Testing methods for quantifying Monte Carlo variation for categorical variables in Probabilistic Genotyping.pdf (507.28 kB)
Testing methods for quantifying Monte Carlo variation for categorical variables in Probabilistic Genotyping
reportposted on 2020-11-03, 03:00 authored by Jo-Anne Bright, Duncan Taylor, James CurranJames Curran, John S. Buckleton
Two methods for applying a lower bound to the variation induced by the Monte Carlo effect are trialled. One of these is implemented in the widely used probabilistic genotyping system, STRmix™. Neither approach is giving the desired 99% coverage. In some cases the coverage is much lower than the desired 99%. The discrepancy (i.e. the distance between the LR corresponding to the desired coverage and the LR observed coverage at 99%) is not large. For example, the discrepancy of 0.23 for approach 1 suggests the lower bounds should be moved downwards by a factor of 1.7 to achieve the desired 99% coverage.
Although less effective than desired these methods provide a layer of conservatism that is additional to the other layers. These other layers are from factors such as the conservatism within the sub-population model, the choice of conservative measures of co-ancestry, the consideration of relatives within the population and the resampling method used for allele probabilities, all of which tend to understate the strength of the findings.