Abstract
Identifying variations in DNA that increase susceptibility to disease is one of the primary aims of genetic studies using a forward genetics approach. However, identification of disease-susceptibility genes by means of such studies provides limited functional information on how genes lead to disease. In fact, in most cases there is an absence of functional information altogether, preventing a definitive identification of the susceptibility gene or genes. Here we develop an alternative to the classic forward genetics approach for dissecting complex disease traits where, instead of identifying susceptibility genes directly affected by variations in DNA, we identify gene networks that are perturbed by susceptibility loci and that in turn lead to disease. Application of this method to liver and adipose gene expression data generated from a segregating mouse population results in the identification of a macrophage-enriched network supported as having a causal relationship with disease traits associated with metabolic syndrome. Three genes in this network, lipoprotein lipase (Lpl), lactamase β (Lactb) and protein phosphatase 1-like (Ppm1l), are validated as previously unknown obesity genes, strengthening the association between this network and metabolic disease traits. Our analysis provides direct experimental support that complex traits such as obesity are emergent properties of molecular networks that are modulated by complex genetic loci and environmental factors.
This is a preview of subscription content, access via your institution
Access options
Subscribe to this journal
Receive 51 print issues and online access
$199.00 per year
only $3.90 per issue
Buy this article
- Purchase on Springer Link
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
Similar content being viewed by others
Accession codes
Primary accessions
Gene Expression Omnibus
Data deposits
The liver and adipose microarray data for the B × H cross have been deposited into the GEO database under accession numbers GSE2814 and GSE3086, respectively. Expression data associated with the diet-induced obesity, Zfp90 transgenic, Alox5-/- and roziglitazone-treated animals have been uploaded to the GEO database under accession numbers GSE7028, GSE7029, GSE7026 and GSE7027, respectively.
References
Edwards, A. O. et al. Complement factor H polymorphism and age-related macular degeneration. Science 308, 421–424 (2005)
Haines, J. L. et al. Complement factor H variant increases the risk of age-related macular degeneration. Science 308, 419–421 (2005)
Klein, R. J. et al. Complement factor H polymorphism in age-related macular degeneration. Science 308, 385–389 (2005)
Sladek, R. et al. A genome-wide association study identifies novel risk loci for type 2 diabetes. Nature 445, 881–885 (2007)
Frayling, T. M. et al. A common variant in the FTO gene is associated with body mass index and predisposes to childhood and adult obesity. Science 316, 889–894 (2007)
Strittmatter, W. J. et al. Apolipoprotein E: high-avidity binding to β-amyloid and increased frequency of type 4 allele in late-onset familial Alzheimer disease. Proc. Natl Acad. Sci. USA 90, 1977–1981 (1993)
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)
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. et al. Genetics of gene expression surveyed in maize, mouse and man. Nature 422, 297–302 (2003)
Hartwell, L. H., Hopfield, J. J., Leibler, S. & Murray, A. W. From molecular to modular cell biology. Nature 402, C47–C52 (1999)
Schadt, E. E., Sachs, A. & Friend, S. Embracing complexity, inching closer to reality. Sci. STKE 2005, pe40 (2005)
Paigen, B., Albee, D., Holmes, P. A. & Mitchell, D. Genetic analysis of murine strains C57BL/6J and C3H/HeJ to confirm the map position of Ath-1, a gene determining atherosclerosis susceptibility. Biochem. Genet. 25, 501–511 (1987)
Yang, X. et al. Tissue-specific expression and regulation of sexually dimorphic genes in mice. Genome Res. 16, 995–1004 (2006)
Wang, S. et al. Genetic and genomic analysis of a fat mass trait with complex inheritance reveals marked sex specificity. PLoS Genet. 2, e15 (2006)
Paigen, B. et al. Ath-1, a gene determining atherosclerosis susceptibility and high density lipoprotein levels in mice. Proc. Natl Acad. Sci. USA 84, 3763–3767 (1987)
Castellani, L. W., Goto, A. M. & Lusis, A. J. Studies with apolipoprotein A-II transgenic mice indicate a role for HDLs in adiposity and insulin resistance. Diabetes 50, 643–651 (2001)
Wang, X., Korstanje, R., Higgins, D. & Paigen, B. Haplotype analysis in multiple crosses to identify a QTL gene. Genome Res. 14, 1767–1772 (2004)
Wang, X. et al. Positional identification of TNFSF4, encoding OX40 ligand, as a gene that influences atherosclerosis susceptibility. Nature Genet. 37, 365–372 (2005)
