Natural genetic variation in whole-genome expression in Arabidopsis thaliana: the impact of physiological QTL introgression

Plant biochemical genetics in the multiomics era

Our understanding of plant biology has been revolutionized by modern genetics and biochemistry. However, biochemical genetics can be traced back to the foundation of Mendelian genetics; indeed, one of Mendel's milestone discoveries of seven characteristics of pea plants later came to be ascribed to a mutation in a starch branching enzyme. Here, we review both current and historical strategies for the elucidation of plant metabolic pathways and the genes that encode their component enzymes and regulators. We use this historical review to discuss a range of classical genetic phenomena including epistasis, canalization, and heterosis as viewed through the lens of contemporary high-throughput data obtained via the array of approaches currently adopted in multiomics studies.

Evolutionary and ecological functional genomics, from lab to the wild

Plant phenotypes are the result of both genetic and environmental forces that act to modulate trait expression. Over the last few years, numerous approaches in functional genomics and systems biology have led to a greater understanding of plant phenotypic variation and plant responses to the environment. These approaches, and the questions that they can address, have been loosely termed evolutionary and ecological functional genomics (EEFG), and have been providing key insights on how plants adapt and evolve. In particular, by bringing these studies from the laboratory to the field, EEFG studies allow us to gain greater knowledge of how plants function in their natural contexts.

Investigation of the multifunctional gene AOP3 expands the regulatory network fine-tuning glucosinolate production in Arabidopsis

Quantitative trait loci (QTL) mapping studies enable identification of loci that are part of regulatory networks controlling various phenotypes. Detailed investigations of genes within these loci are required to ultimately understand the function of individual genes and how they interact with other players in the network. In this study, we use transgenic plants in combination with natural variation to investigate the regulatory role of the AOP3 gene found in GS-AOP locus previously suggested to contribute to the regulation of glucosinolate defense compounds. Phenotypic analysis and QTL mapping in F2 populations with different AOP3 transgenes support that the enzymatic function and the AOP3 RNA both play a significant role in controlling glucosinolate accumulation. Furthermore, we find different loci interacting with either the enzymatic activity or the RNA of AOP3 and thereby extend the regulatory network controlling glucosinolate accumulation.

Extensive cross-environment fitness variation lies along few axes of genetic variation in the model alga, Chlamydomonas reinhardtii

Variation is essential to ecological and evolutionary dynamics, but genetic variation of quantitative traits may be concentrated in a limited number of dimensions, constraining ecoevolutionary dynamics. We describe high-dimension variation in natural accessions of the model alga, Chlamydomonas reinhardtii, and test the hypothesis that extensive fitness variation across 30 environments is constrained to a small number of axes. We used high-throughput phenotyping to investigate morphological, fitness, and genotype × environment (G × E) variation in 18 natural C. reinhardtii accessions in 30 environments. The organismal phenotypes of cell cycle, cell size, and phototactic behavior exhibited substantial genetic variation between lines, and we found up to 74-fold fitness variation across accessions and environments. Approximately 47% of the extensive G × E variation is accounted for by the first two principal components (PCs) of the G-matrix corresponding to covariation in metals response, nitrogen availability, or salt and nutrient response. The natural variation of C. reinhardtii accessions supports the hypothesis that, despite abundant genetic variation across single environments, the species' adaptive response should be constrained along few major axes of selection. These results highlight the utility of natural accessions for integrating ecoevolutionary and genetic research.

