Variations in DNA elucidate molecular networks that cause disease
Open accessAbstract
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 beta (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.
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Genes with local eQTL are promising candidates for underlying disease-associated regions in human GWAS studies.
“genes with local eQTL are promising candidates for underlying disease-associated regions in human GWAS studies (Knight 2005; Chen et al. 2008; Emilsson et al. 2008; Musunuru et al. 2010; Hou and Zhao 2013; Li et al. 2013).”
From RNA-Seq Alignment to Individualized Genomes Improves Transcript Abundance Estimates in Multiparent Populations (2014) · cites this paper as Chen et al. 2008
The passage on the chromosome 1 linkage region (labelled Abstract; page unavailable) reports that some genes with local cis-eQTL were statistically supported as contributing to metabolic traits. But those results come from a mouse cross, not human GWAS, and the paper does not establish that genes with local eQTL generally are promising candidates in human disease-associated regions. (LLM, conf=0.86)
Experimental — accuracy not yet measuredIn biological networks, mathematical derivations show that only intra-modular hub genes are more relevant than other nodes, as opposed to inter-modular hub genes.
“Hubs are frequently more relevant to the functionality of networks than other nodes [69]. This is also the case in biological networks [32], although mathematical derivations show that this is only the case for intra-modular hub genes (as opposed to inter-modular hub genes [64, 65]).”
From Gene co-expression analysis for functional classification and gene–disease predictions (2017) · cites this paper as [64]
The resulting modules from clustering often represent biological processes.
“The resulting modules often represent biological processes [63, 64] and can be phenotype specific [65].”
From Gene co-expression analysis for functional classification and gene–disease predictions (2017) · cites this paper as [64]
Co-expression networks allow analysis of gene expression variation related to multiple disease-related and genetic traits.
“As previously shown for complex diseases 12; 13 co-expression networks allow analysis of gene expression variation related to multiple disease-related and genetic traits.”
From Transcriptomic Analysis of Autistic Brain Reveals Convergent Molecular Pathology (2011) · cites this paper as [12]
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Frayling, T. M., Timpson, N. J., Weedon, M. N., Zeggini, E., Freathy, R. M., Lindgren, C. M., Perry, J. R. B., Elliott, K. S., Lango, H., Rayner, N. W., et al. (2007) · Science · doi:10.1126/science.1141634 · Cited by 4,531 · see citing works
Complement factor H polymorphism in age-related macular degeneration
Klein, R. J., Zeiss, C., Chew, E. Y., Tsai, J.-Y., Sackler, R. S., Haynes, C., Henning, A. K., SanGiovanni, J. P., Mane, S. M., Mayne, S. T., et al. (2005) · Science · doi:10.1126/science.1109557 · Cited by 4,492 · see citing works
Strittmatter, W. J., Saunders, A. M., Schmechel, D., Pericak-Vance, M., Enghild, J., Salvesen, G. S., & Roses, A. D. (1993) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.90.5.1977 · Cited by 4,268 · see citing works
Hierarchical Organization of Modularity in Metabolic Networks
Ravasz, E., Somera, A. L., Mongru, D. A., Oltvai, Z. N., & Barabási, A.-L. (2002) · Science · doi:10.1126/science.1073374 · Cited by 4,224 · see citing works
CD14, a Receptor for Complexes of Lipopolysaccharide (LPS) and LPS Binding Protein
Wright, S. D., Ramos, R., Tobias, P. S., Ulevitch, R. J., & Mathison, J. C. (1990) · Science · doi:10.1126/science.1698311 · Cited by 3,986 · see citing works
From molecular to modular cell biology.
