An integrative genomics approach to the reconstruction of gene networks in segregating populations (original) (raw)
Article navigation
Research Articles| July 14 2004
aRosetta Inpharmatics, Seattle, WA;
Search for other works by this author on:
aRosetta Inpharmatics, Seattle, WA;
Search for other works by this author on:
aRosetta Inpharmatics, Seattle, WA;
Search for other works by this author on:
aRosetta Inpharmatics, Seattle, WA;
Search for other works by this author on:
aRosetta Inpharmatics, Seattle, WA;
Search for other works by this author on:
bDepartment of Atherosclerosis and Endocrinology,
Search for other works by this author on:
cDepartment of Metabolic Disorders,
Search for other works by this author on:
dDepartment of Pharmacology,
Search for other works by this author on:
eDepartment of Molecular Profiling, Merck & Co, Rahway, NJ (USA)
Search for other works by this author on:
aRosetta Inpharmatics, Seattle, WA;
Search for other works by this author on:
aRosetta Inpharmatics, Seattle, WA;
Search for other works by this author on:
Cytogenet Genome Res (2004) 105 (2-4): 363–374.
Citation
J. Zhu, P.Y. Lum, J. Lamb, D. GuhaThakurta, S.W. Edwards, R. Thieringer, J.P. Berger, M.S. Wu, J. Thompson, A.B. Sachs, E.E. Schadt; An integrative genomics approach to the reconstruction of gene networks in segregating populations. _Cytogenet Genome Res 1 June 2004; 105 (2-4): 363–374. https://doi.org/10.1159/000078209
Download citation file:
Abstract
The reconstruction of genetic networks in mammalian systems is one of the primary goals in biological research, especially as such reconstructions relate to elucidating not only common, polygenic human diseases, but living systems more generally. Here we propose a novel gene network reconstruction algorithm, derived from classic Bayesian network methods, that utilizes naturally occurring genetic variations as a source of perturbations to elucidate the network. This algorithm incorporates relative transcript abundance and genotypic data from segregating populations by employing a generalized scoring function of maximum likelihood commonly used in Bayesian network reconstruction problems. The utility of this novel algorithm is demonstrated via application to liver gene expression data from a segregating mouse population. We demonstrate that the network derived from these data using our novel network reconstruction algorithm is able to capture causal associations between genes that result in increased predictive power, compared to more classically reconstructed networks derived from the same data.
References
Alberts P, Engblom L, et al: Selective inhibition of 11beta-hydroxysteroid dehydrogenase type 1 decreases blood glucose concentrations in hyperglycaemic mice. Diabetologia 45:1528–1532 (2002).
Alberts P, Nilsson C, et al: Selective inhibition of 11 beta-hydroxysteroid dehydrogenase type 1 improves hepatic insulin sensitivity in hyperglycemic mice strains. Endocrinology 144:4755–4762 (2003).
Brem RB, Yvert G, et al: Genetic dissection of transcriptional regulation in budding yeast. Science 296:752–755 (2002).
Colinayo VV, Qiao JH, et al: Genetic loci for diet-induced atherosclerotic lesions and plasma lipids in mice. Mammal Genome 14:464–471 (2003).
Davidson EH, Rast JP, et al: A genomic regulatory network for development. Science 295:1669–1678 (2002).
Drake TA, Schadt E, et al: Genetic loci determining bone density in mice with diet-induced atherosclerosis. Physiol Genomics 5:205–215 (2001).
Friedman N, Linial M, et al: Using Bayesian networks to analyze expression data. J Comput Biol 7:601–620 (2000).
Gardner TS, di Bernardo D, et al: Inferring genetic networks and identifying compound mode of action via expression profiling. Science 301:102–105 (2003).
Gordon DA, Shelness GS, et al: Estrogen-induced destabilization of yolk precursor protein mRNAs in avian liver. J Biol Chem 263:2625–2631 (1988).
Hermanowski-Vosatka A, Gerhold A, et al: PPARalpha agonists reduce 11beta-hydroxysteroid dehydrogenase type I in the liver. Biochem Biophys Res Commun 279:330–336 (2000).
Ideker T, Galitski T, et al: A new approach to decoding life: systems biology. Annu Rev Genomics Hum Genet 2:343–372 (2001).
Jackson AL, Bartz SR, et al: Expression profiling reveals off-target gene regulation by RNAi. Nat Biotechnol 21:635–637 (2003).
Jansen R, Yu H, et al: A Bayesian networks approach for predicting protein-protein interactions from genomic data. Science 302:449–453 (2003).