Cytoplasmic genetic variation and extensive cytonuclear interactions influence natural variation in the metabolome

Understanding genome to phenotype linkages has been greatly enabled by genomic sequencing. However, most genome analysis is typically confined to the nuclear genome. We conducted a metabolomic QTL analysis on a reciprocal RIL population structured to examine how variation in the organelle genomes affects phenotypic variation. This showed that the cytoplasmic variation had effects similar to, if not larger than, the largest individual nuclear locus. Inclusion of cytoplasmic variation into the genetic model greatly increased the explained phenotypic variation. Cytoplasmic genetic variation was a central hub in the epistatic network controlling the plant metabolome. This epistatic influence manifested such that the cytoplasmic background could alter or hide pairwise epistasis between nuclear loci. Thus, cytoplasmic genetic variation plays a central role in controlling natural variation in metabolomic networks. This suggests that cytoplasmic genomes must be included in any future analysis of natural variation. DOI: http://dx.doi.org/10.7554/eLife.00776.001.

Development of a next-generation NIL library in Arabidopsis thaliana for dissecting complex traits

Background

The identification of the loci and specific alleles underlying variation in quantitative traits is an important goal for evolutionary biologists and breeders. Despite major advancements in genomics technology, moving from QTL to causal alleles remains a major challenge in genetics research. Near-isogenic lines are the ideal raw material for QTL validation, refinement of QTL location and, ultimately, gene discovery.

Results

In this study, a population of 75 Arabidopsis thaliana near-isogenic lines was developed from an existing recombinant inbred line (RIL) population derived from a cross between physiologically divergent accessions Kas-1 and Tsu-1. First, a novel algorithm was developed to utilize genome-wide marker data in selecting RILs fully isogenic to Kas-1 for a single chromosome. Seven such RILs were used in 2 generations of crossing to Tsu-1 to create BC1 seed. BC1 plants were genotyped with SSR markers so that lines could be selected that carried Kas-1 introgressions, resulting in a population carrying chromosomal introgressions spanning the genome. BC1 lines were genotyped with 48 genome-wide SSRs to identify lines with a targeted Kas-1 introgression and the fewest genomic introgressions elsewhere. 75 such lines were selected and genotyped at an additional 41 SNP loci and another 930 tags using 2b-RAD genotyping by sequencing. The final population carried an average of 1.35 homozygous and 2.49 heterozygous introgressions per line with average introgression sizes of 5.32 and 5.16 Mb, respectively. In a simple case study, we demonstrate the advantage of maintaining heterozygotes in our library whereby fine-mapping efforts are conducted simply by self-pollination. Crossovers in the heterozygous interval during this single selfing generation break the introgression into smaller, homozygous fragments (sub-NILs). Additionally, we utilize a homozygous NIL for validation of a QTL underlying stomatal conductance, a low heritability trait.

Conclusions

The present results introduce a new and valuable resource to the Brassicaceae research community that enables rapid fine-mapping of candidate loci in parallel with QTL validation. These attributes along with dense marker coverage and genome-wide chromosomal introgressions make this population an ideal starting point for discovery of genes underlying important complex traits of agricultural and ecological significance.

Hierarchical nuclear and cytoplasmic genetic architectures for plant growth and defense within Arabidopsis

To understand how genetic architecture translates between phenotypic levels, we mapped the genetic architecture of growth and defense within the Arabidopsis thaliana Kas × Tsu recombinant inbred line population. We measured plant growth using traditional size measurements and size-corrected growth rates. This population contains genetic variation in both the nuclear and cytoplasmic genomes, allowing us to separate their contributions. The cytoplasmic genome regulated a significant variance in growth but not defense, which was due to cytonuclear epistasis. Furthermore, growth adhered to an infinitesimal model of genetic architecture, while defense metabolism was more of a moderate-effect model. We found a lack of concordance between quantitative trait loci (QTL) regulating defense and those regulating growth. Given the published evidence proving the link between glucosinolates and growth, this is likely a false negative result caused by the limited population size. This size limitation creates an inability to test the entire potential genetic landscape possible between these two parents. We uncovered a significant effect of glucosinolates on growth once we accounted for allelic differences in growth QTLs. Therefore, other growth QTLs can mask the effects of defense upon growth. Investigating direct links across phenotypic hierarchies is fraught with difficulty; we identify issues complicating this analysis.

