SFP genotyping from affymetrix arrays is robust but largely detects cis-acting expression regulators

Development of Genome-Wide SNP Markers for Barley via Reference- Based RNA-Seq Analysis

Marker-assisted selection of crop plants requires DNA markers that can distinguish between the closely related strains often used in breeding. The availability of reference genome sequence facilitates the generation of markers, by elucidating the genomic positions of new markers as well as of their neighboring sequences. In 2017, a high quality genome sequence was released for the six-row barley (Hordeum vulgare) cultivar Morex. Here, we developed a de novo RNA-Seq-based genotyping procedure for barley strains used in Japanese breeding programs. Using RNA samples from the seedling shoot, seedling root, and immature flower spike, we mapped next-generation sequencing reads onto the transcribed regions, which correspond to ∼590 Mb of the whole ∼4.8-Gbp reference genome sequence. Using 150 samples from 108 strains, we detected 181,567 SNPs and 45,135 indels located in the 28,939 transcribed regions distributed throughout the Morex genome. We evaluated the quality of this polymorphism detection approach by analyzing 387 RNA-Seq-derived SNPs using amplicon sequencing. More than 85% of the RNA-Seq SNPs were validated using the highly redundant reads from the amplicon sequencing, although half of the indels and multiple-allele loci showed different polymorphisms between the platforms. These results demonstrated that our RNA-Seq-based de novo polymorphism detection system generates genome-wide markers, even in the closely related barley genotypes used in breeding programs.

Quantitative trait loci from identification to exploitation for crop improvement

Advancement in the field of genetics and genomics after the discovery of Mendel's laws of inheritance has led to map the genes controlling qualitative and quantitative traits in crop plant species. Mapping of genomic regions controlling the variation of quantitatively inherited traits has become routine after the advent of different types of molecular markers. Recently, the next generation sequencing methods have accelerated the research on QTL analysis. These efforts have led to the identification of more closely linked molecular markers with gene/QTLs and also identified markers even within gene/QTL controlling the trait of interest. Efforts have also been made towards cloning gene/QTLs or identification of potential candidate genes responsible for a trait. Further new concepts like crop QTLome and QTL prioritization have accelerated precise application of QTLs for genetic improvement of complex traits. In the past years, efforts have also been made in exploitation of a number of QTL for improving grain yield or other agronomic traits in various crops through markers assisted selection leading to cultivation of these improved varieties at farmers' field. In present article, we reviewed QTLs from their identification to exploitation in plant breeding programs and also reviewed that how improved cultivars developed through introgression of QTLs have improved the yield productivity in many crops.

The E-MS Algorithm: Model Selection with Incomplete Data

We propose a procedure associated with the idea of the E-M algorithm for model selection in the presence of missing data. The idea extends the concept of parameters to include both the model and the parameters under the model, and thus allows the model to be part of the E-M iterations. We develop the procedure, known as the E-MS algorithm, under the assumption that the class of candidate models is finite. Some special cases of the procedure are considered, including E-MS with the generalized information criteria (GIC), and E-MS with the adaptive fence (AF; Jiang et al. 2008). We prove numerical convergence of the E-MS algorithm as well as consistency in model selection of the limiting model of the E-MS convergence, for E-MS with GIC and E-MS with AF. We study the impact on model selection of different missing data mechanisms. Furthermore, we carry out extensive simulation studies on the finite-sample performance of the E-MS with comparisons to other procedures. The methodology is also illustrated on a real data analysis involving QTL mapping for an agricultural study on barley grains.

Conserved synteny-based anchoring of the barley genome physical map

Gene order is largely collinear in the small-grained cereals, a feature which has proved helpful in both marker development and positional cloning. The accuracy of a virtual gene order map ("genome zipper") for barley (Hordeum vulgare), developed by combining a genetic map of this species with a large number of gene locations obtained from the maps constructed in other grass species, was evaluated here both at the genome-wide level and at the fine scale in a representative segment of the genome. Comparing the whole genome "genome zipper" maps with a genetic map developed by using transcript-derived markers, yielded an accuracy of >94 %. The fine-scale comparison involved a 14 cM segment of chromosome arm 2HL. One hundred twenty-eight genes of the "genome zipper" interval were analysed. Over 95 % (45/47) of the polymorphic markers were genetically mapped and allocated to the expected region of 2HL, following the predicted order. A further 80 of the 128 genes were assigned to the correct chromosome arm 2HL by analysis of wheat-barley addition lines. All 128 gene-based markers developed were used to probe a barley bacterial artificial chromosome (BAC) library, delivering 26 BAC contigs from which all except two were anchored to the targeted zipper interval. The results demonstrate that the gene order predicted by the "genome zipper" is remarkably accurate and that the "genome zipper" represents a highly efficient informational resource for the systematic identification of gene-based markers and subsequent physical map anchoring of the barley genome.

Conserved synteny-based anchoring of the barley genome physical map

Gene order is largely collinear in the small-grained cereals, a feature which has proved helpful in both marker development and positional cloning. The accuracy of a virtual gene order map ("genome zipper") for barley (Hordeum vulgare), developed by combining a genetic map of this species with a large number of gene locations obtained from the maps constructed in other grass species, was evaluated here both at the genome-wide level and at the fine scale in a representative segment of the genome. Comparing the whole genome "genome zipper" maps with a genetic map developed by using transcript-derived markers, yielded an accuracy of >94 %. The fine-scale comparison involved a 14 cM segment of chromosome arm 2HL. One hundred twenty-eight genes of the "genome zipper" interval were analysed. Over 95 % (45/47) of the polymorphic markers were genetically mapped and allocated to the expected region of 2HL, following the predicted order. A further 80 of the 128 genes were assigned to the correct chromosome arm 2HL by analysis of wheat-barley addition lines. All 128 gene-based markers developed were used to probe a barley bacterial artificial chromosome (BAC) library, delivering 26 BAC contigs from which all except two were anchored to the targeted zipper interval. The results demonstrate that the gene order predicted by the "genome zipper" is remarkably accurate and that the "genome zipper" represents a highly efficient informational resource for the systematic identification of gene-based markers and subsequent physical map anchoring of the barley genome.

Prospects for advancing defense to cereal rusts through genetical genomics

Rusts are one of the most severe threats to cereal crops because new pathogen races emerge regularly, resulting in infestations that lead to large yield losses. In 1999, a new race of stem rust, Puccinia graminis f. sp. tritici (Pgt TTKSK or Ug99), was discovered in Uganda. Most of the wheat and barley cultivars grown currently worldwide are susceptible to this new race. Pgt TTKSK has already spread northward into Iran and will likely spread eastward throughout the Indian subcontinent in the near future. This scenario is not unique to stem rust; new races of leaf rust (Puccinia triticina) and stripe rust (Puccinia striiformis) have also emerged recently. One strategy for countering the persistent adaptability of these pathogens is to stack complete- and partial-resistance genes, which requires significant breeding efforts in order to reduce deleterious effects of linkage drag. These varied resistance combinations are typically more difficult for the pathogen to defeat, since they would be predicted to apply lower selection pressure. Genetical genomics or expression Quantitative Trait Locus (eQTL) analysis enables the identification of regulatory loci that control the expression of many to hundreds of genes. Integrated deployment of these technologies coupled with efficient phenotyping offers significant potential to elucidate the regulatory nodes in genetic networks that orchestrate host defense responses. The focus of this review will be to present advances in genetical genomic experimental designs and analysis, particularly as they apply to the prospects for discovering partial disease resistance alleles in cereals.

