{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/directional-genetic-differentiation-and","title":"Directional genetic differentiation and asymmetric migration","arxiv_id":"1304.0118","date":"2016-09-26","proceeding":null,"authors":[],"abstract":"Understanding the population structure and patterns of gene flow within\nspecies is of fundamental importance to the study of evolution. In the fields\nof population and evolutionary genetics, measures of genetic differentiation\nare commonly used to gather this information. One potential caveat is that\nthese measures assume gene flow to be symmetric. However, asymmetric gene flow\nis common in nature, especially in systems driven by physical processes such as\nwind or water currents. Since information about levels of asymmetric gene flow\namong populations is essential for the correct interpretation of the\ndistribution of contemporary genetic diversity within species, this should not\nbe overlooked. To obtain information on asymmetric migration patterns from\ngenetic data, complex models based on maximum likelihood or Bayesian approaches\ngenerally need to be employed, often at great computational cost. Here, a new\nsimpler and more efficient approach for understanding gene flow patterns is\npresented. This approach allows the estimation of directional components of\ngenetic divergence between pairs of populations at low computational effort,\nusing any of the classical or modern measures of genetic differentiation. These\ndirectional measures of genetic differentiation can further be used to\ncalculate directional relative migration and to detect asymmetries in gene flow\npatterns. This can be done in a user-friendly web application called\ndivMigrate-online introduced in this paper. Using simulated data sets with\nknown gene flow regimes, we demonstrate that the method is capable of resolving\ncomplex migration patterns under a range of study designs.","url_abs":"http://arxiv.org/abs/1304.0118v3","url_pdf":"http://arxiv.org/pdf/1304.0118v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"directional-genetic-differentiation-and","repo_url":"https://github.com/kkeenan02/divMigrate-online","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}