To advance our understanding of the genetic regulation of complex traits and disease we can now generate multiple genome-scale data modalities in a single experiment, allowing unprecedented insights into the molecular mechanisms underlying disease. Moving toward a systems-genetics approach to disease, we combine genomics and network- based analyses for the study of complex traits, including metabolic, cardiovascular, inflammatory and neurological disease. Rather than identifying single susceptibility genes, we use systems-genetics approaches to identify key (epi)genetic regulators of networks, predict their functional consequences in multiple tissues and organs, and facilitate network-based drug discovery in human disease.We combine reverse-engineering strategies for regulatory gene networks with Bayesian modelling approaches in multiple tissues for deeper analyses of the genetics of common diseases. This strategy is used to identify key genes and regulatory networks that modulate disease or physiological traits at the organism level.
Figure 1 – System-genetics to dissect disease mechanisms. Complex gene network driven by the Irf7 transcription factor, which was identified in multiple rat tissues. Nodes represent individual genes: the node representing Irf7 is coloured red and its predicted targets are coloured blue. Edges connect genes that are either predicted Irf7-targets (black) or show significant correlation of expression levels to one of the predicted targets (grey). The network is highly enriched for immune response genes and has been named “Irf7-driven inflammatory gene network” or iDIN. iDIN genes contribute to Type 1 Diabetes (T1D) risk in humans and Ebi2 (or Gpr183), which controls Irf7 in macrophages, represents a candidate for trans-regulation of the human iDIN and for T1D risk (Heinig*, Petretto* et al. Nature 2010).
We have developed a new algorithm to identify common and differential cluster structures simultaneously across multiple conditions. The Cross-Condition-Cluster-Detection or C3D algorithm (Matlab), documentation and example data files are available to download here.
Figure 2 – Multi-tissue networks analysis in the rat using the C3D method identified heat shock protein (Hsp) genes (Hsp90b1, DnaJ (Hsp40) homologs, Hspa5, Hspb8, Hsph1) and the Hsf1 (heat shock transcription factor 1), which were co-expressed with genes known to have disease mutations in hereditary cardiomyopathy in humans (Bag3, Cryab, Kras, Emd, Plec). Conserved co-expression between Hsp and cardiomyopathy genes in rats and humans suggest a potential role for heat shock proteins in cardiovascular disease (Xiao et al PLoS Genetics 2013).
Kang H, Kerloc’h A, Rotival M, Xu X, Zhang Q, D’Souza Z, Kim M, Scholz JC, Ko J-H, Srivastava PK, Genzen JR, Cui W, Aitman TJ, Game L, Melvin JE, Hanidu A, Dimock J, Zheng J, Souza D, Behera A, Nabozny G, Cook HT, Bassett JHD, Williams GR, Li J, Vignery A, Petretto E and Behmoaras J. (2014). Kcnn4 is a regulator of macrophage multinucleation in bone homeostasis and inflammatory disease. Cell Reports, 8(4), 1210-24.
Xiao, X., Moreno-Moral, A., Rotival, M., Bottolo, L., & Petretto, E. (2014). Multi-tissue analysis of co-expression networks by higher-order generalized singular value decomposition identifies functionally coherent transcriptional modules. PLoS Genetics, 10(1), e1004006+.
Heinig, M., Petretto, E., Wallace, C., Bottolo, L., Rotival, M., Lu, H., Li, Y., Sarwar, R., Langley, S. R., Bauerfeind, A., Hummel, O., Lee, Y.-A. A., Paskas, S., Rintisch, C., Saar, K., Cooper, J., Buchan, R., Gray, E. E., Cyster, J. G., Cardiogenics Consortium, Erdmann, J., Hengstenberg, C., Maouche, S., Ouwehand, W. H., Rice, C. M., Samani, N. J., Schunkert, H., Goodall, A. H., Schulz, H., Roider, H. G., Vingron, M., Blankenberg, S., Münzel, T., Zeller, T., Szymczak, S., Ziegler, A., Tiret, L., Smyth, D. J., Pravenec, M., Aitman, T. J., Cambien, F., Clayton, D., Todd, J. A., Hubner, N., & Cook, S. A. (2010). A trans-acting locus regulates an anti-viral expression network and type 1 diabetes risk. Nature, 467(7314), 460–464.
Petretto, E., Bottolo, L., Langley, S. R., Heinig, M., McDermott-Roe, C., Sarwar, R., Pravenec, M., Hübner, N., Aitman, T. J., Cook, S. A., & Richardson, S. (2010). New insights into the genetic control of gene expression using a bayesian multi-tissue approach. PLoS Computational Biology, 6(4), e1000737+.
Ioannidis, J. P. A., Allison, D. B., Ball, C. A., Coulibaly, I., Cui, X., Culhane, A. C., Falchi, M., Furlanello, C., Game, L., Jurman, G., Mangion, J., Mehta, T., Nitzberg, M., Page, G. P., Petretto, E., & van Noort, V. (2009). Repeatability of published microarray gene expression analyses. Nature Genetics, 41(2), 149–155.
Petretto, E., Sarwar, R., Grieve, I., Lu, H., Kumaran, M. K., Muckett, P. J., Mangion, J., Schroen, B., Benson, M., Punjabi, P. P., Prasad, S. K., Pennell, D. J., Kiesewetter, C., Tasheva, E. S., Corpuz, L. M., Webb, M. D., Conrad, G. W., Kurtz, T. W., Kren, V., Fischer, J., Hubner, N., Pinto, Y. M., Pravenec, M., Aitman, T. J., & Cook, S. A. (2008). Integrated genomic approaches implicate osteoglycin (ogn) in the regulation of left ventricular mass. Nature Genetics, 40(5), 546–552.
Petretto, E., Mangion, J., Dickens, N. J., Cook, S. A., Kumaran, M. K., Lu, H., Fischer, J., Maatz, H., Kren, V., Pravenec, M., Hubner, N., & Aitman, T. J. (2006). Heritability and tissue specificity of expression quantitative trait loci. PLoS Genetics, 2(10), e172+.
Hubner, N., Wallace, C. A., Zimdahl, H., Petretto, E., Schulz, H., Maciver, F., Mueller, M., Hummel, O., Monti, J., Zidek, V., Musilova, A., Kren, V., Causton, H., Game, L., Born, G., Schmidt, S., Muller, A., Cook, S. A., Kurtz, T. W., Whittaker, J., Pravenec, M., & Aitman, T. J. (2005). Integrated transcriptional profiling and linkage analysis for identification of genes underlying disease. Nature Genetics, 37(3), 243–253.