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

Ágnes Jónás1, Thomas Taus1, Carolin Kosiol2

  • 1*Vienna Graduate School of Population Genetics, 1210 Vienna, Austria †Institut für Populationsgenetik, Vetmeduni Vienna, 1210 Vienna, Austria.

Genetics
|August 21, 2016
PubMed
Summary
This summary is machine-generated.

We developed a new method to accurately estimate effective population size ([Formula: see text]) using temporal data from pooled sequencing. This approach corrects for sampling variance, improving estimates in experimental evolution studies.

Keywords:
Pool-seqeffective population sizeexperimental evolutiongenetic drift

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Area of Science:

  • Population genetics
  • Genomics
  • Evolutionary biology

Background:

  • Effective population size ([Formula: see text]) is crucial for understanding allele frequency changes.
  • Temporal methods estimate [Formula: see text] using allele frequency shifts, but standard methods struggle with pooled sequencing data (Pool-seq).
  • Pool-seq introduces additional sampling variance, complicating [Formula: see text] estimation.

Purpose of the Study:

  • To develop a novel estimator for [Formula: see text] that accounts for both individual and sequencing sampling steps in Pool-seq data.
  • To provide accurate and reliable [Formula: see text] estimates from experimental evolution studies.
  • To enable localized [Formula: see text] estimation along chromosomes and identify genomic regions with differing [Formula: see text].

Main Methods:

  • Proposed a new estimator for [Formula: see text] based on allele frequency changes in temporal data.
  • Corrected for the increased variance introduced by two sampling steps in Pool-seq.
  • Extended the method using recursive partitioning for localized, genome-wide [Formula: see text] estimation.

Main Results:

  • The new estimator provides accurate [Formula: see text] estimates in simulations when drift variance is sufficiently large.
  • The method successfully identified genomic regions with significantly different [Formula: see text] estimates.
  • Demonstrated application to Pool-seq data from *Drosophila* experimental evolution.

Conclusions:

  • The developed estimator accurately estimates effective population size ([Formula: see text]) from temporal Pool-seq data.
  • The method is computationally efficient and applicable to whole-genome data.
  • Provides a valuable tool for analyzing experimental evolution and population genetics data.