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Towards systems genetic analyses in barley: Integration of phenotypic, expression and genotype data into GeneNetwork

Arnis Druka1, Ilze Druka12, Arthur G Centeno3, Hongqiang Li3, Zhaohui Sun3, William TB Thomas1, Nicola Bonar1, Brian J Steffenson4, Steven E Ullrich5, Andris Kleinhofs5, Roger P Wise67, Timothy J Close8, Elena Potokina9, Zewei Luo9, Carola Wagner10, Günther F Schweizer11, David F Marshall1, Michael J Kearsey9, Robert W Williams3* and Robbie Waugh1*

Author Affiliations

1 Genetics Programme, Scottish Crop Research Institute, Invergowrie, Dundee DD2 5DA, UK

2 School of Computing and Creative Technologies, University of Abertay, Dundee, DD1 1HG, UK

3 Department of Anatomy and Neurobiology, University of Tennessee, Memphis TN 38163, USA

4 Department of Plant Pathology, University of Minnesota, St. Paul, MN 55108, USA

5 Department of Crop and Soil Sciences, Washington State University, Pullman WA99164, USA

6 Corn Insects and Crop Genetics Research, USDA-ARS, Iowa State University, Ames IA 50011, USA

7 Department of Plant Pathology, Iowa State University, Ames IA 50011, USA

8 Department of Botany and Plant Sciences, University of California, Riverside, CA 92521, USA

9 School of Biosciences, University of Birmingham, Birmingham B15 2TT, UK

10 Department of Plant Breeding, Justus Liebig University Giessen, Heinrich-Buff-Ring 26-32, 35392 Giessen, Germany

11 Institute for Crop Production and Plant Breeding, Dep. Genome Analysis, Bavarian State Research Center for Agriculture, Am Gereuth 6, 85354 Freising-Weihenstephan, Germany

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BMC Genetics 2008, 9:73  doi:10.1186/1471-2156-9-73

Published: 18 November 2008



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. Their predictive power 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 has 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.


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 and entered them into the GeneNetwork webcite. GeneNetwork is a unified on-line analytical environment that enables the user to test genetic hypotheses about how component traits, such as mRNA abundance, 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 them.


By integrating barley genotypic, phenotypic and mRNA abundance data sets directly within GeneNetwork's analytical environment we provide simple web access to the data for the research community. 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 unsequenced crop plant (barley) in a database that has been designed for an animal model species (mouse) with a well established genome sequence, we prove the importance of the concept and practice of modular development and interoperability of software engineering for biological data sets.