Mapping salinity tolerance during Arabidopsis thaliana germination and seedling growth

To characterize and dissect genetic variation for salinity tolerance, we assessed variation in salinity tolerance during germination and seedling growth for a worldwide sample of Arabidopsis thaliana accessions. By combining QTL mapping, association mapping and expression data, we identified genomic regions involved in salinity response. Among the worldwide sample, we found germination ability within a moderately saline environment (150 mM NaCl) varied considerable, from >90% among the most tolerant lines to complete inability to germinate among the most susceptible. Our results also demonstrated wide variation in salinity tolerance within A. thaliana RIL populations and identified multiple genomic regions that contribute to this variation. These regions contain known candidate genes, but at least four of the regions contain loci not yet associated with salinity tolerance response phenotypes. Our observations suggest A. thaliana natural variation may be an underutilized resource for investigating salinity stress response.

Advances in genetical genomics of plants

Natural variation provides a valuable resource to study the genetic regulation of quantitative traits. In quantitative trait locus (QTL) analyses this variation, captured in segregating mapping populations, is used to identify the genomic regions affecting these traits. The identification of the causal genes underlying QTLs is a major challenge for which the detection of gene expression differences is of major importance. By combining genetics with large scale expression profiling (i.e. genetical genomics), resulting in expression QTLs (eQTLs), great progress can be made in connecting phenotypic variation to genotypic diversity. In this review we discuss examples from human, mouse, Drosophila, yeast and plant research to illustrate the advances in genetical genomics, with a focus on understanding the regulatory mechanisms underlying natural variation. With their tolerance to inbreeding, short generation time and ease to generate large families, plants are ideal subjects to test new concepts in genetics. The comprehensive resources which are available for Arabidopsis make it a favorite model plant but genetical genomics also found its way to important crop species like rice, barley and wheat. We discuss eQTL profiling with respect to cis and trans regulation and show how combined studies with other ‘omics’ technologies, such as metabolomics and proteomics may further augment current information on transcriptional, translational and metabolomic signaling pathways and enable reconstruction of detailed regulatory networks. The fast developments in the ‘omics’ area will offer great potential for genetical genomics to elucidate the genotype-phenotype relationships for both fundamental and applied research.

RLM3, a potential adaptor between specific TIR-NB-LRR receptors and DZC proteins

In our recent paper, we identified a TIR encoding gene, which is required for resistance against a broad range of necrotrophic fungi. Here we present this finding in a broader perspective and discuss the unique features of this gene which might explain its role as a general regulator of resistance responses against a class of pathogens that have previously not been associated to the classical resistance (R) gene type of defense.

Advances and prospects: biotechnologically improving crop water use efficiency

Bio-water saving can be defined as the reduction of crop water consumption employing biological measures. This is the focus of efforts to save water in agriculture. Different levels of water-use efficiency (WUE) have been developed. The genetic diversity of WUE has been confirmed in several crops. WUE is the basis of bio-watering and physiological WUE is the key. The degree to develop physiological WUE potential decides the performance of bio-watering in the field. During this process, fine management is important. Thus bio-watering is closely related to WUE. Crop WUE has improved and evolved as a result of breeding programs. Many WUE genes have been located in different genomic and aneuploid materials and have been mapped by various molecular markers in a number of crops. Two genes, (Erecta and alx8), which control water use efficiency; have been cloned in Arabidopsis thaliana. Eleven WUE genes have been identified by microarray analysis. Six genes associated with drought resistance and photosynthesis have been transfered into crops which have resulted in improving WUE and drought resistance. WUE is important on the basis of functional identification of more drought resistant gene resources. The popularity on the industrial-scale of transgenic plants is still in its infancy and one of the reasons for this is the lack of knowledge regarding molecular mechanisms and it is a very immature technology. Enhanced agricultural practices and the theoretical aspects of improving crop WUE have been developed and are discussed in this review paper. Rapid progress will be made in bio-water savings and that crop WUE can be substantially improved under both favorable and unfavorable water-limited environments. This will be achieved by a combination of traditional breeding techniques and the introduction of modern biotechnology.