Hartwell, L. H., Hopfield, J. J., Leibler, S., & Murray, A. W. (1999) · Nature · doi:10.1038/35011540 · Cited by 3,632 · see citing works
A genome-wide association study identifies novel risk loci for type 2 diabetes
Sladek, R., Rocheleau, G., Rung, J., Dina, C., Shen, L., Serre, D., Boutin, P., Vincent, D., Belisle, A., Hadjadj, S., et al. (2007) · Nature · doi:10.1038/nature05616 · Cited by 2,993 · see citing works
Complement factor H polymorphism and age-related macular degeneration
Edwards, A. O., Ritter, R., Abel, K. J., Manning, A., Panhuysen, C., & Farrer, L. A. (2005) · Science · doi:10.1126/science.1110189 · Cited by 2,449 · see citing works
Complement factor H variant increases the risk of age-related macular degeneration
Haines, J. L., Hauser, M. A., Schmidt, S., Scott, W. K., Olson, L. M., Gallins, P., Spencer, K. L., Kwan, S. Y., Noureddine, M., Gilbert, J. R., et al. (2005) · Science · doi:10.1126/science.1110359 · Cited by 2,424 · see citing works
Haley, C. S., & Knott, S. A. (1992) · Heredity · doi:10.1038/hdy.1992.131 · Cited by 1,991 · see citing works
Genetics of gene expression surveyed in maize, mouse and man
Schadt, E., Monks, S. A., Drake, T., Lusis, A., Che, N., Colinayo, V. V., Ruff, T., Milligan, S. B., Lamb, J., Cavet, G., et al. (2003) · Nature · doi:10.1038/nature01434 · Cited by 1,594 · see citing works
Self-citationF4/80, a monoclonal antibody directed specifically against the mouse macrophage
Austyn, J. M., & Gordon, S. (1981) · European Journal of Immunology · doi:10.1002/eji.1830111013 · Cited by 1,580 · see citing works
Genetic Dissection of Transcriptional Regulation in Budding Yeast
Brem, R. B., Yvert, G., Clinton, R., & Kruglyak, L. (2002) · Science · doi:10.1126/science.1069516 · Cited by 1,392 · see citing works
PPARγ Mediates High-Fat Diet–Induced Adipocyte Hypertrophy and Insulin Resistance
Kubota, N., Terauchi, Y., Miki, H., Tamemoto, H., Yamauchi, T., Komeda, K., Satoh, S., Nakano, R., Ishii, C., 杉山, 拓., et al. (1999) · Molecular Cell · doi:10.1016/s1097-2765(00)80210-5 · Cited by 1,342 · see citing works
An integrative genomics approach to infer causal associations between gene expression and disease
Schadt, E. E., Lamb, J., Yang, X., Zhu, J., Edwards, S., GuhaThakurta, D., Sieberts, S. K., Monks, S., Reitman, M., Zhang, C., et al. (2005) · Nature Genetics · doi:10.1038/ng1589 · Cited by 1,043 · see citing works
Self-citationTissue-specific expression and regulation of sexually dimorphic genes in mice
Yang, X., Schadt, E. E., Wang, S., Wang, H., Arnold, A. P., Ingram-Drake, L., Drake, T. A., & Lusis, A. J. (2006) · Genome Research · doi:10.1101/gr.5217506 · Cited by 922 · see citing works
Self-citationMootha, V. K., Bunkenborg, J., Olsen, J. V., Hjerrild, M., Wisniewski, J. R., Stahl, E., Bolouri, M. S., Ray, H. N., Sihag, S., Kamal, M., et al. (2003) · Cell · doi:10.1016/s0092-8674(03)00926-7 · Cited by 889 · see citing works
Multiple trait analysis of genetic mapping for quantitative trait loci.
Jiang, C., & Zeng, Z. (1995) · Genetics · doi:10.1093/genetics/140.3.1111 · Cited by 831 · see citing works
Chesler, E. J., Lu, L., Shou, S., Qu, Y., Gu, J., Wang, J., Hsu, H. C., Mountz, J. D., Baldwin, N. E., Langston, M. A., et al. (2005) · Nature Genetics · doi:10.1038/ng1518 · Cited by 719 · see citing works
Characterization of the human heart mitochondrial proteome
Taylor, S. W., Fahy, E., Zhang, B., Glenn, G. M., Warnock, D. E., Wiley, S. E., Murphy, A. N., Gaucher, S. P., Capaldi, R., Gibson, B. W., et al. (2003) · Nature Biotechnology · doi:10.1038/nbt793 · Cited by 702 · see citing works
Self-citationBystrykh, L. V., Weersing, E., Dontje, B., Sutton, S., Pletcher, M. T., Wiltshire, T., Su, A. I., Vellenga, E., Wang, J., Manly, K. F., et al. (2005) · Nature Genetics · doi:10.1038/ng1497 · Cited by 390 · see citing works
