Plant Genome最新文献

Italian ryegrass (Lolium multiflorum Lam.) is an important forage grass, providing a major source of feed for ruminants in temperate regions. Due to its highly heterozygous and repeat-rich genome, high-quality chromosome-level genome assemblies are scarce for Italian ryegrass. Here, we sequenced the genome of a genotype from the Italian ryegrass cultivar 'Rabiosa' (hereafter referred to as Rabiosa), and we obtained Oxford Nanopore Technologies long reads (∼60-fold coverage), Illumina short reads (∼85-fold coverage), and high-throughput chromosome conformation capture data (∼60-fold coverage). With Rabiosa as one of the parents, we constructed an F₁ population consisting of 304 individuals, which were genotyped by reduced representation sequencing for linkage map construction and quantitative trait locus (QTL) analysis. Using whole-genome sequencing data of Rabiosa and the genetic linkage map, we first generated a chromosome-level unphased haploid assembly (scaffold N50 of 338.75 Mb, total Benchmarking Universal Single-Copy Orthologs [BUSCO] score of 94.60%). Then, based on the unphased assembly and a reference-based phasing approach, we generated a chromosome-level haplotype-resolved assembly containing both haplotypes (scaffold N50 of ∼250 Mb and total BUSCO score of ∼90% for each haplome). Between the two haplotypes of Rabiosa, we observed a highly collinear gene order at the chromosome level and a high sequence variation at the local level. With a graph-based reference built from the unphased and the haplotype-resolved assemblies of Rabiosa, we conducted a QTL analysis, and two QTL significantly associated with stem rust resistance were detected. The genome assemblies of Rabiosa will serve as invaluable genomic resources to facilitate genomic applications in forage grass research and breeding.

Multiparent populations are now widespread in crop genetic studies as they capture more genetic diversity and offer high statistical power for detecting quantitative trait loci (QTLs). To confirm the suitability of using a recently developed chickpea (Cicer arietinum L.) multi-parent advanced generation intercross (MAGIC) population for genetic studies, we characterized the diversity of the eight founder lines and explored the linkage disequilibrium decay, marker coverage, segregation distortion, allelic variation, and structure of the population. The MAGIC population was genotyped using whole-genome sequencing; following marker curation, a total of 4255 high-quality polymorphic single nucleotide polymorphism markers were used for genomic analyses. To demonstrate the effectiveness of the MAGIC population to dissect the genetics of key agronomic traits (days to 50% flowering and plant height), we employed both a genome-wide mapping approach using fixed and random model circulating probability unification and a haplotype-based mapping using the local genomic estimated breeding value approach. Our analyses confirmed the role of genomic regions previously reported in the literature and identified several new QTLs for days to 50% flowering and plant height. We also showed the potential for trait improvement through stacking the top 10 haploblocks to develop early flowering chickpea and selection of desirable haplotypes on chromosome 4 to improve plant height. Our results demonstrate the chickpea MAGIC population is a valuable resource for researchers and pre-breeders to study the genetic architecture of complex traits and allelic variation to accelerate crop improvement in chickpea.

Rice (Oryza sativa L.) is a staple food for more than half of the global population. Preharvest sprouting (PHS), which reduces yield and grain quality, presents a major challenge for rice production. The development of PHS-resistant varieties is a major goal in japonica rice breeding. A deep learning model to automate PHS rate measurement was developed using the YOLOv8 algorithm. The model had high mean average precision (0.974). PHS rate measurements made using the model correlated strongly with manual measurements (R²  =  0.9567). A population of 182 F₈ recombinant inbred lines (RILs) was derived from a cross between the japonica rice cultivars, Junam and Nampyeong. The RIL genotypes at 763 single nucleotide polymorphism markers were determined using a rice target capture sequencing system and used to create a genetic map. The RILs were cultivated in the field (summer season) and the greenhouse (winter season) and their PHS rates were measured in both environments. Quantitative trait loci (QTLs) associated with PHS were present on chromosomes 3, 6, and 7 in the field, and on chromosomes 1, 2, 3, 6, 7, 8, and 11 in the greenhouse. Three QTLs on chromosomes 3, 6, and 7 showed stable effects in both environments. A search for candidate genes in the QTL qPHS6 identified Os06g0317200. This gene encodes a glycine-rich protein resembling qLTG3-1, which controls PHS. The QTLs identified in this study and the deep learning model developed for measuring PHS rates will accelerate the development of rice varieties with enhanced resistance to PHS.

