# Barley Genomics: An Overview

Authors: Nese Sreenivasulu; Andreas Graner; Ulrich Wobus
Journal: International Journal of Plant Genomics
 (2008-02-26)

---

## Abstract

Barley (Hordeum vulgare), first domesticated in the Near East, is a well-studied crop in terms of genetics, genomics, and breeding and qualifies as a model plant for Triticeae research. Recent advances made in barley genomics mainly include the following: (i) rapid accumulation of EST sequence data, (ii) growing number of studies on transcriptome, proteome, and metabolome, (iii) new modeling techniques, (iv) availability of genome-wide knockout collections as well as efficient transformation techniques, and (v) the recently started genome sequencing effort. These developments pave the way for a comprehensive functional analysis and understanding of gene expression networks linked to agronomically important traits. Here, we selectively review important technological developments in barley genomics and related fields and discuss the relevance for understanding genotype-phenotype relationships by using approaches such as genetical genomics and association studies. High-throughput genotyping platforms that have recently become available will allow the construction of high-density genetic maps that will further promote marker-assisted selection as well as physical map construction. Systems biology approaches will further enhance our knowledge and largely increase our abilities to design refined breeding strategies on the basis of detailed molecular physiological knowledge.


---

## Body

## 1. Introduction

In the 21st century, cereals continue to constitute the most
important crops with an annual output of 2 billion tons (according to FAO in
2006;http://www.fao.org). In today's worldwide production, barley ranks
fourth among cereals and is preferentially used as feed grain, as a raw material
for beer production and, to a smaller extent, as food. Initially, barley was
domesticated in the fertile crescent of the Neolithic Near East over 10 000
years ago [1]. In the subsequent millennia,
farmers continuously adapted local populations to their needs, leading to a
great variety of landraces. About 100 years ago, these formed the basis for the
development of modern cultivars by cross breeding. During this time, grain
yield was more than   doubled with an estimated genetic contribution to this
increase of about 30–50% [2].  However, to meet the
future challenges imposed by a changing environment, to feed a growing world
population, and to provide renewable resources to satisfy the soaring demand
for energy, genomics-based technologies have to be efficiently implemented to
study the genetic basis of plant performance and to isolate agronomically
important genes from the genetic diversity present in the gene pool of barley.
A broad spectrum of resources has been developed during the last two decades to
facilitate the systematic analysis of the barley genome. These include a large
number of mapped molecular markers, comprehensive EST collections, BAC
libraries, mutant collections, DNA arrays, and enabling technologies such as
the large scale production of doubled haploids and efficient transformation
protocols. Advances made in barley genomics and recent efforts made towards
physical map construction and sequencing of the barley gene space
(http://barleygenome.org) will largely contribute to a comprehensive
understanding of gene functions in the context of agronomical important
phenotypes (refer to Figure 1 and Table 1). Recently, the techniques and
methods employed in cereal genomics have been reviewed [3–8]. In this overview, we have tried to summarize progress in
structural and functional genomics of barley and put emphasis on important
agronomical aspects such as grain yield, seed quality traits, and implications
for malting quality improvement.Table 1
Barley genomic resources.
DatabasesWebsite URLApplicationBarley Genetic Stockshttp://ace.untamo.net/cgi-bin/ace/searches/basicProvides information on the
morphological & genetic background of barley mutants and contains
information on 736 barley translocation and duplication lines.US Barley Germplasmhttp://barleyworld.org/northamericanbarley/germplasm.phpContains information on barley
recombinant chromosome substitution lines and North American barley mapping
populations.EBDBhttp://pgrc-35.ipk-gatersleben.de/portal/page/portal/PG_BICGH/P_BICGH/P_BICGH_RESOURCES/P_BICGHI_RESEBDBThe European Barley
Database (EBDB) contains passport and evaluation data of 155,000 barley
accessions including the international barley core collection.ICARDA Barley varietieshttp://www.icarda.cgiar.org/Crops_Varieties.htm#BarleyProvides an  index of barley variety releases from ICARDA,
1977–2005.Barley TILLINGhttp://www.scri.ac.uk/research/genetics/BarleyTILLINGA reverse genetics platform,
which can be screened for 8,600 barley (cultivar “Optic”) EMS mutagenized lines.CR-ESThttp://pgrc.ipk-gatersleben.de/cr-est/index.phpBarley EST database
containing sequences, functional annotation and clustering information of
more than 232,000 ESTs.HarvESThttp://harvest.ucr.eduBarley EST database
containing unigene sequences and the oligo design of Barley1 Affymetrix
array. It also includes a 1000 barley SNP loci genetic map showing syntenic
information with rice.HvGIhttp://compbio.dfci.harvard.edu/tgi/cgi-bin/tgi/gimain.pl?gudb=barleyThisHordeum vulgare Gene Index provides functional annotation
information, 70-mer oligo predictions and in silico gene expression data for
50,000 unigenes.NCBI Barley genome viewhttp://www.ncbi.nlm.nih.gov/mapview/map_search.cgi?taxid=4513Provides an overview about the
available genomic and genomic survey sequences (GSS) of barley.IBSChttp://barleygenome.orgActivities of the International
Barley Genome Sequencing Consortium (IBSC) are highlighted.Barley genomehttp://phymap.ucdavis.edu:8080/barleyBarley physical mapping
database and available BAC clones together with the accompaning ESTs.Barley physical maphttp://pgrc.ipk-gatersleben.de/kuenzel/barleymap.htmlBarley translocation
breakpoints integrated into the Igri/Franka-derived RFLP linkage map.Barley genomicshttp://barleygenomics.wsu.eduContains information about barley
molecular markers, genetic maps, BACs and mutants.Barley DBhttp://ukcrop.net/perl/ace/search/BarleyDBContains information about barley
germ plasm, molecular markers, genetic maps and BACs.Gramenehttp://www.gramene.orgProvides an overview of
comparative maps of cereals including available updated molecular markers and
maps of barley.GrainGeneshttp://wheat.pw.usda.gov/GG2/index.shtmlTriticeae database provides an overview about available maps, genetic markers, QTLs and gene expression data.Barley dbEST SSRshttp://www.genome.clemson.edu/projects/barley/ssr.dbest.html15,182 barley simple
sequence repeats (SSR) were predicted using the available 328,724 dbEST
dataset.Barley SNP databasehttp://bioinf.scri.ac.uk/barley_snpdbBarley SNP linkage map.Barley RFLP databasehttp://pgrc.ipk-gatersleben.de/rflp/rflp.htmlContains data of mapped
barley RFLP-markers from IPK.Barley DArThttp://www.triticarte.com/content/barley_diversity_analysis.htmlHigh density consensus map
of barley DArT markers linking to existing SSR, RFLP and STS loci.BarleyBasehttp://www.plexdb.org/plex.php?database=BarleyAn online dataset for
storing and visualizing gene expression data of the Barley 1
GeneChip Affymetrix array.BDC-GEDhttp://pgrc.ipk-gatersleben.de/seedsContains barley developing
caryopses gene expression data.Figure 1
Schematic representation of structural and functional genomic aspects of barley.


