Theoretical and Applied Genetics

Published by Springer Nature
Online ISSN: 1432-2242
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  • Panfeng GuanPanfeng Guan
  • Xiaohua LiXiaohua Li
  • Lei ZhuangLei Zhuang
  • [...]
  • Xingwei ZhengXingwei Zheng
Key message Genetic architecture controlling grain lutein content of common wheat was investigated through an integration of genome-wide association study (GWAS) and linkage analysis. Putative candidate genes involved in carotenoid metabolism and regulation were identified, which provide a basis for gene cloning and development of nutrient-enriched wheat varieties through molecular breeding. Abstract Lutein, known as ‘the eye vitamin’, is an important component of wheat nutritional and end-use quality. However, the genetic manipulation of grain lutein content (LUC) in common wheat has not previously been well studied. Here, quantitative trait loci (QTL) associated with the LUC measured by high performance liquid chromatography (HPLC) were first identified by integrating a genome-wide association study (GWAS) and linkage mapping. A Chinese wheat mini-core collection (MCC) of 262 accessions and a doubled haploid (DH) population derived from Jinchun 7 and L1219 were genotyped using the 90K SNP array. A total of 124 significant marker-trait associations (MTAs) on all 21 wheat chromosomes except for 1A, 4D, and 5B that formed 58 QTL were detected. Among them, six stable QTL were identified on chromosomes 2AL, 2DS, 3BL, 3DL, 7AL, and 7BS. Meanwhile, three of the ten QTL identified in the DH population, QLuc.5A.1 and QLuc.5A.2 on chromosome 5AL and QLuc.6A.2 on 6AS, were stable and independently explained 5.58–10.86% of the phenotypic variation. The QLuc.6A.2 region colocalized with two MTAs identified by GWAS. Moreover, 71 carotenoid metabolism-related candidate genes were identified, and the allelic effects were analyzed in the MCC panel based on the 90K array. Results revealed that the genes CYP97A3 (Chr. 6B) and CCD1 (Chr. 5A) were significantly associated with LUC. Additionally, the gene PSY3 (QLuc.5A.1) and several candidate genes involved in the methylerythritol 4-phosphate (MEP) pathways colocalized with stable QTL regions. The present study provides potential targets for future functional gene exploration and molecular breeding in common wheat.
Example marker figure produced by the SNP caller. Seeds homozygous for the HO mutation in FAD2B will produce predominantly HEX fluorescence signal and plot towards the Y-axis (blue cluster in top left corner). Seeds homozygous for the wild type NO allele in FAD2B will produce predominantly FAM fluorescence signal and plot towards the X axis (green cluster in bottom right corner). Heterozygous seeds will produce an equal mix of both signals and cluster in red between the two homozygous clusters. Seeds that fail genotyping will cluster near the origin (yellow in bottom left corner)
Oleic acid distribution of pure Bailey and Bailey II seeds in the experiment. The black circle highlights the sizable overlap between the two distributions particularly for near-isogenic lines. Red bars to the right of the black line indicate NO seeds that would have passed the HO screen and contributed to the contamination issue. The green line indicates the threshold needed to eliminate contamination. Figure was created using the seaborn package in Python and edited in Microsoft PowerPoint
  • R. J. AndresR. J. Andres
  • J. C. DunneJ. C. Dunne
Key message Contamination at the FAD2B locus due to inadequate screening protocols is the primary cause of sporadic, insufficient oleic acid content in Virginia-type peanut. Abstract The high oleic trait in peanut is conditioned by loss-of-function mutations in a pair of homeologous enzymes and is well known to improve the shelf life of peanut products. As such, the trait is given high priority in current and future cultivars by the North Carolina State University peanut breeding program. For unknown reasons, high oleic cultivars and breeding lines intermittently failed to meet self-imposed thresholds for oleic acid content in internal testing. To determine why, a manual seed chipper, crude DNA isolation protocol, genotyping assays for both mutations, and a web-based SNP calling application were developed. The primary cause was determined to be contamination with normal oleic seeds resulting from inadequate screening protocols. In order to correct the problem, a faster screening method was acquired to accommodate a higher oleic acid threshold. Additionally, results showed the mutation in one homeolog is fixed in the program, dig date had no significant effect on oleic acid content, and minor modifiers segregating within the program explained 6% of the variation in oleic acid content.
Key message We report the map-based cloning and functional characterization of SNG1, which encodes OsHXK3, a hexokinase-like protein that plays a pivotal role in controlling grain size in rice. Abstract Grain size is an important agronomic trait determining grain yield and appearance quality in rice. Here, we report the discovery of rice mutant short and narrow grain1 (sng1) with reduced grain length, width and weight. Map-based cloning revealed that the mutant phenotype was caused by loss of function of gene OsHXK3 that encodes a hexokinase-like (HKL) protein. OsHXK3 was associated with the mitochondria and was ubiquitously distributed in various organs, predominately in younger organs. Analysis of glucose (Glc) phosphorylation activities in young panicles and protoplasts showed that OsHXK3 was a non-catalytic hexokinase (HXK). Overexpression of OsHXK3 could not complement the Arabidopsis glucose insensitive2-1 (gin2-1) mutant, indicating that OsHXK3 lacked Glc signaling activity. Scanning electron microscopy analysis revealed that OsHXK3 affects grain size by promoting spikelet husk cell expansion. Knockout of other nine OsHXK genes except OsHXK3 individually did not change grain size, indicating that functions of OsHXKs have differentiated in rice. OsHXK3 influences gibberellin (GA) biosynthesis and homeostasis. Compared with wild type, OsGA3ox2 was significantly up-regulated and OsGA2ox1 was significantly down-regulated in young panicle of sng1, and concentrations of biologically active GAs were significantly decreased in young panicles of the mutants. The yield per plant of OsHXK3 overexpression lines (OE-4 and OE-35) was increased by 10.91% and 7.62%, respectively, compared to that of wild type. Our results provide evidence that an HXK lacking catalytic and sensory functions plays an important role in grain size and has the potential to increase yield in rice.
Schematic overview of the six prediction scenarios representing the associated missing data to be predicted in the MET. Gi\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${G}_{i}$$\end{document} corresponds to the genotype i with i∈1,⋯,NG\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$i\in \left\{1,\dots ,{N}_{G}\right\}$$\end{document} and Ej\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${E}_{j}$$\end{document} corresponds to the environment j of the MET, with j∈1,⋯,NE\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$j\in \left\{1,\dots ,{N}_{E}\right\}$$\end{document}. Filled squares represent data of the training set and crosses represent data of the validation set. Blue shading indicates that a phenotype and a NIR spectrum were acquired from genotype i in environment j. Yellow shading indicates that only the NIR spectrum was acquired, not the phenotyping. Finally, orange shading indicates that neither phenotyping nor NIR spectrum were acquired. oGoE corresponds to prediction in a sparse testing scenario where some combinations of genotypes and environments are not phenotyped. oGnE corresponds to a new environment in which no phenotype is available. nGoE corresponds to the prediction of new genotypes in observed environments. nGnE corresponds to new genotypes to be predicted in a new environment. oGoEref and nGoEref correspond to two scenarios where one environment is considered as a reference where NIR spectrum is acquired on all genotypes. oGoEref therefore is similar to scenario oGoE except that NIR spectra are not acquired on the validation set, while nGoEref is similar to scenario nGoE except that NIR spectra are not acquired on the validation set apart from in the reference environment (colour figure online)
Correlation coefficient matrices between trial environments for heading date adjusted means (upper left) and grain yield adjusted means (lower right). A trial environment is defined as a combination of treatment × year × site. Treatments were denoted by T, treated (equivalent to intensive practices) or LI (low input). Sites were denoted by EM (Estrée-Mons), GL (Genlis), HV (Houville), LC (Lectoure), LM (Le Moulon). Asterisks indicate the significance level: *P-value < 0.05, **P-value < 0.01 and ***P-value < 0.001
Proportion of the genomic (G), the genomic × environment (G × E) and residual variances across the NIR spectra of winter wheat grains from 5 different MET sets
Key message Phenomic prediction of wheat grain yield and heading date in different multi-environmental trial scenarios is accurate. Modelling the genotype-by-environment interaction effect using phenomic data is a potentially low-cost complement to genomic prediction. Abstract The performance of wheat cultivars in multi-environmental trials (MET) is difficult to predict because of the genotype-by-environment interactions (G × E). Phenomic selection is supposed to be efficient for modelling the G × E effect because it accounts for non-additive effects. Here, phenomic data are near-infrared (NIR) spectra obtained from plant material. While phenomic selection has recently been shown to accurately predict wheat grain yield in single environments, its accuracy needs to be investigated for MET. We used four datasets from two winter wheat breeding programs to test and compare the predictive abilities of phenomic and genomic models for grain yield and heading date in different MET scenarios. We also compared different methods to model the G × E using different covariance matrices based on spectra. On average, phenomic and genomic prediction abilities are similar in all different MET scenarios. Better predictive abilities were obtained when G × E effects were modelled with NIR spectra than without them, and it was better to use all the spectra of all genotypes in all environments for modelling the G × E. To facilitate the implementation of phenomic prediction, we tested MET designs where the NIR spectra were measured only on the genotype–environment combinations phenotyped for the target trait. Missing spectra were predicted with a weighted multivariate ridge regression. Intermediate predictive abilities for grain yield were obtained in a sparse testing scenario and for new genotypes, which shows that phenomic selection is an efficient and practicable prediction method for dealing with G × E.
Genetic dissection of yield component traits including spike and kernel characteristics is essential for the continuous improvement in wheat yield. Genome-wide association studies (GWAS) have been frequently used to identify genetic determinants for spike and kernel-related traits in wheat, though none have been employed in hard winter wheat (HWW) which represents a major class in US wheat acreage. Further, most of these studies relied on assembled diversity panels instead of adapted breeding lines, limiting the transferability of results to practical wheat breeding. Here we assembled a population of advanced/elite breeding lines and well-adapted cultivars and evaluated over four environments for phenotypic analysis of spike and kernel traits. GWAS identified 17 significant multi-environment marker–trait associations (MTAs) for various traits, representing 12 putative quantitative trait loci (QTLs), with five QTLs affecting multiple traits. Four of these QTLs mapped on three chromosomes 1A, 5B, and 7A for spike length, number of spikelets per spike (NSPS), and kernel length are likely novel. Further, a highly significant QTL was detected on chromosome 7AS that has not been previously associated with NSPS and putative candidate genes were identified in this region. The allelic frequencies of important quantitative trait nucleotides (QTNs) were deduced in a larger set of 1,124 accessions which revealed the importance of identified MTAs in the US HWW breeding programs. The results from this study could be directly used by the breeders to select the lines with favorable alleles for making crosses, and reported markers will facilitate marker-assisted selection of stable QTLs for yield components in wheat breeding.
Relationship between plant transpiration efficiency and leaf width across genotypes in Exp1 (A) and Exp2 (B) Note: leaf width: the mean of leaves 10–12 in Exp1 (A) and leaves 7–9 in Exp2 (B); R²: coefficient of determination of the relationship; p: significance level; error bars on each data point indicate standard errors of plant transpiration efficiency and leaf width
Correlation among leaf width of the largest leaf within a plant (mm), days to flower and final leaf number in HRF2 (n = 625 lines) Note: Data shown is leaf width, days to flower and final leaf number BLUPs. Leaf width in mm is shown on the x-axes of all three panels on the left, days to flower on the panels in the middle and final leaf number on the panels on the right. Final leaf number is shown on the y-axes of all panels in the bottom row, days to flower on the panels in the middle and leaf width in mm on all panels in the top row. The variable names are displayed on the outer edges of the matrix. The boxes along the diagonals display the density plot for each variable. The boxes in the lower left corner display the scatterplot between each variable. The boxes in the upper right corner display the Pearson correlation coefficient between each variable plus significance levels as stars (***, **, * correspond to p < 0.001, 0.01, 0.05, respectively). Axis labels are for the bottom and left panels
Leaf width for genotypes assigned to five sorghum racial groups in the trials of HRF1 (A), HRF2 (B) and GAT (C) Note: Data shown is leaf width BLUPs of the largest leaf adjusted for days to flower; Min: the minimum leaf width; Max: the maximum leaf width; Mean: the mean leaf width; std.error: standard error; H²: generalised heritability; HRF1: diversity panel grown at Hermitage Research Facility in Warwick QLD in 2017; HRF2: the diversity panel grown at Hermitage Research Facility in Warwick QLD in 2018; GAT: the diversity panel grown at Gatton Research Facility in Gatton, QLD in 2019
Manhattan plot of leaf width in HRF1, HRF2 and GAT Note: HRF1: diversity panel grown at Hermitage Research Facility in Warwick QLD in 2017; HRF2: diversity panel grown at Hermitage Research Facility in Warwick QLD in 2018; GAT: diversity panel grown at Gatton Research Facility in Gatton, QLD in 2019; the SNPs in red are significant ones detected using the p-value < 2e-6
Haplotype network of Sb008G070600 (A) and Sb001G199200 (C), and boxplots showing effects of major haplotypes of Sb008G070600 (B) and Sb001G199200 (D) on leaf width Note: P value in plot B indicates the difference in leaf width between two haplotypes by t-test; different letters over the boxes in plot D mean statistically significant differences in leaf width determined through Tukey-pairwise comparison among the five major haplotypes of Sb001G199200
Key message Leaf width was correlated with plant-level transpiration efficiency and associated with 19 QTL in sorghum, suggesting it could be a surrogate for transpiration efficiency in large breeding program. Abstract Enhancing plant transpiration efficiency (TE) by reducing transpiration without compromising photosynthesis and yield is a desirable selection target in crop improvement programs. While narrow individual leaf width has been correlated with greater intrinsic water use efficiency in C 4 species, the extent to which this translates to greater plant TE has not been investigated. The aims of this study were to evaluate the correlation of leaf width with TE at the whole-plant scale and investigate the genetic control of leaf width in sorghum. Two lysimetry experiments using 16 genotypes varying for stomatal conductance and three field trials using a large sorghum diversity panel ( n = 701 lines) were conducted. Negative associations of leaf width with plant TE were found in the lysimetry experiments, suggesting narrow leaves may result in reduced plant transpiration without trade-offs in biomass accumulation. A wide range in width of the largest leaf was found in the sorghum diversity panel with consistent ranking among sorghum races, suggesting that environmental adaptation may have a role in modifying leaf width. Nineteen QTL were identified by genome-wide association studies on leaf width adjusted for flowering time. The QTL identified showed high levels of correspondence with those in maize and rice, suggesting similarities in the genetic control of leaf width across cereals. Three a priori candidate genes for leaf width, previously found to regulate dorsoventrality, were identified based on a 1-cM threshold. This study provides useful physiological and genetic insights for potential manipulation of leaf width to improve plant adaptation to diverse environments.
