Latent Dirichlet Allocation Uncovers Spectral Characteristics of Drought Stressed Plants

Understanding the adaptation process of plants to drought stress is essential in improving management practices, breeding strategies as well as engineering viable crops for a sustainable agriculture in the coming decades. Hyper-spectral imaging provides a particularly promising approach to gain such understanding since it allows to discover non-destructively spectral characteristics of plants governed primarily by scattering and absorption characteristics of the leaf internal structure and biochemical constituents. Several drought stress indices have been derived using hyper-spectral imaging. However, they are typically based on few hyper-spectral images only, rely on interpretations of experts, and consider few wavelengths only. In this study, we present the first data-driven approach to discovering spectral drought stress indices, treating it as an unsupervised labeling problem at massive scale. To make use of short range dependencies of spectral wavelengths, we develop an online variational Bayes algorithm for latent Dirichlet allocation with convolved Dirichlet regularizer. This approach scales to massive datasets and, hence, provides a more objective complement to plant physiological practices. The spectral topics found conform to plant physiological knowledge and can be computed in a fraction of the time compared to existing LDA approaches.Comment: Appears in Proceedings of the Twenty-Eighth Conference on Uncertainty in Artificial Intelligence (UAI2012

Experimental evolution in barley – 2 decades of natural adaptation to farming systems

Sustainable food production for a growing world population will pose a central challenge in the coming decades. Organic farming is among the most feasible approaches to achieving this goal if the yield gap to conventional farming can be decreased. However, uncertainties exist whether organic and conventional agro-ecosystems require different breeding strategies. A heterogeneous spring barley population was established between a wild barley and an elite cultivar to examine this question. The population was divided into two sets and sown into an organic and a conventional agro-ecosystem, without any artificial selection for two decades. A fraction of seeds harvested each year was sown in the following year. The parents and five generations from both environments up to the 23rd generation were whole-genome pool-sequenced to identify adaptation patterns towards ecosystem and climate conditions in the allele frequency shifts. Additionally, based on previously published QTLs in barley, a meta-data analysis was conducted to link genomic regions' increased fitness to agronomically related traits

Convergently selected NPF2.12 coordinates root growth andnitrogen use efficiency in wheat and barley

- Understanding the genetic and molecular function of nitrate sensing and acquisition across crop species will accelerate breeding of cultivars with improved nitrogen use efficiency (NUE). - Here, we performed a genome-wide scan using wheat and barley accessions characterized under low and high N inputs that uncovered the NPF2.12 gene, encoding a homolog of the Arabidopsis nitrate transceptor NRT1.6 and other low-affinity nitrate transporters that belong to the MAJOR FACILITATOR SUPERFAMILY. - Next, it is shown that variations in the NPF2.12 promoter correlated with altered NPF2.12 transcript levels where decreased gene expression was measured under low nitrate availability. Multiple field trials revealed a significantly enhanced N content in leaves and grains and NUE in the presence of the elite allele TaNPF2.12TT grown under low N conditions. Furthermore, the nitrate reductase encoding gene NIA1 was up-regulated in npf2.12 mutant upon low nitrate concentrations, thereby resulting in elevated levels of nitric oxide (NO) production. This increase in NO correlated with the higher root growth, nitrate uptake, and N translocation observed in the mutant when compared to wild-type. - The presented data indicate that the elite haplotype alleles of NPF2.12 are convergently selected in wheat and barley that by inactivation indirectly contribute to root growth and NUE by activating NO signaling under low nitrate conditions

Major Novel QTL for Resistance to Cassava Bacterial Blight Identified through a Multi-Environmental Analysis

Cassava, Manihot esculenta Crantz, has been positioned as one of the most promising crops world-wide representing the staple security for more than one billion people mainly in poor countries. Cassava production is constantly threatened by several diseases, including cassava bacterial blight (CBB) caused by Xanthomonas axonopodis pv. manihotis (Xam), it is the most destructive disease causing heavy yield losses. Here, we report the detection and localization on the genetic map of cassava QTL (Quantitative Trait Loci) conferring resistance to CBB. An F1 mapping population of 117 full sibs was tested for resistance to two Xam strains (Xam318 and Xam681) at two locations in Colombia: La Vega, Cundinamarca and Arauca. The evaluation was conducted in rainy and dry seasons and additional tests were carried out under controlled greenhouse conditions. The phenotypic evaluation of the response to Xam revealed continuous variation. Based on composite interval mapping analysis, 5 strain-specific QTL for resistance to Xam explaining between 15.8 and 22.1% of phenotypic variance, were detected and localized on a high resolution SNP-based genetic map of cassava. Four of them show stability among the two evaluated seasons. Genotype by environment analysis detected three QTL by environment interactions and the broad sense heritability for Xam318 and Xam681 were 20 and 53%, respectively. DNA sequence analysis of the QTL intervals revealed 29 candidate defense-related genes (CDRGs), and two of them contain domains related to plant immunity proteins, such as NB-ARC-LRR and WRKY

