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Genome Res. 14:651-660, 2004 ©2004 by Cold Spring Harbor Laboratory Press; ISSN 1088-9051/04 $5.00 Methods Integrative Genomics: In Silico Coupling of Rat Physiology and Complex Traits With Mouse and Human Data1 Human and Molecular Genetics Center, Medical College of Wisconsin, Milwaukee, Wisconsin 53226, USA 2 Department of Physiology, Medical College of Wisconsin, Milwaukee, Wisconsin 53226, USA
Integration of the large variety of genome maps from several organisms provides the mechanism by which physiological knowledge obtained in model systems such as the rat can be projected onto the human genome to further the research on human disease. The release of the rat genome sequence provides new information for studies using the rat model and is a key reference against which existing and new rat physiological results can be aligned. Previously, we described comparative maps of the rat, mouse, and human based on EST sequence comparisons combined with radiation hybrid maps. Here, we use new data and introduce the Integrated Genomics Environment, an extensive database of curated and integrated maps, markers, and physiological results. These results are integrated by using VCMapview, a java-based map integration and visualization tool. This unique environment allows researchers to relate results from cytogenetic, genetic, and radiation hybrid studies to the genome sequence and compare regions of interest between human, mouse, and rat. Integrating rat physiology with mouse genetics and clinical results from human by using the respective genomes provides a novel route to capitalize on comparative genomics and the strengths of model organism biology.
The release of the draft Rattus norvegicus genome opens the way for the rat to make further contributions as a model organism of complex human diseases. Known as the preeminent model system for physiological and complex disease studies, there exists substantial literature describing the use of the rat in elucidating physiological mechanisms and factors contributing to complex diseases, their symptoms, and phenotypes (for review, see Jacob and Kwitek 2002  ). Now, researchers are anxious to translate these results into a context more easily applied to human. Besides its obvious importance to basic science and molecular genetics, the rat genome enables this translation through an in silico process we refer to as integrative genomics. We have exploited this process to integrate analyses, data sets, and a map viewer (VCMapView) to create the Integrated Genomics Environment (IGE) that links various maps within and between species. The addition of the rat genome to that of human and mouse provides a third and vital contribution to the genomic "text" and, with IGE, adds a substantial knowledge base of the pharmacology, physiology, and mechanisms important to complex human diseases.
Studies of complex multifactorial diseases such as cancer, hypertension, diabetes, and obesity along with the physiology, mechanisms, and related phenotypes in the rat have necessitated the creation of a variety of mapping resources. Genetic mapping experiments use phenotype and genotype variation and recombination to identify the location of genetic influence of these diseases. These locations, or `quantitative trait loci' (QTL), exist for a wide variety of phenotypes—the Rat Genome Database (RGD) has curated records for 563 unique QTLs representing >60 unique phenotypes (Twigger et al. 2002
Several RH maps have been published for the rat (Steen et al. 1999
Rat genome mapping has progressed from somatic cell hybrids (Szpirer et al. 1984
Sequence-level genome review tools are available from Ensembl (Clamp et al. 2003
We have developed a novel powerful integrative mapping environment with markers, maps, and annotations; a database of the maps, markers, and physiological results; and an in silico cross-organism mapping tool (VCMapView) to address these issues. VCMapView allows an investigator to navigate from map to map, genome to genome, and thus uniquely combines and visualizes distinctive information linked to each type of map. In particular, it enables a powerful strategy to link physiologically important and genetically influenced phenotypes, which can, via their QTL positions, be displayed and traced from genetic maps to genomic sequence and thus linked immediately to genome browsers for review of sequence-level candidate gene annotations (Fig. 1). As such, the IGE is a unique and powerful tool/environment for investigators to translate results from rat physiological research to the molecular genetics of mouse studies and simultaneously into the clinical context of the human genome. The IGE environment and VCMapView tool are available on the RGD at http://rgd.mcw.edu/VCMAP/
Markers and Maps A total of 17 distinct genetic, cytogenetic, and RH maps and related genome annotations have been integrated into the VCMapView database (for complete list, see Table 1), with a further six annotation maps available that show QTLs and cytogenetic features linked to one of the 17 distinct maps. More mapped rat markers (31,136) are contained in VCMapView than in any other single browser environment. Similarly, 44,185 mouse markers and 152,980 human markers are represented. In total, this provides 228,301 mapped markers from the three species integrated into a single searchable cross-mapped environment, providing investigators with an enormously powerful search, exploration, and discovery resource. In addition, 111,064 human, 90,444 mouse, and 63,253 rat UniGene clusters are represented, which nearly quadruples the number of ESTs, sequences, and Unigenes available in our previous static release (Table 2.)
