Mammalian Genome

, Volume 17, Issue 6, pp 496–502 | Cite as

Expression genetics and the phenotype revolution

  • Robert W. Williams


Genetic analysis of variation demands large numbers of individuals and even larger numbers of genotypes. The identification of alleles associated with Mendelian disorders has involved sample sizes of a thousand or more. Pervasive and common diseases that afflict human populations—cancer, heart disease, diabetes, neurodegeneration, addiction—are all polygenic and are even more demanding of large numbers. DeCode Genetics ( has harnessed the human resources of Iceland to unravel genetic and molecular causes of complex disease. The UK BioBank project ( will incorporate 500,000 adult volunteers. The murine Collaborative Cross is the experimental equivalent of these human populations and will consist of a panel of approximately 1000 recombinant strains, expandable by intercrossing to much larger numbers of isogenic but heterozygous F1s. Massive projects of these types require efficient technologies. We have made enormous progress on the genotyping front, and it is now important to focus energy on devising ultrahigh-throughput methods to phenotype. Molecular phenotyping of the transcriptome has matured, and it is now possible to acquire hundreds of thousands of mRNA phenotypes at a cost matching those of SNPs. Proteomic and cell-based assays are also maturing rapidly. The acquisition of a personal genome along with a personal molecular phenome will provide an effective foundation for personalized medicine. Rodent models will be essential to test our ability to predict susceptibility and disease outcome using SNP data, molecular phenomes, and environmental exposures. These models will also be essential to test new treatments in a robust systems context that accounts for genetic variation.


Expression Genetic Molecular Phenotype Recombinant Inbred Strain Collaborative Resource Drop Signal Intensity 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This article is dedicated with lasting gratitude and admiration to Dr. Jing Gu, who has done so much over the past seven years to improve genomics and genetics resources for the entire mouse research community. The author thanks David W. Threadgill for pointing out the potential of microarray technology as a high-throughput phenotyping method to study segregating populations in 1999. He also thanks Jeremy L. Peirce for commenting on drafts and for sharing ideas. The author thanks the Dunavant Endowment, which has supported the Complex Trait Consortium, the production of the first BXD brain transcriptome data set, and a small part of the ongoing Collaborative Cross. The author also thanks his close colleagues, Lu Lu, Jing Gu, Kenneth F. Manly, Elissa J. Chesler, Yanhua Qu, and Jintao Wang for building GeneNetwork and its resource base. Thanks to Kathryn A. Graehl for editing the draft. This work was supported in large part by NIH grants from NIAAA-INIA, NIDA, NIMH, NCI, and NCRR.


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Copyright information

© Springer Science+Business Media, Inc. 2006

Authors and Affiliations

  • Robert W. Williams
    • 1
  1. 1.Department of Anatomy and NeurobiologyUniversity of Tennessee Health Science CenterMemphis TennesseeUSA

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