Population genomics from pool sequencing

Ferretti, Luca; Ramos-Onsins, Sebastián E.; Pérez‐Enciso, Miguel

doi:10.1111/mec.12522

Cited by 139 publications

(182 citation statements)

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“…Errors caused by unequal representation of individual samples in pooled sequencing libraries can be substantially reduced by using large per-pool sample sizes and depth of coverage, and removal of PCR duplicates 88,91,92 . The prevalence of PCR duplicates can be reduced by using a small number of PCR cycles, which should be feasible for pooled sequencing with a large starting amount of genomic DNA.…”

Section: Figurementioning

confidence: 99%

Harnessing the power of RADseq for ecological and evolutionary genomics

et al. 2016

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A revolution is occurring in ecological and evolutionary genetics, driven by the development of techniques such as Restriction-site-Associated DNA sequencing (RADseq) that allow relatively low-cost discovery and genotyping of thousands of genetic markers for any species, including nonmodel species. Here we provide an overview of the diverse RADseq techniques that have been developed and highlight some of the research questions these powerful methods can be used to answer. We discuss how technical differences among the many variant methods lead to trade-offs in experimental design and analysis, and describe general considerations for designing a RADseq study.

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Section: Figurementioning

confidence: 99%

Harnessing the power of RADseq for ecological and evolutionary genomics

et al. 2016

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“…For these reasons, Pool-seq has become the basis of many experimental evolution "evolve and resequence" (E&R) studies (Turner et al 2011;Schlötterer et al 2015). Following the emergence of E&R, many population genetic estimators have been adjusted to handle the properties of Pool-seq data (Futschik and Schlöt-terer 2010; Kofler et al 2011a,b;Kolaczkowski et al 2011;Boitard et al 2013;Ferretti et al 2013). To the best of our knowledge, no N e estimators have been developed so far that properly deal with Pool-seq data.…”

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confidence: 99%

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

Jónás¹,

Taus²,

Kosiol³

et al. 2016

Preprint

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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. KEYWORDS effective population size; genetic drift; Pool-seq; experimental evolution D URING experimental evolution studies, populations are maintained under specific laboratory conditions (Kawecki et al. 2012;Long et al. 2015;Schlötterer et al. 2015). In sexually reproducing organisms, the census population size is typically kept fixed at fairly low numbers, rarely exceeding 2000 individuals. With such small population sizes, genetic drift causes stochastic fluctuations in allele frequencies. Under neutrality, the level of random frequency changes is determined by the effective population size (N e ) (Wright 1931). Furthermore, the efficacy of selection is influenced by N e : For weakly selected alleles, the probability of fixation is directly proportional to the product of N e and the intensity of selection (Fisher 1930;Kimura 1964). As changes in allele frequency are greatly affected by the population size, it is fundamental to estimate N e accurately to understand molecular variation in experimental evolution studies. Krimbas and Tsakas (1971) estimated N e using the standardized variance of allele frequency (F, see also Falconer and Mackay 1996) from longitudinal samples in natural populations of olive flies. As F was calculated from these samples, they accounted for the sampling variance that also contributed to the true allele frequency variance. This approach was further improved and used by sev...

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“…However, a lower level of coverage will not adequately represent the pool of individuals, especially when the pool is small to begin with, and can therefore produce misleading population parameter estimates; see [60,61,67]. Guidelines [51,60,61,67] and software packages such as ngsTools [68] and npstat [69], which carry out likelihood-based estimation of allele frequencies, are now available to help tackle these challenges.…”

Section: Methods For Generating Genomic Datamentioning

confidence: 99%