Quantitative high resolution mapping of HvMLH3 foci in barley pachytene nuclei reveals a strong distal bias and weak interference

In barley (Hordeum vulgare L.), chiasmata (the physical sites of genetic crossovers) are skewed towards the distal ends of chromosomes, effectively consigning a large proportion of genes to recombination coldspots. This has the effect of limiting potential genetic variability, and of reducing the efficiency of map-based cloning and breeding approaches for this crop. Shifting the sites of recombination to more proximal chromosome regions by forward and reverse genetic means may be profitable in terms of realizing the genetic potential of the species, but is predicated upon a better understanding of the mechanisms governing the sites of these events, and upon the ability to recognize real changes in recombination patterns. The barley MutL Homologue (HvMLH3), a marker for class I interfering crossovers, has been isolated and a specific antibody has been raised. Immunolocalization of HvMLH3 along with the synaptonemal complex transverse filament protein ZYP1, used in conjunction with fluorescence in situ hybridization (FISH) tagging of specific barley chromosomes, has enabled access to the physical recombination landscape of the barley cultivars Morex and Bowman. Consistent distal localization of HvMLH3 foci throughout the genome, and similar patterns of HvMLH3 foci within bivalents 2H and 3H have been observed. A difference in total numbers of HvMLH3 foci between these two cultivars has been quantified, which is interpreted as representing genotypic variation in class I crossover frequency. Discrepancies between the frequencies of HvMLH3 foci and crossover frequencies derived from linkage analysis point to the existence of at least two crossover pathways in barley. It is also shown that interference of HvMLH3 foci is relatively weak compared with other plant species.

An improved algorithm for the detection of genomic variation using short oligonucleotide expression microarrays

High-throughput microarray experiments often generate far more biological information than is required to test the experimental hypotheses. Many microarray analyses are considered finished after differential expression and additional analyses are typically not performed, leaving untapped biological information left undiscovered. This is especially true if the microarray experiment is from an ecological study of multiple populations. Comparisons across populations may also contain important genomic polymorphisms, and a subset of these polymorphisms may be identified with microarrays using techniques for the detection of single feature polymorphisms (SFP). SFPs are differences in microarray probe level intensities caused by genetic polymorphisms such as single-nucleotide polymorphisms and small insertions/deletions and not expression differences. In this study, we provide a new algorithm for the detection of SFPs, evaluate the algorithm using existing data from two publicly available Affymetrix Barley (Hordeum vulgare) microarray data sets and compare them to two previously published SFP detection algorithms. Results show that our algorithm provides more consistent and sensitive calling of SFPs with a lower false discovery rate. Simultaneous analysis of SFPs and differential expression is a low-cost method for the enhanced analysis of microarray data, enabling additional biological inferences to be made.

Development and application of a 6.5 million feature Affymetrix Genechip® for massively parallel discovery of single position polymorphisms in lettuce (Lactuca spp.)

BACKGROUND

High-resolution genetic maps are needed in many crops to help characterize the genetic diversity that determines agriculturally important traits. Hybridization to microarrays to detect single feature polymorphisms is a powerful technique for marker discovery and genotyping because of its highly parallel nature. However, microarrays designed for gene expression analysis rarely provide sufficient gene coverage for optimal detection of nucleotide polymorphisms, which limits utility in species with low rates of polymorphism such as lettuce (Lactuca sativa).

RESULTS

We developed a 6.5 million feature Affymetrix GeneChip® for efficient polymorphism discovery and genotyping, as well as for analysis of gene expression in lettuce. Probes on the microarray were designed from 26,809 unigenes from cultivated lettuce and an additional 8,819 unigenes from four related species (L. serriola, L. saligna, L. virosa and L. perennis). Where possible, probes were tiled with a 2 bp stagger, alternating on each DNA strand; providing an average of 187 probes covering approximately 600 bp for each of over 35,000 unigenes; resulting in up to 13 fold redundancy in coverage per nucleotide. We developed protocols for hybridization of genomic DNA to the GeneChip® and refined custom algorithms that utilized coverage from multiple, high quality probes to detect single position polymorphisms in 2 bp sliding windows across each unigene. This allowed us to detect greater than 18,000 polymorphisms between the parental lines of our core mapping population, as well as numerous polymorphisms between cultivated lettuce and wild species in the lettuce genepool. Using marker data from our diversity panel comprised of 52 accessions from the five species listed above, we were able to separate accessions by species using both phylogenetic and principal component analyses. Additionally, we estimated the diversity between different types of cultivated lettuce and distinguished morphological types.

CONCLUSION

By hybridizing genomic DNA to a custom oligonucleotide array designed for maximum gene coverage, we were able to identify polymorphisms using two approaches for pair-wise comparisons, as well as a highly parallel method that compared all 52 genotypes simultaneously.

Development and application of a 6.5 million feature Affymetrix Genechip® for massively parallel discovery of single position polymorphisms in lettuce (Lactuca spp.)

BACKGROUND

High-resolution genetic maps are needed in many crops to help characterize the genetic diversity that determines agriculturally important traits. Hybridization to microarrays to detect single feature polymorphisms is a powerful technique for marker discovery and genotyping because of its highly parallel nature. However, microarrays designed for gene expression analysis rarely provide sufficient gene coverage for optimal detection of nucleotide polymorphisms, which limits utility in species with low rates of polymorphism such as lettuce (Lactuca sativa).

RESULTS

We developed a 6.5 million feature Affymetrix GeneChip® for efficient polymorphism discovery and genotyping, as well as for analysis of gene expression in lettuce. Probes on the microarray were designed from 26,809 unigenes from cultivated lettuce and an additional 8,819 unigenes from four related species (L. serriola, L. saligna, L. virosa and L. perennis). Where possible, probes were tiled with a 2 bp stagger, alternating on each DNA strand; providing an average of 187 probes covering approximately 600 bp for each of over 35,000 unigenes; resulting in up to 13 fold redundancy in coverage per nucleotide. We developed protocols for hybridization of genomic DNA to the GeneChip® and refined custom algorithms that utilized coverage from multiple, high quality probes to detect single position polymorphisms in 2 bp sliding windows across each unigene. This allowed us to detect greater than 18,000 polymorphisms between the parental lines of our core mapping population, as well as numerous polymorphisms between cultivated lettuce and wild species in the lettuce genepool. Using marker data from our diversity panel comprised of 52 accessions from the five species listed above, we were able to separate accessions by species using both phylogenetic and principal component analyses. Additionally, we estimated the diversity between different types of cultivated lettuce and distinguished morphological types.

CONCLUSION

By hybridizing genomic DNA to a custom oligonucleotide array designed for maximum gene coverage, we were able to identify polymorphisms using two approaches for pair-wise comparisons, as well as a highly parallel method that compared all 52 genotypes simultaneously.

Sequence-based marker development in wheat: advances and applications to breeding

In the past two decades, the wheat community has made remarkable progress in developing molecular resources for breeding. A wide variety of molecular tools has been established to accelerate genetic and physical mapping for facilitating the efficient identification of molecular markers linked to genes and QTL of agronomic interest. Already, wheat breeders are benefiting from a wide range of techniques to follow the introgression of the most favorable alleles in elite material and develop improved varieties. Breeders soon will be able to take advantage of new technological developments based on Next Generation Sequencing. In this paper, we review the molecular toolbox available to wheat scientists and breeders for performing fundamental genomic studies and breeding. Special emphasis is given on the production and detection of single nucleotide polymorphisms (SNPs) that should enable a step change in saturating the wheat genome for more efficient genetic studies and for the development of new selection methods. The perspectives offered by the access to an ordered full genome sequence for further marker development and enhanced precision breeding is also discussed. Finally, we discuss the advantages and limitations of marker-assisted selection for supporting wheat improvement.

Fast empirical Bayesian LASSO for multiple quantitative trait locus mapping

Background

The Bayesian shrinkage technique has been applied to multiple quantitative trait loci (QTLs) mapping to estimate the genetic effects of QTLs on quantitative traits from a very large set of possible effects including the main and epistatic effects of QTLs. Although the recently developed empirical Bayes (EB) method significantly reduced computation comparing with the fully Bayesian approach, its speed and accuracy are limited by the fact that numerical optimization is required to estimate the variance components in the QTL model.

Results

We developed a fast empirical Bayesian LASSO (EBLASSO) method for multiple QTL mapping. The fact that the EBLASSO can estimate the variance components in a closed form along with other algorithmic techniques render the EBLASSO method more efficient and accurate. Comparing with the EB method, our simulation study demonstrated that the EBLASSO method could substantially improve the computational speed and detect more QTL effects without increasing the false positive rate. Particularly, the EBLASSO algorithm running on a personal computer could easily handle a linear QTL model with more than 100,000 variables in our simulation study. Real data analysis also demonstrated that the EBLASSO method detected more reasonable effects than the EB method. Comparing with the LASSO, our simulation showed that the current version of the EBLASSO implemented in Matlab had similar speed as the LASSO implemented in Fortran, and that the EBLASSO detected the same number of true effects as the LASSO but a much smaller number of false positive effects.

