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GrainGenes Reference Report: BGE-9-73

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Reference	BGE-9-73
Title	Towards systems genetic analyses in barley: Integration of phenotypic, expression and genotype data into GeneNetwork.
Journal	BMC Genetics
Year	2008
Volume	9
Pages	73
Author	Druka A [ Show all 20 ]
Abstract	ABSTRACT: BACKGROUND: A typical genetical genomics experiment results in four separate data sets; genotype, gene expression, higher-order phenotypic data and metadata that describe the protocols, processing and the array platform. Used in concert, these data sets provide the opportunity to perform genetic analysis at a systems level. The predictive power of these experiments is largely determined by the gene expression dataset where tens of millions of data points can be generated using currently available mRNA profiling technologies. Such large, multidimensional data sets often have value beyond that extracted during their initial analysis and interpretation, particularly if conducted on widely distributed reference genetic materials. Besides quality and scale, access to the data is of primary importance as accessibility potentially allows the extraction of considerable added value from the same primary dataset by the wider research community. Although the number of genetical genomics experiments in different plant species is rapidly increasing, none to date been presented in a form that allows quick and efficient on-line testing for possible associations between genes, loci and traits of interest by an entire research community. Description Using a reference population of 150 recombinant doubled haploid barley lines we generated novel phenotypic, mRNA abundance and SNP-based genotyping data sets, added them to a considerable volume of 'legacy' trait data provided by several of the authors and entered them into the GeneNetwork (www.genenetwork.org). GeneNetwork is unified on-line analytical environment that enables the user to test genetic hypotheses about how component traits, such as mRNA abundance in this case, may interact to condition more complex biological phenotypes (higher-order traits). Here we describe these barley data sets and demonstrate some of the functionalities GeneNetwork provides as an easily accessible and integrated analytical environment for exploring these complex datasets. CONCLUSIONS: By integrating barley genotypic, phenotypic and mRNA abundance data sets directly within GeneNetwork's analytical environment we provide simple desktop community access. In this environment, a combination of correlation analysis and linkage mapping provides the potential to identify and substantiate gene targets for saturation mapping and positional cloning. By integrating datasets from an as yet unsequenced crop plant (barley) in a database that has been designed for mouse, we support the feasibility of 'sustainable programming' practice for biological data sets.
External Databases	http://dx.doi.org/10.1186/1471-2156-9-73

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