Nature Communications (Jul 2024)

Improving laboratory animal genetic reporting: LAG-R guidelines

  • Lydia Teboul,
  • James Amos-Landgraf,
  • Fernando J. Benavides,
  • Marie-Christine Birling,
  • Steve D. M. Brown,
  • Elizabeth Bryda,
  • Rosie Bunton-Stasyshyn,
  • Hsian-Jean Chin,
  • Martina Crispo,
  • Fabien Delerue,
  • Michael Dobbie,
  • Craig L. Franklin,
  • Ernst-Martin Fuchtbauer,
  • Xiang Gao,
  • Christelle Golzio,
  • Rebecca Haffner,
  • Yann Hérault,
  • Martin Hrabe de Angelis,
  • Kevin C. Kent Lloyd,
  • Terry R. Magnuson,
  • Lluis Montoliu,
  • Stephen A. Murray,
  • Ki-Hoan Nam,
  • Lauryl M. J. Nutter,
  • Eric Pailhoux,
  • Fernando Pardo Manuel de Villena,
  • Kevin Peterson,
  • Laura Reinholdt,
  • Radislav Sedlacek,
  • Je Kyung Seong,
  • Toshihiko Shiroishi,
  • Cynthia Smith,
  • Toru Takeo,
  • Louise Tinsley,
  • Jean-Luc Vilotte,
  • Søren Warming,
  • Sara Wells,
  • C. Bruce Whitelaw,
  • Atsushi Yoshiki,
  • Asian Mouse Mutagenesis Resource Association,
  • CELPHEDIA infrastructure,
  • INFRAFRONTIER consortium,
  • International Mammalian Genome Society,
  • International Mouse Phenotyping Consortium,
  • International Society for Transgenic Technologies,
  • Mutant Mouse Resource and Research Centers,
  • Phenomics Australia,
  • RRRC- Rat Resource and Research Center,
  • Guillaume Pavlovic

DOI
https://doi.org/10.1038/s41467-024-49439-y
Journal volume & issue
Vol. 15, no. 1
pp. 1 – 8

Abstract

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Abstract The biomedical research community addresses reproducibility challenges in animal studies through standardized nomenclature, improved experimental design, transparent reporting, data sharing, and centralized repositories. The ARRIVE guidelines outline documentation standards for laboratory animals in experiments, but genetic information is often incomplete. To remedy this, we propose the Laboratory Animal Genetic Reporting (LAG-R) framework. LAG-R aims to document animals’ genetic makeup in scientific publications, providing essential details for replication and appropriate model use. While verifying complete genetic compositions may be impractical, better reporting and validation efforts enhance reliability of research. LAG-R standardization will bolster reproducibility, peer review, and overall scientific rigor.