An evaluation of genotyping by sequencing (GBS) to map the <em>Breviaristatum-e (ari-e)</em> locus in cultivated barley

ABSTRACT: We explored the use of genotyping by sequencing (GBS) on a recombinant inbred line population (GPMx) derived from a cross between the two-rowed barley cultivar ‘Golden Promise’ (ari-e.GP/Vrs1) and the six-rowed cultivar ‘Morex’ (Ari-e/vrs1) to map plant height. We identified three Quantitative Trait Loci (QTL), the first in a region encompassing the spike architecture gene Vrs1 on chromosome 2H, the second in an uncharacterised centromeric region on chromosome 3H, and the third in a region of chromosome 5H coinciding with the previously described dwarfing gene Breviaristatum-e (Ari-e). BACKGROUND: Barley cultivars in North-western Europe largely contain either of two dwarfing genes; Denso on chromosome 3H, a presumed ortholog of the rice green revolution gene OsSd1, or Breviaristatum-e (ari-e) on chromosome 5H. A recessive mutant allele of the latter gene, ari-e.GP, was introduced into cultivation via the cv. ‘Golden Promise’ that was a favourite of the Scottish malt whisky industry for many years and is still used in agriculture today. RESULTS: Using GBS mapping data and phenotypic measurements we show that ari-e.GP maps to a small genetic interval on chromosome 5H and that alternative alleles at a region encompassing Vrs1 on 2H along with a region on chromosome 3H also influence plant height. The location of Ari-e is supported by analysis of near-isogenic lines containing different ari-e alleles. We explored use of the GBS to populate the region with sequence contigs from the recently released physically and genetically integrated barley genome sequence assembly as a step towards Ari-e gene identification. CONCLUSIONS: GBS was an effective and relatively low-cost approach to rapidly construct a genetic map of the GPMx population that was suitable for genetic analysis of row type and height traits, allowing us to precisely position ari-e.GP on chromosome 5H. Mapping resolution was lower than we anticipated. We found the GBS data more complex to analyse than other data types but it did directly provide linked SNP markers for subsequent higher resolution genetic analysis

<i>HvDep</i>1 Is a Positive Regulator of Culm Elongation and Grain Size in Barley and Impacts Yield in an Environment-Dependent Manner

Heterotrimeric G proteins are intracellular membrane-attached signal transducers involved in various cellular processes in both plants and animals. They consist of three subunits denoted as α, β and γ. The γ-subunits of the so-called AGG3 type, which comprise a transmembrane domain, are exclusively found in plants. In model species, these proteins have been shown to participate in the control of plant height, branching and seed size and could therefore impact the harvestable yield of various crop plants. Whether AGG3-type γ-subunits influence yield in temperate cereals like barley and wheat remains unknown. Using a transgenic complementation approach, we show here that the Scottish malting barley cultivar (cv.) Golden Promise carries a loss-of-function mutation in HvDep1, an AGG3-type subunit encoding gene that positively regulates culm elongation and seed size in barley. Somewhat intriguingly, agronomic field data collected over a 12-year period reveals that the HvDep1 loss-of-function mutation in cv. Golden Promise has the potential to confer either a significant increase or decrease in harvestable yield depending on the environment. Our results confirm the role of AGG3-type subunit-encoding genes in shaping plant architecture, but interestingly also indicate that the impact HvDep1 has on yield in barley is both genotypically and environmentally sensitive. This may explain why widespread exploitation of variation in AGG3-type subunit-encoding genes has not occurred in temperate cereals while in rice the DEP1 locus is widely exploited to improve harvestable yield

