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Accurate sex identification of ancient human remains using DNA shotgun sequencing
Stockholms universitet, Humanistiska fakulteten, Institutionen för arkeologi och antikens kultur.
Stockholms universitet, Humanistiska fakulteten, Institutionen för arkeologi och antikens kultur.
2013 (engelsk)Inngår i: Journal of Archaeological Science, ISSN 0305-4403, E-ISSN 1095-9238, Vol. 40, nr 12, s. 4477-4482Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Accurate identification of the biological sex of ancient remains is vital for critically testing hypotheses about social structure in prehistoric societies. However, morphological methods are imprecise for juvenile individuals and fragmentary remains, and molecular methods that rely on particular sex-specific marker loci such as the amelogenin gene suffer from allelic dropout and sensitivity to modern contamination. Analyzing shotgun sequencing data from 14 present-day humans of known biological sex and 16 ancient individuals from a time span of 100 to similar to 70,000 years ago, we show that even relatively sparse shotgun sequencing (about 100,000 human sequences) can be used to reliably identify chromosomal sex simply by considering the ratio of sequences aligning to the X and Y chromosomes, and highlight two examples where the genetic assignments indicate morphological misassignment Furthermore, we show that accurate sex identification of highly degraded remains can be performed in the presence of substantial amounts of present-day contamination by utilizing the signature of cytosine deamination, a characteristic feature of ancient DNA.

sted, utgiver, år, opplag, sider
2013. Vol. 40, nr 12, s. 4477-4482
Emneord [en]
Ancient DNA, Sex identification, DNA analysis, Genomics, Shotgun sequencing
HSV kategori
Identifikatorer
URN: urn:nbn:se:su:diva-99899DOI: 10.1016/j.jas.2013.07.004ISI: 000328015000032OAI: oai:DiVA.org:su-99899DiVA, id: diva2:690359
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AuthorCount:4;

Tilgjengelig fra: 2014-01-23 Laget: 2014-01-20 Sist oppdatert: 2017-12-06bibliografisk kontrollert

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