Introduction

Large-scale genotype datasets can help track the dispersal patterns of epidemiological outbreaks and predict the geographic origins of individuals. Such genetically-based geographic assignments also show a range of possible applications in forensics for profiling both victims and criminals, and in wildlife management, where poaching hotspot areas can be located. They, however, require fast and accurate statistical methods to handle the growing amount of genetic information made available from genotype arrays and next-generation sequencing technologies.We introduce a novel statistical method for geopositioning individuals of unknown origin from genotypes. Our method is based on a geostatistical model trained with a dataset of georeferenced genotypes. Statistical inference under this model can be implemented within the theoretical framework of Integrated Nested Laplace Approximation, which represents one of the major recent breakthroughs in statistics, as it does not require Monte Carlo simulations. We compare the performance of our method and an alternative method for geospatial inference, SPA in a simulation framework. We highlight the accuracy and limits of continuous spatial assignment methods at various scales by analyzing genotype datasets from a diversity of species, including Florida Scrub-jay birds Aphelocoma coerulescens, Arabidopsis thaliana and humans, representing 41-197,146 SNPs. Our method appears to be best suited for the analysis of medium-sized datasets (a few tens of thousands of loci), such as reduced-representation sequencing data that become increasingly available in ecology.http://www2.imm.dtu.dk/∼gigu/Spasiba/gilles.b.guillot@gmail.comSupplementary data are available at Bioinformatics online.

Publications

  1. Accurate continuous geographic assignment from low- to high-density SNP data.
    Cite this
    Guillot G, Jónsson H, Hinge A, Manchih N, Orlando L, 2016-04-01 - Bioinformatics (Oxford, England)

Credits

  1. Gilles Guillot
    Developer

    Department of Applied Mathematics and Computer Science, Technical University of Denmark, Denmark

  2. Hákon Jónsson
    Developer

    Centre for GeoGenetics, Natural History Museum of Denmark and University of Copenhagen, Denmark

  3. Antoine Hinge
    Developer

    Department of Applied Mathematics and Computer Science, Technical University of Denmark, Denmark

  4. Nabil Manchih
    Developer

    Department of Applied Mathematics and Computer Science, Technical University of Denmark, Denmark

  5. Ludovic Orlando
    Investigator

    Centre for GeoGenetics, Natural History Museum of Denmark and University of Copenhagen, Denmark

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Summary
AccessionBT006878
Tool TypeApplication
Category
PlatformsLinux/Unix
TechnologiesR
User InterfaceTerminal Command Line
Download Count0
Country/RegionDenmark
Submitted ByLudovic Orlando