Ramprasad, M. P., Terpstra, V., Kondratenko, N., Quehenberger, O., & Steinberg, D. H. (1996) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.93.25.14833 · Cited by 382 · see citing works
Atherosclerosis in Transgenic Mice Overexpressing Apolipoprotein A-II
Warden, C. H., Hedrick, C. C., Qiao, J.-H., Castellani, L. W., & Lusis, A. J. (1993) · Science · doi:10.1126/science.8332912 · Cited by 359 · see citing works
Self-citationGenetic Inheritance of Gene Expression in Human Cell Lines
Monks, S. A., Leonardson, A. S., Zhu, H., Cundiff, P., Pietrusiak, P., Edwards, S. W., Phillips, J. W., Sachs, A. B., & Schadt, E. E. (2004) · The American Journal of Human Genetics · doi:10.1086/426461 · Cited by 354 · see citing works
Self-citationFamilial combined hyperlipidemia is associated with upstream transcription factor 1 (USF1)
Pajukanta, P., Lilja, H. E., Sinsheimer, J. S., Cantor, R. M., Lusis, A. J., Gentile, M., Duan, X. J., Soro‐Paavonen, A., Naukkarinen, J., Saarela, J., et al. (2004) · Nature Genetics · doi:10.1038/ng1320 · Cited by 347 · see citing works
Self-citationWang, X.-S., Ria, M., Kelmenson, P., Eriksson, P., Higgins, D. C., Samnegård, A., Petros, C., Rollins, J., Bennet, A., Wiman, B., et al. (2005) · Nature Genetics · doi:10.1038/ng1524 · Cited by 289 · see citing works
Paigen, B., Mitchell, D., Reue, K., Morrow, A. L., Lusis, A. J., & LeBoeuf, R. C. (1987) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.84.11.3763 · Cited by 288 · see citing works
Self-citationPreiss-Landl, K., Zimmermann, R., Hämmerle, G., & Zechner, R. (2002) · Current Opinion in Lipidology · doi:10.1097/00041433-200210000-00002 · Cited by 240 · see citing works
Mehrabian, M., Allayee, H., Stockton, J., Lum, P. Y., Drake, T. A., Castellani, L. W., Suh, M., Armour, C. D., Edwards, S. W., Lamb, J. R., et al. (2005) · Nature Genetics · doi:10.1038/ng1619 · Cited by 219 · see citing works
Self-citationWang, S. S.-M., Yehya, N., Schadt, E. E., Wang, H., Drake, T. A., & Lusis, A. J. (2006) · PLoS Genetics · doi:10.1371/journal.pgen.0020015 · Cited by 178 · see citing works
Self-citationHaplotype Analysis in Multiple Crosses to Identify a QTL GenePreprint
Wang, X., Korstanje, R., Higgins, D., & Paigen, B. J. (2004) · Genome Research · doi:10.1101/gr.2668204 · Cited by 112 · see citing works
Multiple Genes for Essential-Hypertension Susceptibility on Chromosome 1q
Chang, Y.-P. C., Liu, X., Kim, J. D. O., Ikeda, M. A., Layton, M. R., Weder, A. B., Cooper, R., Kardia, S. L. R., Rao, D. C., Hunt, S. C., et al. (2007) · The American Journal of Human Genetics · doi:10.1086/510918 · Cited by 109 · see citing works
Babińska, A., Ahmed, T., Batuman, O., et al. (2002) · Thrombosis and Haemostasis · doi:10.1055/s-0037-1613312 · Cited by 108 · see citing works
Doolittle, M. H., LeBoeuf, R. C., Warden, C. H., Bee, L. M., & Lusis, A. J. (1990) · Journal of Biological Chemistry · doi:10.1016/s0021-9258(17)46234-x · Cited by 101 · see citing works
Self-citationGaucher, S. P., Taylor, S. W., Fahy, E., et al. (2004) · Journal of Proteome Research · doi:10.1021/pr034102a · Cited by 99 · see citing works
Schadt, E. E., & Lum, P. Y. (2006) · Journal of Lipid Research · doi:10.1194/jlr.r600026-jlr200 · Cited by 98 · see citing works
Self-citationCastellani, L. W., Goto, A. M., & Lusis, A. J. (2001) · Diabetes · doi:10.2337/diabetes.50.3.643 · Cited by 91 · see citing works
Self-citationLum, P. Y., Chen, Y., Zhu, J., Lamb, J. R., Melmed, S. K., Wang, S. S.-M., Drake, T. A., Lusis, A. J., & Schadt, E. E. (2006) · Journal of Neurochemistry · doi:10.1111/j.1471-4159.2006.03661.x · Cited by 87 · see citing works
