The application and performance of single nucleotide polymorphism markers for population genetic analyses of Lepidoptera

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Hellmich, Richard
Emeritus USDA-ARS Research Entomologist Emeritus Affiliate Professor Emeritus USDA-ARS Research Entomologist Emeritus Affiliate Professor Emeritus USDA-ARS Research Entomologist Emeritus Affiliate Professor Emeritus USDA-ARS Research Entomologist Emeritus Affiliate Professor Emeritus USDA-ARS Research Entomologist Emeritus Affiliate Professor
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Sappington, Thomas
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Entomology
Abstract

Microsatellite markers are difficult to apply within lepidopteran studies due to the lack of locus-specific PCR amplification and the high proportion of “null” alleles, such that erroneous estimations of population genetic parameters often result. Herein single nucleotide polymorphism (SNP) markers are developed from Ostrinia nubilalis (Lepidoptera: Crambidae) using next generation expressed sequence tag (EST) data. A total of 2742 SNPs were predicted within a reference assembly of 7414 EST contigs, and a subset of 763 were incorporated into 24 multiplex PCR reactions. To validate this pipeline, 5 European and North American sample sites were genotyped at 178 SNP loci, which indicated 84 (47.2%) were in Hardy–Weinberg equilibrium. Locus-by-locus FST, analysis of molecular variance, and STRUCTURE analyses indicate significant genetic differentiation may exist between European and North American O. nubilalis. The observed genetic diversity was significantly lower among European sites, which may result from genetic drift, natural selection, a genetic bottleneck, or ascertainment bias due to North American origin of EST sequence data. SNPs are an abundant source of mutation data for molecular genetic marker development in non-model species, with shared ancestral SNPs showing application within closely related species. These markers offer advantages over microsatellite markers for genetic and genomic analyses of Lepidoptera, but the source of mutation data may affect the estimation of population parameters and likely need to be considered in the interpretation of empirical data.

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This article is from Frontiers in Genetics 2 (2011): Article 38, doi:10.3389/fgene.2011.00038.

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