The role of gene expression in ecological speciation

Ecological speciation is the process by which barriers to gene flow between populations evolve due to adaptive divergence via natural selection. A relatively unexplored area in ecological speciation is the role of gene expression. Gene expression may be associated with ecologically important phenotypes not evident from morphology and play a role during colonization of new environments. Here we review two potential roles of gene expression in ecological speciation: (1) its indirect role in facilitating population persistence and (2) its direct role in contributing to genetically based reproductive isolation. We find indirect evidence that gene expression facilitates population persistence, but direct tests are lacking. We also find clear examples of gene expression having effects on phenotypic traits and adaptive genetic divergence, but links to the evolution of reproductive isolation itself remain indirect. Gene expression during adaptive divergence seems to often involve complex genetic architectures controlled by gene networks, regulatory regions, and “eQTL hotspots.” Nonetheless, we review how approaches for isolating the functional mutations contributing to adaptive divergence are proving to be successful. The study of gene expression has promise for increasing our understanding ecological speciation, particularly when integrative approaches are applied.

Exploring genetic and expression differences between physiologically extreme ecotypes: comparative genomic hybridization and gene expression studies of Kas-1 and Tsu-1 accessions of Arabidopsis thaliana

Recent studies have documented remarkable genetic variation among Arabidopsis thaliana accessions collected from diverse habitats. Of particular interest are accessions with putatively locally adapted phenotypes - that is, accessions with attributes that are likely adaptive at their sites of origin. These genotypes may provide insight into the genetic basis of adaptive evolution as well as allow the discovery of genes of ecological importance. We studied the physiology, genome content and gene expression of two physiologically extreme accessions (Tsu-1 from Tsushima, Japan and Kas-1 from Kashmir, India). Our study was conducted under two levels of soil moisture and accompanied by physiological measurements to characterize early responses to soil drying. Genomic hybridizations identified 42,503 single feature polymorphisms (SFP) between accessions, providing an initial screen for genetic differences. Transcript profiling identified a large number (5996) of genes exhibiting constitutive differences in expression including genes involved in many biological pathways. Mild soil drying resulted in only subtle physiological responses but resulted in gene expression changes in hundreds of transcripts, including 352 genes exhibiting differential responses between accessions. Our results highlight the value of genomic studies of natural accessions as well as identify a number of candidate genes underlying physiological differences between Tsu-1 and Kas-1.

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.

NOS2A, TLR4, and IFNGR1 interactions influence pulmonary tuberculosis susceptibility in African-Americans

Tuberculosis (TB) has substantial mortality worldwide with 5-10% of those exposed progressing to active TB disease. Studies in mice and humans indicate that the inducible nitric oxide synthase (iNOS) molecule plays an important role in immune response to TB. A mixed case-control association study of individuals with TB, relatives, or close contact controls was performed in 726 individuals (279 case and 166 control African-Americans; 198 case and 123 control Caucasians). Thirty-nine single nucleotide polymorphisms (SNPs) were selected from the NOS2A gene for single SNP, haplotype, and multilocus interaction analyses with other typed candidate genes using generalized estimating equations. In African-Americans, ten NOS2A SNPs were associated with TB. The strongest associations were observed at rs2274894 (odds ratio (OR) = 1.84, 95% confidence interval (CI) [1.23-2.77], p = 0.003) and rs7215373 (OR = 1.67, 95% CI [1.17-2.37], p = 0.004), both of which passed a false discovery rate correction for multiple comparisons (q* = 0.20). The strongest gene-gene interactions were observed between NOS2A rs2248814 and IFNGR1 rs1327474 (p = 0.0004) and NOS2A rs944722 and IFNGR1 rs1327474 (p = 0.0006). Three other SNPs in NOS2A interacted with TLR4 rs5030729 and five other NOS2A SNPs interacted with IFNGR1 rs1327474. No significant associations were observed in Caucasians. These results suggest that NOS2A variants may contribute to TB susceptibility, particularly in individuals of African descent, and may act synergistically with SNPs in TLR4 and IFNGR1.

