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Flowering time is a key life-history trait in the plant life cycle. Most studies to unravel the genetics of flowering time in Arabidopsis thaliana have been performed under greenhouse conditions. Here, we describe a study about the genetics of flowering time that differs from previous studies in two important ways: first, we measure flowering time in a more complex and ecologically realistic environment; and, second, we combine the advantages of genome-wide association (GWA) and traditional linkage (QTL) mapping. Our experiments involved phenotyping nearly 20,000 plants over 2 winters under field conditions, including 184 worldwide natural accessions genotyped for 216,509 SNPs and 4,366 RILs derived from 13 independent crosses chosen to maximize genetic and phenotypic diversity. Based on a photothermal time model, the flowering time variation scored in our field experiment was poorly correlated with the flowering time variation previously obtained under greenhouse conditions, reinforcing previous demonstrations of the importance of genotype by environment interactions in A. thaliana and the need to study adaptive variation under natural conditions. The use of 4,366 RILs provides great power for dissecting the genetic architecture of flowering time in A. thaliana under our specific field conditions. We describe more than 60 additive QTLs, all with relatively small to medium effects and organized in 5 major clusters. We show that QTL mapping increases our power to distinguish true from false associations in GWA mapping. QTL mapping also permits the identification of false negatives, that is, causative SNPs that are lost when applying GWA methods that control for population structure. Major genes underpinning flowering time in the greenhouse were not associated with flowering time in this study. Instead, we found a prevalence of genes involved in the regulation of the plant circadian clock. Furthermore, we identified new genomic regions lacking obvious candidate genes.

Following the domestication of maize over the past approximately 10,000 years, breeders have exploited the extensive genetic diversity of this species to mold its phenotype to meet human needs. The extent of structural variation, including copy number variation (CNV) and presence/absence variation (PAV), which are thought to contribute to the extraordinary phenotypic diversity and plasticity of this important crop, have not been elucidated. Whole-genome, array-based, comparative genomic hybridization (CGH) revealed a level of structural diversity between the inbred lines B73 and Mo17 that is unprecedented among higher eukaryotes. A detailed analysis of altered segments of DNA conservatively estimates that there are several hundred CNV sequences among the two genotypes, as well as several thousand PAV sequences that are present in B73 but not Mo17. Haplotype-specific PAVs contain hundreds of single-copy, expressed genes that may contribute to heterosis and to the extraordinary phenotypic diversity of this important crop.

Potato is the world's fourth largest food crop yet it continues to endure late blight, a devastating disease caused by the Irish famine pathogen Phytophthora infestans. Breeding broad-spectrum disease resistance (R) genes into potato (Solanum tuberosum) is the best strategy for genetically managing late blight but current approaches are slow and inefficient. We used a repertoire of effector genes predicted computationally from the P. infestans genome to accelerate the identification, functional characterization, and cloning of potentially broad-spectrum R genes. An initial set of 54 effectors containing a signal peptide and a RXLR motif was profiled for activation of innate immunity (avirulence or Avr activity) on wild Solanum species and tentative Avr candidates were identified. The RXLR effector family IpiO induced hypersensitive responses (HR) in S. stoloniferum, S. papita and the more distantly related S. bulbocastanum, the source of the R gene Rpi-blb1. Genetic studies with S. stoloniferum showed cosegregation of resistance to P. infestans and response to IpiO. Transient co-expression of IpiO with Rpi-blb1 in a heterologous Nicotiana benthamiana system identified IpiO as Avr-blb1. A candidate gene approach led to the rapid cloning of S. stoloniferum Rpi-sto1 and S. papita Rpi-pta1, which are functionally equivalent to Rpi-blb1. Our findings indicate that effector genomics enables discovery and functional profiling of late blight R genes and Avr genes at an unprecedented rate and promises to accelerate the engineering of late blight resistant potato varieties.

Background Several conventional genomic Bayesian (or no Bayesian) prediction methods have been proposed including the standard additive genetic effect model for which the variance components are estimated with mixed model equations. In recent years, deep learning (DL) methods have been considered in the context of genomic prediction. The DL methods are nonparametric models providing flexibility to adapt to complicated associations between data and output with the ability to adapt to very complex patterns. Main body We review the applications of deep learning (DL) methods in genomic selection (GS) to obtain a meta-picture of GS performance and highlight how these tools can help solve challenging plant breeding problems. We also provide general guidance for the effective use of DL methods including the fundamentals of DL and the requirements for its appropriate use. We discuss the pros and cons of this technique compared to traditional genomic prediction approaches as well as the current trends in DL applications. Conclusions The main requirement for using DL is the quality and sufficiently large training data. Although, based on current literature GS in plant and animal breeding we did not find clear superiority of DL in terms of prediction power compared to conventional genome based prediction models. Nevertheless, there are clear evidences that DL algorithms capture nonlinear patterns more efficiently than conventional genome based. Deep learning algorithms are able to integrate data from different sources as is usually needed in GS assisted breeding and it shows the ability for improving prediction accuracy for large plant breeding data. It is important to apply DL to large training-testing data sets.