Self-citationFamilial Combined Hyperlipidemia in Mexicans
Huertas‐Vázquez, A., Aguilar-Salinas, C. A., Lusis, A. J., Cantor, R. M., Canizales‐Quinteros, S., Lee, J. C., Mariana-Nuñez, L., Riba-Ramirez, R.-M. L., Jokiaho, A. J., Tusié‐Luna, T., et al. (2005) · Arteriosclerosis Thrombosis and Vascular Biology · doi:10.1161/01.atv.0000175297.37214.a0 · Cited by 79 · see citing works
Self-citationEmbracing Complexity, Inching Closer to Reality
Schadt, E. E., Sachs, A. B., & Friend, S. H. (2005) · Science s STKE · doi:10.1126/stke.2952005pe40 · Cited by 58 · see citing works
Self-citationHu, Y., Liu, W., Huang, R., & Zhang, X. (2006) · Journal of Lipid Research · doi:10.1194/jlr.m600108-jlr200 · Cited by 57 · see citing works
Adams, L. D., Geary, R. L., Li, J., et al. (2005) · Arteriosclerosis Thrombosis and Vascular Biology · doi:10.1161/01.atv.0000196647.45718.d6 · Cited by 50 · see citing works
Smith, T. S., Southan, C., Ellington, K., et al. (2001) · Genomics · doi:10.1006/geno.2001.6643 · Cited by 49 · see citing works
Paigen, B. J., Albee, D., Holmes, P. A., & Mitchell, D. (1987) · Biochemical Genetics · doi:10.1007/bf00554352 · Cited by 38 · see citing works
Visceral Obesity without Insulin Resistance in Late-Onset Obesity Rats
Bains, R. K., Wells, S. E., Flavell, D., Fairhall, K. M., Strom, M., Tissier, P. L., & Robinson, I. C. A. F. (2004) · Endocrinology · doi:10.1210/en.2003-1608 · Cited by 37 · see citing works
Life in the Balance: Cell Walls and Antibiotic Resistance
Jacobs, C. (1997) · Science · doi:10.1126/science.278.5344.1731b · Cited by 35 · see citing works
Liobikas, J., Polianskyte-Prause, Z., Speer, O., Thompson, J. S., Alakoskela, J.-M. I., Peitsaro, N., Franck, M. C. M., Whitehead, M. A., Kinnunen, P. K. J., & Eriksson, O. (2005) · Protein Expression and Purification · doi:10.1016/j.pep.2005.08.006 · Cited by 20 · see citing works
Chlamydia pneumoniae infections augment atherosclerotic lesion formation: a role for serum amyloid P
Ezzahiri, R., Stassen, F. R. M., Kurvers, H. A. J. M., DOLMANS, V., Kitslaar, P. J. E. H. M., & Bruggeman, C. A. (2006) · Apmis · doi:10.1111/j.1600-0463.2006.apm_205.x · Cited by 18 · see citing works
Welch, C. L., Seidelmann, S. B., Wen, P.-Z., Mehrabian, M., Latib, N., Fruchart‐Najib, J., Fruchart, J., Myrick, C., & Lusis, A. J. (2004) · Physiological Genomics · doi:10.1152/physiolgenomics.00124.2003 · Cited by 17 · see citing works
Self-citationYost, T. J., Jensen, D. R., & Eckel, R. H. (1993) · Obesity Research · doi:10.1002/j.1550-8528.1993.tb00002.x · Cited by 11 · see citing works
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METABRIC Group, Curtis, C., Shah, S. P., Chin, S.-F., Turashvili, G., Rueda, O. M., Dunning, M. J., Speed, D., Lynch, A. G., Samarajiwa, S., et al. (2012) · Nature · doi:10.1038/nature10983 · Cited by 6,097 · see citing works
Network medicine: a network-based approach to human disease
Barabási, A.-L., Gulbahce, N., & Loscalzo, J. (2011) · Nature Reviews Genetics · doi:10.1038/nrg2918 · Cited by 5,034 · see citing works
Host–microbe interactions have shaped the genetic architecture of inflammatory bowel disease
The International IBD Genetics Consortium (IIBDGC), Jostins, L., Ripke, S., Weersma, R. K., Duerr, R. H., McGovern, D. P., Hui, K. Y., Lee, J. C., Philip Schumm, L., Sharma, Y., et al. (2012) · Nature · doi:10.1038/nature11582 · Cited by 4,722 · see citing works
Self-citationNetwork pharmacology: the next paradigm in drug discovery
Hopkins, A. L. (2008) · Nature Chemical Biology · doi:10.1038/nchembio.118 · Cited by 4,622 · see citing works
Inflammatory Mechanisms in Obesity
Gregor, M. F., & Hotamisligil, G. S. (2011) · Annual Review of Immunology · doi:10.1146/annurev-immunol-031210-101322 · Cited by 3,754 · see citing works