Key message A reliable locus confers broad-spectrum resistance to multiple plant viruses in soybean under field conditions. Abstract Soybean mosaic disease (SMD) can be caused by a variety of viruses, most of which have been largely overlooked in breeding programs. Effective mitigation of the adverse of SMD might result from breeding cultivars with broad-spectrum resistance. However, reports on broad-spectrum resistance to multiple virus have been limited. To catalog viral community members behind SMD, virus samples were collected from symptomatic field plots, and pathogenicity of component strains was assessed. Preliminary ELISA and PCR detection revealed that 39.58% and 66.67% of samples contained two or more virus strains, respectively. Only three soybean accessions were completely asymptomatic, while 42% exhibited moderate or severe susceptibility, indicating that co-infection of multiple virus remains a significant threat in current soybean production systems. Further, a RIL population consisting of 150 F7:9 strains derived from two soybean genotypes with contrasting reactions to virus infection was constructed and explored for significant markers and resistance genes. QTL analysis returned a reliable locus, named GmRmv, on chromosome 13. Significance of GmRmv in imparting resistance to SMD was further confirmed in NIL lines and delimited into a 157-kb interval that contains 17 annotated genes. Among these genes, three, Glyma.13G190000, Glyma.13G190300 and Glyma.13G190400, each contained LRR domains, as well as significant variation in coding sequences between resistant and susceptible parents. Hence, these three genes are considered strong candidate genes for explaining GmRmv significance. In summary, this research opens a new avenue for formulating strategies to breed soybean varieties with broad-spectrum resistance to multiple virus associated with SMD.
a PC loadings of each trait for the two first standardized principal components. b Plot showing the accessions projected. The first (x-axis) and second (y-axis) PCs explained 19% and 15.8% of variance, respectively
Correlation between observed and predicted phenotypes of Indica improved varieties. In each plot, the first four columns represent the correlation values using Bayes C, while the last four values correspond to RKHS method. Colors represent marker information utilized: green, SNPs; magenta, MITE/DTX; blue, RLX/RIX; brown, all markers. The asterisk shows the best option for each trait. (Color figure online)
Correlation between observed and predicted phenotypes across accessions. All ADM and ADM accessions were predicted using the rest of groups. In each plot, the first four columns represent the correlation values using Bayes C, while the last four values correspond to RKHS method. Colors represent marker information utilized: green, SNPs; magenta, MITE/DTX; blue, RLX/RIX; brown, all markers. The asterisk shows the best option for each trait. (Color figure online)
Predictive accuracy across populations using TIPS from each of 18 recognized MITE families. Each column corresponds to accuracy with one MITE family. Model included only MITEs or MITEs and all SNPs. The asterisk shows the best option
Key message Transposon insertion polymorphisms can improve prediction of complex agronomic traits in rice compared to using SNPs only, especially when accessions to be predicted are less related to the training set . Abstract Transposon insertion polymorphisms (TIPs) are significant sources of genetic variation. Previous work has shown that TIPs can improve detection of causative loci on agronomic traits in rice. Here, we quantify the fraction of variance explained by single nucleotide polymorphisms (SNPs) compared to TIPs, and we explore whether TIPs can improve prediction of traits when compared to using only SNPs. We used eleven traits of agronomic relevance from by five different rice population groups (Aus, Indica, Aromatic, Japonica, and Admixed), 738 accessions in total. We assess prediction by applying data split validation in two scenarios. In the within-population scenario, we predicted performance of improved Indica varieties using the rest of Indica accessions. In the across population scenario, we predicted all Aromatic and Admixed accessions using the rest of populations. In each scenario, Bayes C and a Bayesian reproducible kernel Hilbert space regression were compared. We find that TIPs can explain an important fraction of total genetic variance and that they also improve genomic prediction. In the across population prediction scenario, TIPs outperformed SNPs in nine out of the eleven traits analyzed. In some traits like leaf senescence or grain width, using TIPs increased predictive correlation by 30–50%. Our results evidence, for the first time, that TIPs genotyping can improve prediction on complex agronomic traits in rice, especially when accessions to be predicted are less related to training accessions.
Key message Greater embryo size in a large and carefully phenotyped mapping population was genetically associated with a greater number of longer seminal roots to increase grain yield in droughted field environments. Abstract Breeding modification of root architecture is challenging in field environments owing to genetic and phenotypic complexity, and poor repeatability with root sampling. Seeds from a large mapping population varying in embryo size were harvested from a common glasshouse and standardised to a common size before assessing in rolled germination paper at 12 and 20 °C for seedling growth. Differences in genotype means were large and heritabilities high (h² = 0.55–0.93) indicating strong and repeatable genotypic differences for most root traits. Seminal roots 1 to 3 were produced on all seedlings, whereas growth of seminal roots 4, 5 and 6 was associated with differences in embryo size. Increases in seminal root number from 4 to 6 per plant were strongly, genetically correlated with increases in total seminal length (rg = 0.84, < 0.01). Multivariate analysis confirmed initiation and growth of seminal roots 1, 2 and 3, and of roots 4, 5 and 6 behaved as genetically independent (rPg = 0.15 ns) cohorts. Tails representing extremes in seedling root length and number were associated with significant differences in grain yield of up to 35% in droughted field environments but were not different in irrigated environments. Increases in grain yield were linked to greater lengths of seminal roots 4, 5 and 6 and were largely independent of plant height or development. This is the first report on the genetic relationship of seedling root architecture and embryo size, and potential in selection of seminal root size for accessing deep-soil moisture in droughted environments.
Key message Six wheat-Thinopyrum ponticum disomic addition lines derived from partial amphiploid Xiaoyan 7430 were identified using in situ hybridization and SNP microarray, the homoeologous group and stripe rust resistance of each alien chromosome were determined, and Th. ponticum chromosome-specific markers were developed. Abstract Xiaoyan 7430 is a significant partial amphiploid, which is used to set up a bridge for transferring valuable genes from Thinopyrum ponticum (Podp.) Barkworth & D.R. Dewey into common wheat. To accelerate the application of these useful genes in enriching the genetic variability of cultivated wheat by chromosome engineering, a complete set of derived addition lines has been created from Xiaoyan 7430. The chromosome composition of each line was characterized by the combination of genomic in situ hybridization and multicolor fluorescence in situ hybridization (mc-FISH), and the homoeology of each alien chromosome was determined by wheat SNP microarray analysis. Addition line WTA55 with alien group-6 chromosome was evaluated resistant to stripe rust isolates at both the seedling and grain-filling stages (Zadoks scale at z.11 and z.73). Diagnostic marker analysis proved that it could carry a novel stripe rust resistance gene derived from Th. ponticum. Furthermore, a FISH probe and 45 molecular markers specific for alien chromosomes were developed based on specific-locus amplified fragment sequencing (SLAF-seq). Of which 27 markers were separately located on single alien chromosome, and some of them could be used to identify the derived translocation lines. This set of addition lines as well as the molecular markers and the FISH probe will promote the introgression of abundant variation from Th. ponticum into wheat in wheat improvement programs.
Key message Calibrating a genomic selection model on a sparse factorial design rather than on tester designs is advantageous for some traits, and equivalent for others. Abstract In maize breeding, the selection of the candidate inbred lines is based on topcross evaluations using a limited number of testers. Then, a subset of single-crosses between these selected lines is evaluated to identify the best hybrid combinations. Genomic selection enables the prediction of all possible single-crosses between candidate lines but raises the question of defining the best training set design. Previous simulation results have shown the potential of using a sparse factorial design instead of tester designs as the training set. To validate this result, a 363 hybrid factorial design was obtained by crossing 90 dent and flint inbred lines from six segregating families. Two tester designs were also obtained by crossing the same inbred lines to two testers of the opposite group. These designs were evaluated for silage in eight environments and used to predict independent performances of a 951 hybrid factorial design. At a same number of hybrids and lines, the factorial design was as efficient as the tester designs, and, for some traits, outperformed them. All available designs were used as both training and validation set to evaluate their efficiency. When the objective was to predict single-crosses between untested lines, we showed an advantage of increasing the number of lines involved in the training set, by (1) allocating each of them to a different tester for the tester design, or (2) reducing the number of hybrids per line for the factorial design. Our results confirm the potential of sparse factorial designs for genomic hybrid breeding.
Key Message A genetic framework underpinning salinity tolerance at reproductive stage was revealed by genome-wide SNP markers and major adaptability genes in synthetic-derived wheats, and trait-associated loci were used to predict phenotypes. Abstract Using wild relatives of crops to identify genes related to improved productivity and resilience to climate extremes is a prioritized area of crop genetic improvement. High salinity is a widespread crop production constraint, and development of salt-tolerant cultivars is a sustainable solution. We evaluated a panel of 294 wheat accessions comprising synthetic-derived wheat lines (SYN-DERs) and modern bread wheat advanced lines under control and high salinity conditions at two locations. The GWAS analysis revealed a quantitative genetic framework of more than 200 loci with minor effect underlying salinity tolerance at reproductive stage. The significant trait-associated SNPs were used to predict phenotypes using a GBLUP model, and the prediction accuracy (r²) ranged between 0.57 and 0.74. The r² values for flag leaf weight, days to flowering, biomass, and number of spikes per plant were all above 0.70, validating the phenotypic effects of the loci discovered in this study. Furthermore, the germplasm sets were compared to identify selection sweeps associated with salt tolerance loci in SYN-DERs. Six loci associated with salinity tolerance were found to be differentially selected in the SYN-DERs (12.4 Mb on chromosome (chr)1B, 7.1 Mb on chr2A, 11.2 Mb on chr2D, 200 Mb on chr3D, 600 Mb on chr6B, and 700.9 Mb on chr7B). A total of 228 reported markers and genes, including 17 well-characterized genes, were uncovered using GWAS and EigenGWAS. A linkage disequilibrium (LD) block on chr5A, including the Vrn-A1 gene at 575 Mb and its homeologs on chr5D, were strongly associated with multiple yield-related traits and flowering time under salinity stress conditions. The diversity panel was screened with more than 68 kompetitive allele-specific PCR (KASP) markers of functional genes in wheat, and the pleiotropic effects of superior alleles of Rht-1, TaGASR-A1, and TaCwi-A1 were revealed under salinity stress. To effectively utilize the extensive genetic information obtained from the GWAS analysis, a genetic interaction network was constructed to reveal correlations among the investigated traits. The genetic network data combined with GWAS, selective sweeps, and the functional gene survey provided a quantitative genetic framework for identifying differentially retained loci associated with salinity tolerance in wheat.
Key message: SbWRKY55 functions as a key component of the ABA-mediated signaling pathway; transgenic sorghum regulates plant responses to saline environments and will help save arable land and ensure food security. Salt tolerance in plants is triggered by various environmental stress factors and endogenous hormonal signals. Numerous studies have shown that WRKY transcription factors are involved in regulating plant salt tolerance. However, the underlying mechanism for WRKY transcription factors regulated salt stress response and signal transduction pathways remains largely unknown. In this study, the SbWRKY55 transcription factor was found to be the key component for reduced levels of salt and abscisic acid in SbWRKY55 overexpression significantly reduced salt tolerance in sorghum and Arabidopsis. Mutation of the homologous gene AtWRKY55 in A. thaliana significantly enhanced salt tolerance, and SbWRKY55 supplementation in the mutants restored salt tolerance. In the transgenic sorghum with SbWRKY55 overexpression, the expression levels of genes involved in the abscisic acid (ABA) pathway were altered, and the endogenous ABA content decreased. Yeast one-hybrid assays and dual-luciferase reporter assay showed that SbWRKY55 binds directly to the promoter of SbBGLU22 and inhibits its expression level. In addition, both in vivo and in vitro biochemical analyses showed that SbWRKY55 interacts with the FYVE zinc finger protein SbFYVE1, blocking the ABA signaling pathway. This could be an important feedback regulatory pathway to balance the SbWRKY55-mediated salt stress response. In summary, the results of this study provide convincing evidence that SbWRKY55 functions as a key component in the ABA-mediated signaling pathway, highlighting the dual role of SbWRKY55 in ABA signaling. This study also showed that SbWRKY55 could negatively regulate salt tolerance in sorghum.
The structure of selection cycles in the rye hybrid breeding program. The number of entries decreases due to selection in each GCA trial. In each cycle, inbred lines are crossed with two testers of the opposite gene pool
Illustration of datasets used for the CYC and MY analyses. In CYC, the GCA1, GCA2, and GCA3 datasets from all cycles are combined. In MY, the dataset comprises the GCA1 data of the years 2016 to 2019
Illustration of the selected entries for the GCA2 assessment. The transparent red cylinders illustrate the selected common entries in GCA1 and GCA2
Plots of the probability of obtaining the m truly best entries based on the GBLUPs for each selected number of entries (expressed as percentage of N) from GCA1 assessment of each pool. The different coloured entries indicate the different numbers (m) of truly best entries
Plots of the probability of obtaining the m\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$m$$\end{document} truly best entries based on the GBLUPs for each selected number of entries (expressed as percentage of N) of each cycle in the GCA2 assessment for each pool. The different coloured entries indicate the different numbers (m) of truly best entries. In general, the pollen pool has higher probability to obtain the truly best entries than the seed pool. In Cycle 4, both pools have a relatively lower probability to obtain the truly best entries compared to the other cycles
Key message: We propose a simulation approach to compute response to genomic selection on a multi-environment framework to provide breeders the number of entries that need to be selected from the population to have a defined probability of selecting the truly best entry from the population and the probability of obtaining the truly best entries when some top-ranked entries are selected. The goal of any plant breeding program is to maximize genetic gain for traits of interest. In classical quantitative genetics, the genetic gain can be obtained from what is known as "Breeder's equation". In the past, only phenotypic data were used to compute the genetic gain. The advent of genomic prediction (GP) has opened the door to the utilization of dense markers for estimating genomic breeding values or GBV. The salient feature of GP is the possibility to carry out genomic selection with the assistance of the kinship matrix, hence improving the prediction accuracy and accelerating the breeding cycle. However, estimates of GBV as such do not provide the full information on the number of entries to be selected as in the classical response to selection. In this paper, we use simulation, based on a fitted mixed model for GP in a multi-environmental framework, to answer two typical questions of a plant breeder: (1) How many entries need to be selected to have a defined probability of selecting the truly best entry from the population; (2) what is the probability of obtaining the truly best entries when some top-ranked entries are selected.
Key message: The powdery mildew resistance locus was mapped to A. cristatum chromosome 6PL bin (0.27-0.51) and agronomic traits evaluation indicated that this locus has potential breeding application value. Agropyron cristatum (2n = 4x = 28, PPPP) is a wild relative of wheat with an abundance of biotic and abiotic stress resistance genes and is considered one of the best exogenous donor relatives for wheat breeding. A number of wheat-A. cristatum derived lines have been generated, including addition lines, translocation lines and deletion lines. In this study, the 6P disomic addition line 4844-12 (2n = 2x = 44) was confirmed to have genetic effects on powdery mildew resistance. Four 6P deletion lines (del16a, del19b, del21 and del27) and two translocation lines (WAT638a and WAT638b), derived from radiation treatment of 4844-12, were used to further assess the 6P powdery mildew resistance locus by powdery mildew resistance assessment, genomic in situ hybridization (GISH), fluorescence in situ hybridization (FISH) and 6P specific sequence-tagged-site (STS) markers. Collectively, the locus harboring the powdery mildew resistance gene was genetically mapped to a 6PL bin (0.27-0.51). The genetic effects of this chromosome segment on resistance to powdery mildew were further confirmed by del16a and del27 BC3F2 lines. Comprehensive evaluation of agronomic traits revealed that the powdery mildew resistance locus of 6PL (0.27-0.51) has potential application value in wheat breeding. A total of 22 resistant genes were annotated and 3 specific gene markers were developed for detecting chromatin of the resistant region based on genome re-sequencing. In summary, this study could broaden the powdery mildew resistance gene pool for wheat genetic improvements.
