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


METHODS
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.


CONCLUSIONS
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.
Druka
Arnis
A
Scottish Crop Research Institute, Invergowrie, Dundee, UK. Arnis.Druka@scri.ac.uk
Druka
Ilze
I
Centeno
Arthur G
AG
Li
Hongqiang
H
Sun
Zhaohui
Z
Thomas
William T B
WT
Bonar
Nicola
N
Steffenson
Brian J
BJ
Ullrich
Steven E
SE
Kleinhofs
Andris
A
Wise
Roger P
RP
Close
Timothy J
TJ
Potokina
Elena
E
Luo
Zewei
Z
Wagner
Carola
C
Schweizer
Günther F
GF
Marshall
David F
DF
Kearsey
Michael J
MJ
Williams
Robert W
RW
Waugh
Robbie
R
D002874:Chromosome Mapping; D003628:Database Management Systems; D030541:Databases, Genetic; D018745:Genome, Plant; D005838:Genotype; D001467:Hordeum / Q000235:genetics*; D010641:Phenotype