Macrophages, inflammation, and insulin resistance
Olefsky, J. M., & Glass, C. K. (2010) · Annual Review of Physiology · doi:10.1146/annurev-physiol-021909-135846 · Cited by 2,733 · see citing works
Inflammatory links between obesity and metabolic disease
Lumeng, C. N., & Saltiel, A. R. (2011) · Journal of Clinical Investigation · doi:10.1172/jci57132 · Cited by 2,319 · see citing works
Transcriptomic Analysis of Autistic Brain Reveals Convergent Molecular Pathology
Voineagu, I., Wang, X., Johnston, P., Lowe, J. K., Tian, Y., Horvath, S., Mill, J., Cantor, R. M., Blencowe, B. J., & Geschwind, D. H. (2011) · Nature · doi:10.1038/nature10110 · Cited by 1,874 · see citing works
Self-citationNF-κB, Inflammation, and Metabolic Disease
Baker, R. G., Hayden, M. S., & Ghosh, S. (2011) · Cell Metabolism · doi:10.1016/j.cmet.2010.12.008 · Cited by 1,952 · see citing works
Integrated Systems Approach Identifies Genetic Nodes and Networks in Late-Onset Alzheimer’s Disease
Zhang, B., Gaiteri, C., Bodea, L.-G., Wang, Z., McElwee, J., Podtelezhnikov, A. A., Zhang, C., Xie, T., Tran, L., Dobrin, R., et al. (2013) · Cell · doi:10.1016/j.cell.2013.03.030 · Cited by 1,657 · see citing works
Self-citationThe cellular and signaling networks linking the immune system and metabolism in disease
Osborn, O., & Olefsky, J. M. (2012) · Nature Medicine · doi:10.1038/nm.2627 · Cited by 1,551 · see citing works
Uncovering disease-disease relationships through the incomplete interactome
Menche, J., Sharma, A., Kitsak, M., Ghiassian, S. D., Vidal, M., Loscalzo, J., & Barabási, A.-L. (2015) · Science · doi:10.1126/science.1257601 · Cited by 1,546 · see citing works
Genetics of gene expression and its effect on disease
Emilsson, V., Thorleifsson, G., Zhang, B., Leonardson, A. S., Zink, F., Zhu, J., Carlson, S., Helgason, A., Walters, G. B., Gunnarsdottir, S., et al. (2008) · Nature · doi:10.1038/nature06758 · Cited by 1,296 · see citing works
Self-citationMitonuclear protein imbalance as a conserved longevity mechanism
Houtkooper, R. H., Mouchiroud, L., Ryu, D., Moullan, N., Katsyuba, E., Knott, G., Williams, R. W., & Auwerx, J. (2013) · Nature · doi:10.1038/nature12188 · Cited by 1,035 · see citing works
Gene co-expression analysis for functional classification and gene–disease predictions
van Dam, S., Võsa, U., van der Graaf, A., Franke, L., & de Magalhães, J. P. (2017) · Briefings in Bioinformatics · doi:10.1093/bib/bbw139 · Cited by 1,086 · see citing works
Mapping complex disease traits with global gene expression
Cookson, W., Liang, L., Abecasis, G., Moffatt, M., & Lathrop, M. (2009) · Nature Reviews Genetics · doi:10.1038/nrg2537 · Cited by 849 · see citing works
Functional organization of the transcriptome in human brain
Oldham, M. C., Konopka, G., Iwamoto, K., Langfelder, P., Kato, T., Horvath, S., & Geschwind, D. H. (2008) · Nature Neuroscience · doi:10.1038/nn.2207 · Cited by 752 · see citing works
Self-citationComputational solutions to large‐scale data management and analysis
Schadt, E. E., Linderman, M. D., Sorenson, J., Lee, L., & Nolan, G. P. (2010) · Nature Reviews Genetics · doi:10.1038/nrg2857 · Cited by 631 · see citing works
Self-citationStegle, O., Parts, L., Durbin, R., & Winn, J. (2010) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000770 · Cited by 497 · see citing works
Scalbert, A., Brennan, L., Fiehn, O., Hankemeier, T., Kristal, B. S., van Ommen, B., Pujos-Guillot, E., Verheij, E., Wishart, D., & Wopereis, S. (2009) · Metabolomics · doi:10.1007/s11306-009-0168-0 · Cited by 565 · see citing works
Relton, C. L., & Davey Smith, G. (2012) · International Journal of Epidemiology · doi:10.1093/ije/dyr233 · Cited by 567 · see citing works
