Quantifying Population Genetic Differentiation from Next-Generation Sequencing Data

Matteo Fumagalli, Filipe G. Vieira, Thorfinn Sand Korneliussen, Tyler Linderoth, Emilia Huerta-Sánchez, Anders Albrechtsen, Rasmus Nielsen
2013 Genetics  
Over the last 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 modelling of genotype probability distributions has been
more » ... 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 to investigate 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.
doi:10.1534/genetics.113.154740 pmid:23979584 pmcid:PMC3813878 fatcat:3wnor3s5b5gtnb2exhaaa6dnnq