Defining gene and QTL networks

Current technologies for high-throughput molecular profiling of large numbers of genetically different individuals offer great potential for elucidating the genotype-to-phenotype relationship. Variation in molecular and phenotypic traits can be correlated to DNA sequence variation using the methods of quantitative trait locus (QTL) mapping. In addition, the correlation structure in the molecular and phenotypic traits can be informative for inferring the underlying molecular networks. For this, new methods are emerging to distinguish among causality, reactivity, or independence of traits based upon logic involving underlying QTL. These methods are becoming increasingly popular in plant genetic studies as well as in studies on many other organisms.

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.

Genetics of drought adaptation in Arabidopsis thaliana II. QTL analysis of a new mapping population, KAS-1 x TSU-1

Despite compelling evidence that adaptation to local climate is common in plant populations, little is known about the evolutionary genetics of traits that contribute to climatic adaptation. A screen of natural accessions of Arabidopsis thaliana revealed Tsu-1 and Kas-1 to be opposite extremes for water-use efficiency and climate at collection sites for these accessions differs greatly. To provide a tool to understand the genetic basis of this putative adaptation, Kas-1 and Tsu-1 were reciprocally crossed to create a new mapping population. Analysis of F(3) families showed segregating variation in both delta(13)C and transpiration rate, and as expected these traits had a negative genetic correlation (r(g)=- 0.3). 346 RILs, 148 with Kas-1 cytoplasm and 198 with Tsu-1 cytoplasm, were advanced to the F(9) and genotyped using 48 microsatellites and 55 SNPs for a total of 103 markers. This mapping population was used for QTL analysis of delta(13)C using F(9) RIL means. Analysis of this reciprocal cross showed a large effect of cytoplasmic background, as well as two QTL for delta(13)C. The Kas-1 x Tsu-1 mapping population provides a powerful new resource for mapping QTL underlying natural variation and for dissecting the genetic basis of water-use efficiency differences.

Natural variation in gene expression between wild and weedy populations of Helianthus annuus

The molecular genetic changes underlying the transformation of wild plants into agricultural weeds are poorly understood. Here we use a sunflower cDNA microarray to detect variation in gene expression between two wild (non-weedy) Helianthus annuus populations from Utah and Kansas and four weedy H. annuus populations collected from agricultural fields in Utah, Kansas, Indiana, and California. When grown in a common growth chamber environment, populations differed substantially in their gene expression patterns, indicating extensive genetic differentiation. Overall, 165 uni-genes, representing approximately 5% of total genes on the array, showed significant differential expression in one or more weedy populations when compared to both wild populations. This subset of genes is enriched for abiotic/biotic stimulus and stress response proteins, which may underlie niche transitions from the natural sites to agricultural fields for H. annuus. However, only a small proportion of the differentially expressed genes overlapped in multiple wild vs. weedy comparisons, indicating that most of the observed expression changes are due to local adaptation or neutral processes, as opposed to parallel genotypic adaptation to agricultural fields. These results are consistent with an earlier phylogeographic study suggesting that weedy sunflowers have evolved multiple times in different regions of the United States and further indicate that the evolution of weedy sunflowers has been accompanied by substantial gene expression divergence in different weedy populations.

