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      Computational, Integrative, and Comparative Methods for the Elucidation of Genetic Coexpression Networks

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          Abstract

          Gene expression microarray data can be used for the assembly of genetic coexpression network graphs. Using mRNA samples obtained from recombinant inbred Mus musculus strains, it is possible to integrate allelic variation with molecular and higher-order phenotypes. The depth of quantitative genetic analysis of microarray data can be vastly enhanced utilizing this mouse resource in combination with powerful computational algorithms, platforms, and data repositories. The resulting network graphs transect many levels of biological scale. This approach is illustrated with the extraction of cliques of putatively coregulated genes and their annotation using gene ontology analysis and cis-regulatory element discovery. The causal basis for coregulation is detected through the use of quantitative trait locus mapping.

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          Gene Ontology: tool for the unification of biology

          Michael Ashburner, Catherine A. Ball, Judith Blake … (2002)
          Genomic sequencing has made it clear that a large fraction of the genes specifying the core biological functions are shared by all eukaryotes. Knowledge of the biological role of such shared proteins in one organism can often be transferred to other organisms. The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing. To this end, three independent ontologies accessible on the World-Wide Web (http://www.geneontology.org) are being constructed: biological process, molecular function and cellular component.
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            Genetic dissection of transcriptional regulation in budding yeast.

            Rachel Brem, Gaël Yvert, Rebecca Clinton … (2002)
            To begin to understand the genetic architecture of natural variation in gene expression, we carried out genetic linkage analysis of genomewide expression patterns in a cross between a laboratory strain and a wild strain of Saccharomyces cerevisiae. Over 1500 genes were differentially expressed between the parent strains. Expression levels of 570 genes were linked to one or more different loci, with most expression levels showing complex inheritance patterns. The loci detected by linkage fell largely into two categories: cis-acting modulators of single genes and trans-acting modulators of many genes. We found eight such trans-acting loci, each affecting the expression of a group of 7 to 94 genes of related function.
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              Genetics of gene expression surveyed in maize, mouse and man.

              Eric E. Schadt, Stephanie Monks, Thomas A. Drake … (2003)
              Treating messenger RNA transcript abundances as quantitative traits and mapping gene expression quantitative trait loci for these traits has been pursued in gene-specific ways. Transcript abundances often serve as a surrogate for classical quantitative traits in that the levels of expression are significantly correlated with the classical traits across members of a segregating population. The correlation structure between transcript abundances and classical traits has been used to identify susceptibility loci for complex diseases such as diabetes and allergic asthma. One study recently completed the first comprehensive dissection of transcriptional regulation in budding yeast, giving a detailed glimpse of a genome-wide survey of the genetics of gene expression. Unlike classical quantitative traits, which often represent gross clinical measurements that may be far removed from the biological processes giving rise to them, the genetic linkages associated with transcript abundance affords a closer look at cellular biochemical processes. Here we describe comprehensive genetic screens of mouse, plant and human transcriptomes by considering gene expression values as quantitative traits. We identify a gene expression pattern strongly associated with obesity in a murine cross, and observe two distinct obesity subtypes. Furthermore, we find that these obesity subtypes are under the control of different loci.
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                Author and article information

                Journal
                Journal ID (nlm-ta): J Biomed Biotechnol
                Journal ID (publisher-id): JBB
                Title: Journal of Biomedicine and Biotechnology
                Publisher: Hindawi Publishing Corporation
                ISSN (Print): 1110-7243
                Publication date (Print): 2005
                Volume: 2005
                Issue: 2
                Pages: 172-180
                Affiliations
                1Department of Computer Science, The University of Tennessee, Knoxville, TN 37996, USA
                2Department of Anatomy and Neurobiology, The University of Tennessee, Memphis, TN 38163, USA
                3Life Sciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
                Author notes
                *Michael A. Langston: langston@ 123456cs.utk.edu
                Article
                DOI: 10.1155/JBB.2005.172
                PMC ID: 1184052
                PubMed ID: 16046823
                SO-VID: bc10c72e-cfa3-4049-aa34-80ef090781b2
                Copyright © Copyright © 2005 Hindawi Publishing Corporation
                License:

                This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                Date received : 24 June 2004
                Date revision received : 12 September 2004
                Date accepted : 14 September 2004
                Categories
                Subject: Research Article

                ScienceOpen disciplines: Molecular medicine
                Data availability:
                ScienceOpen disciplines: Molecular medicine

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