Parks, B. W., Nam, E., Org, E., Kostem, E., Norheim, F., Hui, S. T., Pan, C., Civelek, M., Rau, C. D., Bennett, B. J., et al. (2013) · Cell Metabolism · doi:10.1016/j.cmet.2012.12.007 · Cited by 544 · see citing works
Self-citationKell, D. B. (2009) · BMC Medical Genomics · doi:10.1186/1755-8794-2-2 · Cited by 554 · see citing works
Plasma lipoproteins: genetic influences and clinical implications
Hegele, R. A. (2009) · Nature Reviews Genetics · doi:10.1038/nrg2481 · Cited by 462 · see citing works
The IBD interactome: an integrated view of aetiology, pathogenesis and therapy
de Souza, H. S. P., Fiocchi, C., & Iliopoulos, D. (2017) · Nature Reviews Gastroenterology & Hepatology · doi:10.1038/nrgastro.2017.110 · Cited by 447 · see citing works
Hui, K. Y., Fernandez-Hernandez, H., Hu, J., Schaffner, A., Pankratz, N., Hsu, N.-Y., Chuang, L.-S., Carmi, S., Villaverde, N., Li, X., et al. (2018) · Science Translational Medicine · doi:10.1126/scitranslmed.aai7795 · Cited by 384 · see citing works
“We constructed an IBD Bayesian network, using a previously described methodology (20), from gene expression data for 8382 genes.”
MethodTissue-specific regulatory circuits reveal variable modular perturbations across complex diseases
Marbach, D., Lamparter, D., Quon, G., Kellis, M., Kutalik, Z., & Bergmann, S. (2016) · Nature Methods · doi:10.1038/nmeth.3799 · Cited by 326 · see citing works
Timmons, J. A., Knudsen, S., Rankinen, T., Koch, L. G., Sarzynski, M., Jensen, T., Keller, P., Scheele, C., Vollaard, N. B. J., Nielsen, S., et al. (2010) · Journal of Applied Physiology · doi:10.1152/japplphysiol.01295.2009 · Cited by 358 · see citing works
Huang, Y. J., Marsland, B. J., Bunyavanich, S., O'Mahony, L., Leung, D. Y. M., Muraro, A., & Fleisher, T. A. (2017) · Journal of Allergy and Clinical Immunology · doi:10.1016/j.jaci.2017.02.007 · Cited by 330 · see citing works
The Role of the Immune System in Metabolic Health and Disease
Zmora, N., Bashiardes, S., Levy, M., & Elinav, E. (2017) · Cell Metabolism · doi:10.1016/j.cmet.2017.02.006 · Cited by 323 · see citing works
Validation of candidate causal genes for obesity that affect shared metabolic pathways and networks
Yang, X., Deignan, J. L., Qi, H., Zhu, J., Qian, S., Zhong, J., Torosyan, G., Majid, S., Falkard, B., Kleinhanz, R. R., et al. (2009) · Nature Genetics · doi:10.1038/ng.325 · Cited by 288 · see citing works
Self-citationSystem-wide molecular evidence for phenotypic buffering in Arabidopsis
Fu, J., Keurentjes, J. J. B., Bouwmeester, H., America, T., Verstappen, F. W. A., Ward, J. L., Beale, M. H., de Vos, R. C. H., Dijkstra, M., Scheltema, R. A., et al. (2009) · Nature Genetics · doi:10.1038/ng.308 · Cited by 265 · see citing works
A trans-acting locus regulates an anti-viral expression network and type 1 diabetes risk
Cardiogenics Consortium, Heinig, M., Petretto, E., Wallace, C., Bottolo, L., Rotival, M., Lu, H., Li, Y., Sarwar, R., Langley, S. R., et al. (2010) · Nature · doi:10.1038/nature09386 · Cited by 283 · see citing works
Sherlock: Detecting Gene-Disease Associations by Matching Patterns of Expression QTL and GWAS
He, X., Fuller, C. K., Song, Y., Meng, Q., Zhang, B., Yang, X., & Li, H. (2013) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2013.03.022 · Cited by 241 · see citing works
Self-citationGenetic Architecture of Insulin Resistance in the Mouse
Parks, B. W., Sallam, T., Mehrabian, M., Psychogios, N., Hui, S. T., Norheim, F., Castellani, L. W., Rau, C. D., Pan, C., Phun, J., et al. (2015) · Cell Metabolism · doi:10.1016/j.cmet.2015.01.002 · Cited by 249 · see citing works
Self-citationThe study of eQTL variations by RNA-seq: from SNPs to phenotypes
