Amplification of DNA mixtures--Missing data approach

Torben Tvedebrink; Poul Svante Eriksen; Helle Smidt Mogensen; Niels Morling

doi:10.1016/j.fsigss.2007.08.002

Amplification of DNA mixtures--Missing data approach

Torben Tvedebrink, Poul Svante Eriksen, Helle Smidt Mogensen, Niels Morling

Afdeling for Retsgenetik

5 Citationer (Scopus)

Abstract

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

Originalsprog	Engelsk
Titel	Progress in Forensic Genetics 12 : Proceedings of the 22nd International ISFG Congress
Redaktører	Niels Morling
Vol/bind	1
Forlag	Elsevier
Publikationsdato	2008
Sider	664-666
DOI	https://doi.org/10.1016/j.fsigss.2007.08.002
Status	Udgivet - 2008
Begivenhed	22nd International ISFG Congress - Copenhagen, Danmark Varighed: 21 aug. 2007 → 25 aug. 2007

Konference

Konference	22nd International ISFG Congress
Land/Område	Danmark
By	Copenhagen
Periode	21/08/2007 → 25/08/2007

Navn	Forensic Science International: Genetics Supplement Series
Nummer	1
Vol/bind	1
ISSN	1875-1768

Adgang til dokumentet

10.1016/j.fsigss.2007.08.002

Citationsformater

Tvedebrink, T., Eriksen, P. S., Mogensen, H. S., & Morling, N. (2008). Amplification of DNA mixtures--Missing data approach. I N. Morling (red.), Progress in Forensic Genetics 12: Proceedings of the 22nd International ISFG Congress (Bind 1, s. 664-666). Elsevier. Forensic Science International: Genetics Supplement Series Bind 1 Nr. 1 https://doi.org/10.1016/j.fsigss.2007.08.002

Amplification of DNA mixtures--Missing data approach. / Tvedebrink, Torben; Eriksen, Poul Svante; Mogensen, Helle Smidt et al.

Progress in Forensic Genetics 12: Proceedings of the 22nd International ISFG Congress. red. / Niels Morling. Bind 1 Elsevier, 2008. s. 664-666 (Forensic Science International: Genetics Supplement Series; Nr. 1, Bind 1).

Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › peer review

Tvedebrink, T, Eriksen, PS, Mogensen, HS & Morling, N 2008, Amplification of DNA mixtures--Missing data approach. i N Morling (red.), Progress in Forensic Genetics 12: Proceedings of the 22nd International ISFG Congress. bind 1, Elsevier, Forensic Science International: Genetics Supplement Series, nr. 1, bind 1, s. 664-666, 22nd International ISFG Congress, Copenhagen, Danmark, 21/08/2007. https://doi.org/10.1016/j.fsigss.2007.08.002

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abstract = "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",

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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

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