POPMAPS: An R package to estimate ancestry probability surfaces
Dates
Publication Date
2022-05-24
Citation
Massatti, R., 2022, POPMAPS: An R package to estimate ancestry probability surfaces: U.S. Geological Survey Software Release, https://doi.org/10.5066/P96VLOA5.
Summary
This software code was developed to estimate the probability that individuals found at a geographic location will belong to the same genetic cluster as individuals at the nearest empirical sampling location for which ancestry is known. POPMAPS includes 5 main functions to calculate and visualize these results (see Table 1 for functions and arguments). Population assignment coefficients and a raster surface must be estimated prior to using POPMAPS functions (see Fig. 1a and b). With these data in hand, users can run a jackknife function to choose an optimal parameter combination that reconstructs empirical data best (Figs. 2 and S2). Pertinent parameters include 1) how many empirical sampling localities should be used to estimate ancestry [...]
Summary
This software code was developed to estimate the probability that individuals found at a geographic location will belong to the same genetic cluster as individuals at the nearest empirical sampling location for which ancestry is known. POPMAPS includes 5 main functions to calculate and visualize these results (see Table 1 for functions and arguments). Population assignment coefficients and a raster surface must be estimated prior to using POPMAPS functions (see Fig. 1a and b). With these data in hand, users can run a jackknife function to choose an optimal parameter combination that reconstructs empirical data best (Figs. 2 and S2). Pertinent parameters include 1) how many empirical sampling localities should be used to estimate ancestry coefficients and 2) what is the influence of empirical sites on ancestry coefficient estimation as distance increases (Fig. 2). After choosing these parameters, a user can estimate the entire ancestry probability surface (Fig. 1c and d, Fig. 3).
This package can be used to estimate ancestry coefficients from empirical genetic data across a user-defined geospatial layer. Estimated ancestry coefficients are used to calculate ancestry probabilities, which together with 'hard population boundaries,' compose an ancestry probability surface. Within a hard boundary, the ancestry probability informs a user of the confidence that they can have of genetic identity matching the principal population if they were to find individuals of the focal organism at a location. Confidence can be modified across the ancestry probability surface by changing parameters influencing the contribution of empirical data to the estimation of ancestry coefficients. This information may be valuable to inform decision-making for organisms having management needs.
See 'Related External Resources, Type: Source Code' below for direct access to the POPMAPS R software package.
Massatti, R, 2020, Hilaria jamesii data for the Colorado Plateau of the southwestern United States: U.S. Geological Survey data release, https://doi.org/10.5066/P9CNFWOX.
Type: Related Publication For Published Data Release
Massatti, R., and Knowles, L.L., 2020, The historical context of contemporary climatic adaptation: a case study in the climatically dynamic and environmentally complex southwestern United States: Ecography (online), https://doi.org/10.1111/ecog.04840.
Massatti, R., and Winkler, D., 2022, Spatially explicit management of genetic diversity using ancestry probability surfaces: Methods in Ecology and Evolution, online, https://doi.org/10.1111/2041-210X.13902.
This software code was created for interpolating patterns of genetic differentiation across a landscape. This information may be used in downstream ecological or evolutionary research, and it is particularly relevant to species with management needs in which protection of genetic diversity in a primary goal.
Rights
This software has been approved for release by the U.S. Geological Survey (USGS). Although the software has been subjected to rigorous review, the USGS reserves the right to update the software as needed pursuant to further analysis and review. No warranty, expressed or implied, is made by the USGS or the U.S. Government as to the functionality of the software and related material nor shall the fact of release constitute any such warranty. Furthermore, the software is released on condition that neither the USGS nor the U.S. Government shall be held liable for any damages resulting from its authorized or unauthorized use.
