Common human diseases result from the interplay of many genes and environmental factors. Therefore, a more integrative biology approach is needed to unravel the complexity and causes of such diseases. To elucidate the complexity of common human diseases such as obesity, we have analysed the expression of 23,720 transcripts in large population-based blood and adipose tissue cohorts comprehensively assessed for various phenotypes, including traits related to clinical obesity. In contrast to the blood expression profiles, we observed a marked correlation between gene expression in adipose tissue and obesity-related traits. Genome-wide linkage and association mapping revealed a highly significant genetic component to gene expression traits, including a strong genetic effect of proximal (cis) signals, with 50% of the cis signals overlapping between the two tissues profiled. Here we demonstrate an extensive transcriptional network constructed from the human adipose data that exhibits significant overlap with similar network modules constructed from mouse adipose data. A core network module in humans and mice was identified that is enriched for genes involved in the inflammatory and immune response and has been found to be causally associated to obesity-related traits.
Subscribe to Journal
Get full journal access for 1 year
only $3.90 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Rent or Buy article
Get time limited or full article access on ReadCube.
All prices are NET prices.
Gene Expression Omnibus
All the gene expression data generated for this study have been deposited into the GEO database under accession numbers GSE7965 and GPL3991. The authors declare competing financial interests: details accompany the full-text HTML version of the paper at www.nature.com/nature.
Schadt, E. E. et al. Genetics of gene expression surveyed in maize, mouse and man. Nature 422, 297–302 (2003)
Golub, T. R. et al. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286, 531–537 (1999)
Johnson, J. M. et al. Genome-wide survey of human alternative pre-mRNA splicing with exon junction microarrays. Science 302, 2141–2144 (2003)
Shoemaker, D. D. et al. Experimental annotation of the human genome using microarray technology. Nature 409, 922–927 (2001)
Welsh, J. B. et al. Analysis of gene expression profiles in normal and neoplastic ovarian tissue samples identifies candidate molecular markers of epithelial ovarian cancer. Proc. Natl Acad. Sci. USA 98, 1176–1181 (2001)
Schadt, E. E. et al. An integrative genomics approach to infer causal associations between gene expression and disease. Nature Genet. 37, 710–717 (2005)
Schadt, E. E., Sachs, A. & Friend, S. Embracing complexity, inching closer to reality. Sci. STKE 2005, pe40 (2005)
Zhu, J. et al. An integrative genomics approach to the reconstruction of gene networks in segregating populations. Cytogenet. Genome Res. 105, 363–374 (2004)
Brem, R. B., Yvert, G., Clinton, R. & Kruglyak, L. Genetic dissection of transcriptional regulation in budding yeast. Science 296, 752–755 (2002)
Bystrykh, L. et al. Uncovering regulatory pathways that affect hematopoietic stem cell function using ‘genetical genomics’. Nature Genet. 37, 225–232 (2005)
Chesler, E. J. et al. Complex trait analysis of gene expression uncovers polygenic and pleiotropic networks that modulate nervous system function. Nature Genet. 37, 233–242 (2005)
Monks, S. A. et al. Genetic inheritance of gene expression in human cell lines. Am. J. Hum. Genet. 75, 1094–1105 (2004)
Morley, M. et al. Genetic analysis of genome-wide variation in human gene expression. Nature 430, 743–747 (2004)
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)
Brem, R. B., Storey, J. D., Whittle, J. & Kruglyak, L. Genetic interactions between polymorphisms that affect gene expression in yeast. Nature 436, 701–703 (2005)
Cheung, V. G. et al. Mapping determinants of human gene expression by regional and genome-wide association. Nature 437, 1365–1369 (2005)
Ranganathan, P. et al. Expression profiling of genes regulated by TGF-β: differential regulation in normal and tumour cells. BMC Genom. 8 98 doi: 10.1186/1471-2164-8-98 (2007)
Brem, R. B. & Kruglyak, L. The landscape of genetic complexity across 5,700 gene expression traits in yeast. Proc. Natl Acad. Sci. USA 102, 1572–1577 (2005)
Hubbard, T. et al. Ensembl 2005. Nucleic Acids Res. 33, D447–D453 (2005)
Whitney, A. R. et al. Individuality and variation in gene expression patterns in human blood. Proc. Natl Acad. Sci. USA 100, 1896–1901 (2003)
Storey, J. D. & Tibshirani, R. Statistical methods for identifying differentially expressed genes in DNA microarrays. Methods Mol. Biol. 224, 149–157 (2003)
Di Gregorio, G. B. et al. Expression of CD68 and macrophage chemoattractant protein-1 genes in human adipose and muscle tissues: association with cytokine expression, insulin resistance, and reduction by pioglitazone. Diabetes 54, 2305–2313 (2005)
Lumeng, C. N., Bodzin, J. L. & Saltiel, A. R. Obesity induces a phenotypic switch in adipose tissue macrophage polarization. J. Clin. Invest. 117, 175–184 (2007)
Neels, J. G. & Olefsky, J. M. Inflamed fat: what starts the fire? J. Clin. Invest. 116, 33–35 (2006)
Wellen, K. E. & Hotamisligil, G. S. Obesity-induced inflammatory changes in adipose tissue. J. Clin. Invest. 112, 1785–1788 (2003)
Steemers, F. J. & Gunderson, K. L. Illumina, Inc. Pharmacogenomics 6, 777–782 (2005)
Zhang, B. & Horvath, S. A general framework for weighted gene co-expression network analysis. Stat. Appl. Genet. Mol. Biol. 4, Article17 (2005)
