Amplification of DNA mixtures--Missing data approach
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Amplification of DNA mixtures--Missing data approach. / Tvedebrink, Torben; Eriksen, Poul Svante; Mogensen, Helle Smidt; Morling, Niels.
Progress in Forensic Genetics 12: Proceedings of the 22nd International ISFG Congress. ed. / Niels Morling. Vol. 1 Elsevier, 2008. p. 664-666 (Forensic Science International: Genetics Supplement Series; No. 1, Vol. 1).Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
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TY - GEN
T1 - Amplification of DNA mixtures--Missing data approach
AU - Tvedebrink, Torben
AU - Eriksen, Poul Svante
AU - Mogensen, Helle Smidt
AU - Morling, Niels
PY - 2008
Y1 - 2008
N2 - This paper presents a model for the interpretation of results of STR typing of DNA mixtures based on a multivariate normal distribution of peak areas. From previous analyses of controlled experiments with mixed DNA samples, we exploit the linear relationship between peak heights and peak areas, and the linear relations of the means and variances of the measurements. Furthermore, the contribution from one individual's allele to the mean area of this allele is assumed proportional to the average of height measurements on alleles where the individual is the only contributor. For shared alleles in mixed DNA samples, it is only possible to observe the cumulative peak heights and areas. Complying with this latent structure, we use the EM-algorithm to impute the missing variables based on a compound symmetry model. That is the measurements are subject to intra- and inter-loci correlations not depending on the actual alleles of the DNA profiles. Due to factorization of the likelihood, properties of the normal distribution and use of auxiliary variables, an ordinary implementation of the EM-algorithm solves the missing data problem. We estimate the parameters in the model based on a training data set. In order to assess the weight of evidence provided by the model, we use the model with the estimated parameters on STR data from real crime cases with DNA mixtures
AB - This paper presents a model for the interpretation of results of STR typing of DNA mixtures based on a multivariate normal distribution of peak areas. From previous analyses of controlled experiments with mixed DNA samples, we exploit the linear relationship between peak heights and peak areas, and the linear relations of the means and variances of the measurements. Furthermore, the contribution from one individual's allele to the mean area of this allele is assumed proportional to the average of height measurements on alleles where the individual is the only contributor. For shared alleles in mixed DNA samples, it is only possible to observe the cumulative peak heights and areas. Complying with this latent structure, we use the EM-algorithm to impute the missing variables based on a compound symmetry model. That is the measurements are subject to intra- and inter-loci correlations not depending on the actual alleles of the DNA profiles. Due to factorization of the likelihood, properties of the normal distribution and use of auxiliary variables, an ordinary implementation of the EM-algorithm solves the missing data problem. We estimate the parameters in the model based on a training data set. In order to assess the weight of evidence provided by the model, we use the model with the estimated parameters on STR data from real crime cases with DNA mixtures
U2 - 10.1016/j.fsigss.2007.08.002
DO - 10.1016/j.fsigss.2007.08.002
M3 - Article in proceedings
VL - 1
T3 - Forensic Science International: Genetics Supplement Series
SP - 664
EP - 666
BT - Progress in Forensic Genetics 12
A2 - Morling, Niels
PB - Elsevier
Y2 - 21 August 2007 through 25 August 2007
ER -
ID: 14440670