Towards systems genetic analyses in barley: Integration of phenotypic, expression and genotype data into GeneNetwork
- Arnis Druka1,
- Ilze Druka1, 2,
- Arthur G Centeno3,
- Hongqiang Li3,
- Zhaohui Sun3,
- William TB Thomas1,
- Nicola Bonar1,
- Brian J Steffenson4,
- Steven E Ullrich5,
- Andris Kleinhofs5,
- Roger P Wise6, 7,
- Timothy J Close8,
- Elena Potokina9,
- Zewei Luo9,
- Carola Wagner10,
- Günther F Schweizer11,
- David F Marshall1,
- Michael J Kearsey9,
- Robert W Williams3Email author and
- Robbie Waugh1Email author
© Druka et al; licensee BioMed Central Ltd. 2008
Received: 25 April 2008
Accepted: 18 November 2008
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 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.
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.
The systems genetics approach coined 'genetical genomics' aims to decompose phenotypic variation into a series of individual components by simultaneously analysing both 'trait' and 'molecular phenotype' data across genetically defined populations. The approach was originally tested by Damerval et al. in 1994 who applied protein profiling to an F2 population of maize . More recently, genetical genomics has been applied to a range of species using microarray derived mRNA abundance phenotypes [2, 3]. In mouse, such analyses have been used to understand how regulatory networks controlling transcription relate to higher-order phenotypic traits at the genome-wide scale [4, 5]. Analogous genetical genomics experiments in plants have been reported for maize [3, 6], Arabidopsis [7, 8], eucalyptus [9, 10], poplar , wheat  and barley . These experiments demonstrate that the control of gene expression is complex. However, they also can provide insight into the relationships between gene expression and phenotypic traits.
Genetical genomics experiments typically incorporate four separate data sets for each individual in a segregating population; genotype, mRNA abundance, phenotype and associated metadata. When the genetic materials are 'reference strains' that have been analysed by a broad community, there is an opportunity to incorporate legacy phenotypic and genotypic information. While the scale of the mRNA abundance datasets largely determine the predictive power of the approach, a key point is that these large, multidimensional datasets have considerable value beyond that extracted during their initial analysis. This was recognized early by the scientific community and is formally reflected in regulations specifying raw data quality and availability (archiving) by many funding agencies and journals . However, easy access to the data, either raw or processed, is an equally important criterion that may significantly extend its potential usefulness and value [15, 16]. The sheer volume of the genetical genomics data components, if deposited in an open access but unprocessed and in a format designed for archiving, is likely to be of limited value, particularly if only a subset of the data is required for a specific analytical query.
We conducted a genetical genomics experiment in barley using a population of 150 doubled haploid lines . The outcomes of this experiment included two mRNA profiling data sets, a Transcript Derived Marker (TDM)-based barley genetic linkage map and a set of new trait data obtained from over 4 years of field and glasshouse experiments. We also compiled publicly available trait segregation data that has been collected on this reference population by the barley genetics community over the last 15 years. Here we provide open access and availability to these data by integrating them into the GeneNetwork, a web-based analytical tool that has been designed for multiscale integration of networks of genes, transcripts and traits and optimized for on-line analysis of traits controlled by a combination of allelic variants and environmental factors. GeneNetwork with its central module WebQTL facilitates the exploitation of permanent genetic reference populations that are accompanied by genotypic, phenotypic and mRNA abundance datasets. Algorithms for both quantitative trait locus (QTL) mapping and genetic correlation analysis, supported by highly efficient graphical displays facilitate the identification of QTL controlling mRNA transcript abundance (expression-QTL or eQTL) and higher-order phenotypes. Consequently, GeneNetwork is an unique on-line environment for 'trait analysis' at the systems biology level [18, 19].
One of our long term goals is to construct integrated regulatory and structural gene association networks that explain relationships between component gene expression measures and traditional phenotypic traits. We have started this by constructing a trait association network to establish connections and to provide a framework for the identification and mapping of key regulatory genes. Here we describe these barley data sets and demonstrate how GeneNetwork's integrated analytical environment can be exploited to infer map positions of the barley genes and to construct barley trait association networks.
The current barley data set in GeneNetwork
A population of 150 doubled haploid lines (DHLs) derived from a cross between cultivars (cvs.) Steptoe and Morex (St/Mx) was used to generate the mRNA transcript abundance, trait and genotypic data sets. These parents were selected because of their diversity for agronomic traits . Steptoe is a high yielding, broadly adapted six-rowed feed-type barley from the Western United States (US), whereas Morex is a six-rowed malting cultivar from the Midwestern US.
Condensed list of barley traits that have been measured using the Steptoe × Morex DHL population and are available for analysis through GeneNetwork.
