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Linking genomics and population genetics with R.

Emmanuel Paradis1, Thierry Gosselin2, Jérôme Goudet3

  • 1Institut des Sciences de l'Évolution, Université Montpellier - CNRS - IRD - EPHE, Place Eugène Bataillon - CC 065, 34095, Montpellier cédex 05, France.

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Summary

This study explores using the R computing language to address challenges in analyzing large population genomics datasets. It offers practical solutions for handling single-nucleotide polymorphism data and variant call format files.

Keywords:
rmultivariate analysisnext-generation sequencingsingle-nucleotide polymorphismvariant call format

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Area of Science:

  • Population genetics
  • Genomics
  • Bioinformatics

Background:

  • Population genetics and genomics have historically been treated as separate fields.
  • Advancements in sequencing technologies are facilitating their integration.
  • Handling massive genetic datasets presents significant challenges for researchers.

Purpose of the Study:

  • To review the challenges in analyzing large population genomics datasets.
  • To demonstrate how the R computing language can provide solutions for these challenges.
  • To offer practical implementations for common analysis tasks.

Main Methods:

  • Review of existing challenges in population genomics data analysis.
  • Application of R programming language for data handling and analysis.
  • Focus on specific tasks: single-nucleotide polymorphism (SNP) data, Variant Call Format (VCF) files, haplotype and linkage disequilibrium analysis, and multivariate analyses.
  • Illustrative analyses using large-scale, recently published population genomics datasets.

Main Results:

  • R offers robust solutions for managing and analyzing large-scale genetic data.
  • Practical examples demonstrate effective handling of SNP and VCF data.
  • R facilitates complex analyses such as haplotype analysis, linkage disequilibrium, and multivariate statistics.
  • The study successfully applied R to datasets with up to 100 million loci.

Conclusions:

  • R is a powerful and versatile environment for population genomics research.
  • The R software ecosystem is crucial for overcoming big data challenges in the field.
  • Future developments in R will further enhance its capabilities for population genomics.