## 2. Barley ESTs, BACs, and Physical Maps—a  Springboard for the Exploration  of the Genome

The seven barley chromosomes
represent the basic genome of all Triticeae species. Still, the large genome
(∼5500 MB), of which 80% is composed of repetitive DNA is presently not
amenable to whole genome sequencing. Therefore, large scale sequencing programs
for the development of expressed sequence tags (ESTs) from various cDNA
libraries have been initiated. The progress made in the last 5 years resulted
in the generation of 437,713 ESTs covering different cDNA libraries from
various stages of plant development and tissues challenged with abiotic and
biotic stresses (http://www.ncbi.nlm.nih.gov/dbEST/dbEST_summary.html, September 14th 2007 release). Alignment of these ESTs led to the
identification of a representative set of 50,453 unigenes with 23,176 tentative
consensi and 27,094 singletons
(http://compbio.dfci.harvard.edu/tgi/cgi-bin/tgi/gimain.pl?gudb=barley),
representing possibly about 75% of all genes in the barley genome. An earlier
estimate of the barley gene content based on 110,000 ESTs led to the prediction
of around 30 000 unique genes [9]. This number might be an under representation
due to the low EST coverage. The same EST data set, which was generated from
different tissues covering the plant's life cycle, was analyzed to gain insight
into differential gene expression programs in diverse plant tissues by in silico expression
studies [9]. In this way, comprehensive analysis
of extensive EST resources generated from large genomes provides snap shots of
the transcriptome aiding in gene discovery. This also allows identifying coregulated
metabolic and regulatory networks [10, 11] and
helps to establish high-density molecular maps [12–14] which form the basis for comparative genomic studies,
trait mapping, and map-based gene isolation. Thus, in large genome cereal
species like barley, EST sequences facilitate a comprehensive overview of gene
content and represent a resource to study the evolution and organization of a
genome. Regarding the latter, EST-derived information remains limited as it
fails to provide, for instance, regulatory information, since promoters and
full length sequences are not available.Physical maps represent an important
link to connect the genetic level to the sequence level. Similar to genetic
maps, physical maps are available at different levels of resolution.
Wheat-barley addition lines are a useful resource to rapidly assign ESTs to an
entire chromosome or to a chromosome arm [15]. Using this resource, 1787 genes present on the Barley 1
GeneChip could be assigned to the six different chromosomes of barley (365
genes to 2H, 271 to 3H, 265 to 4H, 323 to5H, 194 to 6H, and 369 to 7H) [16]. At a higher resolution, a physical map of all the seven
barley chromosomes has been prepared by mapping DNA markers derived from both
genomic as well as gene-based sequences relative to the translocation
breakpoints of individual chromosomes that had been isolated using
microdissection techniques [17]. The resulting map is of particular
value, as it can be directly aligned to the genetic map of barley by common
markers and thus allows for the estimation of the ratio between genetic and
physical distances. An alternative approach has been described by Masoudi-Nejad
et al. [18]. Here the presence of a wheat
gametocidal chromosome in a wheat barley addition line was exploited to select
90 progeny lines that carried differently sized fragments of barley  chromosome 7H. These were subsequently used
to determine the physical order and distance of markers located on barley
chromosome 7H.During the past several years, core
public resources have been established by generating “bacterial artificial
chromosome” (BAC) libraries from different barley cultivars: “Morex” ([19]; 313,344
clones), “Cebada Capa” ([20]; 177,000 clones) and “Haruno Nijo”
(http://www.intl-pag.org/10/abstracts/PAGX_P393.html). Based on fluorescence in situ hybridization (FISH)
techniques karyotype landmarks were derived for barley, which could be used in
future to place the BAC clones onto the physical map [21]. This map shows that the genetic linkage maps are well
covered with markers among all chromosomes. At the same time, the physical maps
reveal large areas of the barley genome that have yet to be mapped. These
unmapped areas mainly consist of heterochromatin and show very low
recombination rates [17]. In accordance with these findings,
there is increasing evidence that genes are not randomly distributed across the
barley genome but confined to a gene space, which mainly covers the distal
parts of the chromosomes. Experimental evidence for the existence of a gene
space has been gained from screening a barley BAC library with EST-derived
probes, which showed a significant nonrandom distribution across the BAC clones
[22]. More direct evidence has been
reached on the sequence level for barley and other Triticeae species. Although
up to now only a limited amount of sequence data is available, the average
density of annotated genes is much higher than that expected for a random
distribution across the genome. The disproportionate gene number found is
probably due to the preferential selection of gene containing BACs for sequence
analysis. Within single BACs, there is considerable variation ranging, in case
of barley, from 1 gene in 12 kb up to 1 gene in 220 kb (for review see [23]). Thus even the gene space itself seems to be characterized
by a highly variable distribution of genes against the backdrop of noncoding,
mainly repetitive DNA.The existence of a gene space also
opens up new opportunities to focus analyses on gene-rich regions only.
Recently, international efforts have been gearing up to utilize the extensive
barley EST resources for BAC anchoring and genetic mapping. An elegant approach
of screening of the Morex BAC library using EST-derived, pooled
“overgo” probes [24] resulted in the identification of
gene containing BACs. Upon fingerprinting of a subset of 21 161 clones, 2262
contigs could be assembled covering approximately 9.4% of the barley genome.
Furthermore, a database has been set up to search screening results of BAC
libraries as well as to provide an integrative view of data from the existing
barley genetic and physical maps (http://www.genome.clemson.edu). The
identified BAC-based gene-rich regions of the genome have been selected as a
genomic reference from cultivar Morex to initiate sequencing of all
gene-containing regions of the barley genome by an international effort
coordinated through the International Barley Sequencing Consortium (IBSC,
http://barleygenome.org).