Conclusions

The EBLASSO method can handle a large number of effects possibly including both the main and epistatic QTL effects, environmental effects and the effects of gene-environment interactions. It will be a very useful tool for multiple QTL mapping.

Combining genetical genomics and bulked segregant analysis-based differential expression: an approach to gene localization

Positional gene isolation in unsequenced species generally requires either a reference genome sequence or an inference of gene content based on conservation of synteny with a genomic model. In the large unsequenced genomes of the Triticeae cereals the latter, i.e. conservation of synteny with the rice and Brachypodium genomes, provides a powerful proxy for establishing local gene content and order. However, efficient exploitation of conservation of synteny requires 'homology bridges' between the model genome and the target region that contains a gene of interest. As effective homology bridges are generally the sequences of genetically mapped genes, increasing the density of these genes around a target locus is an important step in the process. We used bulked segregant analysis (BSA) of transcript abundance data to identify genes located in a specific region of the barley genome. The approach is valuable because only a relatively small proportion of barley genes are currently placed on a genetic map. We analyzed eQTL datasets from the reference Steptoe × Morex doubled haploid population and showed a strong association between differential gene expression and cis-regulation, with 83% of differentially expressed genes co-locating with their eQTL. We then performed BSA by assembling allele-specific pools based on the genotypes of individuals at the partial resistance QTL Rphq11. BSA identified a total of 411 genes as differentially expressed, including HvPHGPx, a gene previously identified as a promising candidate for Rphq11. The genetic location of 276 of these genes could be determined from both eQTL datasets and conservation of synteny, and 254 (92%) of these were located on the target chromosome. We conclude that the identification of differential expression by BSA constitutes a novel method to identify genes located in specific regions of interest. The datasets obtained from such studies provide a robust set of candidate genes for the analysis and serve as valuable resources for targeted marker development and comparative mapping with other grass species.

A high-density transcript linkage map with 1,845 expressed genes positioned by microarray-based Single Feature Polymorphisms (SFP) in Eucalyptus

Background

Technological advances are progressively increasing the application of genomics to a wider array of economically and ecologically important species. High-density maps enriched for transcribed genes facilitate the discovery of connections between genes and phenotypes. We report the construction of a high-density linkage map of expressed genes for the heterozygous genome of Eucalyptus using Single Feature Polymorphism (SFP) markers.

Results

SFP discovery and mapping was achieved using pseudo-testcross screening and selective mapping to simultaneously optimize linkage mapping and microarray costs. SFP genotyping was carried out by hybridizing complementary RNA prepared from 4.5 year-old trees xylem to an SFP array containing 103,000 25-mer oligonucleotide probes representing 20,726 unigenes derived from a modest size expressed sequence tags collection. An SFP-mapping microarray with 43,777 selected candidate SFP probes representing 15,698 genes was subsequently designed and used to genotype SFPs in a larger subset of the segregating population drawn by selective mapping. A total of 1,845 genes were mapped, with 884 of them ordered with high likelihood support on a framework map anchored to 180 microsatellites with average density of 1.2 cM. Using more probes per unigene increased by two-fold the likelihood of detecting segregating SFPs eventually resulting in more genes mapped. In silico validation showed that 87% of the SFPs map to the expected location on the 4.5X draft sequence of the Eucalyptus grandis genome.

Conclusions

The Eucalyptus 1,845 gene map is the most highly enriched map for transcriptional information for any forest tree species to date. It represents a major improvement on the number of genes previously positioned on Eucalyptus maps and provides an initial glimpse at the gene space for this global tree genome. A general protocol is proposed to build high-density transcript linkage maps in less characterized plant species by SFP genotyping with a concurrent objective of reducing microarray costs. HIgh-density gene-rich maps represent a powerful resource to assist gene discovery endeavors when used in combination with QTL and association mapping and should be especially valuable to assist the assembly of reference genome sequences soon to come for several plant and animal species.

Differential gene expression in nearly isogenic lines with QTL for partial resistance to Puccinia hordei in barley

BACKGROUND

The barley-Puccinia hordei (barley leaf rust) pathosystem is a model for investigating partial disease resistance in crop plants and genetic mapping of phenotypic resistance has identified several quantitative trait loci (QTL) for partial resistance. Reciprocal QTL-specific near-isogenic lines (QTL-NILs) have been developed that combine two QTL, Rphq2 and Rphq3, the largest effects detected in a recombinant-inbred-line (RIL) population derived from a cross between the super-susceptible line L94 and partially-resistant line Vada. The molecular mechanism underpinning partial resistance in these QTL-NILs is unknown.

RESULTS

An Agilent custom microarray consisting of 15,000 probes derived from barley consensus EST sequences was used to investigate genome-wide and QTL-specific differential expression of genes 18 hours post-inoculation (hpi) with Puccinia hordei. A total of 1,410 genes were identified as being significantly differentially expressed across the genome, of which 55 were accounted for by the genetic differences defined by QTL-NILs at Rphq2 and Rphq3. These genes were predominantly located at the QTL regions and are, therefore, positional candidates. One gene, encoding the transcriptional repressor Ethylene-Responsive Element Binding Factor 4 (HvERF4) was located outside the QTL at 71 cM on chromosome 1H, within a previously detected eQTL hotspot for defence response. The results indicate that Rphq2 or Rphq3 contains a trans-eQTL that modulates expression of HvERF4. We speculate that HvERF4 functions as an intermediate that conveys the response signal from a gene(s) contained within Rphq2 or Rphq3 to a host of down-stream defense responsive genes. Our results also reveal that barley lines with extreme or intermediate partial resistance phenotypes exhibit a profound similarity in their spectrum of Ph-responsive genes and that hormone-related signalling pathways are actively involved in response to Puccinia hordei.

CONCLUSIONS

Differential gene expression between QTL-NILs identifies genes predominantly located within the target region(s) providing both transcriptional and positional candidate genes for the QTL. Genetically mapping the differentially expressed genes relative to the QTL has the potential to discover trans-eQTL mediated regulatory relays initiated from genes within the QTL regions.

Intraspecific variation in the Populus balsamifera drought transcriptome

Drought is a major limitation to the growth and productivity of trees in the ecologically and economically important genus Populus. The ability of Populus trees to contend with drought is a function of genome responsiveness to this environmental insult, involving reconfiguration of the transcriptome to appropriately remodel growth, development and metabolism. Here we test hypotheses aimed at examining the extent of intraspecific variation in the drought transcriptome using six different Populus balsamifera L. genotypes and Affymetrix GeneChip technology. Within a given genotype there was a positive correlation between the magnitude of water-deficit induced changes in transcript abundance across the transcriptome, and the capacity of that genotype to maintain growth following water deficit. Genotypes that had more similar drought-responsive transcriptomes also had fewer genotypic differences, as determined by microarray-derived single feature polymorphism (SFP) analysis, suggesting that responses may be conserved across individuals that share a greater degree of genotypic similarity. This work highlights the fact that a core species-level response can be defined; however, the underpinning genotype-derived complexities of the drought response in Populus must be taken into consideration when defining both species- and genus-level responses.

A global analysis of QTLs for expression variations in rice shoots at the early seedling stage

Analyses of quantitative trait loci (QTLs) for expression levels (eQTLs) of genes reveal a genetic relationship between expression variation and the regulator, thus unlocking information for identifying the regulatory network. Oligo-nucleotide expression microarrays hybridized with RNA can simultaneously provide data for molecular markers and transcript abundance. In this study, we used an Affymetrix GeneChip Rice Genome Array to analyze eQTLs in rice shoots at 72 h after germination from 110 recombinant inbred lines (RILs) derived from a cross between Zhenshan 97 and Minghui 63. In total, 1632 single-feature polymorphisms (SFPs) plus 23 PCR markers were identified and placed into 601 recombinant bins, spanning 1459 cM in length, which were used as markers to genotype the RILs. We obtained 16,372 expression traits (e-traits) each with at least one eQTL, resulting in 26,051 eQTLs in total, including both cis- and trans-eQTLs. We also identified 171 eQTL hot spots in the rice genome, each of which controls transcript variations of many e-traits. Gene ontology analysis revealed an enrichment of certain functional categories of genes in some of the eQTL hot spots. In particular, eQTLs for e-traits involving the DNA metabolic process was significantly enriched in several eQTL hot spots on chromosomes 3, 5 and 10. Several e-traits co-localizing with cis-eQTLs showed significant correlations with hundreds of e-traits, indicating possible co-regulation. We also detected correlations between QTLs for shoot dry weight and eQTLs, revealing possible candidate genes for the trait. These results provided clues for the identification and characterization of the regulatory network in the whole genome at the transcriptional level.

