Wheat Breeding through Genetic and Physical Mapping 2

Following the success of the first topic, the special issue of “Wheat breeding through genetic and physical mapping 2” has been re-proposed in order to keep current the recent advancement in research on genetic and physical mapping of candidate genes for agronomically important traits, in studies of the regulatory sequence for biotic and abiotic stress resistance [...]

Using Crop Databases to Explore Phenotypes: From QTL to Candidate Genes

Seeds, especially those of certain grasses and legumes, provide the majority of the protein and carbohydrates for much of the world’s population. Therefore, improvements in seed quality and yield are important drivers for the development of new crop varieties to feed a growing population. Quantitative Trait Loci (QTL) have been identified for many biologically interesting and agronomically important traits, including many seed quality traits. QTL can help explain the genetic architecture of the traits and can also be used to incorporate traits into new crop cultivars during breeding. Despite the important contributions that QTL have made to basic studies and plant breeding, knowing the exact gene(s) conditioning each QTL would greatly improve our ability to study the underlying genetics, biochemistry and regulatory networks. The data sets needed for identifying these genes are increasingly available and often housed in species- or clade-specific genetics and genomics databases. In this demonstration, we present a generalized walkthrough of how such databases can be used in these studies using SoyBase, the USDA soybean Genetics and Genomics Database, as an example.

The Coordinated KNR6–AGAP–ARF1 Complex Modulates Vegetative and Reproductive Traits by Participating in Vesicle Trafficking in Maize

The KERNEL NUMBER PER ROW6 (KNR6)-mediated phosphorylation of an adenosine diphosphate ribosylation factor (Arf) GTPase-activating protein (AGAP) forms a key regulatory module for the numbers of spikelets and kernels in the ear inflorescences of maize (Zea mays L.). However, the action mechanism of the KNR6–AGAP module remains poorly understood. Here, we characterized the AGAP-recruited complex and its roles in maize cellular physiology and agronomically important traits. AGAP and its two interacting Arf GTPase1 (ARF1) members preferentially localized to the Golgi apparatus. The loss-of-function AGAP mutant produced by CRISPR/Cas9 resulted in defective Golgi apparatus with thin and compact cisternae, together with delayed internalization and repressed vesicle agglomeration, leading to defective inflorescences and roots, and dwarfed plants with small leaves. The weak agap mutant was phenotypically similar to knr6, showing short ears with fewer kernels. AGAP interacted with KNR6, and a double mutant produced shorter inflorescence meristems and mature ears than the single agap and knr6 mutants. We hypothesized that the coordinated KNR6–AGAP–ARF1 complex modulates vegetative and reproductive traits by participating in vesicle trafficking in maize. Our findings provide a novel mechanistic insight into the regulation of inflorescence development, and ear length and kernel number, in maize.

Dissection The Practical Soybean Breeding Pipeline By Developing High Throughput Functional Array ZDX1

Microarray technology facilitates rapid, accurate, and economical genotyping. Here, using resequencing data from 2,214 representative soybean accessions, we developed the ZDX1 high-throughput functional soybean array, containing 158,959 SNPs, covering 90.92% of soybean genes and sites related to agronomically important traits. We genotyped 817 soybean accessions using ZDX1, including parental lines, non-parental lines, and progeny from a practical breeding pipeline. It was clarified that non-parental lines had highest genetic diversity, and 235 SNPs were identified to be fixed in the progeny. The unknown soybean cyst nematode-resistant and early maturity accessions were identified by using allele combinations. Notably, we found that breeding index was a good indicator for progeny selection, in which the superior progeny were derived from the crossing more distantly related parents with at least one parent having a higher breeding index. Based on this rule, two varieties were directionally developed. Meanwhile, redundant parents were screened out and potential combinations were formulated. GBLUP analysis displayed that the markers in genic regions had priority to be higher accuracy on predicting four agronomic traits compared with either whole genome or intergenic markers. Then we used progeny to expand the training population to increase the prediction accuracy of breeding selection by 32.1%. Collectively, our work provided a versatile array for high accuracy selecting and predicting both parents and progeny that can greatly accelerate soybean breeding.

Heterotrimeric G-protein α subunit (RGA1) regulates tiller development, yield, cell wall, nitrogen response and biotic stress in rice

G-proteins are implicated in plant productivity, but their genome-wide roles in regulating agronomically important traits remain uncharacterized. Transcriptomic analyses of rice G-protein alpha subunit mutant (rga1) revealed 2270 differentially expressed genes (DEGs) including those involved in C/N and lipid metabolism, cell wall, hormones and stress. Many DEGs were associated with root, leaf, culm, inflorescence, panicle, grain yield and heading date. The mutant performed better in total weight of filled grains, ratio of filled to unfilled grains and tillers per plant. Protein–protein interaction (PPI) network analysis using experimentally validated interactors revealed many RGA1-responsive genes involved in tiller development. qPCR validated the differential expression of genes involved in strigolactone-mediated tiller formation and grain development. Further, the mutant growth and biomass were unaffected by submergence indicating its role in submergence response. Transcription factor network analysis revealed the importance of RGA1 in nitrogen signaling with DEGs such as Nin-like, WRKY, NAC, bHLH families, nitrite reductase, glutamine synthetase, OsCIPK23 and urea transporter. Sub-clustering of DEGs-associated PPI network revealed that RGA1 regulates metabolism, stress and gene regulation among others. Predicted rice G-protein networks mapped DEGs and revealed potential effectors. Thus, this study expands the roles of RGA1 to agronomically important traits and reveals their underlying processes.

The replicative amplification of MITEs and their impact on rice trait variability

Transposable elements (TEs) are a rich source of genetic variability. Among TEs, Miniature Inverted- repeat Transposable Elements (MITEs) are of particular interest as they are present in high copy numbers in plant genomes and are closely associated with genes. MITEs are deletion derivatives of class II transposons, and can be mobilized by the transposases encoded by the latters through a typical cut-and-paste mechanism. However, this mechanism cannot account for the high copy number MITEs attain in plant genomes, and the mechanism by which MITEs amplify remains elusive.We present here an analysis of 103,109 Transposon Insertion Polymorphisms (TIPs) in 1,059 O. sativa genomes representing the main rice population groups. We show that an important fraction of MITE insertions has been fixed in rice concomitantly with rice domestication. However, another fraction of MITE insertions is present at low frequencies. We performed MITE TIP-GWAS to study the impact of these elements on agronomically important traits and found that these elements uncover more trait associations than SNPs on important phenotypes such as grain width. Finally, using SNP-GWAS and TIP-GWAS we provide evidences of the replicative amplification of MITEs, suggesting a mechanism of amplification uncoupled from the typical cut-and-paste mechanism of class II transposons.

Recent Advances in Molecular Research in Rice: Agronomically Important Traits

Rice (Oryza sativa L [...]

Long-reads assembly of the Brassica napus reference genome, Darmor-bzh

BackgroundThe combination of long-reads and long-range information to produce genome assemblies is now accepted as a common standard. This strategy not only allow to access the gene catalogue of a given species but also reveals the architecture and organisation of chromosomes, including complex regions like telomeres and centromeres. The Brassica genus is not exempt and many assemblies based on long reads are now available. The reference genome for Brassica napus, Darmor-bzh, which was published in 2014, has been produced using short-reads and its contiguity was extremely low if compared to current assemblies of the Brassica genus.FindingsHere, we report the new long-reads assembly of Darmor-bzh genome (Brassica napus) generated by combining long-reads sequencing data, optical and genetic maps. Using the PromethION device and six flowcells, we generated about 16M long-reads representing 93X coverage and more importantly 6X with reads longer than 100Kb. This ultralong-reads dataset allows us to generate one of the most contiguous and complete assembly of a Brassica genome to date (contigs N50 > 10Mb). In addition, we exploited all the advantages of the nanopore technology to detect modified bases and sequence transcriptomic data using direct RNA to annotate the genome and focus on resistance genes.ConclusionUsing these cutting edge technologies, and in particular by relying on all the advantages of the nanopore technology, we provide the most contiguous Brassica napus assembly, a resource that will be valuable for the Brassica community for crop improvement and will facilitate the rapid selection of agronomically important traits.

Boosting predictabilities of agronomic traits in rice using bivariate genomic selection

The multivariate genomic selection (GS) models have not been adequately studied and their potential remains unclear. In this study, we developed a highly efficient bivariate (2D) GS method and demonstrated its significant advantages over the univariate (1D) rival methods using a rice dataset, where four traditional traits (i.e. yield, 1000-grain weight, grain number and tiller number) as well as 1000 metabolomic traits were analyzed. The novelty of the method is the incorporation of the HAT methodology in the 2D BLUP GS model such that the computational efficiency has been dramatically increased by avoiding the conventional cross-validation. The results indicated that (1) the 2D BLUP-HAT GS analysis generally produces higher predictabilities for two traits than those achieved by the analysis of individual traits using 1D GS model, and (2) selected metabolites may be utilized as ancillary traits in the new 2D BLUP-HAT GS method to further boost the predictability of traditional traits, especially for agronomically important traits with low 1D predictabilities.