## 3. A Barley Transcriptome Atlas

Despite the lack of a barley genome sequence, functional
genomics efforts have been initiated by taking advantage of the available EST
sequence information generated by multinational coordinated efforts (see
above).  As a first step, efforts were
made to derive functional assignments of the available barley unigene set by
annotation transfer from homologous sequences relying on the available plant
whole genome sequences and by identifying common motifs from Interpro. As a
result, several ontology structures such as MIPS [9] and MAPMAN functional categories (N. Sreenivasulu,
unpublished data; http://mapman.mpimp-golm.mpg.de/index.shtml) were developed.
Such computational methods also yielded putative regulatory networks as well as
metabolic pathway interaction networks, but still about half of the genes have
to be classified as “unknown.”The available barley EST unigene resources played a profound
role in developing several platforms for transcriptome analysis including
cDNA-based macroarrays [11, 25],
microarrays [26], and oligonucleotide-based affymetrix
arrays [10, 27]. Other profiling techniques used in barley include
cDNA-AFLP [28], SAGE (Serial Analysis of Gene
Expression) [29, 30], and iGentifier. The latter method
combines elements of tag sequencing such as SAGE and fragment display [31]. By successfully applying these techniques, barley transcriptome
data have been collected from grain development [11, 25, 32], grain
germination [33, 34], at least 15 different tissues/organs covering
different growth stages [10], and abiotic [26, 35–37] as well
as biotic stress responses [38, 39]. The new insights gained from transcriptome analysis
of host-pathogen studies have lately been reviewed by Wise et al. [40]. These large scale gene expression data sets serve as
baseline experiments to generate a barley transcriptome atlas. Also, an online
Plant Expression Database (PLEXdb), previously known as BarleyBase
(http://www.plexdb.org/plex.php?database=Barley) has been created to store,
visualize, and statistically analyze Barley 1 GeneChip data [41].While transcriptomics have brought about substantial progress in elucidating
biochemical pathways of barley seed metabolism (see reviews [5, 42]), very recent findings  shed light on the interplay of many cellular
and metabolic events that are coordinated by a complex regulatory network
during barley seed development [10, 11, 25]. Studying expression data of nearly 12 000
seed-expressed genes revealed, for instance, the participation of
tissue-specific signaling networks controlling ABA-mediated starch accumulation
(via SNF1 kinase and a set of transcription factors) in the endosperm and
participation of ABA-responsive genes in establishing embryo desiccation
tolerance [11].CpG methylation found in the promoters of prolamin box-binding factor
and B-hordein genes suppresses transcript levels during the prestorage until
the intermediate phase of grain development. This process coincides with the coexpression
of methyltransferases, core histones and DNA-unwinding ATPases [43].  Thus storage protein gene expression may be
regulated by CpG methylation. Using a lys 3a mutant, it has been shown that demethylation of the B-hordein promoter
does not occur in the mutant, hence transcripts encoding storage proteins such
as B-hordeins and C-hordeins are almost absent in the developing endosperm of
this mutant [44]. Transcriptome profiling of barley embryos
using the 22K affymetrix Barley 1
GeneChip revealed activation of developmentally
distinct defense related gene sets including coregulated phenylpropanoid and
phytoalexin related genes around 20 days after flowering (DAF), followed by upregulation
of antioxidant and pathogen related gene sets around 37 DAF [45].
The knowledge obtained on metabolic processes of seed quality traits could
eventually be used to develop superior varieties by genetic engineering or by
marker-assisted selection in conventional breeding programs.Transcriptome
analysis has also been carried out during barley grain germination at
tissue-specific levels [10, 46]. Using cDNA array technology gene
expression was analyzed in germinating seed samples, collected from ten
different barley genotypes showing differential malting response [46].
Based on six different malting quality parameters related to hydrolytic events
connected to protein, starch, and cell wall degradation 19 candidate genes were
identified, whose transcript abundance showed a significant correlation with
some of the malting quality parameters. White et al. [30] analyzed
seven different SAGE libraries derived from malted grains and identified 100
most abundant transcripts showing differential responses during eight different
time points during malting. These transcripts are related to stress and defense
response, hydrolytic processes and translational events. The list of candidate
genes identified in the two studies [30, 46] was
further validated by a genetical genomics approach in which gene expression
studies were conducted with populations segregating for malting traits [34, 47].


## 4. Functional Genomics Approaches in Barley

A major aim of functional genomic studies is to understand
the metabolic and regulatory networks within the structural and functional
context of cells, tissues, and organs often changing with time. Hence in this
review, we update the functional genomic resources available (Table1) to study
gene functions in barley using reverse genetics approaches and highlight the
initial success achieved through genetic engineering based on the manipulation
of individual genes.


### 4.1. Reverse Genetics

To determine gene-function relationships, large scale
genome-wide reverse genetics approaches have been developed in barley (see [48] for review) which includes both nontransgenic technology
platforms such as TILLING (targeting induced local lesions in genomes) [49] and insertional mutagenesis systems based on transgenic technology
[50–54]. Thus, the Scottish Crop Research
Institute generated a large M2 TILLING population in the barley
cultivar “Optic” with leaf material and seeds from 20 000 plants freeze dried and archived [49]. EMS induced mutations were
scored at various growth stages under different conditions and documented [49, 55]. Mutant
phenotypes, candidate genes, and observed DNA sequence variations can be
queried in an SCRI mutant database
(http://germinate.scri.ac.uk/barley/mutants/index.php?option=com_wrapper&Itemid=35).
In a more recent attempt, IPK developed a TILLING population of 10 000 M2 plants in the cultivar `Barke” (N. Stein, personnel
communication). Similarly, a collection of 5000 M3 mutants of the cultivar “Morex”
is provided by the University of Bologna (http://www.intl-pag.org/13/abstracts/PAG13_P081.html).To aid functional gene analysis, insertional mutagenesis
approaches were followed in barley during the last decade (i) to create loss-of-function
mutations by the insertion of transposable elements into a gene of interest [50–53] and (ii) use activation tagging (the random genomic insertion of either promoter or enhancer
sequences) to generate dominant gain-of-function mutations [54, 56]. Insertion lines have been generated
by creating transgenic plants carrying Ac and Ds elements, and crossed them
to induce Ds transposition [50–52]. Ds elements were preferentially found in genic regions and exhibited a high-remobilization
frequency [52, 53]. Such Ds launch pads, represented by barley lines with each harboring a
single copy DS insertion at a well-defined position in the genome, will be
valuable for future targeted gene tagging. Similarly, dominant overexpression
phenotypes [54, 56] will help to study gene functions in the large barley genome where
loss-of-function mutations often may not cause phenotypes because of gene
redundancy.


