Estimating the Effective Population Size from Temporal Allele Frequency Changes in Experimental Evolution. Issue 2 (1st October 2016)

• Record Type: Journal Article
• Authors: Jónás, Ágnes; Taus, Thomas; Kosiol, Carolin; Schlötterer, Christian; Futschik, Andreas
• Is Part Of: Genetics. Volume 204:Issue 2(2016)
• Page Start: 723
• Page End: 735
• Publication Date: 2016-10-01
• Subjects: effective population size -- genetic drift -- Pool-seq -- experimental evolution; Genetics -- Periodicals; 576.5
• DOI: 10.1534/genetics.116.191197
• Languages: English
• ISSNs: 0016-6731

Abstract

Abstract: The effective population size (N e ) 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 N e . They use allele frequency shifts between temporal samples to calculate the standardized variance, which is directly related to N e . 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 N e, which relies on allele frequency changes in temporal data and corrects for the variance in both sampling steps. In simulations, we obtain accurate N e estimates, as long as the drift variance is not too small compared to the sampling and sequencing variance. In addition to genome-wide N e estimates, we extend our method using a recursive partitioning approach to estimate N e locally along the chromosome. Since the type I error is controlled, our method permits the identification of genomic regions that differ significantly in their N e 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 .