RLM3, a TIR domain encoding gene involved in broad-range immunity of Arabidopsis to necrotrophic fungal pathogens

Here, we describe the rapid cloning of a plant gene, Leptosphaeria maculans 3 (RLM3(Col)), which encodes a putative Toll interleukin-1 receptor-nucleotide binding (TIR-NB) class protein, which is involved in defence against the fungal pathogen L. maculans and against three other necrotrophic fungi. We have, through microarray-based case control bulk segregant comparisons of transcriptomes in pools of Col-0 x An-1 progeny, identified the absence of a locus that causes susceptibility in An-1. The significance of this locus on chromosome 4 for L. maculans resistance was supported by PCR-based mapping, and denoted resistance to RLM3(Col). Differential susceptible phenotypes in four independent T-DNA insertion lines support the hypothesis that At4g16990 is required for RLM3(Col) function. The mutants in RLM3(Col) also exhibited an enhanced susceptibility to Botrytis cinerea, Alternaria brassicicola and Alternaria brassicae. Complementations of An-1 and T-DNA mutants using overexpression of a short transcript lacking the NB-ARC domain, or a genomic clone, restored resistance to all necrotrophic fungi. The elevated expression of RLM3(Col) on B. cinerea-susceptible mutants further suggested convergence in signalling and gene regulation between defence against B. cinerea and L. maculans. In the case of L. maculans, RLM3(Col) is required for efficient callose deposition downstream of RLM1(Col).

Stressed genomics-bringing relief to rice fields

Active research in rice genetics aided by full-genome sequence has generated results to address multiple problems caused by biotic and abiotic stresses. The main challenges are achieving stability of resistance against variable biological agents and defining the genetic basis of traits influenced strongly by genotypexenvironment interactions. As shown in bacterial blight disease, detailed knowledge of host-pathogen interactions has enabled a predictive strategy to combine specific genes to provide durable resistance. Large-effect QTLs conferring tolerance to submergence, salinity, and drought have been identified. Marker-aided incorporation of the submergence tolerance gene into popular rice varieties illustrates how gene discovery can be rapidly converted into useful products. Extensive use of parallel whole-genome expression and mapping analyses is expected to improve our understanding of QTL. To accelerate conversion of discoveries into products, much can be gained by using agronomically proven genotypes and by testing in multiple environments.

Identifying the molecular basis of QTLs: eQTLs add a new dimension

Natural genetic variation within plant species is at the core of plant science ranging from agriculture to evolution. Whereas much progress has been made in mapping quantitative trait loci (QTLs) controlling this natural variation, the elucidation of the underlying molecular mechanisms has remained a bottleneck. Recent systems biology tools have significantly shortened the time required to proceed from a mapped locus to testing of candidate genes. These tools enable research on natural variation to move from simple reductionistic studies focused on individual genes to integrative studies connecting molecular variation at multiple loci with physiological consequences. This review focuses on recent examples that demonstrate how expression QTL data can be used for gene discovery and exploited to untangle complex regulatory networks.

Manipulation of thyroid status and/or GH injection alters hepatic gene expression in the juvenile chicken

Both thyroid hormone (T3) and growth hormone (GH) are important regulators of somatic growth in birds and mammals. Although T3-mediated gene transcription is well known, the molecular basis of T3 interaction with GH on growth and development of birds remains unknown. In earlier studies, we discovered that exogenous GH alone increased accumulation of visceral fat in young chickens, while the combination of GH injections and dietary T3 worked synergistically to deplete body fat. In the present study, cDNA microarray and quantitative RT-PCR analyses enabled us to examine hepatic gene expression in young chickens after chronic manipulation of thyroid status and GH injection alone or in combination with T3. Thyroid status modulates expression of common and unique sets of genes involved in a wide range of molecular functions (i.e., energy metabolism, storage and transport, signal transduction, protein turnover and drug detoxification). Hepatic expression of 35 genes was altered by hypothyroidism (e.g., ADFP, ANGPTL3, GSTalpha, CAT, PPARG, HMGCL, GHR, IGF1, STAT3, THRSPalpha), whereas hyperthyroidism affected expression of another cluster of 13 genes (e.g., IGFBP1, KHK, LDHB, BAIA2L1, SULT1B, TRIAD3). Several genes were identified which have not been previously ascribed as T3 responsive (e.g., DEFB9, EPS8L2, ARHGAP1, LASS2, INHBC). Exogenous GH altered expression of 17 genes (e.g., CCAR1, CYP2C45, GYS2, ENOB, HK1, FABP1, SQLE, SOCS2, UPG2). The T3+GH treatment depleted the greatest amount of body fat, where 34 differentially expressed genes were unique to this group (e.g., C/EBP, CDC42EP1, SYDE2, PCK2, PIK4CA, TH1L, GPT2, BHMT). The marked reduction in body fat brought about by the T3+GH synergism could involve modulation of hormone signaling via altered activity of the Ras superfamily of molecular switches, which control diverse biological processes. In conclusion, this study provides the first global analysis of endocrine (T3 and GH) regulation of hepatic gene transcription in the chicken.