### 4.2. Transgenic Barley and Its Potential Applications

In order to functionally characterize candidate genes
identified in functional genomic studies, it was mandatory to establish a
stable and efficient genetic transformation technique in barley. In contrast to
the biolistic gene transfer technique [57], a more
efficient Agrobacterium mediated
barley genetic transformation method based on immature embryos was developed in
spring barley [58]. In a recent attempt to further
improve this technology, Kumlehn et al. [59] developed a transformation method for winter barley based
upon the infection with Agrobacterium of androgenic pollen cultures. By this approach, homozygous double haploid plants could be
immediately obtained at high frequency through chromosome doubling.During the last decade, systematic efforts were made for
genetic engineering of barley to improve seed quality traits including those
related to malting (reviewed in [60]). Malting improvement has been
addressed by altering the expression of hydrolytic enzymes related to the
degradation of storage products such as starch (α and ß-amylases, [61, 62]) and cell wall components. In another approach,
several enzymes such as xylanase, glucanase, endo-, and exoprotease were over
expressed in transgenic barley grains and preferably the enzyme mix necessary
for malting process are provided by transgenic seeds [63].Protein engineering has been used to produce thermostable 1, 3;
1, 4ß-glucanases in transgenic barley grains [64–66]. Such grains can be used to enhance the feed quality
of barley for poultry [67, 68]. In a similar
approach, a hybrid cellulase gene driven by the endosperm specific rice GluB-1
promoter was expressed and produced the enzyme up to 1.5% of total grain
protein [69]. In addition, functions of key genes
involved in determining seed quality traits related to storage product accumulation
were tested. For instance, antisense downregulation of limit dextrinase
inhibitor showed reduced amylose over amylopectin levels and eventually reduced
total starch [70]. Also overexpression of wheat
thioredoxin h in the endosperm of transgenic barley grain leads to increased
activity of the starch debranching enzyme limit dextrinase [71, 72]. Further, a
powerful approach of antisense oligodeoxynucleotide inhibition has been used to
reveal sugar signaling networks. Short stretches of 12–25 nucleotide
long single-strand sequences have been delivered to barley leaf cells to block
the effect of SUSIBA2, a key transcriptional activator involved in plant sugar
signaling [73]. Recently, this approach has been
successfully implemented to deliver antisense oligodeoxynucleotides to barley
seed endosperm to suppress sugar related signaling genes [74]. HvGAMYB,
a transcription factor initially identified in aleurone and shown to be
upregulated by gibberellin, has been shown to be expressed also in barley
anthers. The overexpressing HvGAMYB transgenic lines show reduced anther size
with a male sterility phenotype [75]. Our laboratory has recently characterized
a new protein called Jekyll, which is preferentially expressed in barley grain
nucellar projection tissue [76]. Its downregulation decelerates autolysis
of nurse tissue. As a result, proliferation of endosperm nuclei is impaired and
less starch is finally accumulated in the endosperm [77].


### 4.3. Towards Systems Biology

With respect to applied aspects in crop plants, a
comprehensive knowledge of cellular and functional complexity as related to key
agronomic traits could be revealed using a systems biology approach. With this
in mind, a number of tools and databases were developed at our institute
(Leibniz Institute of Plant Genetics and Crop Plant Research/IPK) to store,
analyze, and display the data derived from multiparallel-OMICs profiling
studies at transcript, metabolite, and protein/enzyme level with the aim to
eventually gain insight into the organization of function-related networks in
barley [78, 79].
These include CR-EST [78] (it provides access to clustering and annotation data of
IPK EST projects),  Meta-All [79], and MetaCrop [80] (they allow to access curated metabolic pathway information and
kinetic reactions of crop plants),  VANTED [81] (for visualization and analysis of metabolic and regulatory
networks),  HiT-MDS [32] (for screening of coexpressed genes and validation of cluster
centroids) as well as barley MapMan and PageMan [http://mapman.mpimp-golm.mpg.de; to index and visualize
overrepresented functional categories and detailed metabolic pathway charts
from throughput transcriptome data]. With the focus of using the “developing
seed” as model for systems biology studies, we investigated transcriptional and
metabolic networks during grain development [11, 25, 82], developed 3D models of the
developing barley grain [83], implemented magnetic resonance-based
techniques to establish 4D models as a framework to store different sets of
data in their spatiotemporal context [84], visualized
the spatial distribution of specific biochemical compounds by noninvasive
NMR-based imaging methods [85] and established kinetic models of
primary metabolism ([86] and E. Grafahrend-Belau
and B. Junker, unpublished data) as already worked out for potato [87]. In addition, a proteomic platform has been successfully
established to study barley grain development [88, 89]. The emerging model (largely qualitative) explaining
how the barley grain develops and functions has to be further validated especially
by the creation and analysis of different lines of transgenic plants with
perturbations at putative key metabolic and/or regulatory sites (see Figure 1).


## 4.1. Reverse Genetics

To determine gene-function relationships, large scale
genome-wide reverse genetics approaches have been developed in barley (see [48] for review) which includes both nontransgenic technology
platforms such as TILLING (targeting induced local lesions in genomes) [49] and insertional mutagenesis systems based on transgenic technology
[50–54]. Thus, the Scottish Crop Research
Institute generated a large M2 TILLING population in the barley
cultivar “Optic” with leaf material and seeds from 20 000 plants freeze dried and archived [49]. EMS induced mutations were
scored at various growth stages under different conditions and documented [49, 55]. Mutant
phenotypes, candidate genes, and observed DNA sequence variations can be
queried in an SCRI mutant database
(http://germinate.scri.ac.uk/barley/mutants/index.php?option=com_wrapper&Itemid=35).
In a more recent attempt, IPK developed a TILLING population of 10 000 M2 plants in the cultivar `Barke” (N. Stein, personnel
communication). Similarly, a collection of 5000 M3 mutants of the cultivar “Morex”
is provided by the University of Bologna (http://www.intl-pag.org/13/abstracts/PAG13_P081.html).To aid functional gene analysis, insertional mutagenesis
approaches were followed in barley during the last decade (i) to create loss-of-function
mutations by the insertion of transposable elements into a gene of interest [50–53] and (ii) use activation tagging (the random genomic insertion of either promoter or enhancer
sequences) to generate dominant gain-of-function mutations [54, 56]. Insertion lines have been generated
by creating transgenic plants carrying Ac and Ds elements, and crossed them
to induce Ds transposition [50–52]. Ds elements were preferentially found in genic regions and exhibited a high-remobilization
frequency [52, 53]. Such Ds launch pads, represented by barley lines with each harboring a
single copy DS insertion at a well-defined position in the genome, will be
valuable for future targeted gene tagging. Similarly, dominant overexpression
phenotypes [54, 56] will help to study gene functions in the large barley genome where
loss-of-function mutations often may not cause phenotypes because of gene
redundancy.