Frequency of the germinating grains
Emergence of the second leaf (ESL)
Single-leaf frequency (ESL-f) and length ratio of the second and the first leaf (ESL-r)
Time interval to heading or anthesis
Distance from ground to collar at maturity
Stems < 45 degree angle to ground (1–9)
Maturity of the plot (1–9)
Normalized difference vegetation index (NDVI)
Distance from peduncle to the awn tip
Post harvest sprouting
Frequency of the germinating grains
Frequency of the tillers with no heads
Frequency of the heads with no spikelets.
Thousand grain weight (TGW).
TGW = 1000 × weight/seed number.
MARVIN and ImageJ
Endosperm cell wall modification.
Calcuflor staining, ImageJ analysis
Nitrogen content or grain protein
Hot water extract
Milling energy (J)
Predicted spirit yield
Grain moisture content
Moisture content of sample %
INTERACTION WITH PATHOGENS
Leaf rust (Puccinia hordeii)
Relative Latency Period
Net bloch (Pyrenophora teres)
Frequency of the infection types
Scald (Rhyncosporium secalis)
Spot bloch (Cochliobolus sativus)
Frequency of the infection types
Stem rust (Puccinia graminis)
Frequency of the infection types
mRNA transcript abundance data
Barley expression data sets available for analysis in GeneNetwork.
Types of the expression data sets
Data processing description
The Affymetrix' CEL files that were generated using MAS 5.0 Suite (Affymetrix, Santa Clara, CA) were imported into the GeneSpring GX 7.3 (Agilent Technologies, Palo Alto, CA) and processed using the RMA algorithm.
MAS 5.0 SCRI
MAS 5.0 SCRI
The MAS 5.0 values were calculated from the DAT files using Affymetrix' MAS 5.0 Suite (Affymetrix, Santa Clara, CA).
The Affymetrix' CEL files were imported into the GeneSpring GX 7.3 (Agilent Technologies, Palo Alto, CA) software and processed using the RMA algorithm. Per-chip and per-gene normalization was done following the standard GeneSpring procedure which includes setting the values below 0.01 to 0.01 and then dividing each measurement by the 50th percentile of all measurements in that sample. Additionally each gene was divided by the median of its measurements in all samples.
The linkage map presented here was generated as part of two barley association mapping projects in the United Kingdom (UK)  and US  (also [26, 27]). To create the genotype file, we used data from a pilot barley Illumina Oligo Pool Assay (POPA1) that employs GoldenGate BeadArray technology (Illumina, SanDiego CA) and tested 1,536 barley SNP markers in each of the 150 St/Mx DHLs. 471 high quality polymorphic SNPs were integrated into the existing St/Mx RFLP map  using Map Manager QTX (ver. 0.27) software . A final map was generated by removing co-segregating markers (leaving a single marker per locus) and manually checking and correcting the relatively rare single marker double recombination events visible in graphical genotypes of the individuals in the population.
Using GeneNetwork for barley
To map genetic loci associated with mRNA abundance or trait phenotypes, any one of the three QTL mapping functions currently employed by GeneNetwork's WebQTL module can be used. These are 1. interval mapping, 2. single-marker regression, or 3. composite mapping [29, 30]. A thousand permutations are used to calculate upper and lower Likelihood Ratio Statistic (LRS) thresholds for each trait , and 1000 bootstrap tests [32, 33] can be employed to determine the confidence intervals (Figure 1B).
The correlation analysis module performs either Pearson product-moment correlation or Spearman rank correlation. Different trait and transcript abundance values (either as integrated or individual probe signals) as well as genotypes can be used to correlate against other data sets of choice. Results of the correlation analyses can be displayed as a table showing correlation coefficients and p-values. The covariates can then be visualized pair-wise as scatter plots (Figure 1C), mapped using the QTL Cluster function (Figure 1D) or combined into association networks [34, 35] (Figure 1E).
Predicting gene position
One of the basic, but arguably most relevant applications of GeneNetwork for barley is to predict the map location of a gene. Until its genome is sequenced or all known barley genes are mapped as genetic markers (e.g. SNPs), the ability to infer a gene's chromosomal position (with a given degree of certainty) by mapping the genetic interval that controls the abundance of its mRNA (as an eQTL) provides valuable information about location of the gene itself. This is easily achieved in the GeneNetwork using its integrated QTL mapping functions.
Support for this simple designation of a gene's map location comes from an analysis of conserved synteny between the rice genome sequence and the barley gene map. The rationale is that an eQTL will more likely reflect the true position of its underlying gene if its rice ortholog is located in the conserved syntenic position. We sub-divided all the probe sets that reported significant eQTLs into the high (LRS > 30) and low (LRS < 30) LRS groups and plotted their barley eQTL peak positions against the physical positions of their putative rice orthologs (Additional file 2). For 9 out of 12 rice chromosomes, clear blocks of conserved synteny were revealed with eQTLs with high LRS values, whereas many low LRS value eQTLs were homogenously distributed across the rice genome (for example rice chromosome 1 in Figure 2B). Conservation of synteny provides additional support for the principle of mapping a barley gene based on QTL mapping of mRNA abundance values.