Intraorganism Maps Of the 23 maps, nine are rat, nine are human, and five are from previous mouse studies. As described above, genetic mapping studies have provided the bulk of historical data linking phenotypes to genes or genomic regions; consequently, these genes or regions are typically identified by specific microsatellite markers placed on these genetic maps. However, because genetic mapping relies on a detectable polymorphism in order to be able to locate a marker via a genetic cross, this reduces the number of markers that can be placed on even the best genetic maps (Table 1). Marker density becomes a problem when trying to narrow regions of interest and when trying to compare genetic maps made with non-overlapping marker sets (not all markers that are polymorphic in one mapping cross will be polymorphic in another cross). The solution to this problem is to relate the genetic maps to a more densely populated map such as the RH or the genome. The maps available in the IGE are listed under seven separate headings—Comparative maps, Radiation Hybrid, Cytogenetic, Genetic, Genome, QTL, and Additional. A simple interface is provided to allow the user to select any combination of maps for display. Once loaded into the java environment, more controls are provided to modify the display to show or hide connections between maps and specific markers and annotations.
Interorganism Maps
Mapping of Physiological Traits Of 563 rat QTLs curated by the RGD project (total as of December 2003), 359 had one or more of their primary flanking or peak markers mapped on the new RH map (version 3.4) thereby allowing the QTLs to be integrated onto a single map. In comparison, only 219 QTLs could be located by using markers mapped on the current genome build because many microsatellite markers are unable to be mapped at high enough confidence using in silico mapping techniques such as BLAT and ePCR. QTLs with both flanking markers mapped could be positioned exactly on the RH map. In situations in which only the peak of the QTL or one flanking marker was available in addition to the peak location, an algorithm was used to conservatively predict the location of the second flanking marker to allow the span of the QTL to be depicted. All curated RGD QTLs were incorporated into the study database, and of these, 151 represent phenotypes exhibited in hypertension, 78 from type I and type II diabetes, 59 from arthritis studies, and the remainder from quantitative traits associated with obesity, renal disease, cardiovascular disease, and other autoimmune diseases. The maps available in the IGE can be used to visualize QTLs alongside a wide variety of additional data linked to other maps in the same organism. This allows researchers to answer questions such as "What genes lie on the genomic sequence within this QTL region, and how do they relate to the cytogenetic map data?" (Fig. 3). In addition to the rat QTLs, 1402 mouse QTLs collected from the Mouse Genome Database (MGD; Blake et al. 2003 ) with corresponding referenced mapped markers were also integrated into the environment, allowing cross-organism disease phenotype testing. As there is no central repository for human QTL results, we have included the OMIM Morbid Map data set linked to the human cytogenetic map (McKusick and Amberger 1993). The morbid map contains the mapping information for the curated disease genes described in Online Mendelian Inheritance in Man (OMIM) and has  9000 genes documented to be involved in human disease.
Integrative Genomics Environment Perhaps the most important result of collecting and integrating this extensive set of maps, data, and annotated comparison analyses from multiple organisms into one environment is the ability to conduct in silico experiments, "virtually" mapping experimental results from one organism onto another. This environment greatly expands the current view of comparative genomics by providing a platform for comparing, collecting, and referencing genetically linked annotations from one organism to that of another organism. Although this type of study is not new (for a review, see Stoll et al. 2000 ; Jacob and Kwitek 2002), previously it entailed a great deal of manual intervention, and hence, the ability to quickly compare mapped traits between multiple organisms in a single environment removes a significant barrier that has typically hindered the use of this comparative data. One example of this virtual experiment arises in the study of arthritis. The q terminal region of rat Chr10 contains QTLs associated with various forms of induced arthritis (oil-induced arthritis QTLs Oia3–6 [Lorentzen et al. 1998; Holm et al. 2001], collagen-induced arthritis QTLs Cia16, Cia20–23, Ciaa2 [Longman et al. 1996; Furuya et al. 2000; Joe et al. 2000]). Confidence in this region and the search for potential candidate genes would be significantly aided by knowledge of other supporting evidence from other organisms such as the mouse. By visualizing the rat QTLs aligned with the rat genome annotations and the rat and mouse comparative maps, this region is found to be syntenic to a portion of mouse Chr11. Aligning this syntenic region of mouse Chr11 with the dense mouse genome annotations and then with the MGI genetic map and its associated QTLs reveals four mouse arthritis QTLs in the syntenic region: Sle1 (systemic lupus erythematosus susceptibility 1; Morel et al. 1994), Bbaa4 (B. burgdorferi–associated arthritis 4; Weis et al. 1999), Orch3 (autoimmune orchitis resistance 3; Meeker et al. 1995), and Pgia17 (proteoglycan induced arthritis QTL 7; Fig. 4; Otto et al. 1999). In this example we have efficiently traversed the data and integrative maps by using the IGE to conduct an in silico experiment, resulting in the association of QTLs derived from physiological experiments studying the same disease in mouse and rat models of arthritis. A review of the related literature from human linkage studies reveals that the syntenic region of human Chr 17 has also been associated with arthritis (Jawaheer et al. 2001, 2003), and in one case, this association was a direct result of using syntenic rat data (Barton et al. 2001). This supports the fundamental paradigm of comparative genomics, namely, that candidate genes in rat and mice models of arthritis may well be characterized by common syntenic location and homology with each other and that these results can be translated to human. If true, such characterizations greatly reduce the number of potential candidate genes for the disease.