Phenotypic characteristics of kernel. a Kernel phenotypes of the parent ‘S849-8,’ ‘SY95-71,’ and partial RILs. The white line represents the scale = 1 cm. b Observation on cell structure of mature seed pericarp. c The size of pericarp cells in mature seeds of parents. **Significance at the 0.01 probability level
BAS-660 K SNP analysis and construction of genetic maps. a, b Overview of PolyHighResolution-SNPs analyses by the 660 K SNP array; ‘No. of SNPs’ represents the number of polymorphic SNPs on each chromosome; ‘Percentage’ represents the ratio of the number of polymorphic SNPs to the total ones. c Genetic maps and physical map of the major QTL for KL identified in SSY population. ‘Genetic Map 1’ represents the genetic linkage map primarily constructed in 1–90 lines; ‘Genetic Map 2’ represents the genetic map constructed in 1–214 lines. ‘CS Physical Map 1 and 2' represent the physical map of ‘Chinese Spring’ corresponding to each genetic map
Genetic effect analysis and validation of major QTL. a, b Genetic effects of QKL.sicau-SSY-2D and QTKW.sicau-SSY-2D in SSY population. n1 and n2 represent the number of homozygous lines carrying ‘S849-8’ and ‘SY95-71’ alleles, respectively. (c.d) Effects of QKL.sicau-SSY-2D and QTKW.sicau-SSY-2D in 218 Sichuan wheat, including 165 SWC and 53 SWL. **Significance at the 0.01 probability level; *significance at the 0.05 probability level
Physical maps of the QKL.sicau-SSY-2D and QTKW.sicau-SSY-2D on ‘CS’ and Ae.tauschii and the orthologs in the target interval
Key message A co-located KL and TKW-related QTL with no negative effect on PH and AD was rapidly identified using BSA and wheat 660 K SNP array. Its effect was validated in a panel of 218 wheat accessions. Abstract Kernel length (KL) and thousand-kernel weight (TKW) of wheat (Triticum aestivum L.) contribute significantly to kernel yield. In the present study, a recombinant inbred line (RIL) population derived from the cross between the wheat line S849-8 with larger kernels and more spikelets per spike and the line SY95-71 was developed. Further, of both the bulked segregant analysis (BSA) and the wheat 660 K single nucleotide polymorphism (SNP) array were used to rapidly identify genomic regions for kernel-related traits from this RIL population. Kompetitive Allele Specific PCR markers were further developed in the SNP-enriched region on the 2D chromosome to construct a genetic map. Both QKL.sicau-SSY-2D for KL and QTKW.sicau-SSY-2D for TKW were identified at multiple environments on chromosome arm 2DL. These two QTLs explained 9.68–23.02% and 6.73–18.32% of the phenotypic variation, respectively. The effects of this co-located QTL were successfully verified in a natural population consisting of 218 Sichuan wheat accessions. Interestingly, the major QTL was significantly and positively correlated with spike length, but did not negatively affect spikelet number per spike (SNS), plant height, or anthesis date. These results indicated that it is possible to synchronously improve kernel weight and SNS by using this QTL. Additionally, several genes associated with kernel development and filling rate were predicted and sequenced in the QTL-containing physical intervals of reference genomes of ‘Chinese spring’ and Aegilops tauschii. Collectively, these results provide a QTL with great breeding potential and its linked markers which should be helpful for fine mapping and molecular breeding.
Key message A melon gene MSO1 located on chromosome 10 by map-based cloning strategy, which encodes an ARGONAUTE 7 protein, is responsible for the development of shoot organization. Abstract Plant endogenous small RNAs (sRNAs) are involved in various plant developmental processes. In Arabidopsis, sRNAs combined with ARGONAUTE (AGO) proteins are incorporated into the RNA-induced silencing complex (RISC), which functions in RNA silencing or biogenesis of trans-acting siRNAs (ta-siRNAs). However, their roles in melon (Cucumis melo L.) are still unclear. Here, the melon shoot organization 1 (mso1) mutant was identified and shown to exhibit pleiotropic phenotypes in leaf morphology and plant architecture. Positional cloning of MSO1 revealed that it encodes a homologue of ArabidopsisAGO7/ZIPPY, which is required for the production of ta-siRNAs. The AG-to-C mutation in the second exon of MSO1 caused a frameshift mutation and significantly reduced its expression. Ectopic expression of MSO1 rescued the Arabidopsisago7 phenotype. RNA-seq analysis showed that several genes involved in transcriptional regulation and plant hormones were significantly altered in mso1 compared to WT. A total of 304 and 231 miRNAs were identified in mso1 and WT by sRNA sequencing, respectively, and among them, 42 known and ten novel miRNAs were differentially expressed. cme-miR390a significantly accumulated, and the expression levels of the two ta-siRNAs were almost completely abolished in mso1. Correspondingly, their targets, the ARF3 and ARF4 genes, showed dramatically upregulated expression, indicating that the miR390-TAS3-ARF pathway has conserved roles in melon. These findings will help us better understand the molecular mechanisms of MSO1 in plant development in melon.
Diagram of the synthesis and genomic relationships in autoallopolyploids. A The genomic relationships of the autoallopolyploids. The triangle formed by purple color lines and boxes represents the genomic relationships of the six Brassica species, known as “U” tringle (U N, 1935). The blue lines indicate the two parents that form the digenomic triploid hybrids. The blue dotted arrows indicate that the hybrid is unobtained. RBR within red text box is the restituted B. rapa from B. napus, and the process is indicated by large blue hole arrow. B The diagram shows how those autoallopolyploids are obtained (colour figure online)
FISH analysis of meiotic chromosome pairings in autoallohexaploids. Red signals are from the C-genome-specific BAC BoB014O06, and green signals are from B-genome-specific centromere DNA. DAPI (blue) and merged images are given for each cell. A1, A2 Cells of NAP × NAP-A (AⁿAⁿAⁿAⁿCⁿCⁿ). One diakinesis with an A-C bivalent (solid arrows with ball tail) due to homeologous chromosome. B1, B2 Cells of NAP × OLE (AⁿAⁿCⁿCⁿCoCo). One diakinesis with an A-C bivalent (solid arrows with ball tail) due to homeologous chromosome. C1, C2 Cells of NAP × OLE (AⁿAⁿCⁿCⁿCoCo). One diakinesis with an A-A-C trivalent (solid arrows with ball tail) due to homeologous chromosome. D1, D2 PMCs of autoallopolyploid NAP × NAP-A (AⁿAⁿCⁿCⁿCoCo). One diakinesis with four quadrivalents (hollow arrow: chain quadrivalent; arrow: ring quadrivalent) of C genome (red), two univalents (arrowhead) of A genome, and two univalents (arrowhead) of C genome (red). E1, E2 Cells of CAR × OLE (BcBcCcCcCoCo). One diakinesis with two quadrivalents (arrow) of C genome (red), fourteen bivalents of C genome (red), and eight bivalents of B genome (green). F1, F2 JUN × NAP-A (AⁿAⁿAjAjBjBj). One diakinesis with one quadrivalent (arrow) of A genome, two univalents (arrowhead) of A genome, seventeen bivalents of A genome, and eight bivalents of B genome (green). Bar, 10 μm (colour figure online)
FISH analysis of chromosome segregation at anaphase I in autoallohexaploid PMCs. With the same probes as in Fig. 2, B-genome chromosomes fluorescence green, and C-subgenome chromosomes fluorescence red. A1, A2 PMCs of NAP × NAP-A (AⁿAⁿ AⁿAⁿCⁿCⁿ). One anaphase I with segregation ratio 20:20 in A subgenome (blue), and segregation ratio 11:7 in C subgenome (red). B1, B2 PMCs of NAP × OLE (AⁿAⁿCⁿCⁿCoCo). One anaphase I with segregation ratio 11:9 in A genome (blue), and segregation ratio 16:20 in C subgenome (red). C1, C2 PMCs of CAR × NIG (Bc BcBⁿⁱBⁿⁱCcCc). One anaphase I with segregation ratio 17:15 in B subgenome (green), and segregation ratio 9:9 in C subgenome (red). D1, D2 PMCs of CAR × OLE (BcBcCoCoCoCo). One anaphase I with segregation ratio 8:8 in B subgenome (green), and segregation ratio 17:19 in C subgenome (red). E1, E2 PMCs of JUN × NAP-A (AⁿAⁿ AjAjBjBj). One anaphase I with segregation ratio 21:19 in A subgenome (blue), and segregation ratio 7:9 in B subgenome (green). Bar, 10 μm (colour figure online)
Number of seeds per silique of autoallohexaploids and their parents
Key message Different digenomic Brassica autoallohexaploids were produced from the crosses of three allotetraploids and ancestral diploids and characterized for the cytological behavior of two subgenomes with two and four copies. Abstract Interspecific hybridization and allopolyploidization present an important pathway for plant evolution and breeding. In this study, different types of digenomic autoallohexaploids with two or four copies of two subgenomes (AAAACC, AACCCC, AAAABB, BBBBCC, BBCCCC) were synthesized by the crosses between three Brassica allotetraploids and their diploid progenitors and the chromosome doubling, and their meiotic behaviors were analyzed by fluorescence in situ hybridization (FISH). These autoallohexaploids showed some variations in pollen fertility and seed-sets and produced both euploid and aneuploid progenies with some chromosomes lost. Two subgenomes in these autoallohexaploids showed some aberrant pairings and segregations, and the degrees of meiotic regularity were negatively associated with the genome affinities. The chromosomes of the subgenome with four copies formed few quadrivalents with the average number < 2, and mainly paired as bivalents, and majority of the chromosomes from the subgenome with two copies gave the expected bivalents. The different extents of the equal and unequal segregations corresponded to the chromosome pairings. The development and cytological investigation of these autoallohexaploids provide not only the new germplasm for genetic research and breeding but also the new clues for the genome behavior and interplay between these subgenomes with different copies.
Key message TaD11-2A affects grain size and root length and its natural variations are associated with significant differences in yield-related traits in wheat. Abstract Brassinosteroids (BRs) control many important agronomic traits and therefore the manipulation of BR components could improve crop productivity and performance. However, the potential effects of BR-related genes on yield-related traits and stress tolerance in wheat (Triticum aestivum L.) remain poorly understood. Here, we identified TaD11 genes in wheat (rice D11 orthologs) that encoded enzymes involved in BR biosynthesis. TaD11 genes were highly expressed in roots (Zadoks scale: Z11) and grains (Z75), while expression was significantly suppressed by exogenous BR (24-epiBL). Ectopic expression of TaD11-2A rescued the abnormal panicle structure and plant height (PH) of the clustered primary branch 1 (cpb1) mutant, and also increased endogenous BR levels, resulting in improved grain yields and grain quality in rice. The tad11-2a-1 mutant displayed dwarfism, smaller grains, sensitivity to 24-epiBL, and reduced endogenous BR contents. Natural variations in TaD11-2A were associated with significant differences in yield-related traits, including PH, grain width, 1000-grain weight, and grain yield per plant, and its favorable haplotype, TaD11-2A-HapI was subjected to positive selection during wheat breeding. Additionally, TaD11-2A influenced root length and salt tolerance in rice and wheat at seedling stages. These results indicated the important role of BR TaD11 biosynthetic genes in controlling grain size and root length, and also highlighted their potential in the molecular biological analysis of wheat.
Comparison of phenotypic and cell morphological characterizations between wild-type cucumber CCMC and cpa-2 mutant. Phenotype comparison between WT and cpa-2 at flowering stage (a) and cotyledon stage (b). bar = 10 cm (a). Bar = 2 cm (b). c The comparison of internodes between CCMC and cpa-2 in mature stage. Stem longitudinal sections of CCMC (d) and cpa-2 (e). Statistics comparison of plant height at flowering stage (f) and hypocotyl length at cotyledon stage (g) and the internode lengths and internode number at flowering stage (h) and internode stem epidermal cell area (i). Values are the mean ± SD (h), **P < 0.01 (f, g, i)
Map-based cloning of cpa-2 locus. a Initial mapping with 195 F2 individuals with Indel and SSR markers placed locus to a region in chromosome 7. b In an enlarged segregating population, cpa-2 was fine mapped to a 109 kb genomic region flanked by indel markers Mutc7-6 and Mutc7-5. And CAPS1 marker was co-segregating with the cpa-2 locus. The numbers in parentheses represent the number of recombinants of the corresponding marker. The black box indicates the genotype of mutant, the white box indicates the genotype of wild-type, and the striped box indicates the heterozygous genotype. c There are 14 genes distributed on this genomic interval. d The structure of predicted CsDWF1 and the coding sequence alignment of CsDWF1 in CCMC and cpa-2. The red arrow points to the mutated position in cpa-2
Recovery of cpa-2 mutant with exogenous spaying of BR. Comparisons of entire shapes (a) and first true leaves(b) of the WT, cpa-2 spayed with different EBR concentration (0.01 μM, 0.1 μM) and cpa-2 at two-leaf stage. Bar = 1 cm
Identification of the CsDWF1 gene and its encoding protein. a Phylogenetic tree of CsDWF1 proteins from in cucumber and its homologs in other species. Numbers on the tree indicate bootstrap values. b Expression analysis of the CsDWF1 in roots (R), stems (S), leaves (L), male flowers (M), ovaries (O),tendrils (T) and shoot apex (SA) of CCMC and cpa-2. The values are the means ± SDs. Asterisks indicate P < 0.01. c Subcellular localization of CsDWF1 from WT and cpa-2. Bar = 20 μm
The analysis of differentially expressed genes related to hormone synthesis and signal transduction pathways between cpa-2 (Da) and CCMC. a Analysis of genes related to BR related pathway. b Analysis of genes involved in other hormone synthesis and signal transduction
Key message A novel compact plant architecture mutant, cpa-2, was identified from EMS-induced mutagenesis. Bulked segregant analysis sequencing and map-based cloning revealed CsDWF1 encoding C-24 reductase enzyme as the candidate gene. Abstract The compact architecture is a vital and valuable agronomic trait that helps to reduce the labor of plant management, and improve the fruit yield by increasing planting density in cucumbers. However, the molecular basis underlying the regulation of plant architecture in cucumber is complex and largely unknown. In this study, a novel recessive compact allele, designated as cpa-2 (compact plant architecture-2) was fine mapped in a 109 kb region on chromosome 7 by the strategy of bulked segregant analysis sequencing combined with map-based cloning. Gene annotation of the corresponding region revealed that the CsaV3_7G030530 (CsDWF1) gene encoding C-24 reductase, which acts as the key enzyme in brassinosteroids biosynthesis, functions as the candidate gene for cpa-2. Sequence analysis showed that a single-nucleotide mutation (G to A) in the second exon of CsaV3_7G030530 caused an amino acid substitution from E⁵⁰² to K⁵⁰². Compared with wild-type CCMC, CsDWF1 had lower expression levels in the stem, leaf and ovary of cpa-2. In addition, the compact phenotype in cpa-2 could be partially restored by exogenous BR application. Transcriptome analysis revealed that many genes related to plant growth hormones were differentially expressed in cpa-2 plants. This is the first report about the characterization and cloning of the CsDWF1 gene. This work revealed the importance of CsDWF1 in plant development regulation and extended our understanding of the interaction between BRs and other hormones for plant architecture development.
