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Comparison of Suitability of the Most Common Ancient DNA Quantification Methods.

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AIMS Ancient DNA (aDNA) extracted from historical bones is damaged and fragmented into short segments, present in low quantity, and usually copurified with microbial DNA. A wide range of DNA… Click to show full abstract

AIMS Ancient DNA (aDNA) extracted from historical bones is damaged and fragmented into short segments, present in low quantity, and usually copurified with microbial DNA. A wide range of DNA quantification methods are available. The aim of this study was to compare the five most common DNA quantification methods for aDNA. MATERIALS AND METHODS Quantification methods were tested on DNA extracted from skeletal material originating from an early medieval burial site. The tested methods included ultraviolet (UV) absorbance, real-time quantitative polymerase chain reaction (qPCR) based on SYBR® green detection, real-time qPCR based on a forensic kit, quantification via fluorescent dyes bonded to DNA, and fragmentary analysis. Differences between groups were tested using a paired t-test. RESULTS Methods that measure total DNA present in the sample (NanoDrop™ UV spectrophotometer and Qubit® fluorometer) showed the highest concentrations. Methods based on real-time qPCR underestimated the quantity of aDNA. The most accurate method of aDNA quantification was fragmentary analysis, which also allows DNA quantification of the desired length and is not affected by PCR inhibitors. CONCLUSIONS Methods based on the quantification of the total amount of DNA in samples are unsuitable for ancient samples as they overestimate the amount of DNA presumably due to the presence of microbial DNA. Real-time qPCR methods give undervalued results due to DNA damage and the presence of PCR inhibitors. DNA quantification methods based on fragment analysis show not only the quantity of DNA but also fragment length.

Keywords: real time; dna; dna quantification; quantification methods; ancient dna; quantification

Journal Title: Genetic testing and molecular biomarkers
Year Published: 2017

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