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ARS Home » Pacific West Area » Hilo, Hawaii » Daniel K. Inouye U.S. Pacific Basin Agricultural Research Center » Tropical Crop and Commodity Protection Research » Research » Publications at this Location » Publication #342865
Research Project: Detection, Control and Area-wide Management of Fruit Flies and Other Quarantine Pests of Tropical/Subtropical Crops

Location: Tropical Crop and Commodity Protection Research

Title: MVMAPPER: Interactive spatial mapping of genetic structures

Author
item DUPUIS, JULIAN - University Of Hawaii
item BREMER, FOREST - University Of Hawaii
item JOMBART, THIBAUT - Imperial College
item Sim, Sheina
item Geib, Scott

Submitted to: Molecular Ecology Resources
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 10/3/2017
Publication Date: 10/23/2017
Citation: Dupuis, J.R., Bremer, F.T., Jombart, T., Sim, S.B., Geib, S.M. 2017. MVMAPPER: Interactive spatial mapping of genetic structures. Molecular Ecology Resources. 18(2):362-367. https://doi.org/10.1111/1755-0998.12724.
DOI: https://doi.org/10.1111/1755-0998.12724

Interpretive Summary: Multivariate statistical analyses, such as principal component analysis, are powerful tools for summarizing datasets of many variables into a few highly informative synthetic variables. In the realm of population genetics, these methods can take a dataset composed of tens to hundreds of genetic markers, and summarize its genetic differentiation into a synthetic assessment of population structure, which is often correlated with geographic features. Despite the widespread use of these statistical tools in population genetics, there are few tools that allow visualization of multivariate analyses across geographic space. Here, we develop a deployable Python-based web-tool, mvMapper, for visualizing and exploring the results of multivariate statistical analyses in geographic space. This tool can ingest results of virtually any multivariate analysis and provides an accessible, open access, user-friendly interface for exploring and visualizing these results. Given its flexibility and user-friendliness, mvMapper should be of broad interest to both researchers and teachers of population genetics.

Technical Abstract: Characterizing population genetic structure across geographic space is a fundamental challenge in population genetics. Multivariate statistical analyses are powerful tools for summarizing genetic variability, but geographic information and accompanying metadata is not always easily integrated into these methods in a user-friendly fashion. Here, we present a deployable Python-based web-tool, mvMapper, for visualizing and exploring the results of multivariate statistical analyses in geographic space. This tool will ingest results of virtually any multivariate analysis of georeferenced data and an automated data preparation function has been integrated into the R library adegenet for many common analyses, including principal components analysis (regular and spatial varieties), discriminant analysis of principal components, principal coordinates analysis, non-metric dimensional scaling, and correspondence analysis. mvMapper’s greatest strength is facilitating dynamic exploration of the statistical and geographic frameworks side-by-side, a task that is difficult and time-consuming to do in static space. The latest version of mvMapper is available as an interactive web application at http://ctahr-peps.colo.hawaii.edu; for stand-alone use, source code available as a Docker container and documentation are available at https://hub.docker.com/r/genomeannotation/mvmapper and https://github.com/genomeannotation/mvMapper, respectively.