Single feature polymorphism detection using recombinant inbred line microarray expression data

MOTIVATION

The Affymetrix GeneChip microarray is currently providing a high-density and economical platform for discovery of genetic polymorphisms. Microarray data for single feature polymorphism (SFP) detection in recombinant inbred lines (RILs) can capitalize on the high level of replication available for each locus in the RIL population. It was suggested that the binding affinities from all of the RILs would form a multimodal distribution for a SFP. This motivated us to estimate the binding affinities from the robust multi-array analysis (RMA) method and formulate the SFP detection problem as a hypothesis testing problem, i.e. testing whether the underlying distribution of the estimated binding affinity (EBA) values of a probe is unimodal or multimodal.

RESULTS

We developed a bootstrap-based hypothesis testing procedure using the 'dip' statistic. Our simulation studies show that the proposed procedure can reach satisfactory detection power with false discovery rate controlled at a desired level and is robust to the unimodal distribution assumption, which facilitates wide application of the proposed procedure. Our analysis of the real data identified more than four times the SFPs compared to the previous studies, covering 96% of their findings. The constructed genetic map using the SFP markers predicted from our procedure shows over 99% concordance of the genetic orders of these markers with their known physical locations on the genome sequence.

AVAILABILITY

The R package 'dipSFP' can be downloaded from http://sites.google.com/a/bioinformatics.ucr.edu/xinping-cui/home/software.

Significance test and genome selection in bayesian shrinkage analysis

Bayesian shrinkage analysis is the state-of-the-art method for whole genome analysis of quantitative traits. It can estimate the genetic effects for the entire genome using a dense marker map. The technique is now called genome selection. A nice property of the shrinkage analysis is that it can estimate effects of QTL as small as explaining 2% of the phenotypic variance in a typical sample size of 300-500 individuals. In most cases, QTL can be detected with simple visual inspection of the entire genome for the effect because the false positive rate is low. As a Bayesian method, no significance test is needed. However, it is still desirable to put some confidences on the estimated QTL effects. We proposed to use the permutation test to draw empirical thresholds to declare significance of QTL under a predetermined genome wide type I error. With the permutation test, Bayesian shrinkage analysis can be routinely used for QTL detection.

A simple optimization can improve the performance of single feature polymorphism detection by Affymetrix expression arrays

Background

High-density oligonucleotide arrays are effective tools for genotyping numerous loci simultaneously. In small genome species (genome size: < ~300 Mb), whole-genome DNA hybridization to expression arrays has been used for various applications. In large genome species, transcript hybridization to expression arrays has been used for genotyping. Although rice is a fully sequenced model plant of medium genome size (~400 Mb), there are a few examples of the use of rice oligonucleotide array as a genotyping tool.

Results

We compared the single feature polymorphism (SFP) detection performance of whole-genome and transcript hybridizations using the Affymetrix GeneChip^® Rice Genome Array, using the rice cultivars with full genome sequence, japonica cultivar Nipponbare and indica cultivar 93-11. Both genomes were surveyed for all probe target sequences. Only completely matched 25-mer single copy probes of the Nipponbare genome were extracted, and SFPs between them and 93-11 sequences were predicted. We investigated optimum conditions for SFP detection in both whole genome and transcript hybridization using differences between perfect match and mismatch probe intensities of non-polymorphic targets, assuming that these differences are representative of those between mismatch and perfect targets. Several statistical methods of SFP detection by whole-genome hybridization were compared under the optimized conditions. Causes of false positives and negatives in SFP detection in both types of hybridization were investigated.

Conclusions

The optimizations allowed a more than 20% increase in true SFP detection in whole-genome hybridization and a large improvement of SFP detection performance in transcript hybridization. Significance analysis of the microarray for log-transformed raw intensities of PM probes gave the best performance in whole genome hybridization, and 22,936 true SFPs were detected with 23.58% false positives by whole genome hybridization. For transcript hybridization, stable SFP detection was achieved for highly expressed genes, and about 3,500 SFPs were detected at a high sensitivity (> 50%) in both shoot and young panicle transcripts. High SFP detection performances of both genome and transcript hybridizations indicated that microarrays of a complex genome (e.g., of Oryza sativa) can be effectively utilized for whole genome genotyping to conduct mutant mapping and analysis of quantitative traits such as gene expression levels.

High-throughput marker discovery in melon using a self-designed oligo microarray

BACKGROUND

Genetic maps constitute the basis of breeding programs for many agricultural organisms. The creation of these maps is dependent on marker discovery. Melon, among other crops, is still lagging in genomic resources, limiting the ability to discover new markers in a high-throughput fashion. One of the methods used to search for molecular markers is DNA hybridization to microarrays. Microarray hybridization of DNA from different accessions can reveal differences between them--single-feature polymorphisms (SFPs). These SFPs can be used as markers for breeding purposes, or they can be converted to conventional markers by sequencing. This method has been utilized in a few different plants to discover genetic variation, using Affymetrix arrays that exist for only a few organisms. We applied this approach with some modifications for marker discovery in melon.

RESULTS

Using a custom-designed oligonucleotide microarray based on a partial EST collection of melon, we discovered 6184 putative SFPs between the parents of our mapping population. Validation by sequencing of 245 SFPs from the two parents showed a sensitivity of around 79%. Most SFPs (81%) contained single-nucleotide polymorphisms. Testing the SFPs on another mapping population of melon confirmed that many of them are conserved.

CONCLUSION

Thousands of new SFPs that can be used for genetic mapping and molecular-assisted breeding in melon were discovered using a custom-designed oligo microarray. A portion of these SFPs are conserved and can be used in different breeding populations. Although improvement of the discovery rate is still needed, this approach is applicable to many agricultural systems with limited genomic resources.

Exploiting induced variation to dissect quantitative traits in barley

The identification of genes underlying complex quantitative traits such as grain yield by means of conventional genetic analysis (positional cloning) requires the development of several large mapping populations. However, it is possible that phenotypically related, but more extreme, allelic variants generated by mutational studies could provide a means for more efficient cloning of QTLs (quantitative trait loci). In barley (Hordeum vulgare), with the development of high-throughput genome analysis tools, efficient genome-wide identification of genetic loci harbouring mutant alleles has recently become possible. Genotypic data from NILs (near-isogenic lines) that carry induced or natural variants of genes that control aspects of plant development can be compared with the location of QTLs to potentially identify candidate genes for development­-related traits such as grain yield. As yield itself can be divided into a number of allometric component traits such as tillers per plant, kernels per spike and kernel size, mutant alleles that both affect these traits and are located within the confidence intervals for major yield QTLs may represent extreme variants of the underlying genes. In addition, the development of detailed comparative genomic models based on the alignment of a high-density barley gene map with the rice and sorghum physical maps, has enabled an informed prioritization of ‘known function’ genes as candidates for both QTLs and induced mutant genes.

An empirical method for establishing positional confidence intervals tailored for composite interval mapping of QTL

BACKGROUND

Improved genetic resolution and availability of sequenced genomes have made positional cloning of moderate-effect QTL realistic in several systems, emphasizing the need for precise and accurate derivation of positional confidence intervals (CIs) for QTL. Support interval (SI) methods based on the shape of the QTL likelihood curve have proven adequate for standard interval mapping, but have not been shown to be appropriate for use with composite interval mapping (CIM), which is one of the most commonly used QTL mapping methods.

