Neuner SM, Heuer SE, Huentelman MJ, O'Connell KM, Kaczorowski CC. Harnessing Genetic Complexity to Enhance Translatability of Alzheimer's Disease Mouse Models: A Path toward Precision Medicine. Neuron. 2019 Feb 6;101(3):399-411.e5. Epub 2018 Dec 27 PubMed.
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In general, teams who are interested in robust results—Catherine Kaczorowski’s and many others—study larger numbers of strains/genomes, with fewer replicates within strain. If a team can afford to study 400 cases, then studying four each of 100 genotypes is about the sweet spot—perhaps two males and two females of each strain. One could bump that up to eight each of 50 genotypes if interested in two time points.
Imagine a human geneticist with the luxury of studying four identical twins (and in male and female versions). At some point, the replication within strain/genome buys you less and less information. Replication in the range from two to eight lets you detect potential problems, such as outliers and errors. But the point is not to understand any one strain. The point is to generate robust data that will generalize (for more, see Williams and Williams, 2017; Williams, 2009).
We are making this work easier for teams: 1. they do not need to do any genotyping; 2. they have easy access to deep electronic health records of all of these genotypes of mice; 3. they have access to the whole statistical workflow at a single website (gn2.genenetwork.org).
The typical functional genomics study (i.e. asking "what does this gene do?") will use 10 to 20 experimental knockout/transgenic animals on one genetic background per treatment group. Perhaps as many controls. This provides results that have good statistical power but not good generality and translatability. However, some of the 5XFAD x BXD lines will be cool to use in more focused studies—particularly interventions that delay or prevent age-related decline in performance.
References:
Williams RW, Williams EG. Resources for Systems Genetics. Methods Mol Biol. 2017;1488:3-29. PubMed.
Williams RW. Herding cats: the sociology of data integration. Front Neurosci. 2009;3(2):154-6. Epub 2009 Sep 15 PubMed.
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