Methods for evaluating gene expression from Affymetrix microarray datasets

<p>Abstract</p> <p>Background</p> <p>Affymetrix high density oligonucleotide expression arrays are widely used across all fields of biological research for measuring genome-wide gene expression. An important step in processing oligonucleotide microarray data is to produce a single value for the gene expression level of an RNA transcript using one of a growing number of statistical methods. The challenge for the researcher is to decide on the most appropriate method to use to address a specific biological question with a given dataset. Although several research efforts have focused on assessing performance of a few methods in evaluating gene expression from RNA hybridization experiments with different datasets, the relative merits of the methods currently available in the literature for evaluating genome-wide gene expression from Affymetrix microarray data collected from real biological experiments remain actively debated.</p> <p>Results</p> <p>The present study reports a comprehensive survey of the performance of all seven commonly used methods in evaluating genome-wide gene expression from a well-designed experiment using Affymetrix microarrays. The experiment profiled eight genetically divergent barley cultivars each with three biological replicates. The dataset so obtained confers a balanced and idealized structure for the present analysis. The methods were evaluated on their sensitivity for detecting differentially expressed genes, reproducibility of expression values across replicates, and consistency in calling differentially expressed genes. The number of genes detected as differentially expressed among methods differed by a factor of two or more at a given false discovery rate (FDR) level. Moreover, we propose the use of genes containing single feature polymorphisms (SFPs) as an empirical test for comparison among methods for the ability to detect true differential gene expression on the basis that SFPs largely correspond to <it>cis</it>-acting expression regulators. The PDNN method demonstrated superiority over all other methods in every comparison, whilst the default Affymetrix MAS5.0 method was clearly inferior.</p> <p>Conclusion</p> <p>A comprehensive assessment of seven commonly used data extraction methods based on an extensive barley Affymetrix gene expression dataset has shown that the PDNN method has superior performance for the detection of differentially expressed genes.</p

Barley stem rust resistance genes: structure and function

Rusts are biotrophic pathogens that attack many plant species but are particularly destructive on cereal crops. The stem rusts (caused by Puccinia graminis) have historically caused severe crop losses and continue to threaten production today. Barley (Hordeum vulgare L.) breeders have controlled major stem rust epidemics since the 1940s with a single durable resistance gene Rpg1. As new epidemics have threatened, additional resistance genes were identified to counter new rust races, such as the rpg4/Rpg5 complex locus against races QCCJ and TTKSK. To understand how these genes work, we initiated research to clone and characterize them. The Rpg1 gene encodes a unique protein kinase with dual kinase domains, an active kinase, and a pseudokinase. Function of both domains is essential to confer resistance. The rpg4 and Rpg5 genes are closely linked and function coordinately to confer resistance to several wheat (Triticum aestivum L.) stem rust races, including the race TTKSK (also called Ug99) that threatens the world's barley and wheat crops. The Rpg5 gene encodes typical resistance gene domains NBS, LRR, and protein kinase but is unique in that all three domains reside in a single gene, a previously unknown structure among plant disease resistance genes. The rpg4 gene encodes an actin depolymerizing factor that functions in cytoskeleton rearrangement

Comparative transcriptomics in the Triticeae

<p>Abstract</p> <p>Background</p> <p>Barley and particularly wheat are two grass species of immense agricultural importance. In spite of polyploidization events within the latter, studies have shown that genotypically and phenotypically these species are very closely related and, indeed, fertile hybrids can be created by interbreeding. The advent of two genome-scale Affymetrix GeneChips now allows studies of the comparison of their transcriptomes.</p> <p>Results</p> <p>We have used the Wheat GeneChip to create a "gene expression atlas" for the wheat transcriptome (cv. Chinese Spring). For this, we chose mRNA from a range of tissues and developmental stages closely mirroring a comparable study carried out for barley (cv. Morex) using the Barley1 GeneChip. This, together with large-scale clustering of the probesets from the two GeneChips into "homologous groups", has allowed us to perform a genomic-scale comparative study of expression patterns in these two species. We explore the influence of the polyploidy of wheat on the results obtained with the Wheat GeneChip and quantify the correlation between conservation in gene sequence and gene expression in wheat and barley. In addition, we show how the conservation of expression patterns can be used to elucidate, probeset by probeset, the reliability of the Wheat GeneChip.</p> <p>Conclusion</p> <p>While there are many differences in expression on the level of individual genes and tissues, we demonstrate that the wheat and barley transcriptomes appear highly correlated. This finding is significant not only because given small evolutionary distance between the two species it is widely expected, but also because it demonstrates that it is possible to use the two GeneChips for comparative studies. This is the case even though their probeset composition reflects rather different design principles as well as, of course, the present incomplete knowledge of the gene content of the two species. We also show that, in general, the Wheat GeneChip is not able to distinguish contributions from individual homoeologs. Furthermore, the comparison between the two species leads us to conclude that the conservation of both gene sequence as well as gene expression is positively correlated with absolute expression levels, presumably reflecting increased selection pressure on genes coding for proteins present at high levels. In addition, the results indicate the presence of a correlation between sequence and expression conservation within the Triticeae.</p

A homolog of <i>blade-on-petiole</i> <i>1</i> and <i>2</i> (<i>BOP1/2</i>) controls internode length and homeotic changes of the barley inflorescence

