Linking genomics and population genetics with R

Emmanuel Paradis, Thierry Gosselin, Jérôme Goudet, Thibaut Jombart, Klaus Schliep

Research output: Contribution to journalArticleResearchpeer-review

Abstract

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.

Original languageEnglish
Pages (from-to)54-66
Number of pages13
JournalMolecular Ecology Resources
Volume17
Issue number1
DOIs
Publication statusPublished - Jan 2017
Externally publishedYes

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Keywords

  • Biostatistics/methods
  • Computational Biology/methods
  • Genetics, Population/methods
  • Genomics/methods
  • Haplotypes
  • Linkage Disequilibrium
  • Polymorphism, Single Nucleotide
  • Software

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