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DNA Quantification of Whole Genome Amplified Samples for Genotyping on a Multiplexed Bead Array Platform

Departments of Neurological Surgery and Epidemiology and Biostatistics, University of California, San Francisco, California

Requests for reprints: John K. Wiencke, Division of Neuroepidemiology, Department of Neurological Surgery, University of California, Suite AC-34, 1 Irving Street, San Francisco, CA 94143-0441. Phone: 415-476-9323; Fax: 415-502-7411. E-mail: john.wiencke{at}ucsf.edu

Whole genome amplification (WGA) permits genotyping DNA samples of limited quantity, expanding the number of samples available for genetic epidemiology studies. WGA, however, produces various nucleic acid side products that may interfere with accurate DNA quantification and further analysis. Although quality control of whole genome amplified DNA (wgaDNA) before genotyping is essential to prevent failed or poor genotyping results, little information is available to choose the best method for wgaDNA quantification. Therefore, we quantified wgaDNA from 54 buccal or poor quality blood samples by four methods: UV absorbance, PicoGreen fluorescence calibrated with bacteriophage or calf thymus DNA, and absolute quantification by real-time PCR amplification of human-specific Alu Yd6. We then genotyped these wgaDNA samples and paired high-quality genomic DNA samples on a custom 384-plex Illumina Golden Gate Panel. Of the 54 paired samples, 39 gave high concordance (>99%), whereas 7 had moderate concordance (>90-99%) and 8 had poor concordance (90%) of wgaDNA and genomic DNA genotyping results. Quantitative PCR of Alu was the only wgaDNA quantification method to distinguish wgaDNA samples that gave high, moderate, or low concordance results (i.e., wgaDNA quantities in the high, moderate, and poor concordance groups ranged at 4.14-118.32, 0.29-2.19, and 0.01-0.27 ng/µL, respectively). Human-specific quantitative PCR is a highly useful guide for determining the suitability of wgaDNA before high-throughput single-nucleotide polymorphism analysis. (Cancer Epidemiol Biomarkers Prev 2007;16(8):1686–90)

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