Redundancy in Genotyping Arrays

Despite their unprecedented density, current SNP genotyping arrays contain large amounts of redundancy, with up to 40 oligonucleotide features used to query each SNP. By using publicly available reference genotype data from the International HapMap, we show that 93.6% sensitivity at <5% false positive rate can be obtained with only four probes per SNP, compared with 98.3% with the full data set. Removal of this redundancy will allow for more comprehensive whole-genome association studies with increased SNP density and larger sample sizes.</p

Genome-wide association studies in plants: the missing heritability is in the field

Genome-wide association studies (GWAS) have been even more successful in plants than in humans. Mapping approaches can be extended to dissect adaptive genetic variation from structured background variation in an ecological context

Natural Genetic Variation for Growth and Development Revealed by High-Throughput Phenotyping in Arabidopsis thaliana

Leaf growth and development determines a plant’s capacity for photosynthesis and carbon fixation. These morphological traits are the integration of genetic and environmental factors through time. Yet fine dissection of the developmental genetic basis of leaf expansion throughout a growing season is difficult, due to the complexity of the trait and the need for real time measurement. In this study, we developed a time-lapse image analysis approach, which traces leaf expansion under seasonal light variation. Three growth traits, rosette leaf area, circular area, and their ratio as compactness, were measured and normalized on a linear timescale to control for developmental heterogeneity. We found high heritability for all growth traits that changed over time. Our study highlights a cost-effective, high-throughput phenotyping approach that facilitates the dissection of genetic basis of plant shoot growth and development under dynamic environmental conditions

Global Analysis of Genetic, Epigenetic and Transcriptional Polymorphisms in <i>Arabidopsis thaliana</i> Using Whole Genome Tiling Arrays

Whole genome tiling arrays provide a high resolution platform for profiling of genetic, epigenetic, and gene expression polymorphisms. In this study we surveyed natural genomic variation in cytosine methylation among Arabidopsis thaliana wild accessions Columbia (Col) and Vancouver (Van) by comparing hybridization intensity difference between genomic DNA digested with either methylation-sensitive (HpaII) or -insensitive (MspI) restriction enzyme. Single Feature Polymorphisms (SFPs) were assayed on a full set of 1,683,620 unique features of Arabidopsis Tiling Array 1.0F (Affymetrix), while constitutive and polymorphic CG methylation were assayed on a subset of 54,519 features, which contain a 5′CCGG3′ restriction site. 138,552 SFPs (1% FDR) were identified across enzyme treatments, which preferentially accumulated in pericentromeric regions. Our study also demonstrates that at least 8% of all analyzed CCGG sites were constitutively methylated across the two strains, while about 10% of all analyzed CCGG sites were differentially methylated between the two strains. Within euchromatin arms, both constitutive and polymorphic CG methylation accumulated in central regions of genes but under-represented toward the 5′ and 3′ ends of the coding sequences. Nevertheless, polymorphic methylation occurred much more frequently in gene ends than constitutive methylation. Inheritance of methylation polymorphisms in reciprocal F1 hybrids was predominantly additive, with F1 plants generally showing levels of methylation intermediate between the parents. By comparing gene expression profiles, using matched tissue samples, we found that magnitude of methylation polymorphism immediately upstream or downstream of the gene was inversely correlated with the degree of expression variation for that gene. In contrast, methylation polymorphism within genic region showed weak positive correlation with expression variation. Our results demonstrated extensive genetic and epigenetic polymorphisms between Arabidopsis accessions and suggested a possible relationship between natural CG methylation variation and gene expression variation.</p

A chromatin modifying enzyme, SDG8, is involved in morphological, gene expression, and epigenetic responses to mechanical stimulation

Thigmomorphogenesis is viewed as being a response process of acclimation to short repetitive bursts of mechanical stimulation or touch. The underlying molecular mechanisms that coordinate changes in how touch signals lead to long-term morphological changes are enigmatic. Touch responsive gene expression is rapid and transient, and no transcription factor or DNA regulatory motif has been reported that could confer a genome wide mechanical stimulus. We report here on a chromatin modifying enzyme, SDG8/ASHH2, which can regulate the expression of many touch responsive genes identified in Arabidopsis. SDG8 is required for the permissive expression of touch induced genes; and the loss of function of sdg8 perturbs the maximum levels of induction on selected touch gene targets. SDG8 is required to maintain permissive H3K4 trimethylation marks surrounding the Arabidopsis touch-inducible gene TOUCH 3 (TCH3), which encodes a calmodulin-like protein (CML12). The gene neighboring was also slightly down regulated, revealing a new target for SDG8 mediated chromatin modification. Finally, sdg8 mutants show perturbed morphological response to wind-agitated mechanical stimuli, implicating an epigenetic memory-forming process in the acclimation response of thigmomorphogenesis

Genome-Wide Expression Profiling of the <i>Arabidopsis</i> Female Gametophyte Identifies Families of Small, Secreted Proteins

