title: Estimating the Effective Population Size from Temporal Allele Frequency Changes in Experimental Evolution.
abstract: The effective population size ([Formula: see text]) is a major factor determining allele frequency changes in natural and experimental populations. Temporal methods provide a powerful and simple approach to estimate short-term [Formula: see text] They use allele frequency shifts between temporal samples to calculate the standardized variance, which is directly related to [Formula: see text] Here we focus on experimental evolution studies that often rely on repeated sequencing of samples in pools (Pool-seq). Pool-seq is cost-effective and often outperforms individual-based sequencing in estimating allele frequencies, but it is associated with atypical sampling properties: Additional to sampling individuals, sequencing DNA in pools leads to a second round of sampling, which increases the variance of allele frequency estimates. We propose a new estimator of [Formula: see text] which relies on allele frequency changes in temporal data and corrects for the variance in both sampling steps. In simulations, we obtain accurate [Formula: see text] estimates, as long as the drift variance is not too small compared to the sampling and sequencing variance. In addition to genome-wide [Formula: see text] estimates, we extend our method using a recursive partitioning approach to estimate [Formula: see text] locally along the chromosome. Since the type I error is controlled, our method permits the identification of genomic regions that differ significantly in their [Formula: see text] estimates. We present an application to Pool-seq data from experimental evolution with Drosophila and provide recommendations for whole-genome data. The estimator is computationally efficient and available as an R package at https://github.com/ThomasTaus/Nest.
authors.affiliation: *Vienna Graduate School of Population Genetics, 1210 Vienna, Austria †Institut für Populationsgenetik, Vetmeduni Vienna, 1210 Vienna, Austria.
authors.forename: Ágnes
authors.initials: Á
authors.lastname: Jónás
authors.affiliation: *Vienna Graduate School of Population Genetics, 1210 Vienna, Austria †Institut für Populationsgenetik, Vetmeduni Vienna, 1210 Vienna, Austria.
authors.forename: Thomas
authors.initials: T
authors.lastname: Taus
authors.affiliation: †Institut für Populationsgenetik, Vetmeduni Vienna, 1210 Vienna, Austria.
authors.forename: Carolin
authors.initials: C
authors.lastname: Kosiol
authors.affiliation: †Institut für Populationsgenetik, Vetmeduni Vienna, 1210 Vienna, Austria.
authors.forename: Christian
authors.initials: C
authors.lastname: Schlötterer
authors.affiliation: †Institut für Populationsgenetik, Vetmeduni Vienna, 1210 Vienna, Austria ‡Department of Applied Statistics, Johannes Kepler Universität Linz, 4040 Linz, Austria andreas.futschik@jku.at.
authors.forename: Andreas
authors.identifier: http://orcid.org/0000-0002-7980-0304
authors.initials: A
authors.lastname: Futschik
mesh_terms: D000483:Alleles; D000818:Animals; D019020:Directed Molecular Evolution; D004330:Drosophila / Q000235:genetics; D005787:Gene Frequency / Q000235:genetics*; D020641:Polymorphism, Single Nucleotide / Q000235:genetics; D011156:Population Density; D017422:Sequence Analysis, DNA
keywords: Pool-seq; effective population size; experimental evolution; genetic drift