Background WRKY proteins comprise a large family of transcription factors that play important roles in many aspects of physiological processes and adaption to environment. However, little information was available about the WRKY genes in pineapple ( Ananas comosus ), an important tropical fruits. The recent release of the whole-genome sequence of pineapple allowed us to perform a genome-wide investigation into the organization and expression profiling of pineapple WRKY genes. Results In the present study, 54 pineapple WRKY (AcWRKY) genes were identified and renamed on the basis of their respective chromosome distribution. According to their structural and phylogenetic features, the 54 AcWRKYs were further classified into three main groups with several subgroups. The segmental duplication events played a major role in the expansion of pineapple WRKY gene family. Synteny analysis and phylogenetic comparison of group III WRKY genes provided deep insight into the evolutionary characteristics of pineapple WRKY genes. Expression profiles derived from transcriptome data and real-time quantitative PCR analysis exhibited distinct expression patterns of AcWRKY genes in various tissues and in response to different abiotic stress and hormonal treatments. Conclusions Fifty four WRKY genes were identified in pineapple and the structure of their encoded proteins, their evolutionary characteristics and expression patterns were examined in this study. This systematic analysis provided a foundation for further functional characterization of WRKY genes with an aim of pineapple crop improvement.

Background Tobacco ( Nicotiana tabacum ) is an important plant model system that has played a key role in the early development of molecular plant biology. The tobacco genome is large and its characterisation challenging because it is an allotetraploid, likely arising from hybridisation between diploid N. sylvestris and N. tomentosiformis ancestors. A draft assembly was recently published for N. tabacum , but because of the aforementioned genome complexities it was of limited utility due to a high level of fragmentation. Results Here we report an improved tobacco genome assembly, which, aided by the application of optical mapping, achieves an N 50 size of 2.17 Mb and enables anchoring of 64% of the genome to pseudomolecules; a significant increase from the previous value of 19%. We use this assembly to identify two homeologous genes that explain the differentiation of the burley tobacco market class, with potential for greater understanding of Nitrogen Utilization Efficiency and Nitrogen Use Efficiency in plants; an important trait for future sustainability of agricultural production. Conclusions Development of an improved genome assembly for N. tabacum enables what we believe to be the first successful map-based gene discovery for the species, and demonstrates the value of an improved assembly for future research in this model and commercially-important species.

A newly developed maize Illumina GoldenGate Assay with 1536 SNPs from 582 loci was used to genotype a highly diverse global maize collection of 632 inbred lines from temperate, tropical, and subtropical public breeding programs. A total of 1229 informative SNPs and 1749 haplotypes within 327 loci was used to estimate the genetic diversity, population structure, and familial relatedness. Population structure identified tropical and temperate subgroups, and complex familial relationships were identified within the global collection. Linkage disequilibrium (LD) was measured overall and within chromosomes, allelic frequency groups, subgroups related by geographic origin, and subgroups of different sample sizes. The LD decay distance differed among chromosomes and ranged between 1 to 10 kb. The LD distance increased with the increase of minor allelic frequency (MAF), and with smaller sample sizes, encouraging caution when using too few lines in a study. The LD decay distance was much higher in temperate than in tropical and subtropical lines, because tropical and subtropical lines are more diverse and contain more rare alleles than temperate lines. A core set of inbreds was defined based on haplotypes, and 60 lines capture 90% of the haplotype diversity of the entire panel. The defined core sets and the entire collection can be used widely for different research targets.

