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Accurate continuous geographic assignment from low- to high-density SNP data
Author(s) -
G. Guillot,
Hákon Jónsson,
Antoine Hinge,
Nabil Manchih,
Ludovic Orlando
Publication year - 2015
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btv703
Subject(s) - computer science , inference , geospatial analysis , data mining , geocoding , geography , cartography , artificial intelligence
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.

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