assumptions about founding genes

TL;DR

This paper explores how assumptions about founding genes affect forensic genetic inferences, presenting methods to assess sensitivity under various violations of standard assumptions using Bayesian networks.

Contribution

It introduces methodologies like constrained steepest descent and linear fractional programming to evaluate the impact of dependence among founding genes in forensic genetics.

Findings

Dependence among founding genes affects inference accuracy.

Sensitivity assessment methods can identify robustness of forensic conclusions.

Application to real forensic cases demonstrates practical utility.

Abstract

Many forensic genetics problems can be handled using structured systems of discrete variables, for which Bayesian networks offer an appealing practical modeling framework, and allow inferences to be computed by probability propagation methods. However, when standard assumptions are violated--for example, when allele frequencies are unknown, there is identity by descent or the population is heterogeneous--dependence is generated among founding genes, that makes exact calculation of conditional probabilities by propagation methods less straightforward. Here we illustrate different methodologies for assessing sensitivity to assumptions about founders in forensic genetics problems. These include constrained steepest descent, linear fractional programming and representing dependence by structure. We illustrate these methods on several forensic genetics examples involving criminal identification, simple and complex disputed paternity and DNA mixtures.