The female gametophyte of flowering plants, the embryo sac, develops within the diploid (sporophytic) tissue of the ovule. While embryo sac–expressed genes are known to be required at multiple stages of the fertilization process, the set of embryo sac–expressed genes has remained poorly defined. In particular, the set of genes responsible for mediating intracellular communication between the embryo sac and the male gametophyte, the pollen grain, is unknown. We used high-throughput cDNA sequencing and whole-genome tiling arrays to compare gene expression in wild-type ovules to that in dif1 ovules, which entirely lack embryo sacs, and myb98 ovules, which are impaired in pollen tube attraction. We identified nearly 400 genes that are downregulated in dif1 ovules. Seventy-eight percent of these embryo sac–dependent genes were predicted to encode for secreted proteins, and 60% belonged to multigenic families. Our results define a large number of candidate extracellular signaling molecules that may act during embryo sac development or fertilization; less than half of these are represented on the widely used ATH1 expression array. In particular, we found that 37 out of 40 genes encoding Domain of Unknown Function 784 (DUF784) domains require the synergid-specific transcription factor MYB98 for expression. Several DUF784 genes were transcribed in synergid cells of the embryo sac, implicating the DUF784 gene family in mediating late stages of embryo sac development or interactions with pollen tubes. The coexpression of highly similar proteins suggests a high degree of functional redundancy among embryo sac genes.</p

HOME: A histogram based machine learning approach for effective identification of differentially methylated regions

Background The development of whole genome bisulfite sequencing has made it possible to identify methylation differences at single base resolution throughout an entire genome. However, a persistent challenge in DNA methylome analysis is the accurate identification of differentially methylated regions (DMRs) between samples. Sensitive and specific identification of DMRs among different conditions requires accurate and efficient algorithms, and while various tools have been developed to tackle this problem, they frequently suffer from inaccurate DMR boundary identification and high false positive rate. Results We present a novel Histogram Of MEthylation (HOME) based method that takes into account the inherent difference in the distribution of methylation levels between DMRs and non-DMRs to discriminate between the two using a Support Vector Machine. We show that generated features used by HOME are dataset-independent such that a classifier trained on, for example, a mouse methylome training set of regions of differentially accessible chromatin, can be applied to any other organism’s dataset and identify accurate DMRs. We demonstrate that DMRs identified by HOME exhibit higher association with biologically relevant genes, processes, and regulatory events compared to the existing methods. Moreover, HOME provides additional functionalities lacking in most of the current DMR finders such as DMR identification in non-CG context and time series analysis. HOME is freely available at https://github.com/ListerLab/HOME . Conclusion HOME produces more accurate DMRs than the current state-of-the-art methods on both simulated and biological datasets. The broad applicability of HOME to identify accurate DMRs in genomic data from any organism will have a significant impact upon expanding our knowledge of how DNA methylation dynamics affect cell development and differentiation.This work was supported by the Australian Research Council (ARC) Centre of Excellence program in Plant Energy Biology (CE140100008). RL was supported by a Sylvia and Charles Viertel Senior Medical Research Fellowship, ARC Future Fellowship (FT120100862), and Howard Hughes Medical Institute International Research Scholarship (RL

Within and between Whorls: Comparative Transcriptional Profiling of <i>Aquilegia</i> and <i>Arabidopsis</i>

Background: The genus Aquilegia is an emerging model system in plant evolutionary biology predominantly because of its wide variation in floral traits and associated floral ecology. The anatomy of the Aquilegia flower is also very distinct. There are two whorls of petaloid organs, the outer whorl of sepals and the second whorl of petals that form nectar spurs, as well as a recently evolved fifth whorl of staminodia inserted between stamens and carpels.Methodology/Principal Findings: We designed an oligonucleotide microarray based on EST sequences from a mixed tissue, normalized cDNA library of an A. formosa x A. pubescens F2 population representing 17,246 unigenes. We then used this array to analyze floral gene expression in late pre-anthesis stage floral organs from a natural A. formosa population. In particular, we tested for gene expression patterns specific to each floral whorl and to combinations of whorls that correspond to traditional and modified ABC model groupings. Similar analyses were performed on gene expression data of Arabidopsis thaliana whorls previously obtained using the Ath1 gene chips (data available through The Arabidopsis Information Resource).Conclusions/Significance: Our comparative gene expression analyses suggest that 1) petaloid sepals and petals of A. formosa share gene expression patterns more than either have organ-specific patterns, 2) petals of A. formosa and A. thaliana may be independently derived, 3) staminodia express B and C genes similar to stamens but the staminodium genetic program has also converged on aspects of the carpel program and 4) staminodia have unique up-regulation of regulatory genes and genes that have been implicated with defense against microbial infection and herbivory. Our study also highlights the value of comparative gene expression profiling and the Aquilegia microarray in particular for the study of floral evolution and ecology.</p

Novel Resampling Improves Statistical Power for Multiple-Trait QTL Mapping

Multiple-trait analysis typically employs models that associate a quantitative trait locus (QTL) with all of the traits. As a result, statistical power for QTL detection may not be optimal if the QTL contributes to the phenotypic variation in only a small proportion of the traits. Excluding QTL effects that contribute little to the test statistic can improve statistical power. In this article, we show that an optimal power can be achieved when the number of QTL effects is best estimated, and that a stringent criterion for QTL effect selection may improve power when the number of QTL effects is small but can reduce power otherwise. We investigate strategies for excluding trivial QTL effects, and propose a method that improves statistical power when the number of QTL effects is relatively small, and fairly maintains the power when the number of QTL effects is large. The proposed method first uses resampling techniques to determine the number of nontrivial QTL effects, and then selects QTL effects by the backward elimination procedure for significance test. We also propose a method for testing QTL-trait associations that are desired for biological interpretation in applications. We validate our methods using simulations and Arabidopsis thaliana transcript data