## 4.2. Transgenic Barley and Its Potential Applications

In order to functionally characterize candidate genes
identified in functional genomic studies, it was mandatory to establish a
stable and efficient genetic transformation technique in barley. In contrast to
the biolistic gene transfer technique [57], a more
efficient Agrobacterium mediated
barley genetic transformation method based on immature embryos was developed in
spring barley [58]. In a recent attempt to further
improve this technology, Kumlehn et al. [59] developed a transformation method for winter barley based
upon the infection with Agrobacterium of androgenic pollen cultures. By this approach, homozygous double haploid plants could be
immediately obtained at high frequency through chromosome doubling.During the last decade, systematic efforts were made for
genetic engineering of barley to improve seed quality traits including those
related to malting (reviewed in [60]). Malting improvement has been
addressed by altering the expression of hydrolytic enzymes related to the
degradation of storage products such as starch (α and ß-amylases, [61, 62]) and cell wall components. In another approach,
several enzymes such as xylanase, glucanase, endo-, and exoprotease were over
expressed in transgenic barley grains and preferably the enzyme mix necessary
for malting process are provided by transgenic seeds [63].Protein engineering has been used to produce thermostable 1, 3;
1, 4ß-glucanases in transgenic barley grains [64–66]. Such grains can be used to enhance the feed quality
of barley for poultry [67, 68]. In a similar
approach, a hybrid cellulase gene driven by the endosperm specific rice GluB-1
promoter was expressed and produced the enzyme up to 1.5% of total grain
protein [69]. In addition, functions of key genes
involved in determining seed quality traits related to storage product accumulation
were tested. For instance, antisense downregulation of limit dextrinase
inhibitor showed reduced amylose over amylopectin levels and eventually reduced
total starch [70]. Also overexpression of wheat
thioredoxin h in the endosperm of transgenic barley grain leads to increased
activity of the starch debranching enzyme limit dextrinase [71, 72]. Further, a
powerful approach of antisense oligodeoxynucleotide inhibition has been used to
reveal sugar signaling networks. Short stretches of 12–25 nucleotide
long single-strand sequences have been delivered to barley leaf cells to block
the effect of SUSIBA2, a key transcriptional activator involved in plant sugar
signaling [73]. Recently, this approach has been
successfully implemented to deliver antisense oligodeoxynucleotides to barley
seed endosperm to suppress sugar related signaling genes [74]. HvGAMYB,
a transcription factor initially identified in aleurone and shown to be
upregulated by gibberellin, has been shown to be expressed also in barley
anthers. The overexpressing HvGAMYB transgenic lines show reduced anther size
with a male sterility phenotype [75]. Our laboratory has recently characterized
a new protein called Jekyll, which is preferentially expressed in barley grain
nucellar projection tissue [76]. Its downregulation decelerates autolysis
of nurse tissue. As a result, proliferation of endosperm nuclei is impaired and
less starch is finally accumulated in the endosperm [77].


## 4.3. Towards Systems Biology

With respect to applied aspects in crop plants, a
comprehensive knowledge of cellular and functional complexity as related to key
agronomic traits could be revealed using a systems biology approach. With this
in mind, a number of tools and databases were developed at our institute
(Leibniz Institute of Plant Genetics and Crop Plant Research/IPK) to store,
analyze, and display the data derived from multiparallel-OMICs profiling
studies at transcript, metabolite, and protein/enzyme level with the aim to
eventually gain insight into the organization of function-related networks in
barley [78, 79].
These include CR-EST [78] (it provides access to clustering and annotation data of
IPK EST projects),  Meta-All [79], and MetaCrop [80] (they allow to access curated metabolic pathway information and
kinetic reactions of crop plants),  VANTED [81] (for visualization and analysis of metabolic and regulatory
networks),  HiT-MDS [32] (for screening of coexpressed genes and validation of cluster
centroids) as well as barley MapMan and PageMan [http://mapman.mpimp-golm.mpg.de; to index and visualize
overrepresented functional categories and detailed metabolic pathway charts
from throughput transcriptome data]. With the focus of using the “developing
seed” as model for systems biology studies, we investigated transcriptional and
metabolic networks during grain development [11, 25, 82], developed 3D models of the
developing barley grain [83], implemented magnetic resonance-based
techniques to establish 4D models as a framework to store different sets of
data in their spatiotemporal context [84], visualized
the spatial distribution of specific biochemical compounds by noninvasive
NMR-based imaging methods [85] and established kinetic models of
primary metabolism ([86] and E. Grafahrend-Belau
and B. Junker, unpublished data) as already worked out for potato [87]. In addition, a proteomic platform has been successfully
established to study barley grain development [88, 89]. The emerging model (largely qualitative) explaining
how the barley grain develops and functions has to be further validated especially
by the creation and analysis of different lines of transgenic plants with
perturbations at putative key metabolic and/or regulatory sites (see Figure 1).


