Abstract
With tens of billions of dollars spent each year on the development of drugs to treat human diseases, and with fewer and fewer applications for investigational new drugs filed each year despite this massive spending, questions now abound on what changes to the drug discovery paradigm can be made to achieve greater success. The high rate of failure of drug candidates in clinical development, where the great majority of these drugs fail due to lack of efficacy, speak directly to the need for more innovative approaches to study the mechanisms of disease and drug discovery. Here we review systems biology approaches that have been devised over the last several years to understand the biology of disease at a more holistic level. By integrating a diversity of data like DNA variation, gene expression, protein–protein interaction, DNA–protein binding, and other types of molecular phenotype data, more comprehensive networks of genes both within and between tissues can be constructed to paint a more complete picture of the molecular processes underlying physiological states associated with disease. These more integrative, systems-level methods lead to networks that are demonstrably predictive, which in turn provides a deeper context within which single genes operate such as those identified from genome-wide association studies or those targeted for therapeutic intervention. The more comprehensive views of disease that result from these methods have the potential to dramatically enhance the way in which novel drug targets are identified and developed, ultimately increasing the probability of success for taking new drugs through clinical development. We highlight a number of the integrative approaches via examples that have resulted not only in the identification of novel genes for diabetes and cardiovascular disease, but in more comprehensive networks as well that describe the context in which the disease genes operate.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Brem RB, Yvert G et al (2002) Genetic dissection of transcriptional regulation in budding yeast. Science 296(5568):752–755
Bystrykh L, Weersing E et al (2005) Uncovering regulatory pathways that affect hematopoietic stem cell function using ‘genetical genomics’. Nat Genet 37(3):225–232
Chen Y, Zhu J et al (2008) Variations in DNA elucidate molecular networks that cause disease. Nature 452(7186):429–435
Chesler EJ, Lu L et al (2005) Complex trait analysis of gene expression uncovers polygenic and pleiotropic networks that modulate nervous system function. Nat Genet 37(3):233–242
Dixon AL, Liang L et al (2007) A genome-wide association study of global gene expression. Nat Genet 39(10):1202–1207
Doss S, Schadt EE et al (2005) Cis-acting expression quantitative trait loci in mice. Genome Res 15(5):681–691
Edwards AO, Ritter R 3rd et al (2005) Complement factor H polymorphism and age-related macular degeneration. Science 308(5720):421–424
Emilsson V, Thorleifsson G et al (2008) Genetics of gene expression and its effect on disease. Nature 452(7186):423–428
Fellay J, Shianna KV et al (2007) A whole-genome association study of major determinants for host control of HIV-1. Science 317(5840):944–947
Frayling TM, Timpson NJ et al (2007) A common variant in the FTO gene is associated with body mass index and predisposes to childhood and adult obesity. Science 316(5826):889–894
Gao F, Foat BC et al (2004) Defining transcriptional networks through integrative modeling of mRNA expression and transcription factor binding data. BMC Bioinformatics 5:31
Gargalovic PS, Imura M et al (2006) Identification of inflammatory gene modules based on variations of human endothelial cell responses to oxidized lipids. Proc Natl Acad Sci USA 103(34):12741–12746
Gerken T, Girard CA et al (2007) The obesity-associated FTO gene encodes a 2-oxoglutarate-dependent nucleic acid demethylase. Science 318(5855):1469–1472
Ghazalpour A, Doss S et al (2006) Integrating genetic and network analysis to characterize genes related to mouse weight. PLoS Genet 2(8):e130
Glymour CN (2001) The mind’s arrows: Bayes Nets and Graphical Causal Models in Psychology. Cambridge, Mass, Bradford Book
Grant SF, Thorleifsson G et al (2006) Variant of transcription factor 7-like 2 (TCF7L2) gene confers risk of type 2 diabetes. Nat Genet 38(3):320–323
Haines JL, Hauser MA et al (2005) Complement factor H variant increases the risk of age-related macular degeneration. Science 308(5720):419–421
Han JD, Bertin N et al (2004) Evidence for dynamically organized modularity in the yeast protein-protein interaction network. Nature 430(6995):88–93
Hartemink AJ, Gifford DK et al. (2002) Combining location and expression data for principled discovery of genetic regulatory network models. Pac Symp Biocomput 7:437–449
Helgadottir A, Thorleifsson G et al (2007) A common variant on chromosome 9p21 affects the risk of myocardial infarction. Science 316(5830):1491–1493
