modelDropout_gui: Model And Plot Drop-out Events

Description

Model the probability of drop-out and plot graphs.

Usage

modelDropout_gui(env = parent.frame(), savegui = NULL, debug = FALSE, parent = NULL)

Arguments

env

environment in wich to search for data frames and save result.

savegui

logical indicating if GUI settings should be saved in the environment.

debug

logical indicating printing debug information.

parent

widget to get focus when finished.

Value

TRUE

Details

calculateDropout score drop-out events relative to a user defined LDT in four different ways: (1) by reference to the low molecular weight allele (Method1), (2) by reference to the high molecular weight allele (Method2), (3) by reference to a random allele (MethodX), and (4) by reference to the locus (MethodL). Options 1-3 are recommended by the DNA commision (see reference), while option 4 is included for experimental purposes. Options 1-3 may discard many dropout events while option 4 catches all drop-out events. On the other hand options 1-3 can score events below the LDT, while option 4 cannot, making accurate predictions possible below the LDT.

Method X/1/2 records the peak height of the partner allele to be used as the explanatory variable in the logistic regression. The locus method L also do this when there has been a drop-out, if not the the mean peak height for the locus is used. Peak heights for the locus method are stored in a separate column.

Using the scored drop-out events and the peak heights of the surviving alleles the probability of drop-out can be modelled by logistic regression as described in Appendix B the first reference. P(dropout|H) = B0 + B1*H, where 'H' is the peak height or log(peak height). This produces a plot with the predicted probabilities for a range of peak heights. There are options to print the model parameters, mark the stochastic threshold at a specified probability of drop-out, include the underlying observations, and to calculate a specified prediction interval. A conservative estimate of the stochastic threshold can be calculated from the prediction interval: the risk of observing a drop-out probability greater than the specified threshold limit, at the conservative peak height, is less than a specified value (e.g. 1-0.95=0.05). By default the gender marker is excluded from the dataset used for modelling, and the peak height is used as explanatory variable. The average peak height 'H' can be used instead of the allele/locus peak height. Optionally, the logarithm of the peak height can be used. To evaluate the goodness of fit for the logistic regression the Hosmer-Lemeshow test is used. A value below 0.05 indicates a poor fit. Alternatives to the logistic regression method are discussed in reference 4 and 5. [13,32].

Explanation of the result: Dropout - all alleles are scored according to the limit of detection threshold (LDT). This is the observations and is not used for modelling. Rfu - peak height of the surviving allele. MethodX - a random reference allele is selected and drop-out is scored in relation to the the partner allele. Method1 - the low molecular weight allele is selected and drop-out is scored if the high molecular weight allele is missing. Method2 - the high molecular weight allele is selected and drop-out is scored if the low molecular weight allele is missing. MethodL - drop-out is scored per locus i.e. drop-out if any allele is missing. MethodL.Ph - peak height of the surviving allele if one allele has droped out, or the average peak height if no drop-out.

References

Peter Gill et.al., DNA commission of the International Society of Forensic Genetics: Recommendations on the evaluation of STR typing results that may include drop-out and/or drop-in using probabilistic methods, Forensic Science International: Genetics, Volume 6, Issue 6, December 2012, Pages 679-688, ISSN 1872-4973, 10.1016/j.fsigen.2012.06.002. http://www.sciencedirect.com/science/article/pii/S1872497312001354

Peter Gill, Roberto Puch-Solis, James Curran, The low-template-DNA (stochastic) threshold-Its determination relative to risk analysis for national DNA databases, Forensic Science International: Genetics, Volume 3, Issue 2, March 2009, Pages 104-111, ISSN 1872-4973, 10.1016/j.fsigen.2008.11.009. http://www.sciencedirect.com/science/article/pii/S1872497308001798

H. DW Jr., S. Lemeshow, Applied Logistic Regression, John Wiley & Sons, 2004.

A.A. Westen, L.J.W. Grol, J. Harteveld, A.S. Matai, P. de Knijff, T. Sijen, Assessment of the stochastic threshold, back- and forward stutter filters and low template techniques for NGM, Forensic Science International: Genetetics, Volume 6, Issue 6 December 2012, Pages 708-715, ISSN 1872-4973, 10.1016/j.fsigen.2012.05.001. http://www.sciencedirect.com/science/article/pii/S1872497312001172