Multiple alleles for resistance and susceptibility modulate the defense response in the interaction of tetraploid potato (Solanum tuberosum) with Synchytrium endobioticum pathotypes 1, 2, 6 and 18

The obligate biotrophic, soil-borne fungus Synchytrium endobioticum causes wart disease of potato (Solanum tuberosum), which is a serious problem for crop production in countries with moderate climates. S. endobioticum induces hypertrophic cell divisions in plant host tissues leading to the formation of tumor-like structures. Potato wart is a quarantine disease and chemical control is not possible. From 38 S. endobioticum pathotypes occurring in Europe, pathotypes 1, 2, 6 and 18 are the most relevant. Genetic resistance to wart is available but only few current potato varieties are resistant to all four pathotypes. The phenotypic evaluation of wart resistance is laborious, time-consuming and sometimes ambiguous, which makes breeding for resistance difficult. Molecular markers diagnostic for genes for resistance to S. endobioticum pathotypes 1, 2, 6 and 18 would greatly facilitate the selection of new, resistant cultivars. Two tetraploid half-sib families (266 individuals) segregating for resistance to S. endobioticum pathotypes 1, 2, 6 and 18 were produced by crossing a resistant genotype with two different susceptible ones. The families were scored for five different wart resistance phenotypes. The distribution of mean resistance scores was quantitative in both families. Resistance to pathotypes 2, 6 and 18 was correlated and independent from resistance to pathotype 1. DNA pools were constructed from the most resistant and most susceptible individuals and screened with genome wide simple sequence repeat (SSR), inverted simple sequence region (ISSR) and randomly amplified polymorphic DNA (RAPD) markers. Bulked segregant analysis identified three SSR markers that were linked to wart resistance loci (Sen). Sen1-XI on chromosome XI conferred partial resistance to pathotype 1, Sen18-IX on chromosome IX to pathotype 18 and Sen2/6/18-I on chromosome I to pathotypes 2,6 and 18. Additional genotyping with 191 single nucleotide polymorphism (SNP) markers confirmed the localization of the Sen loci. Thirty-three SNP markers linked to the Sen loci permitted the dissection of Sen alleles that increased or decreased resistance to wart. The alleles were inherited from both the resistant and susceptible parents

The Transcriptome of Compatible and Incompatible Interactions of Potato (Solanum tuberosum) with Phytophthora infestans Revealed by DeepSAGE Analysis

Late blight, caused by the oomycete Phytophthora infestans, is the most important disease of potato (Solanum tuberosum). Understanding the molecular basis of resistance and susceptibility to late blight is therefore highly relevant for developing resistant cultivars, either by marker-assissted selection or by transgenic approaches. Specific P. infestans races having the Avr1 effector gene trigger a hypersensitive resistance response in potato plants carrying the R1 resistance gene (incompatible interaction) and cause disease in plants lacking R1 (compatible interaction). The transcriptomes of the compatible and incompatible interaction were captured by DeepSAGE analysis of 44 biological samples comprising five genotypes, differing only by the presence or absence of the R1 transgene, three infection time points and three biological replicates. 30.859 unique 21 base pair sequence tags were obtained, one third of which did not match any known potato transcript sequence. Two third of the tags were expressed at low frequency (<10 tag counts/million). 20.470 unitags matched to approximately twelve thousand potato transcribed genes. Tag frequencies were compared between compatible and incompatible interactions over the infection time course and between compatible and incompatible genotypes. Transcriptional changes were more numerous in compatible than in incompatible interactions. In contrast to incompatible interactions, transcriptional changes in the compatible interaction were observed predominantly for multigene families encoding defense response genes and genes functional in photosynthesis and CO2 fixation. Numerous transcriptional differences were also observed between near isogenic genotypes prior to infection with P. infestans. Our DeepSAGE transcriptome analysis uncovered novel candidate genes for plant host pathogen interactions, examples of which are discussed with respect to possible function

Local and Bayesian Survival FDR Estimations to Identify Reliable Associations in Whole Genome of Bread Wheat

