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Estimating the effective population size from temporal allele frequency changes in experimental evolution

Research output: Contribution to journalArticlepeer-review

Author(s)

Ágnes Jónás, Thomas Taus, Carolin Kosiol, Christian Schlötterer, Andreas Futschik

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Abstract

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

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Details

Original languageEnglish
Pages (from-to)723-735
Number of pages13
JournalGenetics
Volume204
Issue number2
DOIs
Publication statusPublished - 11 Oct 2016

    Research areas

  • Effective population size, Genetic drift, Pool-seq, Experimental evolution

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