Thinking too positive? Revisiting current methods of population genetic selection inference

Claudia Bank, Gregory B. Ewing, Anna Ferrer-Admettla, Matthieu Foll, Jeffrey D. Jensen

Research output: Contribution to journalReview article

48 Scopus citations

Abstract

In the age of next-generation sequencing, the availability of increasing amounts and improved quality of data at decreasing cost ought to allow for a better understanding of how natural selection is shaping the genome than ever before. However, alternative forces, such as demography and background selection (BGS), obscure the footprints of positive selection that we would like to identify. In this review, we illustrate recent developments in this area, and outline a roadmap for improved selection inference. We argue (i) that the development and obligatory use of advanced simulation tools is necessary for improved identification of selected loci, (ii) that genomic information from multiple time points will enhance the power of inference, and (iii) that results from experimental evolution should be utilized to better inform population genomic studies.

Original languageEnglish (US)
Pages (from-to)540-546
Number of pages7
JournalTrends in Genetics
Volume30
Issue number12
DOIs
StatePublished - Dec 1 2014
Externally publishedYes

Keywords

  • Background selection
  • Computational biology
  • Evolution
  • Natural selection
  • Population genetic inference

ASJC Scopus subject areas

  • Genetics

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