Herbert A, Gerry NP et al (2006) A common genetic variant is associated with adult and childhood obesity. Science 312(5771):279–283
Hindorff LA, Junkins HA et al. (2008) A catalog of published genome-wide association studies. Science 322(5901):881–888
Hughes TR, Marton MJ et al (2000) Functional discovery via a compendium of expression profiles. Cell 102(1):109–126
International HapMap Consortium (2005) A haplotype map of the human genome. Nature 437(7063):1299–1320. doi:10.1038/nature04226
Jensen FV (1996) An introduction to Bayesian Networks. Springer, New York. ISBN 0387915028
Jiang C, Zeng ZB (1995) Multiple trait analysis of genetic mapping for quantitative trait loci. Genetics 140(3):1111–1127
Kanehisa M, Goto S et al (2006) From genomics to chemical genomics: new developments in KEGG. Nucleic Acids Res 34(Database issue):D354–D357
Klein RJ, Zeiss C et al (2005) Complement factor H polymorphism in age-related macular degeneration. Science 308(5720):385–389
Lander ES, Linton LM et al (2001) Initial sequencing and analysis of the human genome. Nature 409(6822):860–921
McPherson R, Pertsemlidis A et al (2007) A common allele on chromosome 9 associated with coronary heart disease. Science 316(5830):1488–1491
Moffatt MF, Kabesch M et al (2007) Genetic variants regulating ORMDL3 expression contribute to the risk of childhood asthma. Nature 448(7152):470–473
Monks SA, Leonardson A et al (2004) Genetic inheritance of gene expression in human cell lines. Am J Hum Genet 75(6):1094–1105
Morley M, Molony CM et al (2004) Genetic analysis of genome-wide variation in human gene expression. Nature 430(7001):743–747
Ong IM, Glasner JD et al (2002) Modelling regulatory pathways in E. coli from time series expression profiles. Bioinformatics 18(Suppl 1):S241–S248
Pan X, Ye P et al (2006) A DNA integrity network in the yeast Saccharomyces cerevisiae. Cell 124(5):1069–1081
Peacock ML, Warren JT Jr et al (1993) Novel polymorphism in the A4 region of the amyloid precursor protein gene in a patient without Alzheimer’s disease. Neurology 43(6):1254–1256
Rabiner LR (1989) A tutorial on Hidden markov Models and selected applications in speech recognition. Proceedings of the 77th meeting of the institute of electrical and electronics engineers, San Diego, California, IEEE
Rual JF, Venkatesan K et al (2005) Towards a proteome-scale map of the human protein-protein interaction network. Nature 437(7062):1173–1178
Schadt EE, Sachs A et al (2005) Embracing complexity, inching closer to reality. Sci STKE 2005(295):pe40
Schadt EE, Lum PY (2006) Thematic review series: systems biology approaches to metabolic and cardiovascular disorders. Reverse engineering gene networks to identify key drivers of complex disease phenotypes. J Lipid Res 47(12):2601–2613
Schadt EE, Monks SA et al (2003) Genetics of gene expression surveyed in maize, mouse and man. Nature 422(6929):297–302
Schadt EE, Lamb J et al (2005b) An integrative genomics approach to infer causal associations between gene expression and disease. Nat Genet 37(7):710–717
Schadt EE, Molony C et al (2008) Mapping the genetic architecture of gene expression in human liver. PLoS Biol 6(5):e107
Segal E, Shapira M et al (2003) Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data. Nat Genet 34(2):166–176
Sladek R, Rocheleau G et al (2007) A genome-wide association study identifies novel risk loci for type 2 diabetes. Nature 445(7130):881–885
Stuart JM, Segal E et al (2003) A gene-coexpression network for global discovery of conserved genetic modules. Science 302(5643):249–255
Todd JA, Walker NM et al (2007) Robust associations of four new chromosome regions from genome-wide analyses of type 1 diabetes. Nat Genet 39(7):857–864
Venter JC, Adams MD et al (2001) The sequence of the human genome. Science 291(5507):1304–1351
Wellcome Trust Case Control Consortium (2007) Genome-wide association study of 14, 000 cases of seven common diseases and 3, 000 shared controls. Nature 447(7145):661–678. doi:10.1038/nature05911
Willer CJ, Sanna S et al (2008) Newly identified loci that influence lipid concentrations and risk of coronary artery disease. Nat Genet 40(2):161–169
Zhu J, Lum PY et al (2004) An integrative genomics approach to the reconstruction of gene networks in segregating populations. Cytogenet Genome Res 105(2–4):363–374
Zhu J, Wiener MC et al (2007) Increasing the power to detect causal associations by combining genotypic and expression data in segregating populations. PLoS Comput Biol 3(4):e69
Zhu J, Zhang B et al (2008) Integrating large-scale functional genomics data to dissect the complexity of yeast regulatory networks. Nat Genet 40(7):854–861
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Schadt, E.E., Zhang, B. & Zhu, J. Advances in systems biology are enhancing our understanding of disease and moving us closer to novel disease treatments. Genetica 136, 259–269 (2009). https://doi.org/10.1007/s10709-009-9359-x
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10709-009-9359-x