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A Practical and Novel Method to Extract Genomic DNA from Blood Collection Kits for Plasma Protein Preservation
Published on: May 18, 2013
Evaluating genomic DNA extraction methods from human whole blood using endpoint and real-time PCR assays
Linda Koshy1, A L Anju2, S Harikrishnan3
1Department of Biotechnology, Inter-University Centre for Genomics and Gene Technology, University of Kerala, Trivandrum, 695 581, India. lindakoshy@gmail.com.
Abstract:
The extraction of genomic DNA is the crucial first step in large-scale epidemiological studies. Though there are many popular DNA isolation methods from human whole blood, only a few reports have compared their efficiencies using both end-point and real-time PCR assays. Genomic DNA was extracted from coronary artery disease patients using solution-based conventional protocols such as the phenol-chloroform/proteinase-K method and a non-phenolic non-enzymatic Rapid-Method, which were evaluated and compared vis-a-vis a commercially available silica column-based Blood DNA isolation kit. The appropriate method for efficiently extracting relatively pure DNA was assessed based on the total DNA yield, concentration, purity ratios (A260/A280 and A260/A230), spectral profile and agarose gel electrophoresis analysis. The quality of the isolated DNA was further analysed for PCR inhibition using a murine specific ATP1A3 qPCR assay and mtDNA/Y-chromosome ratio determination assay. The suitability of the extracted DNA for downstream applications such as end-point SNP genotyping, was tested using PCR-RFLP analysis of the AGTR1-1166A>C variant, a mirSNP having pharmacogenetic relevance in cardiovascular diseases. Compared to the traditional phenol-chloroform/proteinase-K method, our results indicated the Rapid-Method to be a more suitable protocol for genomic DNA extraction from human whole blood in terms of DNA quantity, quality, safety, processing time and cost. The Rapid-Method, which is based on a simple salting-out procedure, is not only safe and cost-effective, but also has the added advantage of being scaled up to process variable sample volumes, thus enabling it to be applied in large-scale epidemiological studies.
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