Quantifying population genetic differentiation from next-generation sequencing data
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Quantifying population genetic differentiation from next-generation sequencing data. / Fumagalli, Matteo; Garrett Vieira, Filipe Jorge; Korneliussen, Thorfinn Sand; Linderoth, Tyler; Huerta-Sánchez, Emilia; Albrechtsen, Anders; Nielsen, Rasmus.
In: Genetics, Vol. 195, No. 3, 11.2013, p. 979-992.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - Quantifying population genetic differentiation from next-generation sequencing data
AU - Fumagalli, Matteo
AU - Garrett Vieira, Filipe Jorge
AU - Korneliussen, Thorfinn Sand
AU - Linderoth, Tyler
AU - Huerta-Sánchez, Emilia
AU - Albrechtsen, Anders
AU - Nielsen, Rasmus
PY - 2013/11
Y1 - 2013/11
N2 - 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 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 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.
AB - 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 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 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.
U2 - 10.1534/genetics.113.154740
DO - 10.1534/genetics.113.154740
M3 - Journal article
VL - 195
SP - 979
EP - 992
JO - Genetics
JF - Genetics
SN - 1943-2631
IS - 3
ER -
ID: 51121152