Understanding the genetic basis of root system architecture (RSA) in crops requires innovative approaches that enable both high-throughput and precise phenotyping in field conditions. In this study, we evaluated multiple phenotyping and analytical frameworks for quantifying RSA in mature, field-grown maize in three field experiments. We used forward and reverse genetic approaches to evaluate >1700 maize root crowns, including a diversity panel, a biparental mapping population, and maize mutant and wild-type alleles at two known RSA genes, DEEPER ROOTING 1 (DRO1) and Rootless1 (Rt1). We show the utility of increasing the dimensionality of traditional two-dimensional (2D) techniques, referred to as the "2D multi-view" method, to improve the capture of whole root system information for mapping genetic variation influencing RSA. Comparison of univariate and multivariate genome-wide association study (GWAS) approaches revealed that multivariate traits were effective at dissecting complex RSA phenotypes and identifying pleiotropic quantitative trait loci (QTLs). Overall, three-dimensional (3D) root models generated from X-ray computed tomography and digital phenotyping captured a larger proportion of RSA trait variations compared to other methods of root phenotyping, as evidenced by both genome-wide and single-gene analyses. Among the individual root traits, root pulling force emerged as a highly heritable estimate of RSA that identified the largest number of shared QTLs with 3D phenotypes. Our study shows that integrating complementary phenotyping technologies helps to provide a more comprehensive understanding of the genetic architecture of RSA in field-grown maize.

Ascochyta blight of lentil (Lens culinaris Medik.) is a fungal disease caused by Ascochyta lentis. This study was carried out to identify the location of quantitative trait loci (QTL) associated with resistance from the accession Indianhead, and how these vary between the recently identified pathotypes of A. lentis. We performed QTL mapping using F₆ recombinant inbred lines derived from a cross between the resistant cultivar Indianhead and susceptible accession ILL6002, following evaluation in seedling assays and the field. Phenotyping identified nine QTL across the four different isolates. A major QTL effective against Pathotype 1 isolates was identified on chromosome 2 in both the seedling and field phenotyping, explaining 60.5% and 12.6% of the resistance phenotype, respectively. Additional QTL for resistance associated with Pathotype 1 isolates were identified on chromosomes 3, 5, and 7, explaining between 8.5% and 13.1% of the phenotype. In contrast, QTL associated with resistance to Pathotype 2 isolates were identified on chromosomes 1, 2, 3, and 7, in locations distinct from those associated with Pathotype 1 resistance. These loci explained between 8.8% and 29.6% of the phenotypic variation. Additionally, evaluation of a natural powdery mildew infection revealed a major QTL on chromosome 3, explaining 25% of the resistance phenotype. The markers flanking the loci identified herein will allow for lentil breeding programs to trace the associated resistance in their breeding program pedigree.

Multi-environment trials are routinely conducted in plant breeding to capture the genotype-by-environment interaction (G × E) effects. Significant G × E could alter the response pattern of genotypes (the change in rankings of genotypes), subsequently complicating the selection process. Four genomic prediction (GP) models were assessed in three groundnut yield-related traits: pod yield (PY), seed weight (SW), and 100 seed weight (SW100), across four environments. The models, M1 (environment + line), M2 (environment + line + genomic), M3 (environment + line + genomic + genomic × environment interaction), and M4 (environment + line + genomic + genomic × environment interaction + significant markers), were tested using four cross-validation (CV) schemes (CV2, CV1, CV0, and CV00), each simulating different practical breeding scenarios. The results revealed that models incorporating marker data (M2, M3, and M4) consistently improved predictive ability in comparison to the phenotypic model (M1). