Plants have developed effective mechanisms to recognize and respond to infections caused by pathogens. Plant resistance gene analogs (RGAs), as resistance (R) gene candidates, have conserved domains and motifs that play specific roles in pathogens’ resistance. Well-known RGAs are nucleotide binding site leucine rich repeats, receptor like kinases, and receptor like proteins. Others include pentatricopeptide repeats and apoplastic peroxidases. RGAs can be detected using bioinformatics tools based on their conserved structural features. Thousands of RGAs have been identified from sequenced plant genomes. High-density genome-wide RGA genetic maps are useful for designing diagnostic markers and identifying quantitative trait loci (QTL) or markers associated with plant disease resistance. This review focuses on recent advances in structures and mechanisms of RGAs, and their identification from sequenced genomes using bioinformatics tools. Applications in enhancing fine mapping and cloning of plant disease resistance genes are also discussed.

Resistance gene analogs (RGAs), such as NBS-encoding proteins, receptor-like protein kinases (RLKs) and receptor-like proteins (RLPs), are potential R-genes that contain specific conserved domains and motifs. Thus, RGAs can be predicted based on their conserved structural features using bioinformatics tools. Computer programs have been developed for the identification of individual domains and motifs from the protein sequences of RGAs but none offer a systematic assessment of the different types of RGAs. A user-friendly and efficient pipeline is needed for large-scale genome-wide RGA predictions of the growing number of sequenced plant genomes. An integrative pipeline, named RGAugury, was developed to automate RGA prediction. The pipeline first identifies RGA-related protein domains and motifs, namely nucleotide binding site (NB-ARC), leucine rich repeat (LRR), transmembrane (TM), serine/threonine and tyrosine kinase (STTK), lysin motif (LysM), coiled-coil (CC) and Toll/Interleukin-1 receptor (TIR). RGA candidates are identified and classified into four major families based on the presence of combinations of these RGA domains and motifs: NBS-encoding, TM-CC, and membrane associated RLP and RLK. All time-consuming analyses of the pipeline are paralleled to improve performance. The pipeline was evaluated using the well-annotated Arabidopsis genome. A total of 98.5, 85.2, and 100 % of the reported NBS-encoding genes, membrane associated RLPs and RLKs were validated, respectively. The pipeline was also successfully applied to predict RGAs for 50 sequenced plant genomes. A user-friendly web interface was implemented to ease command line operations, facilitate visualization and simplify result management for multiple datasets. RGAugury is an efficiently integrative bioinformatics tool for large scale genome-wide identification of RGAs. It is freely available at Bitbucket: https://bitbucket.org/yaanlpc/rgaugury .

A genome-wide association study (GWAS) was performed on a set of 260 lines which belong to three different bi-parental flax mapping populations. These lines were sequenced to an averaged genome coverage of 19× using the Illumina Hi-Seq platform. Phenotypic data for 11 seed yield and oil quality traits were collected in eight year/location environments. A total of 17,288 single nucleotide polymorphisms were identified, which explained more than 80% of the phenotypic variation for days to maturity (DTM), iodine value (IOD), palmitic (PAL), stearic, linoleic (LIO) and linolenic (LIN) acid contents. Twenty-three unique genomic regions associated with 33 quantitative trait loci (QTL) for the studied traits were detected, thereby validating four genomic regions previously identified. The 33 QTL explained 48–73% of the phenotypic variation for oil content, IOD, PAL, LIO and LIN but only 8–14% for plant height, DTM and seed yield. A genome-wide selective sweep scan for selection signatures detected 114 genomic regions that accounted for 7.82% of the flax pseudomolecule and overlapped with the 11 GWAS-detected genomic regions associated with 18 QTL for 11 traits. The results demonstrate the utility of GWAS combined with selection signatures for dissection of the genetic structure of traits and for pinpointing genomic regions for breeding improvement.

Pasmo (Septoria linicola) is a fungal disease causing major losses in seed yield and quality and stem fibre quality in flax. Pasmo resistance (PR) is quantitative and has low heritability. To improve PR breeding efficiency, the accuracy of genomic prediction (GP) was evaluated using a diverse worldwide core collection of 370 accessions. Four marker sets, including three defined by 500, 134 and 67 previously identified quantitative trait loci (QTL) and one of 52,347 PR-correlated genome-wide single nucleotide polymorphisms, were used to build ridge regression best linear unbiased prediction (RR-BLUP) models using pasmo severity (PS) data collected from field experiments performed during five consecutive years. With five-fold random cross-validation, GP accuracy as high as 0.92 was obtained from the models using the 500 QTL when the average PS was used as the training dataset. GP accuracy increased with training population size, reaching values >0.9 with training population size greater than 185. Linear regression of the observed PS with the number of positive-effect QTL in accessions provided an alternative GP approach with an accuracy of 0.86. The results demonstrate the GP models based on marker information from all identified QTL and the 5-year PS average is highly effective for PR prediction.

Mutations often accompany DNA replication. Since there may be fewer cell cycles per year in the germlines of long-lived than short-lived angiosperms, the genomes of long-lived angiosperms may be diverging more slowly than those of short-lived angiosperms. Here we test this hypothesis. We first constructed a genetic map for walnut, a woody perennial. All linkage groups were short, and recombination rates were greatly reduced in the centromeric regions. We then used the genetic map to construct a walnut bacterial artificial chromosome (BAC) clone-based physical map, which contained 15,203 exonic BAC-end sequences, and quantified with it synteny between the walnut genome and genomes of three long-lived woody perennials, Vitis vinifera, Populus trichocarpa, and Malus domestica, and three short-lived herbs, Cucumis sativus, Medicago truncatula, and Fragaria vesca. Each measure of synteny we used showed that the genomes of woody perennials were less diverged from the walnut genome than those of herbs. We also estimated the nucleotide substitution rate at silent codon positions in the walnut lineage. It was one-fifth and one-sixth of published nucleotide substitution rates in the Medicago and Arabidopsis lineages, respectively. We uncovered a whole-genome duplication in the walnut lineage, dated it to the neighborhood of the Cretaceous-Tertiary boundary, and allocated the 16 walnut chromosomes into eight homoeologous pairs. We pointed out that during polyploidy-dysploidy cycles, the dominant tendency is to reduce the chromosome number. Slow rates of nucleotide substitution are accompanied by slow rates of synteny erosion during genome divergence in woody perennials.

Pseudogenes are paralogs generated from ancestral functional genes (parents) during genome evolution, which contain critical defects in their sequences, such as lacking a promoter, having a premature stop codon or frameshift mutations. Generally, pseudogenes are functionless, but recent evidence demonstrates that some of them have potential roles in regulation. The majority of pseudogenes are generated from functional progenitor genes either by gene duplication (duplicated pseudogenes) or retro-transposition (processed pseudogenes). Pseudogenes are primarily identified by comparison to their parent genes. Bioinformatics tools for pseudogene prediction have been developed, among which PseudoPipe, PSF and Shiu’s pipeline are publicly available. We compared these three tools using the well-annotated Arabidopsis thaliana genome and its known 924 pseudogenes as a test data set. PseudoPipe and Shiu’s pipeline identified ~80% of A. thaliana pseudogenes, of which 94% were shared, while PSF failed to generate adequate results. A need for improvement of the bioinformatics tools for pseudogene prediction accuracy in plant genomes was thus identified, with the ultimate goal of improving the quality of genome annotation in plants.