Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Innovation
  • Published:


New technologies to assess genotype–phenotype relationships


The accelerating pace of the discovery of genes has far surpassed our capabilities to understand their biological function — in other words, the phenotypes they engender. We need efficient and comprehensive large-scale phenotyping technologies. This presents a difficult challenge because phenotypes are numerous and diverse, and they can be observed and annotated at the molecular, cellular and organismal level. New technologies and approaches will therefore be required. Here, I describe recent efforts to develop new and efficient technologies for assessing cellular phenotypes.

This is a preview of subscription content, access via your institution

Access options

Buy this article

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Genotypic and phenotypic maps.
Figure 2: Global cellular analysis.
Figure 3: Phenotype MicroArray comparison of two isogenic strains of E. coli.

Similar content being viewed by others


  1. Miller, J. H. A Short Course in Bacterial Genetics: A Laboratory Manual and Handbook for Escherichia coli and Related Bacteria (Cold Spring Harbor Lab. Press, New York, 1992).

    Google Scholar 

  2. Blattner, F. R. et al. The complete genome sequence of Escherichia coli K-12. Science 277, 1453–1474 (1997).

    Article  CAS  Google Scholar 

  3. Goffeau, A. Life with 6,000 genes. Science 274, 563–567 (1996).

    Article  Google Scholar 

  4. Kaul, S. et al. Analysis of the genome sequence of the flowering plant Arabidopsis thaliana. Nature 408, 796–815 (2000).

    Article  CAS  Google Scholar 

  5. Goff, S. A. et al. A draft sequence of the rice genome (Oryza sativa L. ssp. japonica). Science 296, 92–100 (2002).

    Article  CAS  Google Scholar 

  6. The C. elegans Sequencing Consortium. Genome sequence of C. elegans: a platform for investigating biology. Science 282, 2012–2018 (1998).

  7. Adams, M. D. et al. The genome sequence of Drosophila melanogaster. Science 287, 2185–2195 (2000).

    Article  Google Scholar 

  8. Waterston, R. H. et al. Initial sequencing and comparative analysis of the mouse genome. Nature 420, 520–562 (2002).

    Article  CAS  Google Scholar 

  9. International Human Genome Sequencing Consortium. Initial sequencing and analysis of the human genome. Nature 409, 860–921 (2001).

  10. Venter, J. C. et al. The sequence of the human genome. Science 291, 1304–1351 (2001).

    Article  CAS  Google Scholar 

  11. Nadeau, J. H. et al. Functional annotation of mouse genome sequences. Science 291, 1251–1255 (2001).

    Article  CAS  Google Scholar 

  12. Bassett, D. E. et al. Genome cross-referencing and XREFdb: implications for the identification and analysis of genes mutated in human diseases. Nature Genet. 15, 339–344 (1997).

    Article  CAS  Google Scholar 

  13. Rubin, G. M. et al. Comparative genomics of eukaryotes. Science 287, 2204–2215 (2000).

    Article  CAS  Google Scholar 

  14. LaRossa, R. A. in Escherichia coli and Salmonella Cellular and Molecular Biology 2nd edn Vol. 2 Ch. 39 (ed. Neidhart, F. C.) 2527–2587 (ASM, Washington DC, 1996).

    Google Scholar 

  15. Cheung, V. G. & Spielman, R. S. The genetics of variation in gene expression. Nature Genet. 32 (Suppl.), 522–525 (2002).

    Article  CAS  Google Scholar 

  16. Jansen, R. C. Studying complex biological systems using multifactorial perturbation. Nature Rev. Genet. 4, 1–7 (2003).

    Article  Google Scholar 

  17. Rosenthal, N. & Ashburner, M. Taking stock of our models: the function and future of stock centres. Nature Rev. Genet. 3, 711–717 (2002).

    Article  CAS  Google Scholar 

  18. Eppig, J. T. Algorithms for mutant sorting: the need for phenotype vocabularies. Mamm. Genome 11, 584–589 (2000).

    Article  CAS  Google Scholar 

  19. Pargent, W. et al. MouseNet database: digital management of a large-scale mutagenesis project. Mamm. Genome 11, 590–593 (2000).

    Article  CAS  Google Scholar 

  20. Bochner, B. R. Sleuthing out bacterial identities. Nature 339, 157–158 (1989).

    Article  CAS  Google Scholar 

  21. Brown, S. D. & Peters, J. Combining mutagenesis and genomics in the mouse — closing the phenotype gap. Trends Genet. 12, 433–435 (1996).

    Article  CAS  Google Scholar 

  22. Paigen, K. & Epping, J. T. A mouse phenome project. Mamm. Genome 11, 715–717 (2000).

    Article  CAS  Google Scholar 

  23. Moldin, S. O. et al. Trans-NIH neuroscience initiatives on mouse phenotyping and mutagenesis. Mamm. Genome 12, 575–581 (2001).

    Article  CAS  Google Scholar 

  24. Beckers, J. & Hrabe de Angelis, M. Large-scale mutational analysis for the annotation of the mouse genome. Curr. Opin Chem. Biol. 6, 17–23 (2001).

    Article  Google Scholar 

  25. Somerville, C. & Dangl, L. Genomics: plant biology in 2010. Science 290, 2077–2078 (2000).

    Article  CAS  Google Scholar 

  26. Oliver, S. G. A network approach to the systematic analysis of yeast gene function. Trends Genet. 12, 241–242 (1996).

