Global eQTL mapping reveals the complex genetic architecture of transcript-level variation in Arabidopsis.

The genetic architecture of transcript-level variation is largely unknown. The genetic determinants of transcript-level variation were characterized in a recombinant inbred line (RIL) population (n = 211) of Arabidopsis thaliana using whole-genome microarray analysis and expression quantitative trait loci (eQTL) mapping of transcript levels as expression traits (e-traits). Genetic control of transcription was highly complex: one-third of the quantitatively controlled transcripts/e-traits were regulated by cis-eQTL, and many trans-eQTL mapped to hotspots that regulated hundreds to thousands of e-traits. Several thousand eQTL of large phenotypic effect were detected, but almost all (93%) of the 36,871 eQTL were associated with small phenotypic effects (R(2) < 0.3). Many transcripts/e-traits were controlled by multiple eQTL with opposite allelic effects and exhibited higher heritability in the RILs than their parents, suggesting nonadditive genetic variation. To our knowledge, this is the first large-scale global eQTL study in a relatively large plant mapping population. It reveals that the genetic control of transcript level is highly variable and multifaceted and that this complexity may be a general characteristic of eukaryotes.

A comparison of microarray and MPSS technology platforms for expression analysis of Arabidopsis.

BACKGROUND: Several high-throughput technologies can measure in parallel the abundance of many mRNA transcripts within a sample. These include the widely-used microarray as well as the more recently developed methods based on sequence tag abundances such as the Massively Parallel Signature Sequencing (MPSS) technology. A comparison of microarray and MPSS technologies can help to establish the metrics for data comparisons across these technology platforms and determine some of the factors affecting the measurement of mRNA abundances using different platforms. RESULTS: We compared transcript abundance (gene expression) measurement data obtained using Affymetrix and Agilent microarrays with MPSS data. All three technologies were used to analyze the same set of mRNA samples; these samples were extracted from various wild type Arabidopsis thaliana tissues and floral mutants. We calculated correlations and used clustering methodology to compare the normalized expression data and expression ratios across samples and technologies. Abundance expression measurements were more similar between different samples measured by the same technology than between the same sample measured by different technologies. However, when expression ratios were employed, samples measured by different technologies were found to cluster together more frequently than with abundance expression levels.Furthermore, the two microarray technologies were more consistent with each other than with MPSS. We also investigated probe-position effects on Affymetrix data and tag-position effects in MPSS. We found a similar impact on Affymetrix and MPSS measurements, which suggests that these effects were more likely a characteristic of the RNA sample rather than technology-specific biases. CONCLUSION: Comparisons of transcript expression ratios showed greater consistency across platforms than measurements of transcript abundance. In addition, for measurements based on abundances, technology differences can mask the impact of biological differences between samples and tissues.

Global expression analysis of nucleotide binding site-leucine rich repeat-encoding and related genes in Arabidopsis.

