Ensuring Genetic Integrity and Quality Assurance in GEMMs

Maintaining genetic integrity is fundamental to the reliability and reproducibility of research using Genetically Engineered Mouse Models (GEMMs). While robust genotyping is a critical component of colony management, it is only part of the equation. Comprehensive characterization of genetic background is equally essential for best practice, quality control, and long-term research integrity.

Genetic background can have a profound influence on phenotypic outcomes. Even subtle genetic variation may introduce unintended differences that compromise reproducibility and obscure biological interpretation. Understanding and monitoring the genetic background of GEMM lines helps ensure that experimental results are accurate, comparable, and defensible.

Why Genetic Background Matters

A clear understanding of genetic background supports multiple aspects of responsible research, including:
  • Adherence to the principles of the 3Rs, by reducing unnecessary repeat experiments

  • Best-practice and efficient colony management, through informed breeding decisions

  • Improved reproducibility, by minimizing background-driven phenotypic variability

  • Transparency in reporting, supporting frameworks such as ARRIVE 2.0 and LAG-R

  • Accurate selection of relevant wild-type controls, as well as informed genetic refreshment strategies

Together, these benefits strengthen both scientific outcomes and ethical stewardship.

Introducing the Genetic Integrity Program

To reduce the impact of genetic background variation, a Genetic Integrity Program has been introduced using miniMUGA® Genetic Monitoring at critical points in the mouse lifecycle:
  1. Importation of new lines from external sources, including repositories such as The Jackson Laboratory

  2. Cryopreservation and rederivation, where genetic drift or contamination can occur

  3. Routine quality control of wild-type colonies, ensuring ongoing genetic consistency

By monitoring genetic integrity at these key stages, institutions can proactively identify variation, confirm strain background, and protect the long-term value of their mouse models.

A Collaborative Approach to Responsible Research

Together, this approach provides a scalable, data-driven framework for safeguarding genetic integrity—helping researchers maintain confidence in their models and the data they generate.
GIP Program | Transnetyx