Probabilistic graph models for landscape genetics

Department of Biology, New Mexico State University, Las Cruces, New Mexico, United States
DOI
10.7287/peerj.preprints.2225v1
Subject Areas
Computational Biology, Scientific Computing and Simulation, Spatial and Geographic Information Systems
Keywords
Landscape genetics, Graph models, Bayesian inference, Open source software
Copyright
© 2016 Milligan
Licence
This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
Cite this article
Milligan BG. 2016. Probabilistic graph models for landscape genetics. PeerJ Preprints 4:e2225v1

Abstract

Progress in landscape genetics depends on a strong conceptual foundation and the means of identifying mechanistic connnections between environmental factors, landscape features, and genetic or genomic variation. Many existing approaches and much of the software commonly in use was developed for population genetics or statistics and is not entirely appropriate for landscape genetics. Probabilistic graph models provide a statistically rigorous and flexible means of constructing models directly applicable to landscape genetics. Sophisticated software also exists for the analysis of graph models. However, much of that software does not handle the types data used by landscape geneticis, model structures involving autoregressive spatial interaction between variables, or the scale of landscape genetics problems. Thus, an important priority for the field is to develop suitably flexible software tools for graph models that overcome these problems and allow landscape geneticists to explore meaningfully mechanistic and flexible models. We are developing such a library and applying it to examples in landscape genetics.

Author Comment

This is an article intended for the OGRS2016 Collection. Specifically, it is for the session entitled "Open computational landscape genetics" convened by Stephane Joost and Solange Duruz.