RESULTS

Based on a non-parametric confidence interval (NPCI) method designed for use with the Haley-Knott regression method for mapping QTL, a CIM-specific method (CIM-NPCI) was developed to appropriately account for the selection of background markers during analysis of bootstrap-resampled data sets. Coverage probabilities and interval widths resulting from use of the NPCI, SI, and CIM-NPCI methods were compared in a series of simulations analyzed via CIM, wherein four genetic effects were simulated in chromosomal regions with distinct marker densities while heritability was fixed at 0.6 for a population of 200 isolines. CIM-NPCIs consistently capture the simulated QTL across these conditions while slightly narrower SIs and NPCIs fail at unacceptably high rates, especially in genomic regions where marker density is high, which is increasingly common for real studies. The effects of a known CIM bias toward locating QTL peaks at markers were also investigated for each marker density case. Evaluation of sub-simulations that varied according to the positions of simulated effects relative to the nearest markers showed that the CIM-NPCI method overcomes this bias, offering an explanation for the improved coverage probabilities when marker densities are high.

CONCLUSIONS

Extensive simulation studies herein demonstrate that the QTL confidence interval methods typically used to positionally evaluate CIM results can be dramatically improved by accounting for the procedural complexity of CIM via an empirical approach, CIM-NPCI. Confidence intervals are a critical measure of QTL utility, but have received inadequate treatment due to a perception that QTL mapping is not sufficiently precise for procedural improvements to matter. Technological advances will continue to challenge this assumption, creating even more need for the current improvement to be refined.

Quantification of variation in expression networks

Gene expression microarrays allow rapid and easy quantification of transcript accumulation for almost transcripts present in a genome. This technology has been utilized for diverse investigations from studying gene regulation in response to genetic or environmental fluctuation to global expression QTL (eQTL) analyses of natural variation. Typical analysis techniques focus on responses of individual genes in isolation of other genes. However, emerging evidence indicates that genes are organized into regulons, i.e., they respond as groups due to individual transcription factors binding multiple promoters, creating what is commonly called a network. We have developed a set of statistical approaches that allow researchers to test specific network hypothesis using a priori-defined gene networks. When applied to Arabidopsis thaliana this approach has been able to identify natural genetic variation that controls networks. In this chapter we describe approaches to develop and test specific network hypothesis utilizing natural genetic variation. This approach can be expanded to facilitate direct tests of the relationship between phenotypic trait and transcript genetic architecture. Finally, the use of a priori network definitions can be applied to any microarray experiment to directly conduct hypothesis testing at a genomics level.

An eQTL analysis of partial resistance to Puccinia hordei in barley

BACKGROUND

Genetic resistance to barley leaf rust caused by Puccinia hordei involves both R genes and quantitative trait loci. The R genes provide higher but less durable resistance than the quantitative trait loci. Consequently, exploring quantitative or partial resistance has become a favorable alternative for controlling disease. Four quantitative trait loci for partial resistance to leaf rust have been identified in the doubled haploid Steptoe (St)/Morex (Mx) mapping population. Further investigations are required to study the molecular mechanisms underpinning partial resistance and ultimately identify the causal genes.

METHODOLOGY/PRINCIPAL FINDINGS

We explored partial resistance to barley leaf rust using a genetical genomics approach. We recorded RNA transcript abundance corresponding to each probe on a 15K Agilent custom barley microarray in seedlings from St and Mx and 144 doubled haploid lines of the St/Mx population. A total of 1154 and 1037 genes were, respectively, identified as being P. hordei-responsive among the St and Mx and differentially expressed between P. hordei-infected St and Mx. Normalized ratios from 72 distant-pair hybridisations were used to map the genetic determinants of variation in transcript abundance by expression quantitative trait locus (eQTL) mapping generating 15685 eQTL from 9557 genes. Correlation analysis identified 128 genes that were correlated with resistance, of which 89 had eQTL co-locating with the phenotypic quantitative trait loci (pQTL). Transcript abundance in the parents and conservation of synteny with rice allowed us to prioritise six genes as candidates for Rphq11, the pQTL of largest effect, and highlight one, a phospholipid hydroperoxide glutathione peroxidase (HvPHGPx) for detailed analysis.

CONCLUSIONS/SIGNIFICANCE

The eQTL approach yielded information that led to the identification of strong candidate genes underlying pQTL for resistance to leaf rust in barley and on the general pathogen response pathway. The dataset will facilitate a systems appraisal of this host-pathogen interaction and, potentially, for other traits measured in this population.

Expression quantitative trait loci analysis in plants

An expression Quantitative Trait Locus or eQTL is a chromosomal region that accounts for a proportion of the variation in abundance of a mRNA transcript observed between individuals in a genetic mapping population. A single gene can have one or multiple eQTLs. Large scale mRNA profiling technologies advanced genome-wide eQTL mapping in a diverse range of organisms allowing thousands of eQTLs to be detected in a single experiment. When combined with classical or trait QTLs, correlation analyses can directly suggest candidates for genes underlying these traits. Furthermore, eQTL mapping data enables genetic regulatory networks to be modelled and potentially provide a better understanding of the underlying phenotypic variation. The mRNA profiling data sets can also be used to infer the chromosomal positions of thousands of genes, an outcome that is particularly valuable for species with unsequenced genomes where the chromosomal location of the majority of genes remains unknown. In this review we focus on eQTL studies in plants, addressing conceptual and technical aspects that include experimental design, genetic polymorphism prediction and candidate gene identification.

Oligonucleotide array discovery of polymorphisms in cultivated tomato (Solanum lycopersicum L.) reveals patterns of SNP variation associated with breeding

BACKGROUND

Cultivated tomato (Solanum lycopersicum L.) has narrow genetic diversity that makes it difficult to identify polymorphisms between elite germplasm. We explored array-based single feature polymorphism (SFP) discovery as a high-throughput approach for marker development in cultivated tomato.

RESULTS

Three varieties, FL7600 (fresh-market), OH9242 (processing), and PI114490 (cherry) were used as a source of genomic DNA for hybridization to oligonucleotide arrays. Identification of SFPs was based on outlier detection using regression analysis of normalized hybridization data within a probe set for each gene. A subset of 189 putative SFPs was sequenced for validation. The rate of validation depended on the desired level of significance (alpha) used to define the confidence interval (CI), and ranged from 76% for polymorphisms identified at alpha <or= 10-6 to 60% for those identified at alpha <or= 10-2. Validation percentage reached a plateau between alpha <or= 10-4 and alpha <or= 10-7, but failure to identify known SFPs (Type II error) increased dramatically at alpha <or= 10-6. Trough sequence validation, we identified 279 SNPs and 27 InDels in 111 loci. Sixty loci contained >or= 2 SNPs per locus. We used a subset of validated SNPs for genetic diversity analysis of 92 tomato varieties and accessions. Pairwise estimation of theta (Fst) suggested significant differentiation between collections of fresh-market, processing, vintage, Latin American (landrace), and S. pimpinellifolium accessions. The fresh-market and processing groups displayed high genetic diversity relative to vintage and landrace groups. Furthermore, the patterns of SNP variation indicated that domestication and early breeding practices have led to progressive genetic bottlenecks while modern breeding practices have reintroduced genetic variation into the crop from wild species. Finally, we examined the ratio of non-synonymous (Ka) to synonymous substitutions (Ks) for 20 loci with multiple SNPs (>or= 4 per locus). Six of 20 loci showed ratios of Ka/Ks >or= 0.9.

CONCLUSION

Array-based SFP discovery was an efficient method to identify a large number of molecular markers for genetics and breeding in elite tomato germplasm. Patterns of sequence variation across five major tomato groups provided insight into to the effect of human selection on genetic variation.

Oligonucleotide array discovery of polymorphisms in cultivated tomato (Solanum lycopersicum L.) reveals patterns of SNP variation associated with breeding

BACKGROUND

Cultivated tomato (Solanum lycopersicum L.) has narrow genetic diversity that makes it difficult to identify polymorphisms between elite germplasm. We explored array-based single feature polymorphism (SFP) discovery as a high-throughput approach for marker development in cultivated tomato.