Warden, C. H., Hedrick, C. C., Qiao, J. H., Castellani, L. W. & Lusis, A. J. Atherosclerosis in transgenic mice overexpressing apolipoprotein A-II. Science 261, 469–472 (1993)
Welch, C. L. et al. Novel QTLs for HDL levels identified in mice by controlling for Apoa2 allelic effects: confirmation of a chromosome 6 locus in a congenic strain. Physiol. Genomics 17, 48–59 (2004)
Doolittle, M. H., LeBoeuf, R. C., Warden, C. H., Bee, L. M. & Lusis, A. J. A polymorphism affecting apolipoprotein A-II translational efficiency determines high density lipoprotein size and composition. J. Biol. Chem. 265, 16380–16388 (1990)
Pajukanta, P. et al. Familial combined hyperlipidemia is associated with upstream transcription factor 1 (USF1). Nature Genet. 36, 371–376 (2004)
Babinska, A. et al. F11-receptor (F11R/JAM) mediates platelet adhesion to endothelial cells: role in inflammatory thrombosis. Thromb. Haemost. 88, 843–850 (2002)
Huertas-Vazquez, A. et al. Familial combined hyperlipidemia in Mexicans: association with upstream transcription factor 1 and linkage on chromosome 16q24.1. Arterioscler. Thromb. Vasc. Biol. 25, 1985–1991 (2005)
Ezzahiri, R. et al. Chlamydia pneumoniae infections augment atherosclerotic lesion formation: a role for serum amyloid P. APMIS 114, 117–126 (2006)
Adams, L. D., Geary, R. L., Li, J., Rossini, A. & Schwartz, S. M. Expression profiling identifies smooth muscle cell diversity within human intima and plaque fibrous cap: loss of RGS5 distinguishes the cap. Arterioscler. Thromb. Vasc. Biol. 26, 319–325 (2006)
Chang, Y. P. et al. Multiple genes for essential-hypertension susceptibility on chromosome 1q. Am. J. Hum. Genet. 80, 253–264 (2007)
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)
Austyn, J. M. & Gordon, S. F4/80, a monoclonal antibody directed specifically against the mouse macrophage. Eur. J. Immunol. 11, 805–815 (1981)
Ramprasad, M. P., Terpstra, V., Kondratenko, N., Quehenberger, O. & Steinberg, D. Cell surface expression of mouse macrosialin and human CD68 and their role as macrophage receptors for oxidized low density lipoprotein. Proc. Natl Acad. Sci. USA 93, 14833–14838 (1996)
Wright, S. D., Ramos, R. A., Tobias, P. S., Ulevitch, R. J. & Mathison, J. C. CD14, a receptor for complexes of lipopolysaccharide (LPS) and LPS binding protein. Science 249, 1431–1433 (1990)
Kubota, N. et al. PPARγ mediates high-fat diet-induced adipocyte hypertrophy and insulin resistance. Mol. Cell 4, 597–609 (1999)
Hu, Y., Liu, W., Huang, R. & Zhang, X. A systematic review and meta-analysis of the relationship between lipoprotein lipase Asn291Ser variant and diseases. J. Lipid Res. 47, 1908–1914 (2006)
Preiss-Landl, K., Zimmermann, R., Hammerle, G. & Zechner, R. Lipoprotein lipase: the regulation of tissue specific expression and its role in lipid and energy metabolism. Curr. Opin. Lipidol. 13, 471–481 (2002)
Yost, T. J., Jensen, D. R. & Eckel, R. H. Tissue-specific lipoprotein lipase: relationships to body composition and body fat distribution in normal weight humans. Obes. Res. 1, 1–4 (1993)
Liobikas, J. et al. Expression and purification of the mitochondrial serine protease LACTB as an N-terminal GST fusion protein in Escherichia coli. Protein Expr. Purif. 45, 335–342 (2006)
Smith, T. S. et al. Identification, genomic organization, and mRNA expression of LACTB, encoding a serine β-lactamase-like protein with an amino-terminal transmembrane domain. Genomics 78, 12–14 (2001)
Jacobs, C. Life in the balance: cell walls and antibiotic resistance. Science 278, 1731–1732 (1997)
Gaucher, S. P. et al. Expanded coverage of the human heart mitochondrial proteome using multidimensional liquid chromatography coupled with tandem mass spectrometry. J. Proteome Res. 3, 495–505 (2004)
Mootha, V. K. et al. Integrated analysis of protein composition, tissue diversity, and gene regulation in mouse mitochondria. Cell 115, 629–640 (2003)
Taylor, S. W. et al. Characterization of the human heart mitochondrial proteome. Nature Biotechnol. 21, 281–286 (2003)
Bains, R. K. et al. Visceral obesity without insulin resistance in late-onset obesity rats. Endocrinology 145, 2666–2679 (2004)
Schadt, E. E. & Lum, P. Y. Reverse engineering gene networks to identify key drivers of complex disease phenotypes. J. Lipid Res. 47, 2601–2013 (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)
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)
Haley, C. S. & Knott, S. A. A simple regression method for mapping quantitative trait loci in line crosses using flanking markers. Heredity 69, 315–324 (1992)
Jiang, C. & Zeng, Z. B. Multiple trait analysis of genetic mapping for quantitative trait loci. Genetics 140, 1111–1127 (1995)
Acknowledgements
This work was supported in part by grants from the NIH/NIDDK and NIH/NHLBI to A.J.L. and T.A.D.