## 5. Functional Molecular Markers and their Potential Applications in the Area  of Applied Genomics

### 5.1. Marker Development and Marker-Assisted Selection (MAS)

Almost two decades ago, RFLP markers were employed to develop
the first comprehensive molecular marker maps in barley [90–92]. Using those RFLP maps, a series of agronomic traits
and characters including many quality traits and resistance against several
diseases have been mapped (for review see [93, 94]). Later, the availability of large numbers of ESTs
facilitated the systematic development of functional markers, for example, by
extracting ESTs containing simple sequence repeat (SSR) motifs using
appropriate software tools [95]. Although EST-based SSR markers have
been shown to be less polymorphic than their genomic counterparts, this
drawback is more than compensated for by the ease of their development. Also,
the availability of ESTs from multiple-genotypes/cultivars of barley provides
the possibility to identify sequence polymorphisms (mainly single-nucleotide
polymorphisms and small InDels) in the corresponding EST alignments. These in
turn can be exploited for the development of markers [96, 97].
Kota et al. [98] developed the computer algorithm SNiPping for discovery of
functional markers through browsing EST assemblies in barley. Also an SNP2CAPS
program has been published to facilitate the computational conversion of SNP
markers into CAPS markers [99]. Information generated from the
diverse mapping projects was further enhanced by the development of consensus
maps [14, 100–102]. These provide integrative genetic
information by featuring high marker densities. Although the gel-based
genotyping platforms offer the best quality marker systems, their low
throughput encouraged researchers to explore high-throughput technologies that
can simultaneously assay thousands of markers based on single nucleotide polymorphisms
(SNP). Most recently, genome-wide scans using SNP-based genotyping platforms
such as Illumina GoldenGate BeadArrays [103] and the diversity arrays technology (DArT), which do not
require any sequence information [104] have been successfully established
in barley. Although DArTs are not systematically interrogating expressed
sequences, the choice of appropriate enzymes facilitates their enriched
representation. Based on DArT technology, a high-density consensus map has recently
been established [105]. A number of recent studies also
reported the use of the affymetrix Barley 1
GeneChip [27] for identifying single-feature polymorphisms (SFPs), which
cover not only SNPs but also indels and polymorphisms generated due to alternative
splicing and polyadenylation [34, 106].An important application of the above discussed functional
markers is marker-assisted selection (MAS). MAS is based on linking the DNA polymorphisms
revealed by marker analysis with agronomical traits allowing for their rapid
selection in routine breeding programs. MAS can be performed already at
juvenile growth stages and before flowering, and thus provides breeders with
the opportunity to implement faster back-crossing strategies and allele
enrichment in complex crosses, which eventually reduces the time and costs
required for the development of improved varieties. Despite its inherent
advantages, the application of MAS in barley up to now has mainly been
restricted to monogenic traits such as disease resistances. Here, one of the
most widespread examples is the marker assisted selection of therym4 gene giving resistance to the barley
yellow mosaic virus complex. For this gene, several closely linked and easily
scorable markers have been developed [107, 108]. More recently, cloning of the gene facilitated the exploitation of
functional polymorphisms within the coding region of the resistance gene to
differentiate between alleles [109].
Using MAS, several genes providing full resistance could be readily combined in
complex crosses without time consuming progeny tests in the greenhouse or in
the field (e.g., [110, 111]).MAS for quantitative traits suffers from two major
limitations. (i) Compared to monogenic traits, quantitative traits are
characterized by lower heritabilities impairing their accurate scoring and
entailing a less accurately defined genetic position of the corresponding quantitative
trait locus (QTL). As a result, large chromosomal fragment needs to be selected
for, resulting in the meiotic transfer of many potentially undesired genes.
Meiotic purification of a QTL into a “mendelian” locus, showing
monogenic inheritance, provides a solution to this problem. The feasibility of
downtracking a QTL to a single gene has been initially demonstrated in tomato
and requires the stepwise size reduction of a QTL fragment and its conversion
into a near isogenic line by repeated backcrossing (for review see [112]). In barley, this approach has been
successfully employed to isolate the bot1 gene underlying a major QTL
conferring boron tolerance [113].
(ii) Many of QTL alleles escape detection, when transferred into a different
genetic background. The reasons for the “disappearance of QTLs”
include epistatic interactions, QTL x environment effects, the allelic states
of the parental lines or the small contribution of a single QTL to the overall
variance. As a result, only few common QTLs were detected, when the results of
mapping studies that were performed in different crosses were compared [114].Although the number of successful examples for applying MAS
in barley breeding is still rather limited (see reviews by [114, 115], the recent implementation of high-throughput
genotyping platforms (Illumina, DArT, and SFP identification by using Barley 1 GeneChip affymetrix
array) in barley will significantly increase the identification of marker trait
associations, and the subsequent identification of potential candidate genes.
Finally, this will allow to treat QTLs as monogenic traits and thus spur their
marker assisted manipulation in breeding programs. In combination with a wide
range of mapping populations developed for specific agronomic traits, this
comprehensive resource of markers now allows the identification of
polymorphisms in functionally defined sequences [12, 34, 105, 106]. Functional markers will also be
useful for (i) association studies based on linkage disequilibrium, (ii)
detection of cis and trans-acting regulators either based on
genetical genomics studies using well-defined mapping populations or by
investigating allelic imbalance [116], (iii)  identification of alleles influencing
agronomically important traits using TILLING/EcoTilling approaches (EcoTilling
is a means to determine the extent of natural variation in selected genes), and
(iv) genomics-assisted breeding (see Figure 1).


### 5.2. Linkage Disequilibrium-Based Association Studies

Linkage disequilibrium is the nonrandom distribution of
alleles in a sample population and forms the basis for the construction of
genetic maps and the localization of genetic loci for a variety of traits. The
principles leading to LD apply to both biparental mapping populations (F2,
RILs, etc.) and natural populations. Therefore, LD mapping is the method of
choice for genetic analysis in organisms like humans and animals, where
experimental populations are either not available or difficult to establish [117].Because of its inherent advantages, LD mapping approaches are
increasingly being applied for plant species, in particular maize. Due to the
outbreeding character of this species, LD extends only over a few kb and thus
leads to a high-genetic resolution, up to the level of individual candidate
genes that can be associated with a given trait (see recent reviews [118, 119]). The use of association genetic analyses in
inbreeding species such as barley has been limited so far. However, recent
studies have shown that LD extends over much longer genetic distances in barley
than in maize. A European germplasm collection of 146 two-rowed spring barley
cultivars was used to carry out LD mapping of yield traits using 236 AFLP
markers [120]. Associated markers were identified
that are located in similar regions where QTLs for yield had been found in
barley [93, 121, 122]. A systematic survey of 953 gene bank
accessions representing a broad spectrum of the genetic diversity in barley
genetic resources revealed that LD extends up to 50 cM but is highly dependent
on population structure [120, 123]. On the one
hand, the high level of LD in barley is due to the inbreeding mating type of
this species; on the other hand, the selection of germplasm plays an important
role. Analysis of a germplasm collection of European cultivars, land races, and
wild barley accession from the Fertile Crescent region provided hints that the level of LD decreases from cultivars to
landraces to wild barley [124]. Similarly, Morrell et al. [125] reported low levels of LD in wild barley by examining LD
within and between 18 genes from 25 accessions. Local differences in LD have
been observed at the grain hardness locus comprising four closely linked genes
(hinb, hina, GSP, PG2). Here, a high level of LD was observed in the
intergenic region between hinb-1 and hina probably due to transposable
elements present in this region, which influence the local recombination rate
[114]. By assaying 1524 genome-wide SNPs in elite northwest European barley using
the Illumina GoldenGate BeadArray platform Rostoks et al. [103] concluded that whole-genome association scans can be
exploited for trait mapping in barley. This was further exemplified by the
identification of a marker that showed an association with the winter habit and
which could be tracked to a cluster of CBF (C-repeat/DRE-binding factor)
gene homologs. In a recent whole genome LD-mapping approach, Steffenson
et al. [126] used 318 wild barley accessions to
perform association mapping studies using DArT markers to identify rust
resistance genes. In addition, LD analysis has been performed based on
haplotypes derived from 131 accessions by covering 83 SNPs within 132 kb around
the gene HveIF4E, which confers resistance to barley yellow mosaic virus. The
authors identified three haplogroups discriminating between the alleles rym4 and rym5 [127]. Taken together, the above mentioned
association studies provide starting points for a more systematic analysis of
agronomic traits. These may be selected from the vast ex situ gene bank collections available for this crop. Alone at
the IPK gene bank some 20 000 different barley accessions represent an ample cross section of the
genetic diversity present in this species. However, in order to fully exploit
the potential of LD-based association analysis in this species, populations
have to be carefully selected to minimize the confounding effects of population
structure. This is particularly evident in modern barley germplasm, which is
frequently structured into spring and winter as well as 2-rowed and 6-rowed
types, forming distinct subpopulations (e.g., [95]). If these effects are not adequately accounted for during
association analysis, the risk of detecting spurious associations increases.


