title: Quantifying population genetic differentiation from next-generation sequencing data.
abstract: Over the past few years, new high-throughput DNA sequencing technologies have dramatically increased speed and reduced sequencing costs. However, the use of these sequencing technologies is often challenged by errors and biases associated with the bioinformatical methods used for analyzing the data. In particular, the use of naïve methods to identify polymorphic sites and infer genotypes can inflate downstream analyses. Recently, explicit modeling of genotype probability distributions has been proposed as a method for taking genotype call uncertainty into account. Based on this idea, we propose a novel method for quantifying population genetic differentiation from next-generation sequencing data. In addition, we present a strategy for investigating population structure via principal components analysis. Through extensive simulations, we compare the new method herein proposed to approaches based on genotype calling and demonstrate a marked improvement in estimation accuracy for a wide range of conditions. We apply the method to a large-scale genomic data set of domesticated and wild silkworms sequenced at low coverage. We find that we can infer the fine-scale genetic structure of the sampled individuals, suggesting that employing this new method is useful for investigating the genetic relationships of populations sampled at low coverage.
authors: Department of Integrative Biology, University of California, Berkeley, California 94720.
authors: Matteo
authors: M
authors: Fumagalli
authors: Filipe G
authors: FG
authors: Vieira
authors: Thorfinn Sand
authors: TS
authors: Korneliussen
authors: Tyler
authors: T
authors: Linderoth
authors: Emilia
authors: E
authors: Huerta-Sánchez
authors: Anders
authors: A
authors: Albrechtsen
authors: Rasmus
authors: R
authors: Nielsen
mesh_terms: D000818:Animals; D012831:Bombyx / Q000235:genetics; D019295:Computational Biology; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D040961:Genetic Drift; D014644:Genetic Variation; D005828:Genetics, Population / Q000706:statistics & numerical data*; D005838:Genotype; D059014:High-Throughput Nucleotide Sequencing / Q000706:statistics & numerical data*; D016013:Likelihood Functions; D008957:Models, Genetic; D009154:Mutation; D025341:Principal Component Analysis; D012641:Selection, Genetic
keywords: FST; next-generation sequencing; principal components analysis
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