Allele-specific expression patterns reveal biases and embryo-specific parent-of-origin effects in hybrid maize

We employed allele-specific expression (ASE) analyses to document biased allelic expression in maize (Zea mays). A set of 316 quantitative ASE assays were used to profile the relative allelic expression in seedling tissue derived from five maize hybrids. The different hybrids included in this study exhibit a range of heterosis levels; however, we did not observe differences in the frequencies of allelic bias. Allelic biases in gene expression were consistently observed for approximately 50% of the genes assayed in hybrid seedlings. The relative proportion of genes that exhibit cis- or trans-acting regulatory variation was very similar among the different genotypes. The cis-acting regulatory variation was more prevalent and resulted in greater expression differences than trans-acting regulatory variation for these genes. The ASE assays were further used to compare the relative expression of the B73 and Mo17 alleles in three tissue types (seedling, immature ear, and embryo) derived from reciprocal hybrids. These comparisons provided evidence for tissue-specific cis-acting variation and for a slight maternal expression bias in approximately 20% of genes in embryo tissue. Collectively, these data provide evidence for prevalent cis-acting regulatory variation that contributes to biased allelic expression between genotypes and between tissues.

Microarray challenges in ecology

Microarrays are used to measure simultaneously the amount of mRNAs transcribed from many genes. They were originally designed for gene expression profiling in relatively simple biological systems, such as cell lines and model systems under constant laboratory conditions. This poses a challenge to ecologists who increasingly want to use microarrays to unravel the genetic mechanisms underlying complex interactions among organisms and between organisms and their environment. Here, we discuss typical experimental and statistical problems that arise when analyzing genome-wide expression profiles in an ecological context. We show that experimental design and environmental confounders greatly influence the identification of candidate genes in ecological microarray studies, and that following several simple recommendations could facilitate the analysis of microarray data in ecological settings.

Interpretation of ANOVA models for microarray data using PCA

MOTIVATION

ANOVA is a technique, which is frequently used in the analysis of microarray data, e.g. to assess the significance of treatment effects, and to select interesting genes based on P-values. However, it does not give information about what exactly is causing the effect. Our purpose is to improve the interpretation of the results from ANOVA on large microarray datasets, by applying PCA on the individual variance components. Interaction effects can be visualized by biplots, showing genes and variables in one plot, providing insight in the effect of e.g. treatment or time on gene expression. Because ANOVA has removed uninteresting sources of variance, the results are much more interpretable than without ANOVA. Moreover, the combination of ANOVA and PCA provides a simple way to select genes, based on the interactions of interest.

RESULTS

It is shown that the components from an ANOVA model can be summarized and visualized with PCA, which improves the interpretability of the models. The method is applied to a real time-course gene expression dataset of mesenchymal stem cells. The dataset was designed to investigate the effect of different treatments on osteogenesis. The biplots generated with the algorithm give specific information about the effects of specific treatments on genes over time. These results are in agreement with the literature. The biological validation with GO annotation from the genes present in the selections shows that biologically relevant groups of genes are selected.

AVAILABILITY

R code with the implementation of the method for this dataset is available from http://www.cac.science.ru.nl under the heading "Software".