Key message GS and PS performed similarly in improving resistance to FER and FUM content. With cheaper and faster genotyping methods, GS has the potential to be more efficient than PS. Abstract Fusarium verticillioides is a common maize (Zea mays L.) pathogen that causes Fusarium ear rot (FER) and produces the mycotoxin fumonisin (FUM). This study empirically compared phenotypic selection (PS) and genomic selection (GS) for improving FER and FUM resistance. Three intermating generations of recurrent GS were conducted in the same time frame and from a common base population as two generations of recurrent PS. Lines sampled from each PS and GS cycle were evaluated in three North Carolina environments in 2020. We observed similar cumulative responses to GS and PS, representing decreases of about 50% of mean FER and FUM compared to the base population. The first cycle of GS was more effective than later cycles. PS and GS both achieved about 70% of predicted total gain from selection for FER, but only about 26% of predicted gains for FUM, suggesting that heritability for FUM was overestimated. We observed a 20% decrease in genetic marker variation from PS and 30% decrease from GS. Our greatest challenge was our inability to quickly obtain dense and consistent set of marker genotypes across generations of GS. Practical implementation of GS in individual small-scale breeding programs will require cheaper and faster genotyping methods, and such technological advances will present opportunities to significantly optimize selection and mating schemes for future GS efforts beyond what we were able to achieve in this study.
adult plan stripe rust responses of Baimangmai and homozygous resistant F10 recombinant inbred lines derived from the cross T29/Bm at the adult plan stage in the field at Langfang. T29, Taichung 29; Bm, Baimangmai; 97/98-3 and 97/98-5, resistant RILs; 97/98-14, susceptible RIL
Stripe rust responses of Baimangmai (Bm) and Taichung (T29) to Pst race CYR32 at various growth stages: a seedling; b tillering; c jointing; d pre-booting; and e early-booting under low- (LT) and high-temperature (HT) conditions
Genetic linkage map for YrBm on wheat chromosome 4BL (b) and comparisons with the consensus genetic linkage map (a) developed by Somers et al. (2004) and the linkage maps of stripe rust resistance genes Yr62 (c) developed by Lu et al. (2014), YrZH22 (d) by Wang et al. (2017) and Yr50 (e) by Liu et al. (2013). Dashed lines connect common SSR markers. YrBm, Yr50, Yr62 and YrZH22 are marked in bold italics
Key message A new adult plan resistance gene YrBm for potentially durable resistance to stripe rust was mapped on wheat chromosome arm 4BL in landrace Baimangmai. SSR markers closely flanking YrBm were developed and validated for use in marker-assisted selection. Abstract The wheat stripe rust pathogen Puccinia striiformis f. sp. tritici (Pst) frequently acquires new virulences and rapidly adapts to environmental stress. New virulences in Pst populations can cause previously resistant varieties to become susceptible. If those varieties were widely grown, consequent epidemics can lead to yield losses. Identification and deployment of genes for durable resistance are preferred method for disease control. The Chinese winter wheat landrace Baimangmai showed a high level of adult plant resistance (APR) to stripe rust in a germplasm evaluation trial at Langfang in Hebei province in 2006 and has continued to confer high resistance over the following 15 years in field nurseries in Hebei, Sichuan and Gansu. A recombinant inbred line population of 200 F10 lines developed from a cross of Baimangmai and a susceptible genotype segregated for APR at a single locus on chromosome 4BL; the resistance allele was designated YrBm. Allelism tests of known Yr genes on chromosome 4B and unique closely flanking marker alleles Xgpw7272189 and Xwmc652164 among a panel of Chinese wheat varieties indicated that YrBm was located at a new locus. Moreover, those markers can be used for marker-assisted selection in breeding for stripe rust resistance.
Key message We investigate the genetic basis of panicle architecture in switchgrass in two mapping populations across a latitudinal gradient, and find many stable, repeatable genetic effects and limited genetic interactions with the environment. Abstract Grass species exhibit large diversity in panicle architecture influenced by genes, the environment, and their interaction. The genetic study of panicle architecture in perennial grasses is limited. In this study, we evaluate the genetic basis of panicle architecture including panicle length, primary branching number, and secondary branching number in an outcrossed switchgrass QTL population grown across ten field sites in the central USA through multi-environment mixed QTL analysis. We also evaluate genetic effects in a diversity panel of switchgrass grown at three of the ten field sites using genome-wide association (GWAS) and multivariate adaptive shrinkage. Furthermore, we search for candidate genes underlying panicle traits in both of these independent mapping populations. Overall, 18 QTL were detected in the QTL mapping population for the three panicle traits, and 146 unlinked genomic regions in the diversity panel affected one or more panicle trait. Twelve of the QTL exhibited consistent effects (i.e., no QTL by environment interactions or no QTL × E), and most (four of six) of the effects with QTL × E exhibited site-specific effects. Most (59.3%) significant partially linked diversity panel SNPs had significant effects in all panicle traits and all field sites and showed pervasive pleiotropy and limited environment interactions. Panicle QTL co-localized with significant SNPs found using GWAS, providing additional power to distinguish between true and false associations in the diversity panel.
Key message An alanine to valine mutation of glutamyl-tRNA reductase’s 510th amino acid improves 5-aminolevulinic acid synthesis in rice. Abstract 5-aminolevulinic acid (ALA) is the common precursor of all tetrapyrroles and plays an important role in plant growth regulation. ALA is synthesized from glutamate, catalyzed by glutamyl-tRNA synthetase (GluRS), glutamyl-tRNA reductase (GluTR), and glutamate-1-semialdehyde aminotransferase (GSAT). In Arabidopsis, ALA synthesis is the rate-limiting step in tetrapyrrole production via GluTR post-translational regulations. In rice, mutations of GluTR and GSAT homologs are known to confer chlorophyll deficiency phenotypes; however, the enzymatic activity of rice GluRS, GluTR, and GSAT and the post-translational regulation of rice GluTR have not been investigated experimentally. We have demonstrated that a suppressor mutation in rice partially reverts the xantha trait. In the present study, we first determine that the suppressor mutation results from a G → A nucleotide substitution of OsGluTR (and an A → V change of its 510th amino acid). Protein homology modeling and molecular docking show that the OsGluTRA510V mutation increases its substrate binding. We then demonstrate that the OsGluTRA510V mutation increases ALA synthesis in Escherichia coli without affecting its interaction with OsFLU. We further explore homologous genes encoding GluTR across 193 plant species and find that the amino acid (A) is 100% conserved at the position, suggesting its critical role in GluTR. Thus, we demonstrate that the gain-of-function OsGluTRA510V mutation underlies suppression of the xantha trait, experimentally proves the enzymatic activity of rice GluRS, GluTR, and GSAT in ALA synthesis, and uncovers conservation of the alanine corresponding to the 510th amino acid of OsGluTR across plant species.
Key message The genetic response to changing climatic factors selects consistent across the tested environments and location-specific thermo-sensitive and photoperiod susceptible alleles in lower and higher altitudes, respectively, for starting flowering in winter wheat. Abstract Wheat breeders select heading date to match the most favorable conditions for their target environments and this is favored by the extensive genetic variation for this trait that has the potential to be further explored. In this study, we used a germplasm with broad geographic distribution and tested it in multi-location field trials across Germany over three years. The genotypic response to the variation in the climatic parameters depending on location and year uncovered the effect of photoperiod and spring temperatures in accelerating heading date in higher and lower latitudes, respectively. Spring temperature dominates other factors in inducing heading, whereas the higher amount of solar radiation delays it. A genome-wide scan of marker-trait associations with heading date detected two QTL: an adapted allele at locus TaHd102 on chromosome 5A that has a consistent effect on HD in German cultivars in multiple environments and a non-adapted allele at locus TaHd044 on chromosome 3A that accelerates flowering by 5.6 days. TaHd102 and TaHd044 explain 13.8% and 33% of the genetic variance, respectively. The interplay of the climatic variables led to the detection of environment specific association responding to temperature in lower latitudes and photoperiod in higher ones. Another locus TaHd098 on chromosome 5A showed epistatic interactions with 15 known regulators of flowering time when non-adapted cultivars from outside Germany were included in the analysis.
Key message The CsGAI gene, identified by map-based, was involved in regulating seed germination in low temperature via the GA and ABA signaling pathways. Abstract Low temperature reduces the percentage of seeds germinating and delays seed germinating time, thus posing a threat to cucumber production. However, the molecular mechanism regulating low temperature germination in cucumber is unknown. We here dissected a major quantitative trait locus qLTG1.1 that controls seed germination at low temperature in cucumber. First, we fine-mapped qLTG1.1 to a 46.3-kb interval, containing three candidate genes. Sequence alignment and gene expression analysis identified Csa1G408720 as the gene of interest that was highly expressed in seeds, and encoded a highly conserved, low temperature-regulated DELLA family protein CsGAI. GUS expression analysis indicated that higher promoter activity underscored higher transcriptional expression of the CsGAI gene. Consistent with the known roles of GAI in ABA and GA signaling during germination, genes involved in the GA (CsGA2ox, CsGA3ox) and ABA biosynthetic pathways (CsABA1, CsABA2, CsAAO3 and CsNCED) were found to be differently regulated in the tolerant and sensitive genotypes under low temperatures, and this was reflected in differences in their ratio of GA-to-ABA. Based on these data, we proposed a working model explaining how CsGAI integrates the GA and ABA signaling pathways, to regulate cucumber seed germination at low temperature, thus providing new insights into this mechanism.
Genome-wide linkage disequilibrium (LD) for intermediate wheatgrass (Thinopyrum intermedium) for 200 Mb regions (a) and 5 Mb regions (b). Orange points represent individual marker combinations with a 250-marker sliding window. Average LD has been computed with the Hill and Weir formula (1988) and shown in blue. Vertical line represents the distance at which half-decay value occurs, with the dashed horizontal line showing the half-decay value
Manhattan plots of a shattering, b brittle rachis, and c seed circularity in intermediate wheatgrass (Thinopyrum intermedium) with line indicating 0.05 false discovery rate. Panels d–f show quantile–quantile (QQ) plots for p values under the null hypothesis (no association) and observed p values for shattering (d), brittle rachis (e), and seed circularity (f), respectively
Distribution of phenotypic values for shattering and brittle rachis (a and c respectively), where lower values are preferred, at the marker loci 4S_341952545 and 3J_115931563 in intermediate wheatgrass (Thinopyrum intermedium). Panels b and d display the allele (line plots) and genotype (points, H is heterozygote) frequency change for the shattering marker in The Land Institute (TLI) Cycle 6 to 9 breeding populations; population differentiation expressed with FST between TLI Cycle 6 and TLI Cycle 9
Key message Analysis of multi-year breeding program data revealed that the genetic architecture of an intermediate wheatgrass population was highly polygenic for both domestication and agronomic traits, supporting the use of genomic selection for new crop domestication. Abstract Perennial grains have the potential to provide food for humans and decrease the negative impacts of annual agriculture. Intermediate wheatgrass (IWG, Thinopyrum intermedium, Kernza®) is a promising perennial grain candidate that The Land Institute has been breeding since 2003. We evaluated four consecutive breeding cycles of IWG from 2016 to 2020 with each cycle containing approximately 1100 unique genets. Using genotyping-by-sequencing markers, quantitative trait loci (QTL) were mapped for 34 different traits using genome-wide association analysis. Combining data across cycles and years, we found 93 marker-trait associations for 16 different traits, with each association explaining 0.8–5.2% of the observed phenotypic variance. Across the four cycles, only three QTL showed an FST differentiation > 0.15 with two corresponding to a decrease in floret shattering. Additionally, one marker associated with brittle rachis was 216 bp from an ortholog of the btr2 gene. Power analysis and quantitative genetic theory were used to estimate the effective number of QTL, which ranged from a minimum of 33 up to 558 QTL for individual traits. This study suggests that key agronomic and domestication traits are under polygenic control and that molecular methods like genomic selection are needed to accelerate domestication and improvement of this new crop.
Genetic variation between BoFLC2E and BoFLC2L. a Gene structure of BoFLC2E and BoFLC2L, which were divided into seven exons (E1, E2, E3, E4, E5, E6 and E7) and six introns (black line), BoFLC2L had 215-bp deletion at intron I (215-bp indel), 3-bp SNPs and 3-bp deletion at exon II, compared with BoFLC2E(*); b variations at exon II between BoFLC2E and BoFLC2L; c comparison of BoFLC2E and BoFLC2L with other FLC genes from B. oleracea, B. rapa, B. napus, A. thaliana and R. sativus by cluster analysis via MEGA6
Flowering time variation of B. oleracea verified by indel-FLC2 marker. a Flowering time of extremely early-flowering (BoFLC2E) and extremely late-flowering (BoFLC2L) cabbages among three years (2015/16/17); b flowering time variation of F2 progeny between F416(BoFLC2E) and P1(BoFLC2L); c flowering time variation verified by indel-FLC2 marker in a diverse set of cabbage inbred lines among 2018/19/20; d bolting and flowering time of F2 population between 309 (boflc2) and Hansheng (BoFLC2L, extremely late flower). * means P < 0.05; ** means P < 0.01
a Structure of T-DNA in the plant expression cassette PBoFLC2::BoFLC2Ecds (BoFLC2Ecds), PBoFLC2::BoFLC2Lcds (BoFLC2Lcds) and PBoFLC2::BoFLC2Ecds + BoFLC2LintronI (BoFLC2Ecds + BoFLC2LintronI); bbar gene, target gene and promoter identification of T1 transgenic plants in Col, from the top to the bottom were Bar, target gene and promoter, E means plants of BoFLC2Ecds, L means plants of BoFLC2Lcds, IN means plants of BoFLC2Ecds + BoFLC2LintronI; c, d flowering time variation of T2 lines in A. thaliana transgenic plants; e, f flowering time variation of T1 lines in B. oleracea transgenic plants. *Means P < 0.05; **means P < 0.01
a-dBoFLC2 (a), BoSOC1 (b), BoFT (c) and BoLFY (d) expression in F416 and P1 exposed to 4 °C for 0, 20, 30, 40, 60 days vernalization and 10 days after vernalization (V10); eBoFLC2 expression in F416, P1 and their extremely early flowering (early) and late flowering (late) F2 segregating population planted in the field from November to December (0, 15, 30 and 45 days) in 2020; fBoFLC2 expression in T1 transgenic plants of BoFLC2Ecds, BoFLC2Lcds and BoFLC2Ecds + BoFLC2LintronI exposed to 4 °C for 0, 7, 14, 21 days vernalization. * means P < 0.05; ** means P < 0.01
BoVIN3 (a), BoVRN2 (b), BoVRN5 (c) and BoCLF (d) expression in F416 and P1 exposed to 4 °C for 0, 20, 30, 40, 60 days vernalization and 10 days after vernalization (V10). *Means P < 0.05; ** means P < 0.01
Key message In response to cold, a 215-bp deletion at intron I of BoFLC2 slows its silencing activity by feedback to the core genes of the PHD-PRC2 complex, resulting in late flowering in cabbage. Abstract Cabbage is a plant-vernalization-responsive flowering type. In response to cold, BoFLC2 is an important transcription factor, which allows cabbage plants to remain in the vegetative phase. However, there have been few reports on the detailed and functional effects of genetic variation in BoFLC2 on flowering time in cabbage. Herein, BoFLC2E and BoFLC2L, cloned from extremely early and extremely late flowering cabbages, respectively, exhibited a 215-bp indel at intron I, three non-synonymous SNPs and a 3-bp indel at exon II. BoFLC2L was found to be related to late flowering, as verified in 40 extremely early/late flowering accessions, a diverse set of cabbage inbred lines and two F2 generations by using indel-FLC2 marker. Among the genetic variation of BoFLC2, the 215-bp deletion at intron I was the main reason for the delayed flowering time, as verified in the transgenic progenies of seed-vernalization-responsive Arabidopsis thaliana (Col) and rapid cycler B. oleracea (TO1000, boflc2). This is the first report to show that the intron I indel of BoFLC2 affects the flowering time of cabbage. Although the intron I 215-bp indel between BoFLC2E and BoFLC2L did not cause alternative splicing, it slowed BoFLC2L silencing during vernalization and feedback to the core genes of the PHD-PRC2 complex, resulting in their lower transcription levels. Our study not only provides an effective molecular marker-assisted selective strategy for identifying bolting-resistant resources and breeding improved varieties in cabbage, but also provides an entry point for exploring the mechanisms of flowering time in plant-vernalization-responsive plants.
