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Genome Organisation and Function

An integrative genomics approach to the reconstruction of gene networks in segregating populations

Zhu J.a · Lum P.Y.a · Lamb J.a · GuhaThakurta D.a · Edwards S.W.a · Thieringer R.b · Berger J.P.c · Wu M.S.d · Thompson J.e · Sachs A.B.a · Schadt E.E.a

Author affiliations

aRosetta Inpharmatics, Seattle, WA; bDepartment of Atherosclerosis and Endocrinology, cDepartment of Metabolic Disorders, dDepartment of Pharmacology, eDepartment of Molecular Profiling, Merck & Co, Rahway, NJ (USA)

Related Articles for ""

Cytogenet Genome Res 105:363–374 (2004)

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Article / Publication Details

First-Page Preview
Abstract of Genome Organisation and Function

Published online: July 14, 2004
Issue release date: June 2004

Number of Print Pages: 12
Number of Figures: 5
Number of Tables: 3

ISSN: 1424-8581 (Print)
eISSN: 1424-859X (Online)

For additional information: https://www.karger.com/CGR

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.    

© 2004 S. Karger AG, Basel


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Article / Publication Details

First-Page Preview
Abstract of Genome Organisation and Function

Published online: July 14, 2004
Issue release date: June 2004

Number of Print Pages: 12
Number of Figures: 5
Number of Tables: 3

ISSN: 1424-8581 (Print)
eISSN: 1424-859X (Online)

For additional information: https://www.karger.com/CGR


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