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Mol Ecol Resour. 2017 Jan;17(1):54-66. doi: 10.1111/1755-0998.12577. Epub 2016 Aug 29.

Linking genomics and population genetics with R.

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Institut des Sciences de l'Évolution, Université Montpellier - CNRS - IRD - EPHE, Place Eugène Bataillon - CC 065, 34095, Montpellier cédex 05, France.
Institut de Biologie Intégrative et des Systèmes (IBIS), Université Laval, Québec, QC, G1V 0A6, Canada.
Department of Ecology and Evolution, Swiss Institute of Bioinformatics, Lausanne, CH-1015, Switzerland.
MRC Centre for Outbreak Analysis and Modelling, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College, London, W2 1PG, UK.
Department of Biology, University of Massachusetts Boston, Boston, MA, 02125, USA.


Population genetics and genomics have developed and been treated as independent fields of study despite having common roots. The continuous progress of sequencing technologies is contributing to (re-)connect these two disciplines. We review the challenges faced by data analysts and software developers when handling very big genetic data sets collected on many individuals. We then expose how r, as a computing language and development environment, proposes some solutions to meet these challenges. We focus on some specific issues that are often encountered in practice: handling and analysing single-nucleotide polymorphism data, handling and reading variant call format files, analysing haplotypes and linkage disequilibrium and performing multivariate analyses. We illustrate these implementations with some analyses of three recently published data sets that contain between 60 000 and 1 000 000 loci. We conclude with some perspectives on future developments of r software for population genomics.


r ; multivariate analysis; next-generation sequencing; single-nucleotide polymorphism; variant call format

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