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Genomic inference accurately predicts the timing and severity of a recent bottleneck in a nonmodel insect population. | LitMetric

Genomic inference accurately predicts the timing and severity of a recent bottleneck in a nonmodel insect population.

Mol Ecol

Department of Biology, Stanford University, 371 Serra Mall, Stanford, CA, 94305, USA; Rocky Mountain Biological Laboratory, Crested Butte, CO, 81224, USA.

Published: January 2014

AI Article Synopsis

Article Abstract

The analysis of molecular data from natural populations has allowed researchers to answer diverse ecological questions that were previously intractable. In particular, ecologists are often interested in the demographic history of populations, information that is rarely available from historical records. Methods have been developed to infer demographic parameters from genomic data, but it is not well understood how inferred parameters compare to true population history or depend on aspects of experimental design. Here, we present and evaluate a method of SNP discovery using RNA sequencing and demographic inference using the program δaδi, which uses a diffusion approximation to the allele frequency spectrum to fit demographic models. We test these methods in a population of the checkerspot butterfly Euphydryas gillettii. This population was intentionally introduced to Gothic, Colorado in 1977 and has as experienced extreme fluctuations including bottlenecks of fewer than 25 adults, as documented by nearly annual field surveys. Using RNA sequencing of eight individuals from Colorado and eight individuals from a native population in Wyoming, we generate the first genomic resources for this system. While demographic inference is commonly used to examine ancient demography, our study demonstrates that our inexpensive, all-in-one approach to marker discovery and genotyping provides sufficient data to accurately infer the timing of a recent bottleneck. This demographic scenario is relevant for many species of conservation concern, few of which have sequenced genomes. Our results are remarkably insensitive to sample size or number of genomic markers, which has important implications for applying this method to other nonmodel systems.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4476905PMC
http://dx.doi.org/10.1111/mec.12591DOI Listing

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