    Article  CAS  Google Scholar 

  27. Hampsey, M. H. A review of phenotypes of Saccharomyces cerevisiae. Yeast 13, 1099–1133 (1997).

    Article  CAS  Google Scholar 

  28. Niedenthal, R. et al. Systematic analysis of S. cerevisiae chromosome VII genes. Yeast 15, 1775–1796 (1999).

    Article  CAS  Google Scholar 

  29. Bochner, B. R. 'Breathprints' at the microbial level. ASM News 55, 536–539 (1989).

    Google Scholar 

  30. Bochner, B. R., Gadzinski, P. & Panomitros, E. Phenotype MicroArrays for high-throughput phenotypic testing and assay of gene function. Genome Res. 11, 1246–1255 (2001).

    Article  CAS  Google Scholar 

  31. Vazquez-Boland, J. A. et al. Listeria pathogenesis and molecular virulence determinants. Clin. Micro. Rev. 14, 584–640 (2001).

    Article  CAS  Google Scholar 

  32. Guzman, L. M., Belin, D., Carson, M. J. & Beckwith, J. Tight regulation, modulation, and high-level expression by vectors containing the arabinose PBAD promoter. J. Bacteriol. 177, 4121–4130 (1995).

    Article  CAS  Google Scholar 

  33. Ji, Y. et al. Identification of critical staphylococcal genes using conditional phenotypes generated by antisense RNA. Science 293, 2266–2269 (2001).

    Article  CAS  Google Scholar 

  34. Smith, V. et al. Functional analysis of the genes of yeast chromosome V by genetic footprinting. Science 274, 2069–2074 (1996).

    Article  CAS  Google Scholar 

  35. Shoemaker, D. D. et al. Quantitative phenotypic analysis of yeast deletion mutants using a highly parallel molecular bar-coding strategy. Nature Genet. 14, 450–456 (1996).

    Article  CAS  Google Scholar 

  36. Winzeler, E. A. et al. Functional characterization of the S. cerevisiae genome by gene deletion and parallel analysis. Science 285, 901–906 (1999).

    Article  CAS  Google Scholar 

  37. Giaever, G. et al. Functional profiling of the Saccharomyces cerevisiae genome. Nature 418, 387–391 (2002).

    Article  CAS  Google Scholar 

  38. Rieger, K. J. et al. Large-scale phenotypic analysis — the pilot project on yeast chromosome III. Yeast 13, 1547–1562 (1997).

    Article  CAS  Google Scholar 

  39. Rieger, K. J. et al. in Methods in Microbiology Vol. 28 (ed. Craig, A.G.) 205–227 (Academic Press, London, 1999).

    Google Scholar 

  40. Rieger, K. J. et al. Chemotyping of yeast mutants using robotics. Yeast 15, 973–986 (1999).

    Article  CAS  Google Scholar 

  41. Entian, K. D. et al. Functional analysis of 150 deletion mutants in Saccharomyces cerevisiae by a systematic approach. Mol. Gen. Genet. 262, 683–702 (1999).

    Article  CAS  Google Scholar 

  42. Ross-MacDonald, P. et al. Large-scale analysis of the yeast genome by transposon tagging and gene disruption. Nature 402, 413–418 (1999).

    Article  CAS  Google Scholar 

  43. True, H. L. & Lindquist, S. L. A yeast prion provides a mechanism for genetic variation and phenotypic diversity. Nature 407, 477–483 (2000).

    Article  CAS  Google Scholar 

  44. Sakumoto, N. et al. A series of double disruptants for protein phosphatase genes in Saccharomyces cerevisiae and their phenotypic analysis. Yeast 19, 587–599 (2002).

    Article  CAS  Google Scholar 

  45. Warringer, J. & Blomberg, A. Automated screening in environmental arrays allows analysis of quantitative phenotypic profiles in Saccharomyces cerevisiae. Yeast 20, 53–67 (2003).

    Article  CAS  Google Scholar 

Download references


The author gratefully acknowledges and thanks his colleagues that have participated in the development of Phenotype MicroArray technology: X. H. Lei, A. Franco-Buff, R. Kostriken, J. Argyle, L. Wiater, J. Carlson, A. Morgan, C. Gorman, P. Gadzinski, E. Olender, E. Panomitros and L. He. We are also grateful for partial financial support of this project by Small Business Innovation Research Grants from the National Institutes of Health/National Institute of General Medical Sciences and from the National Aeronautics and Space Administration

Author information

Authors and Affiliations


Related links

Related links






Deltagen, Inc.

Lexicon Genetics, Inc.

Paradigm Genetics, Inc.

Phenomix Corporation

SurroMed, Inc.



A gene that is transferred into a cell but originated in a cell from a different species.


Cells or organisms that are derived from the same parent and have almost identical genomes.


An analytical tool for determining the molecular weight of a chemical.


A self-acting and self-responding machine that has the ability to change itself into multiple states.


Cells that have been repeatedly subcultured, typically under artificial in vitro laboratory-culture conditions and not in more natural in vivo conditions.


A contiguous block of genes, found in pathogenic microorganisms, in which at least a subset of the genes code for virulence factors.


A dye chemistry that absorbs the electrons produced by cellular respiration, causing a colour change as the tetrazolium dye is reduced.


A contiguous block of genes that is derived from the bacterial transposon Tn10, which confers resistance to tetracycline antibiotics.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Bochner, B. New technologies to assess genotype–phenotype relationships. Nat Rev Genet 4, 309–314 (2003).

Download citation

  • Issue Date:

  • DOI:

This article is cited by


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing