Statistical Inference in the Wright–Fisher Model Using Allele Frequency Data

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review

Abstract

TheWright-Fisher model provides an elegant mathematical framework for understanding allele frequency data. In particular, the model can be used to infer the demographic history of species and identify loci under selection. A crucial quantity for inference under the Wright-Fisher model is the distribution of allele frequencies (DAF). Despite the apparent simplicity of the model, the calculation of the DAF is challenging. We review and discuss strategies for approximating the DAF, and howthese are used in methods that performinference from allele frequency data. Various evolutionary forces can be incorporated in the Wright-Fisher model, and we consider these in turn. We begin our review with the basic bi-allelic Wright-Fisher model where random genetic drift is the only evolutionary force. We then consider mutation, migration, and selection. In particular, we compare diffusion-based and moment-based methods in terms of accuracy, computational efficiency, and analytical tractability.We conclude with a brief overview of the multi-allelic process with a general mutation model.

Original languageEnglish
JournalSystematic Biology
Volume66
Issue1
Pages (from-to)e30-e46
ISSN1063-5157
DOIs
Publication statusPublished - 2016

Keywords

  • Allele frequency
  • Diffusion
  • Inference
  • Moments
  • Selection
  • Wright-Fisher

Fingerprint

Dive into the research topics of 'Statistical Inference in the Wright–Fisher Model Using Allele Frequency Data'. Together they form a unique fingerprint.

Cite this