Karp CL, Grupe A, et al: Identification of complement factor 5 as a susceptibility locus for experimental allergic asthma. Nat Immunol 1:221–226 (2000).
Kotelevtsev Y, Holmes MC, et al: 11beta-hydroxysteroid dehydrogenase type 1 knockout mice show attenuated glucocorticoid-inducible responses and resist hyperglycemia on obesity or stress. Proc Natl Acad Sci USA 94:14924–14929 (1997).
Kroetz DL, Yook P, et al: Peroxisome proliferator-activated receptor alpha controls the hepatic CYP4A induction adaptive response to starvation and diabetes. J Biol Chem 273:31581–31589 (1998).
Marcotte EM, Xenarios I, et al: Mining literature for protein-protein interactions. Bioinformatics 17:359–363 (2001).
Mootha VK, Lepage P, et al: Identification of a gene causing human cytochrome c oxidase deficiency by integrative genomics. Proc Natl Acad Sci USA 100:605–610 (2003).
Nguyen TT, Hu Y, et al: Murine GBP-5 a new member of the murine guanylate-binding protein family is coordinately regulated with other GBPs in vivo and in vitro. J Interferon Cytokine Res 22:899–909 (2002).
Pearl J: Probabilistic Reasoning in Intelligent Systems: Net-works of Plausible Inference (Morgan Kaufmann Publishers, San Mateo 1988).
Pe’er D, Regev A, et al: Inferring subnetworks from perturbed expression profiles. Bioinformatics 17(suppl 1):S215–S224 (2001).
Schadt EE, Monks SA, et al: Genetics of gene expression surveyed in maize, mouse and man. Nature 422:297–302 (2003).
Stefansson H, Sigurdsson E, et al: Neuregulin 1 and susceptibility to schizophrenia. Am J Hum Genet 71:877–892 (2002).
Tegner J, Yeung MK, et al: Reverse engineering gene networks: integrating genetic perturbations with dynamical modeling. Proc Natl Acad Sci USA 100:5944–5949 (2003).
Ueda H, Howson JM, et al: Association of the T-cell regulatory gene CTLA4 with susceptibility to autoimmune disease. Nature 423:506–511 (2003).
van de Vijver MJ, He YD, et al: A gene-expression signature as a predictor of survival in breast cancer. New Engl J Med 347:1999–2009 (2002).
van ’t Veer LJ, Dai H, et al: Gene expression profiling predicts clinical outcome of breast cancer. Nature 415:530–536 (2002).
Weyts FA, Flik G, et al: Cortisol induces apoptosis in activated B cells not in other lymphoid cells of the common carp Cyprinus carpio L. Dev Comp Immunol 22:551–562 (1998).
Xenarios I, Salwinski L, et al: DIP the Database of Interacting Proteins: a research tool for studying cellular networks of protein interactions. Nucl Acids Res 30:303–305 (2002).
Yamazaki K, Kuromitsu J, et al: Microarray analysis of gene expression changes in mouse liver induced by peroxisome proliferator-activated receptor alpha agonists. Biochem Biophys Res Commun 290:1114–1122 (2002).
Yang E, van Nimwegen E, et al: Decay rates of human mRNAs: correlation with functional characteristics and sequence attributes. Genome Res 13:1863–1872 (2003).
© 2004 S. Karger AG, Basel
2004
Copyright / Drug Dosage / Disclaimer
Copyright: All rights reserved. No part of this publication may be translated into other languages, reproduced or utilized in any form or by any means, electronic or mechanical, including photocopying, recording, microcopying, or by any information storage and retrieval system, without permission in writing from the publisher.
Drug Dosage: The authors and the publisher have exerted every effort to ensure that drug selection and dosage set forth in this text are in accord with current recommendations and practice at the time of publication. However, in view of ongoing research, changes in government regulations, and the constant flow of information relating to drug therapy and drug reactions, the reader is urged to check the package insert for each drug for any changes in indications and dosage and for added warnings and precautions. This is particularly important when the recommended agent is a new and/or infrequently employed drug.
Disclaimer: The statements, opinions and data contained in this publication are solely those of the individual authors and contributors and not of the publishers and the editor(s). The appearance of advertisements or/and product references in the publication is not a warranty, endorsement, or approval of the products or services advertised or of their effectiveness, quality or safety. The publisher and the editor(s) disclaim responsibility for any injury to persons or property resulting from any ideas, methods, instructions or products referred to in the content or advertisements.
You do not currently have access to this content.
Sign in
Digital Version
Pay-Per-View Access
$39.00
1 Karger Article Bundle Token
$150
Rental
This article is also available for rental through DeepDyve. 