Phenotyping of the mapping populations for stem rust resistance. a MP1: RL 5271 × AL8/78 mapping population inoculated with North American Pgt race TPMKC. b MP2: CPI110672 × CPI110717 mapping population inoculated with Australian Pgt race 34-0. c MP3: Sr672.2 F4 population from CPI110672 × CPI110717 inoculated with Australian Pgt race 98-1,2,3,5,6. R, Resistant; S, Susceptible; R1 + R2, additive effect of two genes (Sr672.1 and Sr672.2b) when combined
Genetic and physical maps of stem rust resistance gene Sr46 in Ae. tauschii. a Genetic map of SR46 (Yu et al. 2015) (Sr46_h1). b Genetic and physical map of SRRL5271 (Sr46_h2). Numbers in brackets followed by marker names indicate marker positions on chromosome 2D. c Genetic map of SR672.1 (Sr46_h2)
Phenotypes of progenies of selected T1 Fielder + Sr46_h1,  Fielder + Sr46_h2 and Fielder + Sr46_h3 plants inoculated with Australian Pgt race 98-1,2,3,5,6. R, resistant; S, susceptible. Infection types 2++ and below are resistant, and 3 and above are susceptible
Key message Stem rust resistance genes, SrRL5271 and Sr672.1 as well as SrCPI110651, from Aegilops tauschii, the diploid D genome progenitor of wheat, are sequence variants of Sr46 differing by 1–2 nucleotides leading to non-synonymous amino acid substitutions. Abstract The Aegilops tauschii (wheat D-genome progenitor) accessions RL 5271 and CPI110672 were identified as resistant to multiple races (including the Ug99) of the wheat stem rust pathogen Puccinia graminis f. sp. tritici (Pgt). This study was conducted to identify the stem rust resistance (Sr) gene(s) in both accessions. Genetic analysis of the resistance in RL 5271 identified a single dominant allele (SrRL5271) controlling resistance, whereas resistance segregated at two loci (SR672.1 and SR672.2) for a cross of CPI110672. Bulked segregant analysis placed SrRL5271 and Sr672.1 in a region on chromosome arm 2DS that encodes Sr46. Molecular marker screening, mapping and genomic sequence analysis demonstrated SrRL5271 and Sr672.1 are alleles of Sr46. The amino acid sequence of SrRL5271 and Sr672.1 is identical but differs from Sr46 (hereafter referred to as Sr46_h1 by following the gene nomenclature in wheat) by a single amino acid (N763K) and is thus designated Sr46_h2. Screening of a panel of Ae. tauschii accessions identified an additional allelic variant that differed from Sr46_h2 by a different amino acid (A648V) and was designated Sr46_h3. By contrast, the protein encoded by the susceptible allele of Ae. tauschii accession AL8/78 differed from these resistance proteins by 54 amino acid substitutions (94% nucleotide sequence gene identity). Cloning and complementation tests of the three resistance haplotypes confirmed their resistance to Pgt race 98-1,2,3,5,6 and partial resistance to Pgt race TTRTF in bread wheat. The three Sr46 haplotypes, with no virulent races detected yet, represent a valuable source for improving stem resistance in wheat.
Box plots of prediction accuracies among agronomic and disease resistance traits evaluated under conventional management (Set-1), organic management (Set-2), all environments regardless of management systems (Set-3), reaction to diseases (Set-4), and all traits in Set-3 and Set-4 combined (Set-5). For each population and data set, predictions were obtained using single-trait (ST) and multi-trait models (MT1 and MT2). Population codes are as follows—ACG: Attila × CDC Go; PAC: Peace × Carberry; and BVC: an association mapping panel. See Supplementary Table S6 for details
Comparison of the mean changes in prediction accuracies between single trait (ST) and multi-trait (MT1 and MT2) models across seven agronomic traits evaluated in all environments regardless of management systems (Set-3), reaction to diseases (Set-4), and all agronomic and disease resistance traits (Set-5). For each population and data set, predictions were obtained using single-trait (ST) and multi-trait (MT1 and MT2) models. Population codes are as follows—ACG: Attila × CDC Go; PAC: Peace × Carberry; and BVC: an association mapping panel. See Table 3 and Supplementary Fig. S4 for details
Key Message This study performed comprehensive analyses on the predictive abilities of single-trait and two multi-trait models in three populations. Our results demonstrated the superiority of multi-traits over single-trait models across seven agronomic and four to seven disease resistance traits of different genetic architecture. Abstract The predictive ability of multi-trait and single-trait prediction models has not been investigated on diverse traits evaluated under organic and conventional management systems. Here, we compared the predictive abilities of 25% of a testing set that has not been evaluated for a single trait (ST), not evaluated for multi-traits (MT1), and evaluated for some traits but not others (MT2) in three spring wheat populations genotyped either with the wheat 90K single nucleotide polymorphisms array or DArTseq. Analyses were performed on seven agronomic traits evaluated under conventional and organic management systems, four to seven disease resistance traits, and all agronomic and disease resistance traits simultaneously. The average prediction accuracies of the ST, MT1, and MT2 models varied from 0.03 to 0.78 (mean 0.41), from 0.05 to 0.82 (mean 0.47), and from 0.05 to 0.92 (mean 0.67), respectively. The predictive ability of the MT2 model was significantly greater than the ST model in all traits and populations except common bunt with the MT1 model being intermediate between them. The MT2 model increased prediction accuracies over the ST and MT1 models in all traits by 9.0–82.4% (mean 37.3%) and 2.9–82.5% (mean 25.7%), respectively, except common bunt that showed up to 7.7% smaller accuracies in two populations. A joint analysis of all agronomic and disease resistance traits further improved accuracies within the MT1 and MT2 models on average by 21.4% and 17.4%, respectively, as compared to either the agronomic or disease resistance traits, demonstrating the high potential of the multi-traits models in improving prediction accuracies.
Morphological features of the accession G1812 and the tiller-reduced mutant tin5 of T. urartu. a Seedling stage in the field; b Three-leaf stage, Bar, 2 cm; c and d Magnified views of the boxed in b; e Grain filling stage, Bar, 10 cm. Distribution of tiller number per plant f and aboveground biomass g in the segregating F2 population (n = 240)
Mapping of TIN5 on chromosome Tu7. a Primary mapping. b Fine physical mapping of TIN5 using 2545 F2 individuals. Rec: number of recombinants. Numbers above marker indicate the number of recombinants. Red block indicates the candidate region of TIN5. c Graphical illustrations of the recombinant genotypes and phenotypes in the TIN5 interval
Structure of the candidate gene. a Structure of TuG1812G0700004539. The black box shows the exon. Coding sequence (c.) and corresponding protein (p.). Nucleotide and amino acid sequences of G1812 and the tin5 mutant are shown in red and black font, respectively. b Transcript levels of TuG1812G0700004539 in G1812 and the tin5 mutant at the four-leaf stage. Error bars represent ± SD of the values of three biological repeats. Student’s t-test (*P < 0.05) was used for statistical analysis
Key message A tiller inhibition gene TIN5 was delimited to an approximate 2.1 Mb region on chromosome Tu7 that contains 24 annotated genes. Abstract Grain yield in wheat (Triticum aestivum L.) is a polygenic trait representing many developmental processes and their interactions with the environments. Among them, tillering capacity is an important agronomic trait for plant architecture and grain yield, but the genetic basis of tiller formation in wheat remains largely unknown. In this study, we identified a tiller inhibition 5 (tin5) mutant from ethyl methane sulfonate treated G1812 (Triticum urartu Thumanjan ex Gandilyan). A mapping population was constructed with tin5/G3146. Based on the sequence differences between G1812 and G3146, large insertions and deletions (≥ 5 bp) were selected and verified, and a skeleton physical map was constructed with genome-wide 168 polymorphic InDel markers. Genetic analysis revealed that the low-tiller phenotype was controlled by a single recessive locus, which we named TIN5. This locus was mapped to a 2.1-Mb region that contained 24 annotated genes on chromosome Tu7. Among these annotated genes, only TuG1812G0700004539 showed a non-synonymous polymorphism between tin5 and the wild type. Our finding will facilitate its map-based cloning and pave the way for an in-depth analysis of the underlying genetic basis of tiller formation and regulation patterns.
Linkage (left), physical (middle), and deletion bin map (right) of Lr81 (formerly Lr470121). Markers are shown on the right side of the linkage map with their genomic locations given in the physical map. Genetic distances in cM are presented on the left of the linkage map. Molecular markers flanking Lr81 were connected to their appropriate physical bin. The breakpoint of the Chinese Spring deletion line 2AS-5 is shown with an arrow, and the corresponding fraction length (FL) value is given in parenthesis
Key message The novel, leaf rust seedling resistance gene, Lr81, was identified in a Croatian breeding line and mapped to a genomic region of less than 100 Kb on chromosome 2AS. Abstract Leaf rust, caused by Puccinia triticina, is the most common and widespread rust disease in wheat. Races of Puccinia triticina evolve rapidly in the southern Great Plains of the USA, and leaf rust resistance genes often lose effectiveness shortly after deployment in wheat production. PI 470121, a wheat breeding line developed by the University of Zagreb in Croatia, showed high resistance to Puccinia triticina races collected from Oklahoma, suggesting that PI 470121 could be a leaf rust resistance source for the southern Great Plains of the USA. Genetic analysis based on an F2 population and F2:3 families derived from the cross PI 470121 × Stardust indicated that PI 470121 carries a dominant seedling resistance gene, designated as Lr81. Linkage mapping delimited Lr81 to a genomic region of 96,148 bp flanked by newly developed KASP markers Xstars-KASP320 and Xstars-KASP323 on the short arm of chromosome 2A, spanning 67,030,206–67,132,354 bp in the Chinese Spring reference assembly (IWGSC RefSeq v1.0). Deletion bin mapping assigned Lr81 to the terminal bin 2AS-0.78–1.00. Allelism tests indicated that Lr81 is a distinctive leaf rust resistance locus with the physical order Lr65-Lr17-Lr81. Marker-assisted selection based on a set of markers closely linked to leaf rust resistance genes in PI 470121 and Stardust enabled identification of a recombinant inbred line RIL92 carrying Lr81 only. Lr81 is a valuable leaf rust resistance source that can be rapidly introgressed into locally adapted cultivars using KASP markers Xstars-KASP320 and Xstars-KASP323.
Phenotypic distribution of crude fat content in cultivated rice and an F2 population. a, b The appearance of cooked rice (a) (from top left to button right) and crude fat content (b) of Zhenshan97 (ZS97), Nipponbare (Nip), Buphopa (W127) and 88B-2. c–e The distribution of crude fat content in 533 diverse rice accessions (c), Indica subpopulation (d) and Japonica subpopulation (e). Numbers of Indica and Japonica accession were 303 and 181, respectively. f The distribution of crude fat content in the F2 population derived from the cross between 88B-2 and Hua2613S
QTL dissection on crude fat content in diverse rice accessions and the F2 population. a–c Manhattan plots and quantile–quantile plots depicting genome-wide association analysis (GWAS) results using a mixed model. Associations identified in whole population (a), Indica subspecies (b) and Japonica subspecies (c). The x axis depicts the physical location of SNPs across the 12 chromosomes of rice and the y axis depicts the -log10(P) value. d QTL mapping of crude fat content in 88B-2/Hua 2613S F2 population. e Karyotype showing crude fat QTL identified in GWAS and linkage analysis. c1–c12 represent the rice 12 chromosomes
Identification and validation of the candidate gene for qFC6. a The gene-based association analysis of crude fat content and the LD plot of polymorphisms in the candidate region. Variations were divided into four groups according to their effect annotation. b The most significant functional variation, and the gene structure of the causal gene. c–d Contents of crude fat in transgenic plants. e–f Contents of free lipids in transgenic plants. KO-Wx and CO-Wx, respectively, represent knockout and complementation vectors. ( −) and ( +) indicate transgene-negative and transgene-positive plants. ***P < 0.001. All P values were based on two-tailed t tests
The crude fat content and amylose content varied with the Wx function. a The allele variation of Wx gene in 533 cultivated rice accessions. b–c The phenotypic distribution of crude fate content (b) and amylose content (c) in varieties with different Wx alleles in 533 cultivated rice accessions. d–e the crude fat content (d) and amylose content (e) in Wx NILs with different Wx alleles. Different letters above bars indicate significant differences at P < 0.05, using Tukey’s multiple-comparison test
The glossiness of rice is affected by lipid composition which defined by the function of Wx alleles. a–d The free lipids (a), bound lipids (b), total lipids (c) content and appearance of cooked rice (d) in Wx NIL with different Wx alleles. e–f The appearance (e) and the taste value (f) of cooked rice with varying degrees of extra oil. Different letters above bars indicate significant differences at P < 0.05, using Tukey’s multiple-comparison test
Key message qFC6, a major quantitative trait locus for rice crude fat content, was fine mapped to be identical with Wx. FC6 negatively regulates crude fat content and rice quality. Abstract Starch, protein and lipids are the three major components in rice endosperm. The lipids content in rice influences both storage and quality. In this study, we identified a quantitative trait locus (QTL), qFC6, for crude fat (free lipids) content through association analysis and linkage analysis. Gene-based association analysis revealed that LOC_Os06g04200, also known as Wx, was the candidate gene for qFC6. Complementation and knockout transgenic lines revealed that Wx negatively regulates crude fat content. Lipid composition and content analysis by gas chromatography and taste evaluation analysis showed that FC6 positively influenced bound lipids content and negatively affected both free lipids content and taste. Besides, higher free lipids content rice varieties exhibit more lustrous appearance after cooking and by adding extra oil during cooking could improve rice luster and taste score, indicating that higher free lipids content may make rice more lustrous and delicious. Together, we cloned a QTL coordinating rice crude fat content and eating quality and assisted in uncovering the genetic basis of rice lipid content and in the improvement of rice eating quality.