RESULTS

Three varieties, FL7600 (fresh-market), OH9242 (processing), and PI114490 (cherry) were used as a source of genomic DNA for hybridization to oligonucleotide arrays. Identification of SFPs was based on outlier detection using regression analysis of normalized hybridization data within a probe set for each gene. A subset of 189 putative SFPs was sequenced for validation. The rate of validation depended on the desired level of significance (alpha) used to define the confidence interval (CI), and ranged from 76% for polymorphisms identified at alpha <or= 10-6 to 60% for those identified at alpha <or= 10-2. Validation percentage reached a plateau between alpha <or= 10-4 and alpha <or= 10-7, but failure to identify known SFPs (Type II error) increased dramatically at alpha <or= 10-6. Trough sequence validation, we identified 279 SNPs and 27 InDels in 111 loci. Sixty loci contained >or= 2 SNPs per locus. We used a subset of validated SNPs for genetic diversity analysis of 92 tomato varieties and accessions. Pairwise estimation of theta (Fst) suggested significant differentiation between collections of fresh-market, processing, vintage, Latin American (landrace), and S. pimpinellifolium accessions. The fresh-market and processing groups displayed high genetic diversity relative to vintage and landrace groups. Furthermore, the patterns of SNP variation indicated that domestication and early breeding practices have led to progressive genetic bottlenecks while modern breeding practices have reintroduced genetic variation into the crop from wild species. Finally, we examined the ratio of non-synonymous (Ka) to synonymous substitutions (Ks) for 20 loci with multiple SNPs (>or= 4 per locus). Six of 20 loci showed ratios of Ka/Ks >or= 0.9.

CONCLUSION

Array-based SFP discovery was an efficient method to identify a large number of molecular markers for genetics and breeding in elite tomato germplasm. Patterns of sequence variation across five major tomato groups provided insight into to the effect of human selection on genetic variation.

Single-feature polymorphism discovery by computing probe affinity shape powers

Background

Single-feature polymorphism (SFP) discovery is a rapid and cost-effective approach to identify DNA polymorphisms. However, high false positive rates and/or low sensitivity are prevalent in previously described SFP detection methods. This work presents a new computing method for SFP discovery.

Results

The probe affinity differences and affinity shape powers formed by the neighboring probes in each probe set were computed into SFP weight scores. This method was validated by known sequence information and was comprehensively compared with previously-reported methods using the same datasets. A web application using this algorithm has been implemented for SFP detection. Using this method, we identified 364 SFPs in a barley near-isogenic line pair carrying either the wild type or the mutant uniculm2 (cul2) allele. Most of the SFP polymorphisms were identified on chromosome 6H in the vicinity of the Cul2 locus.

Conclusion

This SFP discovery method exhibits better performance in specificity and sensitivity over previously-reported methods. It can be used for other organisms for which GeneChip technology is available. The web-based tool will facilitate SFP discovery. The 364 SFPs discovered in a barley near-isogenic line pair provide a set of genetic markers for fine mapping and future map-based cloning of the Cul2 locus.

Comparative transcriptomics in the Triticeae

Background

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.

Results

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.

Conclusion

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.

Single feature polymorphisms between two rice cultivars detected using a median polish method

Expression levels measured in microarrays of oligonucleotide probes have now been adapted as a high throughput approach for identifying DNA sequence variation between genotypes, referred to as single feature polymorphisms (SFPs). Although there have been increasing interests in this approach, there is still need for improving the algorithm in order to achieve high sensitivity and specificity especially with complex genome and large datasets, while maintaining optimal computational performance. We obtained microarray datasets for expression profiles of two rice cultivars and adapted a median polish method to detect SFPs. The analysis identified 6,655 SFPs between two the rice varieties representing 3,131 rice unique genes. We showed that the median polish method has the advantage of avoiding fitting complex linear models thus can be used to analyze complex transcriptome datasets like the ones in this study. The method is also superior in sensitivity, accuracy and computing time requirement compared with two previously used methods. A comparison with data from a resequencing project indicated that 75.6% of the SFPs had SNP supports in the probe regions. Further comparison revealed that SNPs in sequences immediately flanking the probes also had contributions to the detection of SFPs in cases where the probes and the targets had perfectly matched sequences. It was shown that differences in minimum free energies caused by flanking SNPs, which may change the stability of RNA secondary structure, may partly explain the SFPs as detected. These SFPs may facilitate gene discovery in future studies.

A microarray-based genotyping and genetic mapping approach for highly heterozygous outcrossing species enables localization of a large fraction of the unassembled Populus trichocarpa genome sequence

Microarrays have demonstrated significant power for genome-wide analyses of gene expression, and recently have also revolutionized the genetic analysis of segregating populations by genotyping thousands of loci in a single assay. Although microarray-based genotyping approaches have been successfully applied in yeast and several inbred plant species, their power has not been proven in an outcrossing species with extensive genetic diversity. Here we have developed methods for high-throughput microarray-based genotyping in such species using a pseudo-backcross progeny of 154 individuals of Populus trichocarpa and P. deltoides analyzed with long-oligonucleotide in situ-synthesized microarray probes. Our analysis resulted in high-confidence genotypes for 719 single-feature polymorphism (SFP) and 1014 gene expression marker (GEM) candidates. Using these genotypes and an established microsatellite (SSR) framework map, we produced a high-density genetic map comprising over 600 SFPs, GEMs and SSRs. The abundance of gene-based markers allowed us to localize over 35 million base pairs of previously unplaced whole-genome shotgun (WGS) scaffold sequence to putative locations in the genome of P. trichocarpa. A high proportion of sampled scaffolds could be verified for their placement with independently mapped SSRs, demonstrating the previously un-utilized power that high-density genotyping can provide in the context of map-based WGS sequence reassembly. Our results provide a substantial contribution to the continued improvement of the Populus genome assembly, while demonstrating the feasibility of microarray-based genotyping in a highly heterozygous population. The strategies presented are applicable to genetic mapping efforts in all plant species with similarly high levels of genetic diversity.

Discovery and mapping of single feature polymorphisms in wheat using Affymetrix arrays

BACKGROUND

Wheat (Triticum aestivum L.) is a staple food crop worldwide. The wheat genome has not yet been sequenced due to its huge genome size (approximately 17,000 Mb) and high levels of repetitive sequences; the whole genome sequence may not be expected in the near future. Available linkage maps have low marker density due to limitation in available markers; therefore new technologies that detect genome-wide polymorphisms are still needed to discover a large number of new markers for construction of high-resolution maps. A high-resolution map is a critical tool for gene isolation, molecular breeding and genomic research. Single feature polymorphism (SFP) is a new microarray-based type of marker that is detected by hybridization of DNA or cRNA to oligonucleotide probes. This study was conducted to explore the feasibility of using the Affymetrix GeneChip to discover and map SFPs in the large hexaploid wheat genome.

RESULTS

Six wheat varieties of diverse origins (Ning 7840, Clark, Jagger, Encruzilhada, Chinese Spring, and Opata 85) were analyzed for significant probe by variety interactions and 396 probe sets with SFPs were identified. A subset of 164 unigenes was sequenced and 54% showed polymorphism within probes. Microarray analysis of 71 recombinant inbred lines from the cross Ning 7840/Clark identified 955 SFPs and 877 of them were mapped together with 269 simple sequence repeat markers. The SFPs were randomly distributed within a chromosome but were unevenly distributed among different genomes. The B genome had the most SFPs, and the D genome had the least. Map positions of a selected set of SFPs were validated by mapping single nucleotide polymorphism using SNaPshot and comparing with expressed sequence tags mapping data.

CONCLUSION

The Affymetrix array is a cost-effective platform for SFP discovery and SFP mapping in wheat. The new high-density map constructed in this study will be a useful tool for genetic and genomic research in wheat.

SNEP: Simultaneous detection of nucleotide and expression polymorphisms using Affymetrix GeneChip

BACKGROUND

High-density short oligonucleotide microarrays are useful tools for studying biodiversity, because they can be used to investigate both nucleotide and expression polymorphisms. However, when different strains (or species) produce different signal intensities after mRNA hybridization, it is not easy to determine whether the signal intensities were affected by nucleotide or expression polymorphisms. To overcome this difficulty, nucleotide and expression polymorphisms are currently examined separately.

