Skip to main content

Thank you for visiting nature.com. 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.

  • Review Article
  • Published:

Molecular networks as sensors and drivers of common human diseases

Abstract

The molecular biology revolution led to an intense focus on the study of interactions between DNA, RNA and protein biosynthesis in order to develop a more comprehensive understanding of the cell. One consequence of this focus was a reduced attention to whole-system physiology, making it difficult to link molecular biology to clinical medicine. Equipped with the tools emerging from the genomics revolution, we are now in a position to link molecular states to physiological ones through the reverse engineering of molecular networks that sense DNA and environmental perturbations and, as a result, drive variations in physiological states associated with disease.

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: Hierarchy of causal relationships.
Figure 2: Linking molecular biology to physiology through molecular networks.

Similar content being viewed by others

References

  1. McKusick, V. A. Mendelian Inheritance in Man: A Catalog of Human Genes and Genetic Disorders (Johns Hopkins Univ. Press, 1998).

    Google Scholar 

  2. Chen, Y. et al. Variations in DNA elucidate molecular networks that cause disease. Nature 452, 429–435 (2008). This paper was the first demonstration that coherent networks of genes respond to genetic and environmental perturbations and in turn influence disease-associated traits, directly showing that common forms of disease are probably emergent properties of networks rather than the result of single gene changes.

    Article  ADS  CAS  Google Scholar 

  3. Altshuler, D., Daly, M. J. & Lander, E. S. Genetic mapping in human disease. Science 322, 881–888 (2008).

    Article  ADS  CAS  Google Scholar 

  4. Barrett, J. C. et al. Genome-wide association defines more than 30 distinct susceptibility loci for Crohn's disease. Nature Genet. 40, 955–962 (2008).

    Article  CAS  Google Scholar 

  5. Zeggini, E. et al. Meta-analysis of genome-wide association data and large-scale replication identifies additional susceptibility loci for type 2 diabetes. Nature Genet. 40, 638–645 (2008).

    Article  CAS  Google Scholar 

  6. Kathiresan, S. et al. Six new loci associated with blood low-density lipoprotein cholesterol, high-density lipoprotein cholesterol or triglycerides in humans. Nature Genet. 40, 189–197 (2008).

    Article  CAS  Google Scholar 

  7. Willer, C. J. et al. Newly identified loci that influence lipid concentrations and risk of coronary artery disease. Nature Genet. 40, 161–169 (2008).

    Article  CAS  Google Scholar 

  8. Haiman, C. A. et al. A common genetic risk factor for colorectal and prostate cancer. Nature Genet. 39, 954–956 (2007).

    Article  CAS  Google Scholar 

  9. Haiman, C. A. et al. Multiple regions within 8q24 independently affect risk for prostate cancer. Nature Genet. 39, 638–644 (2007).

    Article  CAS  Google Scholar 

  10. Li, M. et al. CFH haplotypes without the Y402H coding variant show strong association with susceptibility to age-related macular degeneration. Nature Genet. 38, 1049–1054 (2006).

    Article  CAS  Google Scholar 

  11. Maller, J. et al. Common variation in three genes, including a noncoding variant in CFH, strongly influences risk of age-related macular degeneration. Nature Genet. 38, 1055–1059 (2006).

    Article  CAS  Google Scholar 

  12. Thorleifsson, G. et al. Genome-wide association yields new sequence variants at seven loci that associate with measures of obesity. Nature Genet. 41, 18–24 (2009).

    Article  CAS  Google Scholar 

  13. Emilsson, V. et al. Genetics of gene expression and its effect on disease. Nature 452, 423–428 (2008). This paper is a confirmation in a human population that common diseases like obesity are the result of complex molecular networks responding to genetic and environmental perturbations.

    Article  ADS  CAS  Google Scholar 

  14. Schadt, E. E. et al. Mapping the genetic architecture of gene expression in human liver. PLoS Biol. 6, e107 (2008).

    Article  Google Scholar 

  15. Lum, P. Y., Derry, J. M. & Schadt, E. E. Integrative genomics and drug development. Pharmacogenomics 10, 203–212 (2009).

    Article  CAS  Google Scholar 

  16. Schadt, E. E., Friend, S. H. & Shaywitz, D. A. A network view of disease and compound screening. Nature Rev. Drug Discov. 8, 286–295 (2009).