Breeding scheme used to create single Rf2 gene-segregating populations via hybridization, self-pollination, and molecular marker-assisted selection. Markers S1597 and S1609 (Zhang et al. 2021) were used to select CaPPR6 (Rf1) homozygous recessive fertile single plants (genotypes rf1rf1Rf2Rf2 or rf1rf1Rf2rf2) from an F2 (7G) population, which were then backcrossed with the sterile parent, 77013A. After statistical analysis of the segregation of fertility traits in four backcross progeny populations (Table 2), a single plant, 7G-112 with the genotype rf1rf1Rf2rf2, was identified in the F2 population. Because fruits were immature owing to winter, we were unable to harvest seeds from the 7G-112 plant. Five fertile plants were therefore randomly selected from the backcross population (7G7) to obtain self-crossed seeds and used to develop a population for genetic mapping of the Rf2 gene
The genotypes and phenotypes of F2 (7G) plants. a Comparison of the average production of pollen grains per flower among different genotypes. The genotypes were detected using markers S1597 and S1609 (Zhang et al. 2021) linked to the Rf1 (CaPPR6) gene and the S1820 marker linked to the Rf2 (Capana06g000193) gene. Different lowercase letters indicate a significant difference (Student’s t-test, p < 0.05). b The abundance of pollen grains in male fertile plants dominant for Rf1. c Male fertile plants recessive for Rf1 produce less pollen grains than dominant Rf1 plants. d The absence of pollen grains in male sterile plants
Genetic linkage map of Rf1 and Rf2. The markers S1585, S1597, and S1609 were co-segregated with Rf1, and S1803, S1820, and S1718 were co-segregated with Rf2. On the right side of the genetic map were the physical locations of the markers on the reference genome Zunla-1 v2.0 (Qin et al. 2014). The markers S1585, S1597, S1609, S1350, and S1436 were developed by Zhang et al. (2021). The underlined physical location number was obtained by BLAST searches. The physical location of S1585 was not retrieved in Zunla-1 v2.0 (Qin et al. 2014)
Recombinants between phenotypes and marker genotypes in Rf2 gene-segregating populations. Sixteen marker genotypes for 44 recombinants were detected. Physical locations of the flanking markers on chromosome 6 of the Zunla-1 reference genome are 2,560,823 bp for S1736 and 2,740,136 bp for S1719. Fifteen genes were located in the 179.3-kb candidate interval between the two flanking markers. Red markers indicate co-segregation with the phenotype. A, genotype of sterile line 77013A; B, genotype of restorer line G164; H, heterozygous genotype; MS, male-sterile; MF, male-fertile
Expression analysis of the male fertility restoration genes CaPPR6 and Capana06g000193 in the flower buds of 77013A, 0601 M, and G164. 77013A is a male-sterile line carrying homozygous recessive alleles of CaPPR6 and Capana06g000193, and 0601 M and G164 are male fertility restoration lines harboring CaPPR6 and CaPPR6 + Capana06g000193, respectively. The average standard deviation was determined from three biological replicates; different lowercase letters indicate a significant difference (Student’s t-test, p < 0.05)
Key message Genome re-sequencing and recombination analyses identified Capana06g000193 as a strong candidate for the minor male fertility restoration locus Rf2 in chili pepper G164 harboring two dominant male fertility restoration genes. Abstract Male fertility restoration genes of chili pepper restorer line G164 (Capsicum annuum L.) were studied using molecular marker genotypes of an F2 population (7G) of G164 crossed with the cytoplasmic male sterility line 77013A. The ratio of sterile to fertile single plants in the F2 population was 1:15. This result indicates that chili pepper G164 has two dominant restoration genes, which we designated as Rf1 and Rf2. An individual plant recessive for Rf1 and heterozygous for Rf2, 7G-112 (rf1rf1Rf2rf2), was identified by molecular marker selection and genetic analysis, and a single Rf2 gene-segregating population with a 3:1 ratio of fertile to sterile plants was developed from the self-pollination of male fertile individuals of 77013A and 7G-112 hybrid progeny. Bulk segregant analysis of fertile and sterile pools from the segregating populations was used to genetically map Rf2 to a 3.1-Mb region on chromosome 6. Rf2 was further narrowed to a 179.3-kb interval through recombination analysis of molecular markers and obtained the most likely candidate gene, Capana06g000193.
Key message Map-based cloning and photoperiod response detection suggested that CsFT is the critical gene for cucumber photoperiod domestication. Abstract Photoperiod sensitivity is important for sensing seasonal changes and local adaptation. However, day-length sensitivity limits crop geographical adaptation and it should be modified during domestication. Cucumber was domesticated in southern Asia and is currently cultivated worldwide across a wide range of latitudes, but its photoperiod sensitivity and its change during cucumber domestication are unknown. Here, we confirmed wild cucumber (Hardwickii) was a short-day plant, and its flowering depends on short-day (SD) conditions, while the cultivated cucumber (9930) is a day-neutral plant that flowers independently of day length. A photoperiod sensitivity locus (ps-1) was identified by the 9930 × Hardwickii F2 segregating populations, which span a ~ 970 kb region and contain 60 predicted genes. RNA-seq analysis showed that the critical photoperiod pathway gene FLOWERING LOCUS T (CsFT) within the ps-1 locus exhibits differential expression between 9930 and Hardwickii, which was confirmed by qRT-PCR detection. CsFT in Hardwickii was sensitive to day length and could be significantly induced by SD conditions, whereas CsFT was highly expressed in 9930 and was insensitive to day length. Moreover, the role of CsFT in promoting flowering was verified by overexpression of CsFT in Arabidopsis. We also identified the genetic variations existing in the promoter of CsFT among the different geographic cucumbers and suggest they have possible roles in photoperiod domestication. The results of this study suggest that a variation in photoperiod sensitivity of CsFT is associated with day neutrality and early flowering in cultivated cucumber and could contribute to cucumber cultivation in diverse regions throughout the world.
Graphical genotypes of the CSSL lines C119 (a), C57 (b), C54 (c), and C122 (d). The position of the introgressed chromosomal segments originating from ‘Nipponbare’ is shown in blue. The red and yellow lines represent the homozygous 9311 genotype and the heterozygous genotypes, respectively. The position indicated by the arrow represents the Rf5 and Rf6 location, respectively
Gross morphology of rice plants and pollen viability. a WufengA-WFA. B–d F1 plants of WFA × 9311, WFA x C119, TFA × 9311, TFA xC119, Guang8A × 9311, and Guang8A x C119. E–k Pollen grains stained with I2–KI from corresponding plants of WFA, WFA × 9311, WFA x C119, TFA × 9311, TFA x C119, Guang8A × 9311, and Guang8A x C119, respectively. Scale bars = 20 cm in a–d, 50 μm in e–k
Primary mapping of Rf18(t). Numbers under the partial map of chromosome 1 indicate physical distances. Black and white rectangles represent regions homozygous for NIP and 9311, respectively. “F” and “S” indicate the fertile and sterile testcross F1 hybrids, respectively
Fine mapping and candidate gene analysis of Rf18(t). a and b Fine mapping of Rf18(t) based on the genotypes (left) and phenotypes (right) of the recombinant substitution individuals selected from a segregating populations derived from 9311 and CSSL line C119. c Two open reading frames (ORFs) were found to be present in the 48 kb target region, and comparisons of the genomic sequences of the two ORFs between 9311 and NIP. d Comparisons of the relative expression of LOC_Os01g71310 and LOC_Os01g71320 mRNA in fertile and sterile plants. Black and white rectangles represent regions homozygous for NIP and 9311 genotypes, respectively, in A and B. “F” and “S” indicate the fertile and sterile testcross F1 hybrids, respectively. ▲ indicates the position of inserted nucleotides in 9311. Gene expression levels were normalized against the expression of the rice UBQ gene. Means ± SD were obtained from three technical replicates and three biological replicates
Relative expression of WA352 in young panicles of sterile and fertile testcross F1 plants. Left to right: WFA × 9311 F1, WFA x CZ6 F1, WFA x C119 F1, and WFA x CZ1 F1. Expression levels were normalized against the expression of the rice UBQ gene. Means ± SD were obtained from three technical replicates and three biological replicates
Key message We mapped Rf18(t), a Restorer-of-fertility gene for wild abortive cytoplasmic male sterility from the japonica maintainer ‘Nipponbare’, to chromosome 1. The best candidate gene, LOC_Os01g71320, is predicted to encode hexokinase. Abstract Three-line hybrid rice obtained through cytoplasmic male sterility (CMS) has helped increase the yield of rice globally, and the wild abortive (WA)-type cytoplasm from wild rice (Oryza rufipogon Griff.) is used widely in three-line indica hybrids. The identification and mapping of the Restorer-of-fertility (Rf) genes in maintainer lines aided in uncovering the genetic basis of fertility restoration of WA-type CMS and the development of WA-type hybrids. In this study, we identified a new Rf gene, Rf18(t), for WA-type CMS from the japonica maintainer line ‘Nipponbare’ using a chromosome segment substitution line population derived from a cross between the indica line 9311 and ‘Nipponbare.’ Using a substitution mapping strategy, Rf18(t) was delimited to a 48-kb chromosomal region flanked by molecular marker loci ID01M28791 and ID01M28845 on chromosome 1. By comparative sequence analyses, we propose that LOC_Os01g71320 is the most likely candidate gene for Rf18(t), and it is predicted to encode hexokinase. Furthermore, Rf18(t) was found to function in fertility restoration probably by a posttranscriptional mechanism and its function is dependent on the genetic background of 9311. These results broaden our knowledge on the mechanism of fertility restoration of WA-type CMS lines and will facilitate the development of WA-type rice hybrids.
Key message In this study, we present AAQSP as an extension of existing NGS-BSA applications for identifying stable QTLs at high resolution. GhPAP16 and GhIQD14 fine mapped on chromosome D09 of upland cotton are identified as important candidate genes for lint percentage (LP). Abstract Bulked segregant analysis combined with next generation sequencing (NGS-BSA) allows rapid identification of genome sequence differences responsible for phenotypic variation. The NGS-BSA approach applied to crops mainly depends on comparing two bulked DNA samples of individuals from an F2 population. Since some F2 individuals still maintain high heterozygosity, heterosis will exert complications in pursuing NGS-BSA in such populations. In addition, the genetic background influences the stability of gene expression in crops, so some QTLs mapped in one segregating population may not be widely applied in crop improvement. The AAQSP (Association Analysis of QTL-seq on Semi-homologous Populations) reported in our study combines the optimized scheme of constructing BSA bulks with NGS-BSA analysis in two (or more) different parental genetic backgrounds for isolating the stable QTLs. With application of AAQSP strategy and construction of a high-density linkage map, we have successfully identified a QTL significantly related to lint percentage (LP) in cultivated upland cotton, followed by map-based cloning to dissect two candidate genes, GhPAP16 and GhIQD14. This study demonstrated that AAQSP can efficiently identify stable QTLs for complex traits of interest, and thus accelerate the genetic improvement of upland cotton and other crop plants.
Fine mapping of QTKW.caas-5DL.a Confirmation of QTKW.caas-5DL in the Doumai/Shi4185 RIL population using new phenotypic data. LOD contours of QTKW.caas-5DL generated from inclusive composite interval mapping. The vertical and horizontal axes indicate the LOD score and genetic positions (cM) of markers, respectively. The QTL mapping region is labeled with the flanking SNP markers IWB63123 and IWA1681. DZ, Dezhou; GY, Gaoyi; Average, mean value for two environments. b Physical locations of molecular markers in the target interval of QTKW.caas-5DL on chromosome 5DL according to Chinese Spring reference genome. Twenty-nine markers (M1-M29) were used to screen and confirm recombinants. c Graphical genotypes (left panel) and phenotypic assays (right panel) of 10 types of recombinants. Dark blue, orange and green rectangles represent the genotypes of Doumai (5D +), Shi4185 (5D-) and heterozygotes, respectively. Note: the homozygous lines in the L2-1 descendent population were genotyped by M10. Statistical analyses of TKW between 5D + and 5D- genotypes from self-pollinated progenies of each recombinant are shown in the right panel. *, **, *** and ns, significant at P < 0.05, P < 0.01 and P < 0.001, and non-significant, respectively
Collinearity analysis of 64 genes within the QTKW.caas-5DL physical interval among Chinese Spring (marked in red at the left panel) and eight varieties from the 10 + Genome project by the MicroCollinearity function of TGT (; Chen et al. 2020b). The gene accession numbers are marked in red in the right panel. RBH and SBH represent reciprocal and single-side best hits, respectively (colour figure online)
Genetic effect of QTKW.caas-5DL on thousand kernel weight (TKW) in a panel of 150 wheat cultivars. a Genotyping results for the panel using marker KASP306. Red, blue and black dots represent Doumai allele, Shi4185 allele and NTC (no-template control), respectively. b Comparisons of TKW between panel members with contrasting genotypes. AY, Anyang; SX, Suixi. *, ** and ns, significant at P < 0.05, P < 0.01 and non-significant, respectively
Key message We fine mapped QTL QTKW.caas-5DL for thousand kernel weight in wheat, predicted candidate genes and developed a breeding-applicable marker. Abstract Thousand kernel weight (TKW) is an important yield component trait in wheat, and identification of the underlying genetic loci is helpful for yield improvement. We previously identified a stable quantitative trait locus (QTL) QTKW.caas-5DL for TKW in a Doumai/Shi4185 recombinant inbred line (RIL) population. Here we performed fine mapping of QTKW.caas-5DL using secondary populations derived from 15 heterozygous recombinants and delimited the QTL to an approximate 3.9 Mb physical interval from 409.9 to 413.8 Mb according to the Chinese Spring (CS) reference genome. Analysis of genomic synteny showed that annotated genes in the physical interval had high collinearity among CS and eight other wheat genomes. Seven genes with sequence variation and/or differential expression between parents were predicted as candidates for QTKW.caas-5DL based on whole-genome resequencing and transcriptome assays. A kompetitive allele-specific PCR (KASP) marker for QTKW.caas-5DL was developed, and genotyping confirmed a significant association with TKW but not with other yield component traits in a panel of elite wheat cultivars. The superior allele of QTKW.caas-5DL was frequent in a panel of cultivars, suggesting that it had undergone positive selection. These findings not only lay a foundation for map-based cloning of QTKW.caas-5DL but also provide an efficient tool for marker-assisted selection.
Plot of principal coordinate analysis for a panel of soybean accessions evaluated for their reactions to soybean rust in the southeastern USA. Dots representing plant introductions (PIs) are color-coded based on country of origin. Clusters of PIs from Japan, Indonesia and Vietnam are mostly independent from one another, and the susceptible checks from the USA also formed an independent cluster
Dendrogram depicting the genetic relationship among soybean plant introductions in a panel of germplasm accessions evaluated for their reactions to soybean rust in the southeastern USA. The grouping patterns also indicate that the majority of the accessions from Vietnam, Indonesia and especially Japan formed distinct groups based on country of origin. The genetic similarities among US cultivars used as checks in the disease assays are also evident
Manhattan plot generated from a genome-wide association analysis of a panel of soybean accessions evaluated for their reactions to soybean rust in the southeastern USA. The X-axis shows the location of SNPs along each chromosome in the genome, and the Y-axis shows the − log10 of the p-values. The significance threshold was − log10(P) = 4.84
Quantile–quantile (QQ) plot of expected vs. observed p-values for each SNP marker used in the GWAS analysis
Key message Eight soybean genomic regions, including six never before reported, were found to be associated with resistance to soybean rust (Phakopsora pachyrhizi) in the southeastern USA. Abstract Soybean rust caused by Phakopsora pachyrhizi is one of the most important foliar diseases of soybean [Glycine max (L.) Merr.]. Although seven Rpp resistance gene loci have been reported, extensive pathotype variation in and among fungal populations increases the importance of identifying additional genes and loci associated with rust resistance. One hundred and ninety-one soybean plant introductions from Japan, Indonesia and Vietnam, and 65 plant introductions from other countries were screened for resistance to P. pachyrhizi under field conditions in the southeastern USA between 2008 and 2015. The results indicated that 84, 69, and 49% of the accessions from southern Japan, Vietnam or central Indonesia, respectively, had negative BLUP values, indicating less disease than the panel mean. A genome-wide association analysis using SoySNP50K Infinium BeadChip data identified eight genomic regions on seven chromosomes associated with SBR resistance, including previously unreported regions of Chromosomes 1, 4, 6, 9, 13, and 15, in addition to the locations of the Rpp3 and Rpp6 loci. The six unreported genomic regions might contain novel Rpp loci. The identification of additional sources of rust resistance and associated genomic regions will further efforts to develop soybean cultivars with broad and durable resistance to soybean rust in the southern USA.
Pearson correlation coefficients, histograms and scatterplots between across-year best linear unbiased estimates (BLUEs) for normalized incidences of dwarf bunt (DB-NI) and common bunt (CB–NI) as well as plant height (PH) and heading date (HD) in the common bunt trials across all years (2019–2021) (Gordon et al. 2020)
a Best linear unbiased estimates (BLUEs) across three years for common bunt normalized incidence (CB–NI) in percentages for genotypes assigned to different subpopulations. Number of genotypes per subpopulation is shown on the x-axis, crosses mark average CB–NI. b Heatmap comparing subpopulation averages of BLUEs across years for normalized incidence (NI) of dwarf bunt (DB-NI) and CB–NI (Gordon et al. 2020)
Scatterplot of the first two principal components of the 238 accessions used for association mapping. Individual subpopulations in the panel are discriminated by shapes of the data points. Colours of individual data points indicate across-year best linear unbiased estimates (BLUEs) of normalized common bunt incidence (CB–NI) levels of the respective genotypes (Gordon et al. 2020)
Manhattan plot showing marker-trait associations for best linear unbiased estimates (BLUEs) of normalized common bunt incidence across all three years (2019–2021). The dashed line marks a significance threshold of α=0.05\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\alpha = 0.05$$\end{document}
Key message Association mapping and phenotypic analysis of a diversity panel of 238 bread wheat accessions highlights differences in resistance against common vs. dwarf bunt and identifies genotypes valuable for bi-parental crosses. Abstract Common bunt caused by Tilletia caries and T. laevis was successfully controlled by seed dressings with systemic fungicides for decades, but has become a renewed threat to wheat yield and quality in organic agriculture where such treatments are forbidden. As the most efficient way to address this problem is the use of resistant cultivars, this study aims to broaden the spectrum of resistance sources available for breeders by identifying resistance loci against common bunt in bread wheat accessions of the USDA National Small Grains Collection. We conducted three years of artificially inoculated field trials to assess common bunt infection levels in a diversity panel comprising 238 wheat accessions for which data on resistance against the closely related pathogen Tilletia controversa causing dwarf bunt was already available. Resistance levels against common bunt were higher compared to dwarf bunt with 99 accessions showing ≤\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\le$$\end{document} 1% incidence. Genome-wide association mapping identified six markers significantly associated with common bunt incidence in regions already known to confer resistance on chromosomes 1A and 1B and novel loci on 2B and 7A. Our results show that resistance against common and dwarf bunt is not necessarily controlled by the same loci but we identified twenty accessions with high resistance against both diseases. These represent valuable new resources for research and breeding programs since several bunt races have already been reported to overcome known resistance genes.
Population structure and genome-wide association analysis (GWAS) of pre-harvest sprouting tolerance (PHST) in 302 wheat accessions. A Stacked barplot for 302 wheat accessions consisting of nine major subpopulations. The colors indicate the nine different subpopulations identified through the ADMIXTURE program. B Manhattan plot and C Quantile–quantile (Q–Q) plot of germination percentage for the best linear unbiased estimator (GP_BLUE). The Manhattan plot indicates the −log10 (observed P-value) for genome-wide SNPs (y-axis) plotted against their respective positions on each chromosome (x-axis). The threshold of 4.0 was adopted with a red line. For the Q–Q plot, the x-axis shows the −log10 transformed expected P-value, and the y-axis represents the −log10 transformed observed P-value (colour figure online)
Detected QTL for pre-harvest sprouting tolerance (PHST). The red blocks indicate the positions of the significant candidate interval. Black text indicates QTL co-located with PHST genes, blue text indicates QTL co-located with PHST QTL reported in previous studies, and green text indicates novel QTL in the present study (colour figure online)
Sequence and Mann–Whitney U-test of germination percentage in 233 wheat accessions (GP_2021ZX) of two haplotypes for the molecular markers PHST3A9200-18 and PHST7B1193-Hae3 A Sequence information of the two haplotypes for PHST3A9200-18. B Boxplot of the Mann-Whitney U-test of germination percentage in 233 wheat accessions (GP_2021ZX) for the two haplotypes of PHST3A9200-18. *indicates a significant difference at the 0.05 level of probability. C Sequence information of the two haplotypes for PHST7B1193-Hae3. D Boxplot of the Mann-Whitney U-test for the two haplotypes of PHST7B1193-Hae3. ***indicates a significant difference at the 0.001 level of probability
Expression and phenotypic analysis of wild-type and transgenic plants with different alleles of TaNAC074. A Transcript level of TaNAC074 in different tissues measured by qRT-PCR. Total RNA was extracted from different wheat tissues (seminal root, first leaf, root, the second internode, flag leaf blade, and spike) at 9 days after anthesis (DAA), from dry seeds, and from seeds at 12 h (h) after imbibition. The mean value is an average ± standard error (SE) of three biological replicates. B Germination percentage for the two haplotypes as determined by molecular marker based on a SNP at position -46 of the TaNAC074 promoter in 233 wheat accessions. *indicates a Mann–Whitney U-test significant difference at the 0.05 level. C Seed images of Fielder (wild-type) and TaNAC074 transgenic lines (OE1 and OE2) 96 h after imbibition. D Relative expression of TaNAC074 in 48 h imbibed seeds of Fielder and the transgenic lines (OE1 and OE2). E Seed germination of Fielder (wild-type) and TaNAC074 transgenic lines (OE1 and OE2). GP3D, GP6D, GP9D, GP12D, and GP15D indicate 3 days (d), 6 d, 9 d, 12 d, and 15 d post-imbibition, respectively. Data are means ± standard deviation (SD), n = 10–15. Significant differences of the transgenic lines compared with Fielder (wild-type) line were determined using Student’s t-test: **P < 0.01 and *P < 0.05 (colour figure online)
Key message Twelve QTL associated with pre-harvest sprouting tolerance were identified using association analysis in wheat. Two markers were validated and a candidate gene TaNAC074 for Qgpf.cas-3B.2 was verified using Agrobacterium-mediated transformation. Abstract Pre-harvest sprouting (PHS) is a considerable global threat to wheat yield and quality. Due to this threat, breeders must identify quantitative trait loci (QTL) and genes conferring PHS-tolerance (PHST) to reduce the negative effects of PHS caused by low seed dormancy. In this study, we evaluated a panel of 302 diverse wheat genotypes for PHST in four environments and genotyped the panel with a high-density wheat 660 K SNP array. By using a genome-wide association study (GWAS), we identified 12 stable loci significantly associated with PHST (P < 0.0001), explaining 3.34 − 9.88% of the phenotypic variances. Seven of these loci co-located with QTL and genes reported previously. Five loci (Qgpf.cas-3B.2, Qgpf.cas-3B.3, Qgpf.cas-3B.4, Qgpf.cas-7B.2, and Qgpf.cas-7B.3), located in genomic regions with no known PHST QTL or genes, are likely to be new QTL conferring PHST. Additionally, two molecular markers were developed for Qgpf.cas-3A and Qgpf.cas-7B.3, and validated using a different set of 233 wheat accessions. Finally, the PHST-related function of candidate gene TaNAC074 for Qgpf.cas-3B.2 was confirmed by CAPS (cleaved amplified polymorphic sequences) marker association analysis in 233 wheat accessions and by expression and phenotypic analysis of transgenic wheat. Overexpression of TaNAC074 significantly reduced seed dormancy in wheat. This study contributes to broaden the genetic basis and molecular marker-assisted breeding of PHST.
Key message Key genes controlling flowering and interactions of different photoperiod alleles with various environments were identified in a barley MAGIC population. A new candidate gene for vernalisation requirements was also detected. Abstract Optimal flowering time has a major impact on grain yield in crop species, including the globally important temperate cereal crop barley (Hordeum vulgare L.). Understanding the genetics of flowering is a key avenue to enhancing yield potential. Although bi-parental populations were used intensively to map genes controlling flowering, their lack of genetic diversity requires additional work to obtain desired gene combinations in the selected lines, especially when the two parental cultivars did not carry the genes. Multi-parent mapping populations, which use a combination of four or eight parental cultivars, have higher genetic and phenotypic diversity and can provide novel genetic combinations that cannot be achieved using bi-parental populations. This study uses a Multi-parent advanced generation intercross (MAGIC) population from four commercial barley cultivars to identify genes controlling flowering time in different environmental conditions. Genome-wide association studies (GWAS) were performed using 5,112 high-quality markers from Diversity Arrays Technology sequencing (DArT-seq), and Kompetitive allele-specific polymerase chain reaction (KASP) genetic markers were developed. Phenotypic data were collected from fifteen different field trials for three consecutive years. Planting was conducted at various sowing times, and plants were grown with/without additional vernalisation and extended photoperiod treatments. This study detected fourteen stable regions associated with flowering time across multiple environments. GWAS combined with pangenome data highlighted the role of CEN gene in flowering and enabled the prediction of different CEN alleles from parental lines. As the founder lines of the multi-parental population are elite germplasm, the favourable alleles identified in this study are directly relevant to breeding, increasing the efficiency of subsequent breeding strategies and offering better grain yield and adaptation to growing conditions.
Key message A novel rice resistance gene, Xo2, influencing pathogenesis of the bacterial leaf streak disease, has been identified, and candidate genes for Xo2 in the fine mapping region have been shown to be involved in bacterial leaf streak resistance. Abstract Rice (Oryza sativa) bacterial leaf streak, caused by Xanthomonas oryzae pv. oryzicola (Xoc), is one of the most serious rice bacterial diseases. The deployment of host resistance genes is an effective approach for controlling this disease. The cultivar BHADOIA 303 (X455) from Bangladesh is resistant to most of Chinese Xoc races. To identify and map the resistance gene(s) involved in Xoc resistance, we examined the association between phenotypic and genotypic variations in two F2 populations derived from crosses between X455/Jingang 30 and X455/Wushansimiao. The segregation ratios of the F2 progeny were consistent with the action of a single dominant resistance gene, which was designated as Xo2. Based on rice SNP chip (GSR40K) assays of X455, Jingang 30, and resistant and susceptible pools thereof, we mapped Xo2 to the region from 10 Mb to 12.5 Mb on chromosome 2. The target gene was further finely mapped between the markers RM12941 and D6-1 within an approximately 110-kb region. The de novo sequencing and gene annotation of X455 and Jingang 30 revealed nineteen predicted genes within the target region. RNA-seq and expression analysis showed that four candidate genes, including Osa002T0115800, encoding an NLR resistance protein, were distinctly upregulated. Differential sequence and synteny analysis between X455 and Jingang 30 suggested that Osa002T0115800 is likely the functional Xo2 gene. This study lays a foundation for marker-assisted selection resistance breeding against rice bacterial leaf streak and the further cloning of Xo2.
Schematic of non-imputation analysis. The total analysis was conducted 30 times to obtain averages of estimates and model performance criterion. (1) The SunGrains 2020 (SG20) or SunGrains 2021 (SG21) data were randomly partitioned into different sizes. (2) The training population created in (1) was used to train and tune parameters for gradient boosting machine (GBM), k-nearest neighbor (KNN), naive classification with the most correlated marker (NCOR), and random forest (RF). (3) The trained models from (2) were used to predict the classes of the held-out testing portion of either SG20 or SG21. (4) The trained models from (2) were used to predict the QTL haplotype calls of the Southern Uniform Winter Wheat Scab Nursery (SUWWSN) and calculate confusion matrices and coefficients. (5) The QTL haplotype calls predicted in (4) were used to estimate the group means of the predicted QTL haplotype call across the available data for the SUWWSN
A Accuracy, specificity, and sensitivity boxplots of the 30 iterations for 10, 50, and 90% training sizes (denoted by actual number of individuals in training) of the 2020 SunGrains trained model forward predictions made on the Uniform Southern Winter Wheat Scab Nursery for, Qfhb.vt-1B,, and Fhb1. Models, beagle (BEAGLE), gradient boosting machine (GBM), k-nearest neighbor (KNN), naive classification with the most correlated marker (NCOR), and random forest (RF), are denoted by color and listed on the x-axis. Training sizes are denoted by gray banners in each subgraph. The y-axis denotes the response. B Accuracy, specificity, and sensitivity boxplots of the 30 iterations for 10, 50, and 90% training sizes (denoted by actual number of individuals in training) of the 2021 SunGrains trained model forward predictions made on the Uniform Southern Winter Wheat Scab Nursery for, Qfhb.vt-1B,, and Fhb1. Models, beagle (BEAGLE), gradient boosting machine (GBM), k-nearest neighbor (KNN), naive classification with the most correlated marker (NCOR), and random forest (RF), are denoted by color and listed on the x-axis. Training sizes are denoted by gray banners in each subgraph. The y-axis denotes the response
A 2020 SunGrains average GBS SNP marker importance values. Importance values are scaled between 0 and 100 for interpretability. Training size is denoted by the color of the point. Importance value is denoted by the y-axis. The x-axis denotes the position of the marker in mega base pairs (Mbp). The red vertical lines indicates the interval of KASP markers used in haplotyping. B 2021 SunGrains average GBS SNP marker importance values. Importance values are scaled between 0 and 100 for interpretability. Training size is denoted by the color of the point. Importance value is denoted by the y-axis. The x-axis denotes the position of the marker in mega base pairs (Mbp). The red vertical lines indicates the interval of KASP markers used in haplotyping
A Estimated means of predicted QTL haplotype calls using the SG20 population versus observed QTL haplotype calls in the SUWWSN averaged over 30 iterations. Each sub-figure is labeled with the QTL to which the results displayed belong. The averaged estimated group means for severity (SEV), percent Fusarium damaged kernels (FDK), and deoxynivalenol content (DON) are presented and indicated on the y-axis. The x-axis denotes a haplotype call of resistant (R) or susceptible (S). Line color and point shape denote what model a prediction came from or if the QTL haplotype calls were observed. The training size of the population used to train the models is denoted above in gray banners. Bars surrounding points represent the averaged standard error about the averaged estimated group mean. B Estimated means of predicted QTL haplotype calls using the SG21 population versus observed QTL haplotype calls in the SUWWSN averaged over 30 iterations. Each sub-figure is labeled with the QTL to which the results displayed belong. The averaged estimated group means for severity (SEV), percent Fusarium damaged kernels (FDK), and deoxynivalenol content (DON) are presented and indicated on the y-axis. The x-axis denotes a haplotype call of resistant (R) or susceptible (S). Line color and point shape denote what model a prediction came from or if the QTL haplotype calls were observed. The training size of the population used to train the models is denoted above in gray banners. Bars surrounding points represent the averaged standard error about the averaged estimated group mean (gm)
A hypothetical general schematic of how predictive QTL haplotyping could be incorporated into a breeding pipeline. All boxes in black and all black text near black boxes relate to the phenotypic breeding program method. Displayed is the mass–selection–pedigree method of a single cross. Red boxes and lines relate to the marker-assisted selection (MAS) pipeline where lines are genotyped using molecular markers to make a QTL haplotype call. Blue boxes and lines relate to the genotyping-by-sequencing (GBS) pipeline. Green boxes and arrows involve data from both the MAS and GBS pipeline to train machine learning models to predict QTL haplotype calls
Key message Marker-assisted selection is important for cultivar development. We propose a system where a training population genotyped for QTL and genome-wide markers may predict QTL haplotypes in early development germplasm. Abstract Breeders screen germplasm with molecular markers to identify and select individuals that have desirable haplotypes. The objective of this research was to investigate whether QTL haplotypes can be accurately predicted using SNPs derived by genotyping-by-sequencing (GBS). In the SunGrains program during 2020 (SG20) and 2021 (SG21), 1,536 and 2,352 lines submitted for GBS were genotyped with markers linked to the Fusarium head blight QTL:, Qfhb.vt-1B, Fhb1, and In parallel, data were compiled from the 2011–2020 Southern Uniform Winter Wheat Scab Nursery (SUWWSN), which had been screened for the same QTL, sequenced via GBS, and phenotyped for: visual Fusarium severity rating (SEV), percent Fusarium damaged kernels (FDK), deoxynivalenol content (DON), plant height, and heading date. Three machine learning models were evaluated: random forest, k-nearest neighbors, and gradient boosting machine. Data were randomly partitioned into training–testing splits. The QTL haplotype and 100 most correlated GBS SNPs were used for training and tuning of each model. Trained machine learning models were used to predict QTL haplotypes in the testing partition of SG20, SG21, and the total SUWWSN. Mean disease ratings for the observed and predicted QTL haplotypes were compared in the SUWWSN. For all models trained using the SG20 and SG21, the observed Fhb1 haplotype estimated group means for SEV, FDK, DON, plant height, and heading date in the SUWWSN were not significantly different from any of the predicted Fhb1 calls. This indicated that machine learning may be utilized in breeding programs to accurately predict QTL haplotypes in earlier generations.