Estimating the FDR significance threshold in genome-wide association studies remains a major challenge in distinguishing true positive hypotheses from false positive and negative errors. Several comparative methods for multiple testing comparison have been developed to determine the significance threshold; however, these methods may be overly conservative and lead to an increase in false negative results. The local FDR approach is suitable for testing many associations simultaneously based on the empirical Bayes perspective. In the local FDR, the maximum likelihood estimator is sensitive to bias when the GWAS model contains two or more explanatory variables as genetic parameters simultaneously. The main criticism of local FDR is that it focuses only locally on the effects of single nucleotide polymorphism (SNP) in tails of distribution, whereas the signal associations are distributed across the whole genome. The advantage of the Bayesian perspective is that knowledge of prior distribution comes from other genetic parameters included in the GWAS model, such as linkage disequilibrium (LD) analysis, minor allele frequency (MAF) and call rate of significant associations. We also proposed Bayesian survival FDR to solve the multi-collinearity and large-scale problems, respectively, in grain yield (GY) vector in bread wheat with large-scale SNP information. The objective of this study was to obtain a short list of SNPs that are reliably associated with GY under low and high levels of nitrogen (N) in the population. The five top significant SNPs were compared with different Bayesian models. Based on the time to events in the Bayesian survival analysis, the differentiation between minor and major alleles within the association panel can be identified

Genetic Parameter and Hyper-Parameter Estimation Underlie Nitrogen Use Efficiency in Bread Wheat

Estimation and prediction play a key role in breeding programs. Currently, phenotyping of complex traits such as nitrogen use efficiency (NUE) in wheat is still expensive, requires high-throughput technologies and is very time consuming compared to genotyping. Therefore, researchers are trying to predict phenotypes based on marker information. Genetic parameters such as population structure, genomic relationship matrix, marker density and sample size are major factors that increase the performance and accuracy of a model. However, they play an important role in adjusting the statistically significant false discovery rate (FDR) threshold in estimation. In parallel, there are many genetic hyper-parameters that are hidden and not represented in the given genomic selection (GS) model but have significant effects on the results, such as panel size, number of markers, minor allele frequency, number of call rates for each marker, number of cross validations and batch size in the training set of the genomic file. The main challenge is to ensure the reliability and accuracy of predicted breeding values (BVs) as results. Our study has confirmed the results of bias–variance tradeoff and adaptive prediction error for the ensemble-learning-based model STACK, which has the highest performance when estimating genetic parameters and hyper-parameters in a given GS model compared to other models

Transcriptome profiling at the transition to the reproductive stage uncovers stage and tissue-specific genes in wheat

Abstract Background The transition from vegetative to floral phase is the result of complex crosstalk of exogenous and endogenous floral integrators. This critical physiological event is the response to environmental interaction, which causes biochemical cascades of reactions at different internal tissues, organs, and releases signals that make the plant moves from vegetative status to a reproductive phase. This network controlling flowering time is not deciphered largely in bread wheat. In this study, a comparative transcriptome analysis at a transition time in combination with genetic mapping was used to identify responsible genes in a stage and tissue-specific manner. For this reason, two winter cultivars that have been bred in Germany showing contrasting and stable heading time in different environments were selected for the analysis. Results In total, 670 and 1075 differentially expressed genes in the shoot apical meristem and leaf tissue, respectively, could be identified in 23 QTL intervals for the heading date. In the transition apex, Histone methylation H3-K36 and regulation of circadian rhythm are both controlled by the same homoeolog genes mapped in QTL TaHd112, TaHd124, and TaHd137. TaAGL14 gene that identifies the floral meristem was mapped in TaHd054 in the double ridge. In the same stage, the homoeolog located on chromosome 7D of FLOWERING TIME LOCUS T mapped on chr 7B, which evolved an antagonist function and acts as a flowering repressor was uncovered. The wheat orthologue of transcription factor ASYMMETRIC LEAVES 1 (AS1) was identified in the late reproductive stage and was mapped in TaHd102, which is strongly associated with heading date. Deletion of eight nucleotides in the AS1 promoter could be identified in the binding site of the SUPPRESSOR OF CONSTANS OVEREXPRESSION 1 (SOC1) gene in the late flowering cultivar. Both proteins AS1 and SOC1 are inducing flowering time in response to gibberellin biosynthesis. Conclusion The global transcriptomic at the transition phase uncovered stage and tissue-specific genes mapped in QTL of heading date in winter wheat. In response to Gibberellin signaling, wheat orthologous transcription factor AS1 is expressed in the late reproductive phase of the floral transition. The locus harboring this gene is the strongest QTL associated with the heading date trait in the German cultivars. Consequently, we conclude that this is another indication of the Gibberellin biosynthesis as the mechanism behind the heading variation in wheat