    Article  CAS  Google Scholar 

  17. Goldstein, D. B. Common genetic variation and human traits. N. Engl. J. Med. 360, 1696–1698 (2009).

    Article  CAS  Google Scholar 

  18. Hardy, J. & Singleton, A. Genomewide association studies and human disease. N. Engl. J. Med. 360, 1759–1768 (2009).

    Article  CAS  Google Scholar 

  19. Kraft, P. & Hunter, D. J. Genetic risk prediction — are we there yet? N. Engl. J. Med. 360, 1701–1703 (2009).

    Article  CAS  Google Scholar 

  20. Moffatt, M. F. et al. Genetic variants regulating ORMDL3 expression contribute to the risk of childhood asthma. Nature 448, 470–473 (2007). This was among the first studies to identify a disease-susceptibility gene by restricting attention to DNA variants that simultaneously associate with the disease and the expression levels of genes in the neighbourhood of the disease-associated variant.

    Article  ADS  CAS  Google Scholar 

  21. Schadt, E. E. et al. An integrative genomics approach to infer causal associations between gene expression and disease. Nature Genet. 37, 710–717 (2005). This was the first study to demonstrate that causal relationships between molecular-profiling traits (such as gene expression) and disease traits could be systematically inferred by integrating these data with genotypic data in human and experimental populations.

    Article  CAS  Google Scholar 

  22. Schadt, E. E. et al. Genetics of gene expression surveyed in maize, mouse and man. Nature 422, 297–302 (2003).

    Article  ADS  CAS  Google Scholar 

  23. Monks, S. A. et al. Genetic inheritance of gene expression in human cell lines. Am. J. Hum. Genet. 75, 1094–1105 (2004).

    Article  CAS  Google Scholar 

  24. Morley, M. et al. Genetic analysis of genome-wide variation in human gene expression. Nature 430, 743–747 (2004).

    Article  ADS  CAS  Google Scholar 

  25. Foss, E. J. et al. Genetic basis of proteome variation in yeast. Nature Genet. 39, 1369–1375 (2007).

    Article  CAS  Google Scholar 

  26. Fraser, H. B. & Xie, X. Common polymorphic transcript variation in human disease. Genome Res. 19, 567–575 (2009).

    Article  CAS  Google Scholar 

  27. Smirnov, D. A., Morley, M., Shin, E., Spielman, R. S. & Cheung, V. G. Genetic analysis of radiation-induced changes in human gene expression. Nature 459, 587–591 (2009).

    Article  ADS  CAS  Google Scholar 

  28. Mehrabian, M. et al. Integrating genotypic and expression data in a segregating mouse population to identify 5-lipoxygenase as a susceptibility gene for obesity and bone traits. Nature Genet. 37, 1224–1233 (2005).

    Article  CAS  Google Scholar 

  29. Yang, X. et al. Validation of candidate causal genes for abdominal obesity that affect shared metabolic pathways and networks. Nature Genet. 41, 415–423 (2009).

    Article  CAS  Google Scholar 

  30. Goldstein, D. B. Genomics and biology come together to fight HIV. PLoS Biol. 6, e76 (2008).

    Article  Google Scholar 

  31. Jones, S. et al. Core signaling pathways in human pancreatic cancers revealed by global genomic analyses. Science 321, 1801–1806 (2008).

    Article  ADS  CAS  Google Scholar 

  32. Parsons, D. W. et al. An integrated genomic analysis of human glioblastoma multiforme. Science 321, 1807–1812 (2008).

    Article  ADS  CAS  Google Scholar 

  33. Zhu, J. et al. Integrating large-scale functional genomic data to dissect the complexity of yeast regulatory networks. Nature Genet. 40, 854–861 (2008). This paper generalizes the early idea of integrating gene expression and DNA-variation data to infer causal relationships among gene expression traits and between gene expression and disease traits by integrating diverse types of data, including genotype, gene expression, protein-interaction and DNA–protein-binding data.

    Article  CAS  Google Scholar 

  34. Keller, M. P. et al. A gene expression network model of type 2 diabetes links cell cycle regulation in islets with diabetes susceptibility. Genome Res. 18, 706–716 (2008).

    Article  CAS  Google Scholar 

  35. Meng, H. et al. Identification of Abcc6 as the major causal gene for dystrophic cardiac calcification in mice through integrative genomics. Proc. Natl Acad. Sci. USA 104, 4530–4535 (2007).

    Article  ADS  CAS  Google Scholar 

  36. Ghazalpour, A. et al. Genomic analysis of metabolic pathway gene expression in mice. Genome Biol. 6, R59 (2005).

    Article  Google Scholar 

  37. Ghazalpour, A. et al. Integrating genetic and network analysis to characterize genes related to mouse weight. PLoS Genet. 2, e130 (2006).