Integrated Maps In Silico VCMapView is a java-based tool that allows navigation between multiple genomes and within individual organisms by providing map integration between the cytogenetic, genetic, and RH maps and related genomic sequence contained within the IGE. VCMapview displays multiple maps from one or more organisms in the native coordinate system of each map and thus allows the comparative relationships between the maps to be explored on the fly. Visual anchor lines between shared objects (such as markers and genes) and the ability to highlight markers shared between maps aid map integration. Individual maps can be displayed as required, as can map units and the marker names. Additional annotations such as QTLs can be toggled on or off and, as with markers, are hyperlinked to database reports. Whereas the strength of the genome browsers is their ability to display very detailed annotation at a base-pair resolution, the strength of VCMapview is that such detailed annotations can be turned off, allowing a clearer view of an entire chromosome. VCMapview complements and greatly extends existing browsers and resources and includes a wide spectrum of functionality including zooming, cross-organism annotation, dynamic resizing, and orientation and on-the-fly visual modifications. An online help environment explains all features and options and user support is provided through the RGD support team.
The human, rat, and mouse genome projects require substantial analysis and interpretation to provide the detailed data and information required by an individual investigator exploring his or her respective mechanisms, phenotypes, and diseases. The Integrated Genomics Environment combines analysis, an extensive mapping database, and the VCMapView visualization tool to provide a resource allowing individuals to mine the depth and richness of the genomes. The future of physiological and clinical research will depend on the availability of such resources that will compare multiple maps within and between organisms. A map integration/comparative-mapping paradigm strategy (Fig. 1) demonstrates how to investigate a phenotype mapped onto the rat genome and exploit results from the human and mouse genomes. Currently, IGE represents the most comprehensive collection of cross-organism map data of any utility available to the research community. In particular, this is the largest collection of map and reference information for rat. An example of the integration of the data is presented in Figure 1 of Kwitek et al. 2004 (this issue) a visualization of the three major genetic, RH, and genome maps used to curate and analyze the quality of the new rat RH map.
Markers and Maps
Intraorganism Maps
Integrative Map Browser Our earlier results provided static views of comparative maps between rat, mouse, and human constructed from the respective RH maps combined with sequence-based homology with the three organisms (Kwitek et al. 2001 ). These results and site supported the translation of rat QTLs to the RH map and to syntenic regions in human and/or mouse. However, the original static release required substantial manual effort to update and use and did not include important cytogenetic and genetic data, or reference to rat genome sequence. VCMapView overcomes these manual bottlenecks and provides investigators with a powerful tool to perform in silico physiological comparative mapping experiments, thus translating results from one organism to another and at the same time connecting the powerful approach to complex disease modeling in the rat, to the genetics of the mouse, and the clinical importance of human.
Integrative Genomics Environment The IGE allows in silico research supporting new hypothesis development and testing. In providing a comprehensive collection of marker and map data from the three species and a visualization tool that allows the intra- and intergenome comparisons, IGE is a uniquely valuable tool for data integration within organisms and for comparative genomics studies between organisms.
Data Sources All published maps contained within the IGE are listed in Table 1 along with citations listing the sources for the mapping data. Four of the annotation data sets included in IGE have not been published, and they were created as follows.
Rat Genome
Rat Cytogenetic Map
Rat QTL Maps
Mouse QTL Maps
VCMapView
We would like to acknowledge the RGD bioinformatics and curation teams for their great contributions to the collection and validation of map data and for contributions to VCMapView testing and integration into the RGD environment, as well as past and present members of the S.N.T., H.J.J., and P.J.T. groups for their technical assistance. Thank you to Mike Jensen-Seaman for providing the photograph of the Brown Norway rat used in Figure 1. VCMapView is licensed to PointOne Systems, LLC, Milwaukee, WI. All data integrated into the tool is publicly available. This work has been supported by RO1s HL59826 (H.J.J.), HL54508, HL066579 (H.J.J.), HL54998, and HL64541 (P.J.T.). The publication costs of this article were defrayed in part by payment of page charges. This article must therefore be hereby marked "advertisement" in accordance with 18 USC section 1734 solely to indicate this fact.
Article and publication are at http://www.genome.org/cgi/doi/10.1101/gr.1974504.
5 Corresponding author.
3 Present address: PointOne Systems, LLC, Milwaukee, Wisconsin, USA
4 Present address: Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, USA
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Received September 13, 2003;
accepted in revised format December 27, 2003.
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ABSTRACT
RESULTS
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fibrinogen genes by in situ hybridization. Cytogenet. Cell Genet. 42: 36-41.
