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Quantitative high resolution melting: two methods to determine SNP allele frequencies from pooled samples

BACKGROUND: The advent of next-generation sequencing has brought about an explosion of single nucleotide polymorphism (SNP) data in non-model organisms; however, profiling these SNPs across multiple natural populations still requires substantial time and resources. RESULTS: Here, we introduce two co...

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Detalles Bibliográficos
Autores principales: Capper, Roxana L., Jin, Young K., Lundgren, Petra B., Peplow, Lesa M., Matz, Mikhail V., van Oppen, Madeleine J. H.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4465018/
https://www.ncbi.nlm.nih.gov/pubmed/26070466
http://dx.doi.org/10.1186/s12863-015-0222-z
Descripción
Sumario:BACKGROUND: The advent of next-generation sequencing has brought about an explosion of single nucleotide polymorphism (SNP) data in non-model organisms; however, profiling these SNPs across multiple natural populations still requires substantial time and resources. RESULTS: Here, we introduce two cost-efficient quantitative High Resolution Melting (qHRM) methods for measuring allele frequencies at known SNP loci in pooled DNA samples: the “peaks” method, which can be applied to large numbers of SNPs, and the “curves” method, which is more labor intensive but also slightly more accurate. Using the reef-building coral Acropora millepora, we show that both qHRM methods can recover the allele proportions from mixtures prepared using two or more individuals of known genotype. We further demonstrate advantages of each method over previously published methods; specifically, the “peaks” method can be rapidly scaled to screen several hundred SNPs at once, whereas the “curves” method is better suited for smaller numbers of SNPs. CONCLUSIONS: Compared to genotyping individual samples, these methods can save considerable effort and genotyping costs when relatively few candidate SNPs must be profiled across a large number of populations. One of the main applications of this method could be validation of SNPs of interest identified in population genomic studies. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12863-015-0222-z) contains supplementary material, which is available to authorized users.