Data from: Multiplex preamplification PCR and microsatellite validation allows accurate single nucleotide polymorphism (SNP) genotyping of historical fish scales
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Incorporating historical tissues into the study of ecological, conservation, and management questions can broaden the scope of population genetic research by enhancing our understanding of evolutionary processes and anthropogenic influences on natural populations. Genotyping historical and low-quality samples has been plagued by challenges associated with low amounts of template DNA and the potential for preexisting DNA contamination among samples. We describe a two-step process designed to (i) accurately genotype large numbers of historical low-quality scale samples in a high-throughput format and (ii) screen samples for preexisting DNA contamination. First, we describe how an efficient multiplex preamplification PCR of 45 single nucleotide polymorphisms (SNPs) can generate highly accurate genotypes with low failure and error rates in subsequent SNP genotyping reactions of individual historical scales from sockeye salmon (Oncorhynchus nerka). Second, we demonstrate how the method can be modified for the amplification of microsatellite loci to detect preexisting DNA contamination. A total of 760 individual historical scale and 182 contemporary fin clip samples were genotyped and screened for contamination. Genotyping failure and error rates were exceedingly low and similar for both historical and contemporary samples. Preexisting contamination in 21% of the historical samples was successfully identified by screening the amplified microsatellite loci. The potential for automation, low failure and error rates, and ability to multiplex both the preamplification and subsequent genotyping reactions combine to make the protocol ideally suited for efficiently genotyping large numbers of potentially contaminated low-quality sources of DNA.
Citations (1)
- https://doi.org/10.1111/j.1755-0998.2010.02965.xDataCite MDC
Cited on 01 March 2011
Weight: 1.23
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Publication Details
Subfield
Plant Science
Field
Agricultural and Biological Sciences
Domain
Life Sciences
Confidence Score
51%
Source
Scholar Data Model