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Measuring Genetic Differentiation from Pool-seq Data

Abstract : The advent of high throughput sequencing and genotyping technologies enables the comparison of patterns of polymorphisms at a very large number of markers. While the characterization of genetic structure from individual sequencing data remains expensive for many nonmodel species, it has been shown that sequencing pools of individual DNAs (Pool-seq) represents an attractive and cost-effective alternative. However, analyzing sequence read counts from a DNA pool instead of individual genotypes raises statistical challenges in deriving correct estimates of genetic differentiation. In this article, we provide a method-of-moments estimator of F-ST for Pool-seq data, based on an analysis-of-variance framework. We show, by means of simulations, that this new estimator is unbiased and outperforms previously proposed estimators. We evaluate the robustness of our estimator to model misspecification, such as sequencing errors and uneven contributions of individual DNAs to the pools. Finally, by reanalyzing published Pool-seq data of different ecotypes of the prickly sculpin Cottus asper, we show how the use of an unbiased F-ST estimator may question the interpretation of population structure inferred from previous analyses.
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Valentin Hivert, Raphael Leblois, Eric Petit, Mathieu Gautier, Renaud Vitalis. Measuring Genetic Differentiation from Pool-seq Data. Genetics, Genetics Society of America, 2018, 210 (1), pp.315-330. ⟨10.1534/genetics.118.300900⟩. ⟨hal-01936905⟩

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