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International Journal of Legal Medicine

, Volume 128, Issue 2, pp 259–267 | Cite as

Modeling one complete versus triplicate analyses in low template DNA typing

  • Jianye Ge
  • Bruce Budowle
Original Article

Abstract

There are generally two strategies for low template DNA typing: the complete strategy, which uses all available DNA in a single PCR and subsequent typing, and the consensus strategy, in which the biological sample is divided into two or more aliquots and the genotype profile is determined by consensus from these “replicates.” In this study, the consensus and complete strategies are compared by a statistical approach in terms of the accuracy of obtaining the correct genotype at a single locus for single source samples. Logistic models were employed to describe the allele drop-out and drop-in events. The parameters of the models were estimated with empirical or hypothetical data. The probabilities of obtaining the true genotype and the chances to observe drop-out and drop-in alleles were estimated and compared for both strategies. Consistent with a previous experimental study, this study found that, with relatively high input DNA (e.g., ≥100 pg), the complete strategy performs better than the consensus strategy to obtain the true genotype and the complete strategy will display less dropped out alleles. The consensus strategy had less drop-in alleles for ≤100 pg DNA samples. Moreover, the limitations of the logistic models were discussed. Ideal models with better fit of empirical data approximating casework conditions were proposed for future studies.

Keywords

Forensic genetics Low template DNA Statistical modeling Consensus Allele drop-out Allele drop-in Logistic model 

Supplementary material

414_2013_924_MOESM1_ESM.docx (33 kb)
ESM 1 (DOCX 33 kb)

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Institute of Applied Genetics, Department of Forensics and Investigative GeneticsUniversity of North Texas Health Science CenterFort WorthUSA
  2. 2.Center of Excellence in Genomic Medicine Research (CEGMR)King Abdulaziz UniversityJeddahSaudi Arabia

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