Probabilistic graph models for landscape genetics - Milligan,Brook G (PeerJ Preprints, 2016)
Progress in landscape genetics depends on a strong conceptual foundation and the means of identifying mechanistic connections 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 of data used for landscape genetics, 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.
Keywords: Landscape genetics; Graph models; Bayesian inference; Open source software