Estimating the Effective Population Size from Temporal Allele Frequency Changes in Experimental Evolution

Abstract

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

Do estimates of contemporary effective population size tell us what we want to know?

“…A large number of approaches and computer programs are available for estimating effective size from genetic marker data (reviews by e.g., Gilbert & Whitlock, 2015;Luikart et al, 2010;Palstra & Ruzzante, 2008;Wang, 2005Wang, ,2016. Until recently, most studies were based on the "temporal method" that compares allele frequencies in samples collected one or more generations apart to assess variance effective size (N eV ; e.g., Jónás, Taus, Kosiol, Schlötterer, & Futschik, 2016;Jorde & Ryman, 1995,2007Nei & Tajima, 1981;Wang & Whitlock, 2003;Waples, 1989). During the past decade, however, estimation procedures that only require a single sample, collected at one point in time, have become prevailing (Palstra & Fraser, 2012;Waples, 2016).…”

A comprehensive assessment of inbreeding and laboratory adaptation in Aedes aegypti mosquitoes

“…These files were converted to sync format using the mpileup2sync.jar tool from Popoolation2 (Kofler, Pandey, & Schlotterer, 2011). We then estimated effective population size (N e ) using the Nest R package v1.1.9 with three different methods (Jonas, Taus, Kosiol, Schlotterer, & Futschik, 2016). were still significant when adjusted for the number of traits by the Bonferroni procedure.…”

Clear: Composition of Likelihoods for Evolve and Resequence Experiments

“…Others have made similar observations on this data. In particular, Jónás et al (2016) showed that the chromosome-wise population size varies even more when it is computed for each replicate separately (see table 1 in Jónás et al 2016). For instance, $\stackrel{true^}{N}$ is 131 for chromosome 3 R replicate 1, while it is 328 for chromosome X replicate 2.…”