Inflorescence architecture in small-grain cereals has a direct effect on yield and is an important selection target in breeding for yield improvement. We analyzed the recessive mutation laxatum-a (lax-a) in barley (Hordeum vulgare), which causes pleiotropic changes in spike development, resulting in (1) extended rachis internodes conferring a more relaxed inflorescence, (2) broadened base of the lemma awns, (3) thinner grains that are largely exposed due to reduced marginal growth of the palea and lemma, and (4) and homeotic conversion of lodicules into two stamenoid structures. Map-based cloning enforced by mapping-by-sequencing of the mutant lax-a locus enabled the identification of a homolog of BLADE-ON-PETIOLE1 (BOP1) and BOP2 as the causal gene. Interestingly, the recently identified barley uniculme4 gene also is a BOP1/2 homolog and has been shown to regulate tillering and leaf sheath development. While the Arabidopsis (Arabidopsis thaliana) BOP1 and BOP2 genes act redundantly, the barley genes contribute independent effects in specifying the developmental growth of vegetative and reproductive organs, respectively. Analysis of natural genetic diversity revealed strikingly different haplotype diversity for the two paralogous barley genes, likely affected by the respective genomic environments, since no indication for an active selection process was detected

Robust Detection and Genotyping of Single Feature Polymorphisms from Gene Expression Data

It is well known that Affymetrix microarrays are widely used to predict genome-wide gene expression and genome-wide genetic polymorphisms from RNA and genomic DNA hybridization experiments, respectively. It has recently been proposed to integrate the two predictions by use of RNA microarray data only. Although the ability to detect single feature polymorphisms (SFPs) from RNA microarray data has many practical implications for genome study in both sequenced and unsequenced species, it raises enormous challenges for statistical modelling and analysis of microarray gene expression data for this objective. Several methods are proposed to predict SFPs from the gene expression profile. However, their performance is highly vulnerable to differential expression of genes. The SFPs thus predicted are eventually a reflection of differentially expressed genes rather than genuine sequence polymorphisms. To address the problem, we developed a novel statistical method to separate the binding affinity between a transcript and its targeting probe and the parameter measuring transcript abundance from perfect-match hybridization values of Affymetrix gene expression data. We implemented a Bayesian approach to detect SFPs and to genotype a segregating population at the detected SFPs. Based on analysis of three Affymetrix microarray datasets, we demonstrated that the present method confers a significantly improved robustness and accuracy in detecting the SFPs that carry genuine sequence polymorphisms when compared to its rivals in the literature. The method developed in this paper will provide experimental genomicists with advanced analytical tools for appropriate and efficient analysis of their microarray experiments and biostatisticians with insightful interpretation of Affymetrix microarray data

Development and implementation of high-throughput SNP genotyping in barley

<p>Abstract</p> <p>Background</p> <p>High density genetic maps of plants have, nearly without exception, made use of marker datasets containing missing or questionable genotype calls derived from a variety of genic and non-genic or anonymous markers, and been presented as a single linear order of genetic loci for each linkage group. The consequences of missing or erroneous data include falsely separated markers, expansion of cM distances and incorrect marker order. These imperfections are amplified in consensus maps and problematic when fine resolution is critical including comparative genome analyses and map-based cloning. Here we provide a new paradigm, a high-density consensus genetic map of barley based only on complete and error-free datasets and genic markers, represented accurately by graphs and approximately by a best-fit linear order, and supported by a readily available SNP genotyping resource.</p> <p>Results</p> <p>Approximately 22,000 SNPs were identified from barley ESTs and sequenced amplicons; 4,596 of them were tested for performance in three pilot phase Illumina GoldenGate assays. Data from three barley doubled haploid mapping populations supported the production of an initial consensus map. Over 200 germplasm selections, principally European and US breeding material, were used to estimate minor allele frequency (MAF) for each SNP. We selected 3,072 of these tested SNPs based on technical performance, map location, MAF and biological interest to fill two 1536-SNP "production" assays (BOPA1 and BOPA2), which were made available to the barley genetics community. Data were added using BOPA1 from a fourth mapping population to yield a consensus map containing 2,943 SNP loci in 975 marker bins covering a genetic distance of 1099 cM.</p> <p>Conclusion</p> <p>The unprecedented density of genic markers and marker bins enabled a high resolution comparison of the genomes of barley and rice. Low recombination in pericentric regions is evident from bins containing many more than the average number of markers, meaning that a large number of genes are recombinationally locked into the genetic centromeric regions of several barley chromosomes. Examination of US breeding germplasm illustrated the usefulness of BOPA1 and BOPA2 in that they provide excellent marker density and sensitivity for detection of minor alleles in this genetically narrow material.</p