Key message Two candidate genes (Csa6G046210 and Csa6G046240) were identified by fine-mapping gsb-s6.2 for gummy stem blight resistance in cucumber stem. Abstract Gummy stem blight (GSB) is a serious fungal disease caused by Didymella bryoniae, that affects cucumber yield and quality worldwide. However, no GSB-resistant genes have been identified in cucumber cultivars. In this study, the wild cucumber accession ‘PI 183967’ was used as a source of resistance to GSB in adult stems. An F2 population was mapped using resistant line ‘LM189’ and susceptible line ‘LM6’ derived from a cross between ‘PI 183967’ and ‘931’. By developing InDel and SNP markers, the gsb-s6.2 QTL on Chr. 6 was fine-mapped to a 34 kb interval harboring six genes. Gene Expression analysis after inoculation showed that two candidate genes (Csa6G046210 and Csa6G046240) were induced and differentially expressed between the resistant and susceptible parents, and may be involved in disease defense. Sequence alignment showed that Csa6G046210 encodes a multiple myeloma tumor-associated protein, and it harbored two nonsynonymous SNPs and one InDel in the third and the fourth exons, and two InDels in the TATA-box of the basal promoter region. Csa6G046240 encodes a MYB transcription factor with six variants in the AP2/ERF and MYB motifs in the promoter. These two candidate genes lay the foundation for revealing the mechanism of GSB resistance and may be useful for marker-assisted selection in cucumber disease-resistant breeding.
Key Message Modeling of the distribution of allele frequency over year of variety release identifies major loci involved in historical breeding of winter wheat. Abstract Winter wheat is a major crop with a rich selection history in the modern era of crop breeding. Genetic gains across economically important traits like yield have been well characterized and are the major force driving its production. Winter wheat is also an excellent model for analyzing historical genetic selection. As a proof of concept, we analyze two major collections of winter wheat varieties that were bred in Western Europe from 1916 to 2010, namely the Triticeae Genome (TG) and WAGTAIL panels, which include 333 and 403 varieties, respectively. We develop and apply a selection mapping approach, Regression of Alleles on Years (RALLY), in these panels, as well as in simulated populations. RALLY maps loci under sustained historical selection by using a simple logistic model to regress allele counts on years of variety release. To control for drift-induced allele frequency change, we develop a hybrid approach of genomic control and delta control. Within the TG panel, we identify 22 significant RALLY quantitative selection loci (QSLs) and estimate the local heritabilities for 12 traits across these QSLs. By correlating predicted marker effects with RALLY regression estimates, we show that alleles whose frequencies have increased over time are heavily biased toward conferring positive yield effect, but negative effects in flowering time, lodging, plant height and grain protein content. Altogether, our results (1) demonstrate the use of RALLY to identify selected genomic regions while controlling for drift, and (2) reveal key patterns in the historical selection in winter wheat and guide its future breeding.
Frequency distribution of SGC in the KN DH population. In each plot, a marker denotes the median of the data, a box indicates the interquartile range, and spikes extend to the upper and lower adjacent values. The distribution density is overlaid
Collinearity of the updated high-density linkage map and B. napus reference genome and distribution of identified QTLs, consensus QTLs and candidate genes in each linkage group. The color blocks at the outermost circle represent the 19 chromosomes. Short bands adjacent to chromosomes represent QTLs or associated loci reported in other research for SGC. (The loci from different studies are labeled with different colors, and the corresponding colors are displayed in the lower right corner.) The black circle in the middle represents the 19 linkage groups of the updated genetic map. The lines connecting them represent their collinearity relationship. The 15 inner circles with 3 colors represent 15 environments in winter-type, semi-winter-type and spring-type eco-environments. The short bars with brown color within the 15 inner circles represent QTLs identified in different environments, and the short bars with brown color located between the 15th inner circle and the black circle represent consensus QTLs. Candidate genes falling within CIs of SGC-QTL are shown at the edge of the chromosomes
QTL comparison between this study and previous reports. Vertical bars represent chromosomes, and the lines on chromosomes represent markers near QTLs. The color bars on the right side of the chromosomes represent QTLs detected from different populations. QTLs detected in different research (except for the KN DH population) are named by the first author's surname and year of publication, connected with "-"
Cumulative effects of four QTL-HRs. On the abscissa, the chromosomes represent DH lines with favorable alleles from N53-3 in QTL-HRs of the corresponding chromosome, and ‘0’ represents DH lines with undesirable haplotypes from Ken-C8 and no favorable alleles from N53-3 on four QTL-HRs
Potential regulatory model of SGC variation in B. napus. The numbers after the gene symbol represent its copy number in the ‘Darmor-bzh’ reference genomes, and only the copies with different expression in leaves between Ken-C8 and N53-2 followed, and the neighboring bars above the copies represent the expression levels in Ken-C8 and N53-2. Transcription factor 1 includes the TFs regulating direct GSL-related pathways, and Transcription factor 2 includes the TFs regulating the sulfur assimilation process. The red dots indicate that there are copies that are identified underlying SGC-QTL, and the number of dots represents the number of genes that underlie SGC-QTL. The large and brown dots represent GSL types. The GTR2 transporters annotated on the side of the silique show the expression level in seeds between Ken-C8 and N53-2
Key message: The QTL hotspots determining seed glucosinolate content instead of only four HAG1 loci and elucidation of a potential regulatory model for rapeseed SGC variation. Glucosinolates (GSLs) are amino acid-derived, sulfur-rich secondary metabolites that function as biopesticides and flavor compounds, but the high seed glucosinolate content (SGC) reduces seed quality for rapeseed meal. To dissect the genetic mechanism and further reduce SGC in rapeseed, QTL mapping was performed using an updated high-density genetic map based on a doubled haploid (DH) population derived from two parents that showed significant differences in SGC. In 15 environments, a total of 162 significant QTLs were identified for SGC and then integrated into 59 consensus QTLs, of which 32 were novel QTLs. Four QTL hotspot regions (QTL-HRs) for SGC variation were discovered on chromosomes A09, C02, C07 and C09, including seven major QTLs that have previously been reported and four novel major QTLs in addition to HAG1 loci. SGC was largely determined by superimposition of advantage allele in the four QTL-HRs. Important candidate genes directly related to GSL pathways were identified underlying the four QTL-HRs, including BnaC09.MYB28, BnaA09.APK1, BnaC09.SUR1 and BnaC02.GTR2a. Related differentially expressed candidates identified in the minor but environment stable QTLs indicated that sulfur assimilation plays an important rather than dominant role in SGC variation. A potential regulatory model for rapeseed SGC variation constructed by combining candidate GSL gene identification and differentially expressed gene analysis based on RNA-seq contributed to a better understanding of the GSL accumulation mechanism. This study provides insights to further understand the genetic regulatory mechanism of GSLs, as well as the potential loci and a new route to further diminish the SGC in rapeseed.
Key message: MutMap and KASP analyses revealed that the BrGGL7 gene is responsible for the male-sterile trait of ftms1 in Chinese cabbage, with functional verification in Arabidopsis. The application of a male-sterile line is an ideal approach of hybrid seed production in Chinese cabbage. In this study, we obtained a male-sterile mutant (ftms1) from the double haploid line 'FT' using ethyl methane sulfonate (EMS) mutagenesis. The mutant was completely sterile due to abnormal enlargement and vacuolization of the tapetum cells. A single recessive nuclear gene was found to control male sterility in the mutant, while MutMap and KASP analyses identified BraA05g022470.3C (BrGGL7), which encodes a GDSL esterase / lipase, as the candidate mutant gene. A single nucleotide substitution from C to T occurred within the domain of BrGGL7 in ftms1, resulting in premature translation termination in the fourth exon. Meanwhile, qRT-PCR analysis indicated that BrGGL7 was prominently expressed in the anothers, and expression was greater in the wild-type 'FT' than ftms1. Genetic complementation of the orthologous Arabidopsis ggl7 mutant further confirmed the role of BrGGL7 in pollen development. These findings suggest that BrGGL7 plays a fundamental role in pollen formation, providing important insight into the molecular mechanisms underlying male sterility in Chinese cabbage.
2Mb-specific molecular markers analysis, GISH and FISH patterns of 12 CS-Ae. biuncialis 2Mb recombinants. a GISH patterns of CS-Ae. biuncialis 2Mb recombinants. Total genomic DNA of Ae. biuncialis was labeled with fluorescein-12-dUTP and visualized with green fluorescence. Wheat chromatin was counterstained with DAPI and visualized with blue fluorescence. b FISH patterns of CS-Ae. biuncialis 2Mb recombinants. TAMRA-modified oligonucleotides (pAs1-1, pAs1-3, pAs1-4, pAs1-6, AFA-3 and AFA-4) are in red color. FAM-modified oligonucleotides (pSc119.2–1 and (GAA)10) are in green color. Wheat chromatin was counterstained with DAPI and visualized with blue fluorescence. ‘ + ’ indicates the presence of the 2Mb-specific markers, while ‘-’ indicates the absence of the 2Mb-specific markers. FL: fragment length
GISH and dual-color ND-FISH identification of 12 different types of CS-Ae. biuncialis 2Mb recombinants. a1-l1 GISH analysis of 12 different types of CS-Ae. biuncialis 2Mb recombinants. Total genomic DNA of Ae. biuncialis was labeled with fluorescein-12-dUTP and visualized with green fluorescence. Wheat chromosomes were counterstained with DAPI and visualized with blue fluorescence. a2-l2 ND-FISH analysis of 12 different types of CS-Ae. biuncialis 2Mb recombinants. TAMRA-modified oligonucleotides (pAs1-1, pAs1-3, pAs1-4, pAs1-6, AFA-3 and AFA-4) are in red color. FAM-modified oligonucleotides (pSc119.2–1 and (GAA)10) are in green color. Wheat chromosomes were counterstained with DAPI and visualized with blue fluorescence. a-l CS-Ae. biuncialis 2Mb recombinants T-1, T-2, T-3, T-4, T-5, T-6, T-7, T-8, T-9, T-10, T-11 and T-12. Arrows point to recombined 2Mb chromosomes
Molecular marker physical map of Ae. biuncialis chromosome 2Mb. The numbers on the left of chromosome 2Mb display the FL of 2Mb breakpoints
Evaluation of powdery mildew resistance of 12 different types of CS-Ae. biuncialis 2Mb recombinants and their parents
Cytological mapping of novel powdery mildew resistance gene Pm2Mb. Chromosome 2Mb chromatin is shown in green and common wheat chromatin in blue. R indicates resistance to powdery mildew, while S indicates susceptibility to powdery mildew
Key message: A novel powdery mildew resistance gene Pm2Mb from Aegilops biuncialis was transferred into common wheat and mapped to chromosome 2MbL bin FL 0.49-0.66 by molecular cytogenetic analysis of 2Mb recombinants. Aegilops biuncialis, a wild relative of common wheat, is highly resistant to powdery mildew. Previous studies identified that chromosome 2Mb in Chinese Spring (CS)-Ae. biuncialis 2Mb disomic addition line TA7733 conferred high resistance to powdery mildew, and the resistance gene was temporarily designated as Pm2Mb. In this study, a total of 65 CS-Ae. biuncialis 2Mb recombinants were developed by ph1b-induced homoeologous recombination and they were grouped into 12 different types based on the presence of different markers of 2Mb-specificity. Segment sizes and breakpoints of each 2Mb recombinant type were further characterized using in situ hybridization and molecular marker analyses. Powdery mildew responses of each type were assessed by inoculation of each 2Mb recombinant-derived F2 progenies using the isolate E05. Combined analyses of in situ hybridization, molecular markers and powdery mildew resistance data of the 2Mb recombinants, the gene Pm2Mb was cytologically located to an interval of FL 0.49-0.66 in the long arm of 2Mb, where 19 2Mb-specific markers were located. Among the 65 2Mb recombinants, T-11 (T2DS.2DL-2MbL) and T-12 (Ti2DS.2DL-2MbL-2DL) contained a small 2MbL segment harboring Pm2Mb. Besides, a physical map of chromosome 2Mb was constructed with 70 2Mb-specific markers in 10 chromosomal bins and the map showed that submetacentric chromosome 2Mb of Ae. biuncialis was rearranged by a terminal intrachromosomal translocation. The newly developed 2Mb recombinants with powdery mildew resistance, the 2Mb-specific molecular markers and the physical map of chromosome 2Mb will benefit wheat disease breeding as well as fine mapping and cloning of Pm2Mb.
Application of nitrogen in field trials in Germany and Poland from 1987 to 2018
Year-wise yield (dt/ha) of maize field trials in Germany (left) and Poland (right) from 1987 to 2017. The lines represent yearly mean yield
Maize agroecological zones of Germany and Poland. a Maize-growing area in Germany is classified into 22 agroecological zones, while maize-growing area in Poland is classified into 6 zones. b merged agroecological zones
Yield prediction of 43 common varieties in Germany and Poland from a FG, b FGC, c RG-UN, d RGC-UN and e RC4 models
Genetic correlations between German zones and Poland using RGC model
Key message We assess the genetic gain and genetic correlation in maize yield using German and Polish official variety trials. The random coefficient models were fitted to assess the genetic correlation. Abstract Official variety testing is performed in many countries by statutory agencies in order to identify the best candidates and make decisions on the addition to the national list. Neighbouring countries can have similarities in agroecological conditions, so it is worthwhile to consider a joint analysis of data from national list trials to assess the similarity in performance of those varieties tested in both countries. Here, maize yield data from official German and Poland variety trials for cultivation and use (VCU) were analysed for the period from 1987 to 2017. Several statistical models that incorporate environmental covariates were fitted. The best fitting model was used to compute estimates of genotype main effects for each country. It is demonstrated that a model with random genotype-by-country effects can be used to borrow strength across countries. The genetic correlation between cultivars from the two countries equalled 0.89. The analysis based on agroecological zones showed high correlation between zones in the two countries. The results also showed that 22 agroecological zones in Germany can be merged into five zones, whereas the six zones in Poland had very high correlation and can be considered as a single zone for maize. The 43 common varieties which were tested in both countries performed equally in both countries. The mean performances of these common varieties in both countries were highly correlated.
Top-cited authors
Rajeev K Varshney
  • Murdoch University
Steve Tanksley
  • Cornell University
Matthew Reynolds
  • Consultative Group on International Agricultural Research
Susan Mccouch
  • Cornell University
Ravi Singh
  • International Maize and Wheat Improvement Center