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Mol Biol Evol. 2006 Sep;23(9):1801-7. Epub 2006 Jun 29.

Tracking down human contamination in ancient human teeth.

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  • 1Unitat de Biologia Evolutiva, Departament de Ciències Experimentals i de la Salut, Universitat Pompeu Fabra, Barcelona, Spain.

Abstract

DNA contamination arising from the manipulation of ancient calcified tissue samples is a poorly understood, yet fundamental, problem that affects the reliability of ancient DNA (aDNA) studies. We have typed the mitochondrial DNA hypervariable region I of the only 6 people involved in the excavation, washing, and subsequent anthropological and genetic study of 23 Neolithic remains excavated from Granollers (Barcelona, Spain) and searched for their presence among the 572 clones generated during the aDNA analyses of teeth from these samples. Of the cloned sequences, 17.13% could be unambiguously identified as contaminants, with those derived from the people involved in the retrieval and washing of the remains present in higher frequencies than those of the anthropologist and genetic researchers. This finding confirms, for the first time, previous hypotheses that teeth samples are most susceptible to contamination at their initial excavation. More worrying, the cloned contaminant sequences exhibit substitutions that can be attributed to DNA damage after the contamination event, and we demonstrate that the level of such damage increases with time: contaminants that are >10 years old have approximately 5 times more damage than those that are recent. Furthermore, we demonstrate that in this data set, the damage rate of the old contaminant sequences is indistinguishable from that of the endogenous DNA sequences. As such, the commonly used argument that miscoding lesions observed among cloned aDNA sequences can be used to support data authenticity is misleading in scenarios where the presence of old contaminant sequences is possible. We argue therefore that the typing of those involved in the manipulation of the ancient human specimens is critical in order to ensure that generated results are accurate.

PMID:
16809622
[PubMed - indexed for MEDLINE]
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