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      Genome-Wide Detection of Gene Coexpression Domains Showing Linkage to Regions Enriched with Polymorphic Retrotransposons in Recombinant Inbred Mouse Strains

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          Abstract

          Although gene coexpression domains have been reported in most eukaryotic organisms, data available to date suggest that coexpression rarely concerns more than doublets or triplets of adjacent genes in mammals. Using expression data from hearts of mice from the panel of AxB/BxA recombinant inbred mice, we detected (according to window sizes) 42−53 loci linked to the expression levels of clusters of three or more neighboring genes. These loci thus formed “ cis-expression quantitative trait loci (eQTL) clusters” because their position matched that of the genes whose expression was linked to the loci. Compared with matching control regions, genes contained within cis-eQTL clusters showed much greater levels of coexpression. Corresponding regions showed: (1) a greater abundance of polymorphic elements (mostly short interspersed element retrotransposons), and (2) significant enrichment for the motifs of binding sites for various transcription factors, with binding sites for the chromatin-organizing CCCTC-binding factor showing the greatest levels of enrichment in polymorphic short interspersed elements. Similar cis-eQTL clusters also were detected when we used data obtained with several tissues from BxD recombinant inbred mice. In addition to strengthening the evidence for gene expression domains in mammalian genomes, our data suggest a possible mechanism whereby noncoding polymorphisms could affect the coordinate expression of several neighboring genes.

          Most cited references34

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          Genetics of gene expression surveyed in maize, mouse and man.

          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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            Genomic Views of Distant-Acting Enhancers

            Preface In contrast to changes in protein-coding sequences, the significance of noncoding DNA variation in human disease has been minimally explored. A recent torrent of genome-wide association studies suggests that noncoding variation represents a significant risk factor for common disorders, but the mechanisms by which they contribute to disease remain largely obscure. Distant-acting transcriptional enhancers - a major category of functional noncoding DNA - are likely involved in many developmental and disease-relevant processes. Genome-wide approaches for their discovery and functional characterization are now available and provide a growing knowledgebase for the systematic exploration of their role in human biology and disease susceptibility.
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              Complex trait analysis of gene expression uncovers polygenic and pleiotropic networks that modulate nervous system function.

              Patterns of gene expression in the central nervous system are highly variable and heritable. This genetic variation among normal individuals leads to considerable structural, functional and behavioral differences. We devised a general approach to dissect genetic networks systematically across biological scale, from base pairs to behavior, using a reference population of recombinant inbred strains. We profiled gene expression using Affymetrix oligonucleotide arrays in the BXD recombinant inbred strains, for which we have extensive SNP and haplotype data. We integrated a complementary database comprising 25 years of legacy phenotypic data on these strains. Covariance among gene expression and pharmacological and behavioral traits is often highly significant, corroborates known functional relations and is often generated by common quantitative trait loci. We found that a small number of major-effect quantitative trait loci jointly modulated large sets of transcripts and classical neural phenotypes in patterns specific to each tissue. We developed new analytic and graph theoretical approaches to study shared genetic modulation of networks of traits using gene sets involved in neural synapse function as an example. We built these tools into an open web resource called WebQTL that can be used to test a broad array of hypotheses.
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                Author and article information

                Journal
                G3 (Bethesda)
                Genetics
                G3: Genes, Genomes, Genetics
                G3: Genes, Genomes, Genetics
                G3: Genes, Genomes, Genetics
                G3: Genes|Genomes|Genetics
                Genetics Society of America
                2160-1836
                1 April 2013
                April 2013
                : 3
                : 4
                : 597-605
                Affiliations
                [1]Cardiovascular Biology Research Unit, Institut de recherches cliniques de Montréal (IRCM) and Université de Montréal, Montréal, Québec, H2W 1R7, Canada
                Author notes

                Supporting information is available online at http://www.g3journal.org/lookup/suppl/doi:10.1534/g3.113.005546/-/DC1.

                Processed data from this article have been submitted for public access to GeneNetwork ( www.genenetwork.org; accession number GN421).

                [1 ]Corresponding author: IRCM, 110, avenue des Pins Ouest, Montréal (QC), Canada H2W 1R7. E-mail: deschec@ 123456ircm.qc.ca
                Article
                GGG_005546
                10.1534/g3.113.005546
                3618347
                23550129
                f8ef7aac-d844-4965-bd8f-fb8bb4280f7b
                Copyright © 2013 Scott-Boyer, Deschepper

                This is an open-access article distributed under the terms of the Creative Commons Attribution Unported License ( http://creativecommons.org/licenses/by/3.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 01 November 2012
                : 26 January 2013
                Page count
                Pages: 9
                Categories
                Investigations
                Custom metadata
                v1

                Genetics
                clustering of coexpressed genes,genetics of gene expression,quantitative trait loci,structural variants,transposable elements

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