RESULTS

We have developed SNEP, a new method that allows simultaneous detection of both nucleotide and expression polymorphisms. SNEP involves a robust statistical procedure based on the idea that a nucleotide polymorphism observed at the probe level can be regarded as an outlier, because the nucleotide polymorphism can reduce the hybridization signal intensity. To investigate the performance of SNEP, we used three species: barley, rice and mice. In addition to the publicly available barley data, we obtained new rice and mouse data from the strains with available genome sequences. The sensitivity and false positive rate of nucleotide polymorphism detection were estimated based on the sequence information. The robustness of expression polymorphism detection against nucleotide polymorphisms was also investigated.

CONCLUSION

SNEP performed well regardless of the genome size and showed a better performance for nucleotide polymorphism detection, when compared with other previously proposed methods. The R-software 'SNEP' is available at http://www.ism.ac.jp/~fujisawa/SNEP/.

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.

One of the ultimate goals of genomics is to explore structural and functional variations of all genes in a genome. High-density oligo-microarray techniques enable prediction of genome-wide gene expression and genome-wide genetic polymorphisms from using RNA and genomic DNA samples, respectively. A recent proposal to integrate the two predictions by use of RNA microarray data alone has great practical implications in genomics. However, it is essential but very challenging to develop an appropriate analytical method for detecting genetic polymorphisms (SFPs) from RNA expression data, which are inherently coupled with various sources of biological and technical variations. This paper presents a novel statistical approach to detect SFPs from gene expression data. We demonstrated that the new method is significantly more robust to variation due to differential expression of genes and improves the reliability of calling SFPs that bear genuine sequence polymorphisms than the other five methods in the mainstream literature on SFP prediction from microarray data. The improved predictability of detecting SFPs not only confers accuracy in evaluating gene expression from microarray information, but also opens up an opportunity to integrate structural and functional analyses by using only one set of microarray data.

Comprehensive analysis of Arabidopsis expression level polymorphisms with simple inheritance

In Arabidopsis thaliana, gene expression level polymorphisms (ELPs) between natural accessions that exhibit simple, single locus inheritance are promising quantitative trait locus (QTL) candidates to explain phenotypic variability. It is assumed that such ELPs overwhelmingly represent regulatory element polymorphisms. However, comprehensive genome-wide analyses linking expression level, regulatory sequence and gene structure variation are missing, preventing definite verification of this assumption. Here, we analyzed ELPs observed between the Eil-0 and Lc-0 accessions. Compared with non-variable controls, 5' regulatory sequence variation in the corresponding genes is indeed increased. However, approximately 42% of all the ELP genes also carry major transcription unit deletions in one parent as revealed by genome tiling arrays, representing a >4-fold enrichment over controls. Within the subset of ELPs with simple inheritance, this proportion is even higher and deletions are generally more severe. Similar results were obtained from analyses of the Bay-0 and Sha accessions, using alternative technical approaches. Collectively, our results suggest that drastic structural changes are a major cause for ELPs with simple inheritance, corroborating experimentally observed indel preponderance in cloned Arabidopsis QTL.

Quantitative genomics: analyzing intraspecific variation using global gene expression polymorphisms or eQTLs

Scientific inquiries in fields ranging from ecology to plant breeding assess phenotypic variation within a plant species either to explain its presence or utilize its consequences. Frequently this natural genetic variation is studied via mapping quantitative trait loci (QTLs); however, elucidation of the underlying molecular mechanisms is a continuing bottleneck. The genomic analysis of transcripts as individual phenotypes has led to the emerging field of expression QTL analysis. This field has begun both to delve into the ecological/evolutionary significance of this transcript variation as well as to use specific eQTLs to speed up our analysis of the molecular basis of quantitative traits. This review introduces eQTL analysis and begins to illustrate how these data can be applied to multiple research fields.

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

BACKGROUND

A typical genetical genomics experiment results in four separate data sets; genotype, gene expression, higher-order phenotypic data and metadata that describe the protocols, processing and the array platform. Used in concert, these data sets provide the opportunity to perform genetic analysis at a systems level. Their predictive power is largely determined by the gene expression dataset where tens of millions of data points can be generated using currently available mRNA profiling technologies. Such large, multidimensional data sets often have value beyond that extracted during their initial analysis and interpretation, particularly if conducted on widely distributed reference genetic materials. Besides quality and scale, access to the data is of primary importance as accessibility potentially allows the extraction of considerable added value from the same primary dataset by the wider research community. Although the number of genetical genomics experiments in different plant species is rapidly increasing, none to date has been presented in a form that allows quick and efficient on-line testing for possible associations between genes, loci and traits of interest by an entire research community.

DESCRIPTION

Using a reference population of 150 recombinant doubled haploid barley lines we generated novel phenotypic, mRNA abundance and SNP-based genotyping data sets, added them to a considerable volume of legacy trait data and entered them into the GeneNetwork http://www.genenetwork.org. GeneNetwork is a unified on-line analytical environment that enables the user to test genetic hypotheses about how component traits, such as mRNA abundance, may interact to condition more complex biological phenotypes (higher-order traits). Here we describe these barley data sets and demonstrate some of the functionalities GeneNetwork provides as an easily accessible and integrated analytical environment for exploring them.

CONCLUSION

By integrating barley genotypic, phenotypic and mRNA abundance data sets directly within GeneNetwork's analytical environment we provide simple web access to the data for the research community. In this environment, a combination of correlation analysis and linkage mapping provides the potential to identify and substantiate gene targets for saturation mapping and positional cloning. By integrating datasets from an unsequenced crop plant (barley) in a database that has been designed for an animal model species (mouse) with a well established genome sequence, we prove the importance of the concept and practice of modular development and interoperability of software engineering for biological data sets.

Tissue-dependent limited pleiotropy affects gene expression in barley

Non-synonymous coding mutations in a gene change the resulting protein, no matter where it is expressed, but the effects of cis-regulatory mutations could be spatially or temporally limited - a phenomenon termed limited pleiotropy. Here, we report the genome-wide occurrence of limited pleiotropy of cis-regulatory mutations in barley (Hordeum vulgare L.) using Affymetrix analysis of 22,840 genes in a population of 139 doubled haploid lines derived from a cross between the cultivars Steptoe (St) and Morex (Mx). We identified robust cis-acting expression regulators that segregate as major genes in two successive ontogenetic stages: germinating embryo tissues and seedling leaves from the embryonic axis. We show that these polymorphisms may be consistent in both tissues or may cause a dramatic change in transcript abundance in one tissue but not in another. We also show that the parental allele that increases expression can vary with the tissue, suggesting nucleotide polymorphism in enhancer sequences. Because of the limited pleiotropy of cis-regulating mutations, the number of cis expression quantitative trait loci (cis-eQTLs) discovered by 'genetical genomics' is strongly affected by the particular tissue or developmental stage studied. Given that limited pleiotropy is a common feature of cis-regulatory mutations in barley, we predict that the phenomenon would be relevant to developmental and/or tissue-specific interactions across wide taxonomic boundaries in both plants and animals.

Exploiting regulatory variation to identify genes underlying quantitative resistance to the wheat stem rust pathogen Puccinia graminis f. sp. tritici in barley

We previously mapped mRNA transcript abundance traits (expression-QTL or eQTL) using the Barley1 Affymetrix array and 'whole plant' tissue from 139 progeny of the Steptoe x Morex (St/Mx) reference barley mapping population. Of the 22,840 probesets (genes) on the array, 15,987 reported transcript abundance signals that were suitable for eQTL analysis, and this revealed a genome-wide distribution of 23,738 significant eQTLs. Here we have explored the potential of using these mRNA abundance eQTL traits as surrogates for the identification of candidate genes underlying the interaction between barley and the wheat stem rust fungus Puccinia graminis f. sp. tritici. We re-analysed quantitative 'resistance phenotype' data collected on this population in 1990/1991 and identified six loci associated with barley's reaction to stem rust. One of these coincided with the major stem rust resistance locus Rpg1, that we had previously positionally cloned using this population. Correlation analysis between phenotype values for rust infection and mRNA abundance values reported by the 22,840 GeneChip probe sets placed Rpg1, which is on the Barley1 GeneChip, in the top five candidate genes for the major QTL on chromosome 7H corresponding to the location of Rpg1. A second co-located with the rpg4/Rpg5 stem rust resistance locus that has been mapped in a different population and the remaining four were novel. Correlation analyses identified candidate genes for the rpg4/Rpg5 locus on chromosome 5H. By combining our data with additional published mRNA profiling data sets, we identify a putative sensory transduction histidine kinase as a strong candidate for a novel resistance locus on chromosome 2H and compile candidate gene lists for the other three loci.

