Detecting and quantifying changing selection intensities from time-sampled polymorphism data

Hyunjin Shim, Stefan Laurent, Sebastian Matuszewski, Matthieu Foll, Jeffrey D. Jensen

Research output: Contribution to journalArticlepeer-review

18 Scopus citations


During his well-known debate with Fisher regarding the phenotypic dataset of Panaxia dominula, Wright suggested fluctuating selection as a potential explanation for the observed change in allele frequencies. This model has since been invoked in a number of analyses, with the focus of discussion centering mainly on random or oscillatory fluctuations of selection intensities. Here, we present a novel method to consider nonrandom changes in selection intensities using Wright-Fisher approximate Bayesian (ABC)-based approaches, in order to detect and evaluate a change in selection strength from time-sampled data. This novel method jointly estimates the position of a change point as well as the strength of both corresponding selection coefficients (and dominance for diploid cases) from the allele trajectory. The simulation studies of this method reveal the combinations of parameter ranges and input values that optimize performance, thus indicating optimal experimental design strategies. We apply this approach to both the historical dataset of P. dominula in order to shed light on this historical debate, as well as to whole-genome time-serial data from influenza virus in order to identify sites with changing selection intensities in response to drug treatment.

Original languageEnglish (US)
Pages (from-to)893-904
Number of pages12
JournalG3: Genes, Genomes, Genetics
Issue number4
StatePublished - 2016
Externally publishedYes


  • Approximate Bayesian computation
  • Change point analysis
  • Fluctuating selection
  • Time-sampled data
  • Wright-Fisher model

ASJC Scopus subject areas

  • Molecular Biology
  • Genetics
  • Genetics(clinical)


Dive into the research topics of 'Detecting and quantifying changing selection intensities from time-sampled polymorphism data'. Together they form a unique fingerprint.

Cite this