title: Accurate continuous geographic assignment from low- to high-density SNP data.
abstract: 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.com
  
  
  
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark and Centre for GeoGenetics, Natural History Museum of Denmark and University of Copenhagen, Copenhagen, Denmark.
authors.forename: Gilles
authors.initials: G
authors.lastname: Guillot
authors.affiliation: Centre for GeoGenetics, Natural History Museum of Denmark and University of Copenhagen, Copenhagen, Denmark.
authors.forename: Hákon
authors.initials: H
authors.lastname: Jónsson
authors.affiliation: Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark and.
authors.forename: Antoine
authors.initials: A
authors.lastname: Hinge
authors.affiliation: Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark and.
authors.forename: Nabil
authors.initials: N
authors.lastname: Manchih
authors.affiliation: Centre for GeoGenetics, Natural History Museum of Denmark and University of Copenhagen, Copenhagen, Denmark.
authors.forename: Ludovic
authors.initials: L
authors.lastname: Orlando
mesh_terms: D003627:Data Interpretation, Statistical; D005838:Genotype; D059014:High-Throughput Nucleotide Sequencing; D006801:Humans; D008962:Models, Theoretical; D009010:Monte Carlo Method; D020641:Polymorphism, Single Nucleotide