Towards systems genetic analyses in barley

Integration of phenotypic, expression and genotype data into GeneNetwork

Arnis Druka, Ilze Druka, Arthur G. Centeno, Hongqiang Li, Zhaohui Sun, William T.B. Thomas, Nicola Bonar, Brian J. Steffenson, Steven E. Ullrich, Andris Kleinhofs, Roger P. Wise, Timothy J. Close, Elena Potokina, Zewei Luo, Carola Wagner, Günther F. Schweizer, David F. Marshall, Michael J. Kearsey, Robert Williams, Robbie Waugh

Research output: Contribution to journalArticle

22 Citations (Scopus)

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. 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. 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 and entered them into the GeneNetwork http://www.genenetwork.org. 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. Conclusion: 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.

Original languageEnglish (US)
Article number73
JournalBMC Genetics
Volume9
DOIs
StatePublished - Nov 18 2008

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Hordeum
Genotype
Messenger RNA
Genomics
Research
Datasets
Genes
Gene Expression
Chromosome Mapping
Haploidy
Single Nucleotide Polymorphism
Organism Cloning
Software
Animal Models
Genome
Databases
Technology
Phenotype

All Science Journal Classification (ASJC) codes

  • Genetics
  • Genetics(clinical)

Cite this

Towards systems genetic analyses in barley : Integration of phenotypic, expression and genotype data into GeneNetwork. / Druka, Arnis; Druka, Ilze; Centeno, Arthur G.; Li, Hongqiang; Sun, Zhaohui; Thomas, William T.B.; Bonar, Nicola; Steffenson, Brian J.; Ullrich, Steven E.; Kleinhofs, Andris; Wise, Roger P.; Close, Timothy J.; Potokina, Elena; Luo, Zewei; Wagner, Carola; Schweizer, Günther F.; Marshall, David F.; Kearsey, Michael J.; Williams, Robert; Waugh, Robbie.

In: BMC Genetics, Vol. 9, 73, 18.11.2008.

Research output: Contribution to journalArticle

Druka, A, Druka, I, Centeno, AG, Li, H, Sun, Z, Thomas, WTB, Bonar, N, Steffenson, BJ, Ullrich, SE, Kleinhofs, A, Wise, RP, Close, TJ, Potokina, E, Luo, Z, Wagner, C, Schweizer, GF, Marshall, DF, Kearsey, MJ, Williams, R & Waugh, R 2008, 'Towards systems genetic analyses in barley: Integration of phenotypic, expression and genotype data into GeneNetwork', BMC Genetics, vol. 9, 73. https://doi.org/10.1186/1471-2156-9-73
Druka, Arnis ; Druka, Ilze ; Centeno, Arthur G. ; Li, Hongqiang ; Sun, Zhaohui ; Thomas, William T.B. ; Bonar, Nicola ; Steffenson, Brian J. ; Ullrich, Steven E. ; Kleinhofs, Andris ; Wise, Roger P. ; Close, Timothy J. ; Potokina, Elena ; Luo, Zewei ; Wagner, Carola ; Schweizer, Günther F. ; Marshall, David F. ; Kearsey, Michael J. ; Williams, Robert ; Waugh, Robbie. / Towards systems genetic analyses in barley : Integration of phenotypic, expression and genotype data into GeneNetwork. In: BMC Genetics. 2008 ; Vol. 9.
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AU - Druka, Arnis

AU - Druka, Ilze

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AU - Li, Hongqiang

AU - Sun, Zhaohui

AU - Thomas, William T.B.

AU - Bonar, Nicola

AU - Steffenson, Brian J.

AU - Ullrich, Steven E.

AU - Kleinhofs, Andris

AU - Wise, Roger P.

AU - Close, Timothy J.

AU - Potokina, Elena

AU - Luo, Zewei

AU - Wagner, Carola

AU - Schweizer, Günther F.

AU - Marshall, David F.

AU - Kearsey, Michael J.

AU - Williams, Robert

AU - Waugh, Robbie

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N2 - 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. 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. 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 and entered them into the GeneNetwork http://www.genenetwork.org. 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. Conclusion: 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.

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