Majewski, J., & Pastinen, T. (2011) · Trends in Genetics · doi:10.1016/j.tig.2010.10.006 · Cited by 232 · see citing works
Neuroscience in the era of functional genomics and systems biology
Geschwind, D. H., & Konopka, G. (2009) · Nature · doi:10.1038/nature08537 · Cited by 213 · see citing works
Common dysregulation network in the human prefrontal cortex underlies two neurodegenerative diseases
Narayanan, M., Huynh, J. L., Wang, K., Yang, X., Yoo, S., McElwee, J., Zhang, B., Zhang, C., Lamb, J. R., Xie, T., et al. (2014) · Molecular Systems Biology · doi:10.15252/msb.20145304 · Cited by 208 · see citing works
Self-citationIntegrative Genomics Reveals Novel Molecular Pathways and Gene Networks for Coronary Artery Disease
Mäkinen, V.-P., Civelek, M., Meng, Q., Zhang, B., Zhu, J., Levian, C., Huan, T., Segrè, A. V., Ghosh, S., Vivar, J., et al. (2014) · PLoS Genetics · doi:10.1371/journal.pgen.1004502 · Cited by 196 · see citing works
Self-citationZhu, J., Sova, P., Xu, Q., Dombek, K. M., Xu, E. Y., Vu, H., Tu, Z., Brem, R. B., Bumgarner, R. E., & Schadt, E. E. (2012) · PLoS Biology · doi:10.1371/journal.pbio.1001301 · Cited by 205 · see citing works
Sharing and Specificity of Co-expression Networks across 35 Human Tissues
Pierson, E., the GTEx Consortium, Koller, D., Battle, A., & Mostafavi, S. (2015) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1004220 · Cited by 177 · see citing works
The initiation of metabolic inflammation in childhood obesity
Singer, K., & Lumeng, C. N. (2017) · Journal of Clinical Investigation · doi:10.1172/jci88882 · Cited by 194 · see citing works
What would you do if you could sequence everything?
Kahvejian, A., Quackenbush, J., & Thompson, J. F. (2008) · Nature Biotechnology · doi:10.1038/nbt1494 · Cited by 189 · see citing works
Toward a unifying hypothesis of metabolic syndrome
Bremer, A. A., Mietus-Snyder, M., & Lustig, R. H. (2012) · PEDIATRICS · doi:10.1542/peds.2011-2912 · Cited by 186 · see citing works
Kodama, K., Horikoshi, M., Toda, K., Yamada, S., Hara, K., Irie, J., Sirota, M., Morgan, A. A., Chen, R., Ohtsu, H., et al. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1114513109 · Cited by 171 · see citing works
A Systems Biology Framework Identifies Molecular Underpinnings of Coronary Heart Disease
Huan, T., Zhang, B., Wang, Z., Joehanes, R., Zhu, J., Johnson, A. D., Ying, S., Munson, P. J., Raghavachari, N., Wang, R., et al. (2013) · Arteriosclerosis, Thrombosis, and Vascular Biology · doi:10.1161/atvbaha.112.300112 · Cited by 167 · see citing works
Self-citationModulatory profiling identifies mechanisms of small molecule-induced cell death
Wolpaw, A. J., Shimada, K., Skouta, R., Welsch, M. E., Akavia, U. D., Pe'er, D., Shaik, F., Bulinski, J. C., & Stockwell, B. R. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1106149108 · Cited by 133 · see citing works
Bremer, A. A., Auinger, P., & Byrd, R. S. (2009) · Archives of Pediatrics & Adolescent Medicine · doi:10.1001/archpediatrics.2009.21 · Cited by 132 · see citing works
Yepuru, M., Eswaraka, J., Kearbey, J. D., Barrett, C. M., Raghow, S., Veverka, K. A., Miller, D. D., Dalton, J. T., & Narayanan, R. (2010) · Journal of Biological Chemistry · doi:10.1074/jbc.m110.147850 · Cited by 125 · see citing works
Ray, M., Ruan, J., & Zhang, W. (2008) · Genome Biology · doi:10.1186/gb-2008-9-10-r148 · Cited by 119 · see citing works
Getting away from glucose: fanning the flames of obesity-induced inflammation
Shoelson, S. E., & Goldfine, A. B. (2009) · Nature Medicine · doi:10.1038/nm0409-373 · Cited by 98 · see citing works
Wang, L., Sfakianos, J. P., Beaumont, K. G., Akturk, G., Horowitz, A., Sebra, R. P., Farkas, A. M., Gnjatic, S., Hake, A., Izadmehr, S., et al. (2021) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-20-4574 · Cited by 97 · see citing works