### 5.3. Genetical Genomics Studies

The genetical genomics strategy was first outlined by Jansen and Nap
[128]. It combines gene expression studies
with genetic linkage analysis. Differentially expressed genes (but also
proteins and metabolites) involved in metabolic and regulatory pathways and
identified by high-throughput technologies are treated as phenotypes, and genetic
variants that influence gene expression are identified in genetically related
lines. This strategy has been successfully applied also in plant systems, and
relevant data were reviewed elsewhere [128–130].
Here, we will focus on the latest development in expression QTL
(eQTL) mapping in barley. Using the Barley 1
GeneChip affymetrix array SFP genotyping
has been performed in 35 recombinant lines of a Steptoe × Morex doubled-haploid
population, enabling eQTL studies [34]. Using a
high-throughput SFP genotyping platform, genome-wide linkage analysis has been
performed based on 22 000 transcript data collected from 139 DH lines (Steptoe × Morex). The most significant eQTLs derived from germinating barley grain are
linked to cis regulation [47]. Using the same mapping population, a serine
carboxypeptidase 1 eQTL has been mapped on chromosome 3H to the same region
where a QTL for the malting quality trait “diastatic power” has been mapped [131]. In another study, instead of a segregating population a set
of 47 BC3 DH introgression lines was employed (wild barley [H. spontaneum] is introgressed in the
genetic background of the elite line “Brenda” [H. vulgare]) in order to understand gene expression networks
controlling seed traits. Initially, this BC3 DH population was used to identify
QTLs for yield and yield components [132]. In
further experiments, expression data from nearly 12 000 genes interrogated by using a barley seed specific array were used to
calculate eQTLs (C. Pietsch et al., unpublished). Although such initial studies
provide evidence that genetical genomics is a promising concept which assists
to expose gene-trait relationships, an extensive exploration of the technology
needs the full barley genome sequence and improved high-throughput genotyping
information.


## 5.1. Marker Development and Marker-Assisted Selection (MAS)

Almost two decades ago, RFLP markers were employed to develop
the first comprehensive molecular marker maps in barley [90–92]. Using those RFLP maps, a series of agronomic traits
and characters including many quality traits and resistance against several
diseases have been mapped (for review see [93, 94]). Later, the availability of large numbers of ESTs
facilitated the systematic development of functional markers, for example, by
extracting ESTs containing simple sequence repeat (SSR) motifs using
appropriate software tools [95]. Although EST-based SSR markers have
been shown to be less polymorphic than their genomic counterparts, this
drawback is more than compensated for by the ease of their development. Also,
the availability of ESTs from multiple-genotypes/cultivars of barley provides
the possibility to identify sequence polymorphisms (mainly single-nucleotide
polymorphisms and small InDels) in the corresponding EST alignments. These in
turn can be exploited for the development of markers [96, 97].
Kota et al. [98] developed the computer algorithm SNiPping for discovery of
functional markers through browsing EST assemblies in barley. Also an SNP2CAPS
program has been published to facilitate the computational conversion of SNP
markers into CAPS markers [99]. Information generated from the
diverse mapping projects was further enhanced by the development of consensus
maps [14, 100–102]. These provide integrative genetic
information by featuring high marker densities. Although the gel-based
genotyping platforms offer the best quality marker systems, their low
throughput encouraged researchers to explore high-throughput technologies that
can simultaneously assay thousands of markers based on single nucleotide polymorphisms
(SNP). Most recently, genome-wide scans using SNP-based genotyping platforms
such as Illumina GoldenGate BeadArrays [103] and the diversity arrays technology (DArT), which do not
require any sequence information [104] have been successfully established
in barley. Although DArTs are not systematically interrogating expressed
sequences, the choice of appropriate enzymes facilitates their enriched
representation. Based on DArT technology, a high-density consensus map has recently
been established [105]. A number of recent studies also
reported the use of the affymetrix Barley 1
GeneChip [27] for identifying single-feature polymorphisms (SFPs), which
cover not only SNPs but also indels and polymorphisms generated due to alternative
splicing and polyadenylation [34, 106].An important application of the above discussed functional
markers is marker-assisted selection (MAS). MAS is based on linking the DNA polymorphisms
revealed by marker analysis with agronomical traits allowing for their rapid
selection in routine breeding programs. MAS can be performed already at
juvenile growth stages and before flowering, and thus provides breeders with
the opportunity to implement faster back-crossing strategies and allele
enrichment in complex crosses, which eventually reduces the time and costs
required for the development of improved varieties. Despite its inherent
advantages, the application of MAS in barley up to now has mainly been
restricted to monogenic traits such as disease resistances. Here, one of the
most widespread examples is the marker assisted selection of therym4 gene giving resistance to the barley
yellow mosaic virus complex. For this gene, several closely linked and easily
scorable markers have been developed [107, 108]. More recently, cloning of the gene facilitated the exploitation of
functional polymorphisms within the coding region of the resistance gene to
differentiate between alleles [109].
Using MAS, several genes providing full resistance could be readily combined in
complex crosses without time consuming progeny tests in the greenhouse or in
the field (e.g., [110, 111]).MAS for quantitative traits suffers from two major
limitations. (i) Compared to monogenic traits, quantitative traits are
characterized by lower heritabilities impairing their accurate scoring and
entailing a less accurately defined genetic position of the corresponding quantitative
trait locus (QTL). As a result, large chromosomal fragment needs to be selected
for, resulting in the meiotic transfer of many potentially undesired genes.
Meiotic purification of a QTL into a “mendelian” locus, showing
monogenic inheritance, provides a solution to this problem. The feasibility of
downtracking a QTL to a single gene has been initially demonstrated in tomato
and requires the stepwise size reduction of a QTL fragment and its conversion
into a near isogenic line by repeated backcrossing (for review see [112]). In barley, this approach has been
successfully employed to isolate the bot1 gene underlying a major QTL
conferring boron tolerance [113].
(ii) Many of QTL alleles escape detection, when transferred into a different
genetic background. The reasons for the “disappearance of QTLs”
include epistatic interactions, QTL x environment effects, the allelic states
of the parental lines or the small contribution of a single QTL to the overall
variance. As a result, only few common QTLs were detected, when the results of
mapping studies that were performed in different crosses were compared [114].Although the number of successful examples for applying MAS
in barley breeding is still rather limited (see reviews by [114, 115], the recent implementation of high-throughput
genotyping platforms (Illumina, DArT, and SFP identification by using Barley 1 GeneChip affymetrix
array) in barley will significantly increase the identification of marker trait
associations, and the subsequent identification of potential candidate genes.
Finally, this will allow to treat QTLs as monogenic traits and thus spur their
marker assisted manipulation in breeding programs. In combination with a wide
range of mapping populations developed for specific agronomic traits, this
comprehensive resource of markers now allows the identification of
polymorphisms in functionally defined sequences [12, 34, 105, 106]. Functional markers will also be
useful for (i) association studies based on linkage disequilibrium, (ii)
detection of cis and trans-acting regulators either based on
genetical genomics studies using well-defined mapping populations or by
investigating allelic imbalance [116], (iii)  identification of alleles influencing
agronomically important traits using TILLING/EcoTilling approaches (EcoTilling
is a means to determine the extent of natural variation in selected genes), and
(iv) genomics-assisted breeding (see Figure 1).