BACKGROUND: Nucleotide binding site-leucine rich repeat (NBS-LRR)-encoding genes comprise the largest class of plant disease resistance genes. The 149 NBS-LRR-encoding genes and the 58 related genes that do not encode LRRs represent approximately 0.8% of all ORFs so far annotated in Arabidopsis ecotype Col-0. Despite their prevalence in the genome and functional importance, there was little information regarding expression of these genes. RESULTS: We analyzed the expression patterns of approximately 170 NBS-LRR-encoding and related genes in Arabidopsis Col-0 using multiple analytical approaches: expressed sequenced tag (EST) representation, massively parallel signature sequencing (MPSS), microarray analysis, rapid amplification of cDNA ends (RACE) PCR, and gene trap lines. Most of these genes were expressed at low levels with a variety of tissue specificities. Expression was detected by at least one approach for all but 10 of these genes. The expression of some but not the majority of NBS-LRR-encoding and related genes was affected by salicylic acid (SA) treatment; the response to SA varied among different accessions. An analysis of previously published microarray data indicated that ten NBS-LRR-encoding and related genes exhibited increased expression in wild-type Landsberg erecta (Ler) after flagellin treatment. Several of these ten genes also showed altered expression after SA treatment, consistent with the regulation of R gene expression during defense responses and overlap between the basal defense response and salicylic acid signaling pathways. Enhancer trap analysis indicated that neither jasmonic acid nor benzothiadiazole (BTH), a salicylic acid analog, induced detectable expression of the five NBS-LRR-encoding genes and one TIR-NBS-encoding gene tested; however, BTH did induce detectable expression of the other TIR-NBS-encoding gene analyzed. Evidence for alternative mRNA polyadenylation sites was observed for many of the tested genes. Evidence for alternative splicing was found for at least 12 genes, 11 of which encode TIR-NBS-LRR proteins. There was no obvious correlation between expression pattern, phylogenetic relationship or genomic location of the NBS-LRR-encoding and related genes studied. CONCLUSION: Transcripts of many NBS-LRR-encoding and related genes were defined. Most were present at low levels and exhibited tissue-specific expression patterns. Expression data are consistent with most Arabidopsis NBS-LRR-encoding and related genes functioning in plant defense responses but do not preclude other biological roles.

Genomic survey of gene expression diversity in Arabidopsis thaliana.

Differential gene expression controls variation in numerous plant traits, such as flowering time and plant/pest interactions, but little is known about the genomic distribution of the determinants of transcript levels and their associated variation. Affymetrix ATH1 GeneChip microarrays representing 22,810 genes were used to survey the transcriptome of seven Arabidopsis thaliana accessions in the presence and absence of exogenously applied salicylic acid (SA). These accessions encompassed approximately 80% of the moderate- to high-frequency nucleotide polymorphisms in Arabidopsis. A factorial design, consisting of three biological replicates per accession for the two treatments at three time points (4, 28, and 52 hr post-treatment), and a total of 126 microarrays were used. Between any pair of Arabidopsis accessions, we detected on average 2234 genes (ranging from 1428 to 3334) that were significantly differentially expressed under the conditions of this experiment, using a split-plot analysis of variance. Upward of 6433 genes were differentially expressed between at least one pair of accessions. These results suggest that analysis of additional genetic, developmental, and environmental conditions may show that a significant fraction of the Arabidopsis genome is differentially expressed. Examination of sequence diversity demonstrated a significant positive association with diversity in gene expression.

Identification of QTLs controlling gene expression networks defined a priori.

BACKGROUND: Gene expression microarrays allow the quantification of transcript accumulation for many or all genes in a genome. This technology has been utilized for a range of investigations, from assessments of gene regulation in response to genetic or environmental fluctuation to global expression QTL (eQTL) analyses of natural variation. Current analysis techniques facilitate the statistical querying of individual genes to evaluate the significance of a change in response, also known as differential expression. Since genes are also known to respond as groups due to their membership in networks, effective approaches are needed to investigate transcriptome variation as related to gene network responses. RESULTS: We describe a statistical approach that is capable of assessing higher-order a priori defined gene network response, as measured by microarrays. This analysis detected significant network variation between two Arabidopsis thaliana accessions, Bay-0 and Shahdara. By extending this approach, we were able to identify eQTLs controlling network responses for 18 out of 20 a priori-defined gene networks in a recombinant inbred line population derived from accessions Bay-0 and Shahdara. CONCLUSION: This approach has the potential to be expanded to facilitate direct tests of the relationship between phenotypic trait and transcript genetic architecture. The use of a priori definitions for network eQTL identification has enormous potential for providing direction toward future eQTL analyses.

Natural variation among Arabidopsis thaliana accessions for transcriptome response to exogenous salicylic acid.

