The impact of genetic background on mouse models of disease

The mouse is the most commonly used mammalian model to study disease, including kidney disease. However, close attention needs to be paid to the differences and effects of genetic background. The default choice of most investigators is to use C57BL/6 mice, but not all C57BL/6 mice are the same. Ever since the C57BL/6 line was first established, differences in the genetic background have risen between substrains, which have major implications in the phenotypes expressed in kidney disease. Furthermore, considering that C57BL/6 substrains are relatively resistant to kidney damage, there can be major benefits in selecting other mouse inbred strains when studying kidney disease. These strains can show more similar responses regarding kidney damage as in humans, and results may therefore translate better to human application. Genetically diverse mice, such as the Diversity Outbred mice, allow investigators to study kidney phenotypes with comparable levels of genetic diversity as seen in humans, which yield results that more closely reflect the variation in human disease outcomes due to genetic variation. Hence, embracing the genetic diversity that is present in mice can lead to better translational research methods. Investigators need to always take into consideration that genetic background is a variable that can alter results significantly, and optimization of translational research asks for careful strain selection and more rigorous reporting of the genetic background that is being used in experiment.

Automated summary

The mouse is commonly used to study kidney disease, but genetic background differences and effects need to be considered. Choosing the right mouse strain can improve translational research and make the results more applicable to humans. Using genetically diverse mice can provide results that better reflect the variation in human disease outcomes. Therefore, embracing the genetic diversity in mice is important for better translational research methods.