Author Contributions S.P., D.J.M. and M.-F.C. constructed and characterized the Ppm1l knockout mouse. X.Y., L.W.C., S.W., S.D., A.G., T.A.D. and A.J.L. constructed and characterized the B × H cross, the Lpl knockout mouse and the Lactb transgenic mouse. S.H., A.G., S.D. and B.Z. assisted in the co-expression network analyses. S.E. and A.J.L. performed bioinformatic analyses. All authors discussed the results and commented on the manuscript. S.K.S. and C.Z. aided in the data analysis. P.Y.L. and J.L. aided in the study design and interpretation of the experimental results. Y.C., J.Z. and E.E.S. designed the study, developed methods, analysed the data and wrote the paper.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Competing interests
The following authors own stocks or stock options of Merck & Co. Inc.: Y.C., J.Z., P.Y.L., X.Y., S.P., D.J.M., C.Z., J.L., S.E., S.K.S., A.L., B.Z., V.E. and E.E.S.
Supplementary information
Supplementary Information
The file contains Supplementary Discussion, Supplementary Notes, Supplementary Figures 1-11 and Legends and Supplementary Tables 1-2 The Supplementary Discussion and Supplementary Figures 7-9 provide details on inferring causal relationships for traits under feedback control. In addition, the Supplementary Discussion and Supplementary Figures 1 and 2, additional information on Tnfsf4 and Apoa2 expression in the BXH cross. Supplementary Figures 3-5 provide the coexpression networks, enrichments, and characteristics that are critical to the results in the main text. Supplementary Figure 6 provides details on the oral glucose tolerance test carried out on the Ppm1l knockout mice. Supplementary Figure 10 provides details on the Ppm1l knockout construct. Supplementary Figure 11 provides details on the selection of the p-value threshold used to construct the liver and adipose coexpression networks (PDF 1554 kb)
Supplementary Table 3
The file contains Supplementary Table 3 which lists genes contained in the macrophage-enriched network described in the main text. (PDF 103 kb)
Supplementary Table 4
The file contains Supplementary Table 4. The file includes series of indicators for each gene in the macrophage enriched network listed in Supplementary Table 3 that indicate whether a given gene in this network was annotated as belonging to an inflammatory pathway; expressed in macrophage-related tissues; a member of the diet-induced obesity, Zfp90, Alox5, or Pparg perturbation gene expression signatures; or supported as causal for any of the metabolic traits. (PDF 99 kb)
Supplementary Table 5
The file contains Supplementary Table 5 For each gene in the macrophage-enriched metabolic network that was supported as causal for at least one metabolic trait, this table provides the number of metabolic traits a given gene was supported as causal for. (PDF 71 kb)
Supplementary Table 6
The file contains Supplementary Table 6 which includes the list of genes comprising each module in the liver coexpression network described in the text and depicted in Supplementary Figure 3A. (PDF 128 kb)
Supplementary Table 7
The file contains Supplementary Table 7 which lists genes and annotations represented on the microarray used to profile the BXH cross tissues. (PDF 9553 kb)
Supplementary Methods
This file includes Supplementary Methods. This file was uploaded on 2nd April 2008. (PDF 165 kb)
Rights and permissions
About this article
Cite this article
Chen, Y., Zhu, J., Lum, P. et al. Variations in DNA elucidate molecular networks that cause disease. Nature 452, 429–435 (2008). https://doi.org/10.1038/nature06757
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/nature06757
This article is cited by
-
DrGA: cancer driver gene analysis in a simpler manner
BMC Bioinformatics (2022)
-
Coding and regulatory variants are associated with serum protein levels and disease
Nature Communications (2022)
-
Traversing industry and academia in biomedicine: the best of both worlds?
Nature Reviews Genetics (2022)
-
Systemic cytokines and GlycA discriminate disease status and predict corticosteroid response in HTLV-1-associated neuroinflammation
Journal of Neuroinflammation (2022)
-
Competing endogenous RNA network analysis explores the key lncRNAs, miRNAs, and mRNAs in type 1 diabetes
BMC Medical Genomics (2021)
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.