Photosynthetic diatoms are exposed to rapid and unpredictable changes in irradiance and spectral quality, and must be able to acclimate their light harvesting systems to varying light conditions. Molecular mechanisms behind light acclimation in diatoms are largely unknown. We set out to investigate the mechanisms of high light acclimation in Phaeodactylum tricornutum using an integrated approach involving global transcriptional profiling, metabolite profiling and variable fluorescence technique. Algae cultures were acclimated to low light (LL), after which the cultures were transferred to high light (HL). Molecular, metabolic and physiological responses were studied at time points 0.5 h, 3 h, 6 h, 12 h, 24 h and 48 h after transfer to HL conditions. The integrated results indicate that the acclimation mechanisms in diatoms can be divided into an initial response phase (0-0.5 h), an intermediate acclimation phase (3-12 h) and a late acclimation phase (12-48 h). The initial phase is recognized by strong and rapid regulation of genes encoding proteins involved in photosynthesis, pigment metabolism and reactive oxygen species (ROS) scavenging systems. A significant increase in light protecting metabolites occur together with the induction of transcriptional processes involved in protection of cellular structures at this early phase. During the following phases, the metabolite profiling display a pronounced decrease in light harvesting pigments, whereas the variable fluorescence measurements show that the photosynthetic capacity increases strongly during the late acclimation phase. We show that P. tricornutum is capable of swift and efficient execution of photoprotective mechanisms, followed by changes in the composition of the photosynthetic machinery that enable the diatoms to utilize the excess energy available in HL. Central molecular players in light protection and acclimation to high irradiance have been identified.

Background Suaeda glauca ( S. glauca ) is a halophyte widely distributed in saline and sandy beaches, with strong saline-alkali tolerance. It is also admired as a landscape plant with high development prospects and scientific research value. The S. glauca chloroplast (cp) genome has recently been reported; however, the mitochondria (mt) genome is still unexplored. Results The mt genome of S. glauca were assembled based on the reads from Pacbio and Illumina sequencing platforms. The circular mt genome of S. glauca has a length of 474,330 bp. The base composition of the S. glauca mt genome showed A (28.00%), T (27.93%), C (21.62%), and G (22.45%). S. glauca mt genome contains 61 genes, including 27 protein-coding genes, 29 tRNA genes, and 5 rRNA genes. The sequence repeats, RNA editing, and gene migration from cp to mt were observed in S. glauca mt genome. Phylogenetic analysis based on the mt genomes of S. glauca and other 28 taxa reflects an exact evolutionary and taxonomic status of S. glauca . Furthermore, the investigation on mt genome characteristics, including genome size, GC contents, genome organization, and gene repeats of S. gulaca genome, was investigated compared to other land plants, indicating the variation of the mt genome in plants. However, the subsequently Ka/Ks analysis revealed that most of the protein-coding genes in mt genome had undergone negative selections, reflecting the importance of those genes in the mt genomes. Conclusions In this study, we reported the mt genome assembly and annotation of a halophytic model plant S. glauca. The subsequent analysis provided us a comprehensive understanding of the S. glauca mt genome, which might facilitate the research on the salt-tolerant plant species.

Background Plant mitochondrial genomes (mitogenomes) can be structurally complex while their size can vary from ~ 222 Kbp in Brassica napus to 11.3 Mbp in Silene conica . To date, in comparison with the number of plant species, only a few plant mitogenomes have been sequenced and released, particularly for conifers (the Pinaceae family). Conifers cover an ancient group of land plants that includes about 600 species, and which are of great ecological and economical value. Among them, Siberian larch ( Larix sibirica Ledeb.) represents one of the keystone species in Siberian boreal forests. Yet, despite its importance for evolutionary and population studies, the mitogenome of Siberian larch has not yet been assembled and studied. Results Two sources of DNA sequences were used to search for mitochondrial DNA (mtDNA) sequences: mtDNA enriched samples and nucleotide reads generated in the de novo whole genome sequencing project, respectively. The assembly of the Siberian larch mitogenome contained nine contigs, with the shortest and the largest contigs being 24,767 bp and 4,008,762 bp, respectively. The total size of the genome was estimated at 11.7 Mbp. In total, 40 protein-coding, 34 tRNA, and 3 rRNA genes and numerous repetitive elements (REs) were annotated in this mitogenome. In total, 864 C-to-U RNA editing sites were found for 38 out of 40 protein-coding genes. The immense size of this genome, currently the largest reported, can be partly explained by variable numbers of mobile genetic elements, and introns, but unlikely by plasmid-related sequences. We found few plasmid-like insertions representing only 0.11% of the entire Siberian larch mitogenome. Conclusions Our study showed that the size of the Siberian larch mitogenome is much larger than in other so far studied Gymnosperms, and in the same range as for the annual flowering plant Silene conica (11.3 Mbp). Similar to other species, the Siberian larch mitogenome contains relatively few genes, and despite its huge size, the repeated and low complexity regions cover only 14.46% of the mitogenome sequence.