Self-citationMunger, S. C., Raghupathy, N., Choi, K., Simons, A. K., Gatti, D. M., Hinerfeld, D. A., Svenson, K. L., Keller, M. P., Attie, A. D., Hibbs, M. A., et al. (2014) · Genetics · doi:10.1534/genetics.114.165886 · Cited by 85 · see citing works
Xiao, X., Moreno-Moral, A., Rotival, M., Bottolo, L., & Petretto, E. (2014) · PLoS Genetics · doi:10.1371/journal.pgen.1004006 · Cited by 63 · see citing works
Wingo, A. P., & Gibson, G. (2015) · Brain, Behavior, and Immunity · doi:10.1016/j.bbi.2014.09.016 · Cited by 59 · see citing works
Evolving toward a human-cell based and multiscale approach to drug discovery for CNS disorders
Schadt, E. E., Buchanan, S., Brennand, K. J., & Merchant, K. M. (2014) · Frontiers in Pharmacology · doi:10.3389/fphar.2014.00252 · Cited by 39 · see citing works
Self-citationMore powerful genetic association testing via a new statistical framework for integrative genomics
Zhao, S. D., Cai, T. T., & Li, H. (2014) · Biometrics · doi:10.1111/biom.12206 · Cited by 31 · see citing works
Genomics and Cardiovascular Drug Development
Plump, A. S., & Lum, P. Y. (2009) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2008.11.050 · Cited by 31 · see citing works
Self-citationBremer, A. A., Byrd, R. S., & Auinger, P. (2010) · Clinical Pediatrics · doi:10.1177/0009922810379043 · Cited by 24 · see citing works
Genomic Analysis of Left Ventricular Remodeling
Sarwar, R., & Cook, S. A. (2009) · Circulation · doi:10.1161/circulationaha.108.797225 · Cited by 13 · see citing works
- Authors
- Chen, Y., Zhu, J., Lum, P. Y., Yang, X., Pinto, S., MacNeil, D. J., Zhang, C., Lamb, J., Edwards, S., Sieberts, S. K., Leonardson, A., Castellini, L. W., Wang, S., Champy, M.-F., Zhang, B., Emilsson, V., Doss, S., Ghazalpour, A., Horvath, S., Drake, T. A., Lusis, A. J., & Schadt, E. E.
- Affiliations
- Merck & Co., Inc., Rahway, NJ, USA (United States) (US)
- Centre National de la Recherche Scientifique (FR)
- University of California, Los Angeles (US)
- Corresponding author
- Schadt, Eric E.
- ORCID
- Venue
- Nature
- Published
- 2008-03
- Volume
- 452
- Issue
- 7186
- Pages
- 429-435
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- PMC
- PMC2841398
- OpenAlex
- W2091901410
- Cited by
- 887
- References
- 50
- Citations by year
- 2026: 8 · 2025: 16 · 2024: 15 · 2023: 17 · 2022: 31 · 2021: 22 · 2020: 28 · 2019: 25 · 2018: 37 · 2017: 51 · 2016: 38 · 2015: 52 · 2014: 66 · 2013: 63 · 2012: 75
- FWCI
- 46.85
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- HAL (submittedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- National Institutes of Health
- National Heart, Lung, and Blood Institute (P01HL030568, P01HL028481)
- National Institute of Diabetes and Digestive and Kidney Diseases (R01DK071673)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Nutrition, Genetics, and Disease
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Keywords
- genetic susceptibility loci, gene regulatory networks, obesity, metabolic syndrome, lipoprotein lipase, adipose tissue gene expression, hepatic gene expression, disease networks, complex disease genetics, diabetes mellitus, atherosclerosis, body mass index
- MeSH terms
- Adipose Tissue, Animals, Female, Lipoprotein Lipase, Liver, Lod Score, Macrophages, Male, Membrane Proteins, Obesity, Phenotype, Phosphoprotein Phosphatases, Ribosomal Proteins, Genetic Variation, Reproducibility of Results, Linkage Disequilibrium, Apolipoprotein A-II, Genetic Predisposition to Disease, Metabolic Syndrome, Chromosomes, Mammalian, Quantitative Trait Loci, Mice, Gene Regulatory Networks
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