Constructing trait association networks
An association network for a given set of traits is a graphical display of all pair-wise correlations that are above an arbitrarily assigned correlation threshold value . GeneNetwork has a function that constructs such association networks using either phenotype or transcript abundance, or indeed both simultaneously. It provides a visualization of the relative positions and numbers of possible interacting partners, how they interact (positive or negative correlation) and in some situations, based on prior knowledge, it may suggest the directionality of the interaction.
Since association networks are based on correlation, they differentiate neither causal from reactive traits, nor genetic from environmental factors. Genetic linkage mapping, of course, can provide this distinction if a mapping population with sufficiently high resolution is used and sufficient replication is incorporated in the experimental design. Furthermore, in the case of transcript abundance traits, the integration of data from 'classical' or 'treatment-response' type profiling experiments as well as fine scale haplotype map information may clarify the difference between causal and reactive traits . However we note that there is an extra layer of complexity when dealing with an unsequenced genome. Without knowing the regulatory genes underlying key phenotypic traits, and without having precise map positions for the majority of the genes, it is critical that any mRNA abundance based association network analysis is conducted with caution and stringent validation strategies deployed to support any putative links.
The GeneNetwork is an acknowledged and widely used integrated platform designed primarily for analysis of data from mouse genetical genomics experiments [18, 19, 36]. In the future we intend to integrate mRNA profiling, phenotypic and genotypic data from alternative populations that have a different genetic architecture along with molecular profiling data, such as proteins or metabolites, together with access to gene and pathway models and annotations from model plant genomes.
Incorporating algorithms and data handling functions for mapping dynamic traits, also known as functional mapping [38, 39] is also a priority. The approach has been applied to diverse range of species, including humans, animals and plants, to uncover novel information [38, 40–46]. However, to our knowledge, there are no available barley data sets that are suitable for dynamic trait mapping. Preliminary experiments on grain development  and interactions with pathogens [48–51] provide examples and methodologies for obtaining trait values that could be easily applied to an expanded sample population, however, this hasn't been done yet. Functional mapping of data relating to classical traits such as height, flowering time and malting quality could also reveal novel QTL or relationships between existing QTL. However, this knowledge will only improve our understanding of the causal biological process if the genes underlying the QTL are cloned.
The collection of precise phenotypic data across a population and over time would reveal more significant QTL and provide a better link to 'surrogates' such as mRNA abundance, especially if the latter was derived from specific and relevant cell types. As an example, endosperm modification is a key barley quality trait central to both malting and distilling. We mapped endosperm modification as the area ratio of endosperm stained with calcuflor to the unstained area. Calcuflor stains polymeric 1,3–1,4 -beta glucans which are important barley cell wall constituents and their amount decreases when the cell walls are broken down by cellulytic enzymes. The collection of calcuflor staining data on a population of plants over time is an eminently feasible experiment and would allow endosperm modification to be considered as a dynamic trait with the obvious potential of revealing novel QTL controlling biochemical processes activated during germination.
The object models underlying GeneNetwork have been designed for handling data linked to a well established, stable sequencing data that for the mouse have been available for years. For barley and other less thoroughly researched species this is still in a distant future. This is viewed as a major hindrance for high level genetical genomics analysis by many researchers. However, we were able to integrate barley data in the software designed for mouse without any changes to the software itself and just minor adjustments to the existing barley data. This suggests that software that is designed according to the nature of the biological object can be easily adopted to work with objects of the same kind but lacking some essential property values. Therefore the lack of sequence shouldn't be an obstacle for genetical genomics analysis. By integrating datasets from an unsequenced crop plant (barley) in a database that has been designed for an animal model species (mouse) with 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.
Linking barley data in the GeneNetwork to other relevant genomic resources, such as the Barley SNP Database (SNPDb) , Harvest , BarleyBase (within PLEXdb) , GrainGenes  and Gramene  will significantly enhance the interpretation of the molecular basis of higher order phenotypes in barley. The success of this implementation largely depends on the development of flexible and streamlined data processing and submission procedures that can handle heterogeneous data types and provide efficient cross-referencing. XML-based technologies seem well suited to handle this .
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 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.
Availability and requirements
This work was supported by the BBSRC/SEERAD response mode grant SCR/910/04 to Robbie Waugh and Mike Kearsey. GeneNetwork is funded by the NIH (U01AA13499, P20-DA 21131, U01CA105417, and U24 RR021760). Genotyping was funded by the NSF Plant Genome Research Program grant DBI-0321756.
Thanks to Kazuhiro Sato at the Barley Germplasm Center, Okayama University for providing the Steptoe × Morex population for the grain morphometric analysis. Beth Tacke and Howard Casper are acknowledged for the DON tests. The authors are also thankful to an anonymous reviewer for illustrating the potential of functional mapping for efficiently establishing associations between existing QTL, as well as for novel QTL discovery.
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