Little is known about how gene expression variation within a given species controls phenotypic variation under different treatments or environments. Here, we surveyed the transcriptome response of seven diverse Arabidopsis thaliana accessions in response to two treatments: the presence and absence of exogenously applied salicylic acid (SA), an important signaling molecule in plant defense. A factorial experiment was conducted with three biological replicates per accession with and without applications of SA and sampled at three time points posttreatment. Transcript level data from Affymetrix ATH1 microarrays were analyzed on both per-gene and gene-network levels to detect expression level polymorphisms associated with SA response. Significant variation in transcript levels for response to SA was detected among the accessions, with relatively few genes responding similarly across all accessions and time points. Twenty-five of 54 defined gene networks identified from other microarray studies (pathogen-challenged Columbia [Col-0]) showed a significant response to SA in one or more accessions. A comparison of gene-network relationships in our data to the pathogen-challenged Col-0 data demonstrated a higher-order conservation of linkages between defense response gene networks. Cvi-1 and Mt-0 appeared to have globally different SA responsiveness in comparison to the other five accessions. Expression level polymorphisms for SA response were abundant at both individual gene and gene-network levels in the seven accessions, suggesting that natural variation for SA response is prevalent in Arabidopsis.

High-density haplotyping with microarray-based expression and single feature polymorphism markers in Arabidopsis.

Expression microarrays hybridized with RNA can simultaneously provide both phenotypic (gene expression) and genotypic (marker) data. We developed two types of genetic markers from Affymetrix GeneChip expression data to generate detailed haplotypes for 148 recombinant inbred lines (RILs) derived from Arabidopsis thaliana accessions Bayreuth and Shahdara. Gene expression markers (GEMs) are based on differences in transcript levels that exhibit bimodal distributions in segregating progeny, while single feature polymorphism (SFP) markers rely on differences in hybridization to individual oligonucleotide probes. Unlike SFPs, GEMs can be derived from any type of DNA-based expression microarray. Our method identifies SFPs independent of a gene's expression level. Alleles for each GEM and SFP marker were ascertained with GeneChip data from parental accessions as well as RILs; a novel algorithm for allele determination using RIL distributions capitalized on the high level of genetic replication per locus. GEMs and SFP markers provided robust markers in 187 and 968 genes, respectively, which allowed estimation of gene order consistent with that predicted from the Col-0 genomic sequence. Using microarrays on a population to simultaneously measure gene expression variation and obtain genotypic data for a linkage map will facilitate expression QTL analyses without the need for separate genotyping. We have demonstrated that gene expression measurements from microarrays can be leveraged to identify polymorphisms across the genome and can be efficiently developed into genetic markers that are verifiable in a large segregating RIL population. Both marker types also offer opportunities for massively parallel mapping in unsequenced and less studied species.

Evaluation of AFLPs for germplasm fingerprinting and assessment of genetic diversity in cultivars of tomato (Lycopersicon esculentum L.).

Cultivated tomato (L. esculentum L.) germplasm exhibits limited genetic variation compared with wild Lycopersicon species. Amplified fragment length polymorphism (AFLP) markers were used to evaluate genetic variation among 74 cultivars, primarily from California, and to fingerprint germplasm to determine if cultivar-specific patterns could be obtained. All 74 cultivars were genotyped using 26 AFLP primer combinations; of the 1092 bands scored, 102 AFLP bands (9.3%) were polymorphic. Pair-wise genetic similarity coefficients (Jaccard and Nei-Li) were calculated. Jaccard coefficients varied from 0.16 to 0.98 among cultivar pairs, and 72% of pair-wise comparisons exceeded 0.5. UPGMA (unweighted pair-group method with arithmetic averaging) clustering and principle component analysis revealed four main clusters, I-IV; most modern hybrid cultivars grouped in II, whereas most vintage cultivars grouped in I. Clusters III and IV contained three and two cultivars, respectively. Some groups of cultivars closely related by pedigree exhibited high bootstrap values, but lower values (<50%) were obtained for cluster II and its four subgroups. Unique fingerprints for all 74 cultivars were obtained by a minimum of seven AFLP primer pairs, despite inclusion of some closely related cultivars. This study demonstrated that AFLP markers are effective for obtaining unique fingerprints of, and assessing genetic diversity among, tomato cultivars.