Methods for evaluating gene expression from Affymetrix microarray datasets

Background

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.

Results

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 cis-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.

Conclusion

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.

Identification of SNPs and INDELS in swine transcribed sequences using short oligonucleotide microarrays

Background

Genome-wide detection of single feature polymorphisms (SFP) in swine using transcriptome profiling of day 25 placental RNA by contrasting probe intensities from either Meishan or an occidental composite breed with Affymetrix porcine microarrays is presented. A linear mixed model analysis was used to identify significant breed-by-probe interactions.

Results

Gene specific linear mixed models were fit to each of the log₂ transformed probe intensities on these arrays, using fixed effects for breed, probe, breed-by-probe interaction, and a random effect for array. After surveying the day 25 placental transcriptome, 857 probes with a q-value ≤ 0.05 and |fold change| ≥ 2 for the breed-by-probe interaction were identified as candidates containing SFP. To address the quality of the bioinformatics approach, universal pyrosequencing assays were designed from Affymetrix exemplar sequences to independently assess polymorphisms within a subset of probes for validation. Additionally probes were randomly selected for sequencing to determine an unbiased confirmation rate. In most cases, the 25-mer probe sequence printed on the microarray diverged from Meishan, not occidental crosses. This analysis was used to define a set of highly reliable predicted SFPs according to their probability scores.

Conclusion

By applying a SFP detection method to two mammalian breeds for the first time, we detected transition and transversion single nucleotide polymorphisms, as well as insertions/deletions which can be used to rapidly develop markers for genetic mapping and association analysis in species where high density genotyping platforms are otherwise unavailable.

SNPs and INDELS discovered by this approach have been publicly deposited in NCBI's SNP repository dbSNP. This method is an attractive bioinformatics tool for uncovering breed-by-probe interactions, for rapidly identifying expressed SNPs, for investigating potential functional correlations between gene expression and breed polymorphisms, and is robust enough to be used on any Affymetrix gene expression platform.

Array-based high-throughput DNA markers for crop improvement

The last two decades have witnessed a remarkable activity in the development and use of molecular markers both in animal and plant systems. This activity started with low-throughput restriction fragment length polymorphisms and culminated in recent years with single nucleotide polymorphisms (SNPs), which are abundant and uniformly distributed. Although the latter became the markers of choice for many, their discovery needed previous sequence information. However, with the availability of microarrays, SNP platforms have been developed, which allow genotyping of thousands of markers in parallel. Besides SNPs, some other novel marker systems, including single feature polymorphisms, diversity array technology and restriction site-associated DNA markers, have also been developed, where array-based assays have been utilized to provide for the desired ultra-high throughput and low cost. These microarray-based markers are the markers of choice for the future and are already being used for construction of high-density maps, quantitative trait loci (QTL) mapping (including expression QTLs) and genetic diversity analysis with a limited expense in terms of time and money. In this study, we briefly describe the characteristics of these array-based marker systems and review the work that has already been done involving development and use of these markers, not only in simple eukaryotes like yeast, but also in a variety of seed plants with simple or complex genomes.

Diversification of Lrk/Tak kinase gene clusters is associated with subfunctionalization and cultivar-specific transcript accumulation in barley

Lrk (Lr10 receptor-like kinase) and Tak (Triticum aestivum kinase) belong to the receptor-like kinase (RLK) supergene family in higher plants. Three Lrk/Tak gene regions spanning greater than 600 kb were identified via a genome-wide survey of barley gene-rich BAC clones. Two Lrk/Tak gene clusters are positioned on barley chromosome 3 (3H) and another is localized on chromosome 5 (1H), with each Lrk and Tak open reading frame physically positioned in a back-to-back orientation. Thirteen new Lrk/Tak-like fragments were cloned from the two clusters on 3H and the single cluster on 1H, respectively, and compared phylogenetically with other grass Lrk/Tak-like genes, including a 280-kb Lrk/Tak cluster on rice chromosome 1S. Physically clustered Lrk/Tak-like genes always form monophyletic groups; this suggests that the primary mechanism of expansion of the Lrk/Tak RLK super family was by tandem duplication, of which most members were duplicated after speciation of the Poaceae. Cultivar-dependent transcript accumulation of some Lrk/Tak family members on 3H, as revealed via Barley1 GeneChip microarray analysis, is consistent with the hypothesis of subfunctionalization of Lrk/Tak members following tandem duplication.

Two alternative recessive quantitative trait loci influence resistance to spring black stem and leaf spot in Medicago truncatula

BACKGROUND

Knowledge of the genetic basis of plant resistance to necrotrophic pathogens is incomplete and has been characterised in relatively few pathosystems. In this study, the cytology and genetics of resistance to spring black stem and leaf spot caused by Phoma medicaginis, an economically important necrotrophic pathogen of Medicago spp., was examined in the model legume M. truncatula.

RESULTS

Macroscopically, the resistant response of accession SA27063 was characterised by small, hypersensitive-like spots following inoculation while the susceptible interaction with accessions A17 and SA3054 showed necrotic lesions and spreading chlorosis. No unique cytological differences were observed during early infection (<48 h) between the resistant and susceptible genotypes, except pathogen growth was restricted to one or a few host cells in SA27063. In both interactions reactive oxygen intermediates and phenolic compounds were produced, and cell death occurred. Two F2 populations segregating for resistance to spring black stem and leaf spot were established between SA27063 and the two susceptible accessions, A17 and SA3054. The cross between SA27063 and A17 represented a wider cross than between SA27063 and SA3054, as evidenced by higher genetic polymorphism, reduced fertility and aberrant phenotypes of F2 progeny. In the SA27063 x A17 F2 population a highly significant quantitative trait locus (QTL, LOD = 7.37; P < 0.00001) named resistance to the necrotroph Phoma medicaginis one (rnpm1) genetically mapped to the top arm of linkage group 4 (LG4). rnpm1 explained 33.6% of the phenotypic variance in the population's response to infection depicted on a 1-5 scale and was tightly linked to marker AW256637. A second highly significant QTL (LOD = 6.77; P < 0.00001), rnpm2, was located on the lower arm of LG8 in the SA27063 x SA3054 map. rnpm2 explained 29.6% of the phenotypic variance and was fine mapped to a 0.8 cM interval between markers h2_16a6a and h2_21h11d. rnpm1 is tightly linked to a cluster of Toll/Interleukin1 receptor-nucleotide binding site-leucine-rich repeat (TIR-NBS-LRR) genes and disease resistance protein-like genes, while no resistance gene analogues (RGAs) are apparent in the genomic sequence of the reference accession A17 at the rnpm2 locus.

CONCLUSION

The induction of defence responses and cell death in the susceptible interaction following infection by P. medicaginis suggested this pathogen is not negatively affected by these responses and may promote them. A QTL for resistance was revealed in each of two populations derived from crosses between a resistant accession and two different susceptible accessions. Both loci are recessive in nature, and the simplest explanation for the existence of two separate QTLs is the occurrence of host genotype-specific susceptibility loci that may interact with undetermined P. medicaginis virulence factors.

Combining classical trait and microarray data to dissect transcriptional regulation: a case study

The selective transcriptional profiling approach involves selecting an optimal subset of individuals to microarray from a larger set of individuals for which relatively inexpensive quantitative trait and molecular marker data are available. The goal of the selection and subsequent analyses is to identify genes whose expression is associated with a quantitative trait or quantitative trait locus (QTL). In this paper, we applied the selective transcriptional profiling approach to data sets concerning flowering time and gene transcription levels of Arabidopsis recombinant inbred lines. Our results confirm that the selective transcriptional profiling approach can achieve much greater power for uncovering associations than standard approaches that ignore information from classical traits. In addition, we show that selective transcriptional profiling can achieve power similar to standard approaches at a fraction of the cost and effort. We also identified three groups of genes which show distinctive patterns with regard to gene expression levels, QTL genotype, and a classical trait. This study represents the first application of selective transcriptional profiling to real data and serves as a template for dissecting gene regulation networks related to a classical trait using the selective transcriptional profiling approach.