Research highlight
JAX® Mouse Diversity Genotyping Array announced
To fully explore complex genomic and genetic questions, researchers need better tools. The JAX® Mouse Diversity Genotyping Array, designed by Jackson Laboratory Professor Gary Churchill, Ph.D., and Fernando Pardo Manuel de Villena, Ph.D., of the University of North Carolina, provides an efficient and powerful platform for characterizing mice and gaining insight into a variety of genomic inquiries.
"You can look at new types of mouse populations, outcross populations, that have an incredibly high density of recombination," says Churchill. "The array allows us to do fine mapping, very similar to the types of fine mapping that are going on in the human GWAS studies right now. The diversity outcross mice being developed at The Jackson Laboratory are an extremely useful mapping population, but they're only useful in conjunction with a high-density genotyping array."
The array can simultaneously assay approximately 625,000 single nucleotide polymorphisms (SNPs) and over 900,000 invariant genomic probes (IGPs) in the mouse genome. Researchers who work with mice can use it to genotype virtually any mouse. They can also better monitor genetic quality and assay gene expression and copy number variations. Furthermore, de Villena has developed a way to use the array to do methylation assays, which Churchill sees as one of the most exciting applications of the array going forward.
"We're going to be able to look at many different tissues in multiple environmental conditions and ask about the epigenome. Methylation modifications are fundamental mechanisms that the genome uses to adapt to the environment on a relatively rapid time scale. I think in a matter of months we'll be able to report hundreds of novel methylated genes."
The details about the development of the array can be found in Nature Methods (Yang et al. 2009).
See interviews with Gary Churchill.
How did you choose the array’s SNPs? (4:08)How did you ensure the array’s reliability? (2:07)How are IGPs different from SNPs, and how will they increase the array’s utility? (4:23)What are some of the quality control measures you used to validate the array? (3:28)What will be some to the most important uses of the array? (2:41)How will the array help researchers identify genes associated with disease? (3:35)