## 5.2. Linkage Disequilibrium-Based Association Studies

Linkage disequilibrium is the nonrandom distribution of
alleles in a sample population and forms the basis for the construction of
genetic maps and the localization of genetic loci for a variety of traits. The
principles leading to LD apply to both biparental mapping populations (F2,
RILs, etc.) and natural populations. Therefore, LD mapping is the method of
choice for genetic analysis in organisms like humans and animals, where
experimental populations are either not available or difficult to establish [117].Because of its inherent advantages, LD mapping approaches are
increasingly being applied for plant species, in particular maize. Due to the
outbreeding character of this species, LD extends only over a few kb and thus
leads to a high-genetic resolution, up to the level of individual candidate
genes that can be associated with a given trait (see recent reviews [118, 119]). The use of association genetic analyses in
inbreeding species such as barley has been limited so far. However, recent
studies have shown that LD extends over much longer genetic distances in barley
than in maize. A European germplasm collection of 146 two-rowed spring barley
cultivars was used to carry out LD mapping of yield traits using 236 AFLP
markers [120]. Associated markers were identified
that are located in similar regions where QTLs for yield had been found in
barley [93, 121, 122]. A systematic survey of 953 gene bank
accessions representing a broad spectrum of the genetic diversity in barley
genetic resources revealed that LD extends up to 50 cM but is highly dependent
on population structure [120, 123]. On the one
hand, the high level of LD in barley is due to the inbreeding mating type of
this species; on the other hand, the selection of germplasm plays an important
role. Analysis of a germplasm collection of European cultivars, land races, and
wild barley accession from the Fertile Crescent region provided hints that the level of LD decreases from cultivars to
landraces to wild barley [124]. Similarly, Morrell et al. [125] reported low levels of LD in wild barley by examining LD
within and between 18 genes from 25 accessions. Local differences in LD have
been observed at the grain hardness locus comprising four closely linked genes
(hinb, hina, GSP, PG2). Here, a high level of LD was observed in the
intergenic region between hinb-1 and hina probably due to transposable
elements present in this region, which influence the local recombination rate
[114]. By assaying 1524 genome-wide SNPs in elite northwest European barley using
the Illumina GoldenGate BeadArray platform Rostoks et al. [103] concluded that whole-genome association scans can be
exploited for trait mapping in barley. This was further exemplified by the
identification of a marker that showed an association with the winter habit and
which could be tracked to a cluster of CBF (C-repeat/DRE-binding factor)
gene homologs. In a recent whole genome LD-mapping approach, Steffenson
et al. [126] used 318 wild barley accessions to
perform association mapping studies using DArT markers to identify rust
resistance genes. In addition, LD analysis has been performed based on
haplotypes derived from 131 accessions by covering 83 SNPs within 132 kb around
the gene HveIF4E, which confers resistance to barley yellow mosaic virus. The
authors identified three haplogroups discriminating between the alleles rym4 and rym5 [127]. Taken together, the above mentioned
association studies provide starting points for a more systematic analysis of
agronomic traits. These may be selected from the vast ex situ gene bank collections available for this crop. Alone at
the IPK gene bank some 20 000 different barley accessions represent an ample cross section of the
genetic diversity present in this species. However, in order to fully exploit
the potential of LD-based association analysis in this species, populations
have to be carefully selected to minimize the confounding effects of population
structure. This is particularly evident in modern barley germplasm, which is
frequently structured into spring and winter as well as 2-rowed and 6-rowed
types, forming distinct subpopulations (e.g., [95]). If these effects are not adequately accounted for during
association analysis, the risk of detecting spurious associations increases.


## 5.3. Genetical Genomics Studies

The genetical genomics strategy was first outlined by Jansen and Nap
[128]. It combines gene expression studies
with genetic linkage analysis. Differentially expressed genes (but also
proteins and metabolites) involved in metabolic and regulatory pathways and
identified by high-throughput technologies are treated as phenotypes, and genetic
variants that influence gene expression are identified in genetically related
lines. This strategy has been successfully applied also in plant systems, and
relevant data were reviewed elsewhere [128–130].
Here, we will focus on the latest development in expression QTL
(eQTL) mapping in barley. Using the Barley 1
GeneChip affymetrix array SFP genotyping
has been performed in 35 recombinant lines of a Steptoe × Morex doubled-haploid
population, enabling eQTL studies [34]. Using a
high-throughput SFP genotyping platform, genome-wide linkage analysis has been
performed based on 22 000 transcript data collected from 139 DH lines (Steptoe × Morex). The most significant eQTLs derived from germinating barley grain are
linked to cis regulation [47]. Using the same mapping population, a serine
carboxypeptidase 1 eQTL has been mapped on chromosome 3H to the same region
where a QTL for the malting quality trait “diastatic power” has been mapped [131]. In another study, instead of a segregating population a set
of 47 BC3 DH introgression lines was employed (wild barley [H. spontaneum] is introgressed in the
genetic background of the elite line “Brenda” [H. vulgare]) in order to understand gene expression networks
controlling seed traits. Initially, this BC3 DH population was used to identify
QTLs for yield and yield components [132]. In
further experiments, expression data from nearly 12 000 genes interrogated by using a barley seed specific array were used to
calculate eQTLs (C. Pietsch et al., unpublished). Although such initial studies
provide evidence that genetical genomics is a promising concept which assists
to expose gene-trait relationships, an extensive exploration of the technology
needs the full barley genome sequence and improved high-throughput genotyping
information.


## 6. Outloook

In recent years, we experienced a dramatic development of new
tools and technologies for genome research and a concomitantly dramatic
increase in data leading to a much improved and advanced knowledge base. Barley
research gained a lot of momentum from this development but the nonavailability
of a whole genome sequence is still a serious limitation. However, due to
consortial efforts (see above) and the rapidly developing sequencing
technologies that are relevant for even complex genomes like that of barley [133] this limitation will be largely overcome, hopefully within
the next five years. High-throughput transcriptome analysis techniques have
already provided numerous new insights in transcriptional networks. They will,
together with rapidly improving protein and metabolite profiling techniques and
in combination with new genetic analysis concepts such as genetical genomics
and association genetics, improve our knowledge on the relationship between the
genetic and the phenotypic architecture of agronomic traits and thus create a basis
for knowledge-based molecular breeding [134]. As a next step systems biology approaches are emerging,
which attempt to model complex cellular or organismic functions in response to
changing internal and external factors [135]. Until now molecular markers have had limited success in
barley breeding programs, but due to recent advancement of barley genomics a
stronger impact on breeding strategies is expected. For instance, marker
technologies together with double haploid production have almost halved the time
of variety development in Australian wheat and barley breeding programs [136]. However, new whole-genome breeding strategies have to be
developed to make full use of the ever increasing knowledge about crop plant
genomes and their behavior.



---

*Source: 486258-2008-03-13.xml*