Ravasz, E., Somera, A. L., Mongru, D. A., Oltvai, Z. N. & Barabasi, A. L. Hierarchical organization of modularity in metabolic networks. Science 297, 1551–1555 (2002)
Ghazalpour, A. et al. Integrating genetic and network analysis to characterize genes related to mouse weight. PLoS Genet 2, e130 (2006)
Lum, P. Y. et al. Elucidating the murine brain transcriptional network in a segregating mouse population to identify core functional modules for obesity and diabetes. J. Neurochem. 97 (suppl. 1). 50–62 (2006)
Chen, Y. et al. Variations in DNA elucidate molecular networks that cause disease. Nature doi: 10.1038/nature06757 (this issue)
Gulcher, J. R., Kristjansson, K., Gudbjartsson, H. & Stefansson, K. Protection of privacy by third-party encryption in genetic research in Iceland. Eur. J. Hum. Genet. 8, 739–742 (2000)
He, Y. D. et al. Microarray standard data set and figures of merit for comparing data processing methods and experiment designs. Bioinformatics 19, 956–965 (2003)
Hughes, T. R. et al. Functional discovery via a compendium of expression profiles. Cell 102, 109–126 (2000)
Kong, A. et al. A high-resolution recombination map of the human genome. Nature Genet. 31, 241–247 (2002)
Almasy, L. & Blangero, J. Multipoint quantitative-trait linkage analysis in general pedigrees. Am. J. Hum. Genet. 62, 1198–1211 (1998)
Gudbjartsson, D. F., Jonasson, K., Frigge, M. L. & Kong, A. Allegro, a new computer program for multipoint linkage analysis. Nature Genet. 25, 12–13 (2000)
Kong, A. & Cox, N. J. Allele-sharing models: LOD scores and accurate linkage tests. Am. J. Hum. Genet. 61, 1179–1188 (1997)
Badner, J. A., Gershon, E. S. & Goldin, L. R. Optimal ascertainment strategies to detect linkage to common disease alleles. Am. J. Hum. Genet. 63, 880–888 (1998)
Amos, C. I. Robust variance-components approach for assessing genetic linkage in pedigrees. Am. J. Hum. Genet. 54, 535–543 (1994)
Churchill, G. A. & Doerge, R. W. Empirical threshold values for quantitative trait mapping. Genetics 138, 963–971 (1994)
The authors acknowledge the participating families and the staff at the Clinical Research Centre for their cooperation. Genotyping service was provided at the deCode Genetics genotyping facilities.
Author Contributions V.E., E.E.S., K.S. and G.T. wrote the paper. G.T., E.E.S., A.K., D.G. and F.Z. performed statistical analysis. Tissue sampling and/or molecular profiling was carried out by H.G.G., T.S., B.G.L., G.H.E., S.C., M.M., Aslaug Jonasdottir, Adalbjorg Jonasdottir, G.B. and K.K. V.E., J.Z., U.T., A.S.L., A.H., B.Z., G.B.W., S. Gunnarsdottir, S. Gretarsdottir, K.P.M., V.S., I.R., A.H., U.S., H.S., R.F., J.R.G., K.S., M.L.R. and J.R.L. performed the genetic analysis and/or data-mining. K.S. and E.E.S. contributed equally to this work.
The following authors own stocks in either deCode Genetics, Inc. or Merck & Co., Inc.: V.E., E.E.S., G.T., G.B., S.C., M.M., J.Z., Aslaug Jonasdottir, Adalbjorg Jonasdottir, K.K., U.T., A.S.L., A.H., B.Z., G.B.W., F.Z., S. Gunnarsdottir, S. Gretarsdottir, K.P.M., V.S., I.R., D.G., A.H., U.S. H.S., R.F., A.K., K.S., J.R.G., M.L.R. and J.R.L.
The file contains Supplementary Results with additional references, Supplementary Tables 1-7 and Supplementary Figures 1-7 with Legends. The file contains Supplementary Results on the probes overlapping SNPs, distribution of cis eSNPs as regards the location of probes, detection of trans eQTLs and eQTL hotspots, comparison of expression linkage and association results and finally the additional information regarding genes in the mouse MEMN that have been shown to be causal for metabolic diseases. Tables include a summary of cohort description (Supplementary Table 1), results on the re-sequencing of array probes (Supplementary Table 2), expression trait vs. clinical trait correlations (Supplementary Table 3), heritability and eQTL results (Supplementary Tables 4 and 5), detection of significant trans eQTLs and eQTL hotspots (Supplementary Table 6) and finally the pathway enrichment for the gene sets in the MEMN module (Supplementary Table 7). The figures show chromosomal distribution of eQTLs in IFB in the real data and a simulated dataset (Supplementary Figure 1), distribution of BMI, gene expression vs. BMI correlations and heritability in the IFA cohort (Supplementary Figure 2), the agreement between the linkage and association data (Supplementary Figure 3), distribution of cis eSNPs as regards the location of probes (Supplementary Figure 4), the localization and specificity of the association signal (Supplementary Figure 5), the gene set overlap between the male and female specific MEMN module in humans (Supplementary Figure 6) and finally the comparison between the connectivity structure of the MEMN genes in between the females and males. (PDF 828 kb)
The file contains description of the RNA sample processing, the design of the arrays, quality controlling and processing of the probe hybridization. Here, appropriate references and Figure 1 (as an example of the data display) are provided as well. (PDF 179 kb)
About this article
Cite this article
Emilsson, V., Thorleifsson, G., Zhang, B. et al. Genetics of gene expression and its effect on disease. Nature 452, 423–428 (2008) doi:10.1038/nature06758
A Novel Gene Selection Algorithm based on Sparse Representation and Minimum-redundancy Maximum-relevancy of Maximum Compatibility Center
Current Proteomics (2019)
Analysis of potential roles of combinatorial microRNA regulation in occurrence of valvular heart disease with atrial fibrillation based on computational evidences
PLOS ONE (2019)