    Article  Google Scholar 

  38. Zhu, J. et al. An integrative genomics approach to the reconstruction of gene networks in segregating populations. Cytogenet. Genome Res. 105, 363–374 (2004).

    Article  CAS  Google Scholar 

  39. Rual, J. F. et al. Towards a proteome-scale map of the human protein–protein interaction network. Nature 437, 1173–1178 (2005).

    Article  ADS  CAS  Google Scholar 

  40. Han, J. D. et al. Evidence for dynamically organized modularity in the yeast protein–protein interaction network. Nature 430, 88–93 (2004).

    Article  ADS  CAS  Google Scholar 

  41. Gargalovic, P. S. et al. Identification of inflammatory gene modules based on variations of human endothelial cell responses to oxidized lipids. Proc. Natl Acad. Sci. USA 103, 12741–12746 (2006).

    Article  ADS  CAS  Google Scholar 

  42. Dobrin, R. et al. Multi-tissue coexpression networks reveal unexpected subnetworks associated with disease. Genome Biol. 10, R55 (2009).

    Article  Google Scholar 

  43. Pe'er, D., Regev, A., Elidan, G. & Friedman, N. Inferring subnetworks from perturbed expression profiles. Bioinformatics 17 (suppl. 1), S215–S224 (2001).

    Article  Google Scholar 

  44. Zhu, J. et al. Increasing the power to detect causal associations by combining genotypic and expression data in segregating populations. PLOS Comput. Biol. 3, e69 (2007).

    Article  ADS  MathSciNet  Google Scholar 

  45. Schadt, E. E., Sachs, A. & Friend, S. Embracing complexity, inching closer to reality. Sci. STKE 2005, pe40 (2005).

    PubMed  Google Scholar 

  46. Zeyda, M. & Stulnig, T. M. Adipose tissue macrophages. Immunol. Lett. 112, 61–67 (2007).

    Article  CAS  Google Scholar 

  47. Eid, J. et al. Real-time DNA sequencing from single polymerase molecules. Science 323, 133–138 (2009).

    Article  ADS  CAS  Google Scholar 

  48. Cokus, S. J. et al. Shotgun bisulphite sequencing of the Arabidopsis genome reveals DNA methylation patterning. Nature 452, 215–219 (2008).

    Article  ADS  CAS  Google Scholar 

  49. Wang, Z., Gerstein, M. & Snyder, M. RNA-Seq: a revolutionary tool for transcriptomics. Nature Rev. Genet. 10, 57–63 (2009).

    Article  CAS  Google Scholar 

  50. Ingolia, N. T., Ghaemmaghami, S., Newman, J. R. & Weissman, J. S. Genome-wide analysis in vivo of translation with nucleotide resolution using ribosome profiling. Science 324, 218–223 (2009).

    Article  ADS  CAS  Google Scholar 

  51. Morgan, T. M., Krumholz, H. M., Lifton, R. P. & Spertus, J. A. Nonvalidation of reported genetic risk factors for acute coronary syndrome in a large-scale replication study. J. Am. Med. Assoc. 297, 1551–1561 (2007).

    Article  CAS  Google Scholar 

  52. Stolovitsky, G. & Califano, A. (eds). Reverse Engineering Biological Networks: Opportunities and Challenges in Computational Methods for Pathway Inference (Wiley, 2007).

    Google Scholar 

  53. Schmidt, M. & Lipson, H. Distilling free-form natural laws from experimental data. Science 324, 81–85 (2009).

    Article  ADS  CAS  Google Scholar 

  54. Bock, G. & Goode, J. A. (eds). 'In Silico' Simulation of Biological Processes 91–103; 119–128; 244–252 (Wiley, 2002).

    Book  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Ethics declarations

Competing interests

E.E.S. was recently employed by, and owns stock in, Merck & Co. At present, he is chief scientific officer of Pacific Biosciences.

Additional information

Reprints and permissions information is available at http://www.nature.com/reprints.

Correspondence should be addressed to E.E.S. (eschadt@pacificbiosciences.com).

Rights and permissions

Reprints and permissions

About this article

Cite this article

Schadt, E. Molecular networks as sensors and drivers of common human diseases. Nature 461, 218–223 (2009). https://doi.org/10.1038/nature08454

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nature08454

This article is cited by

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

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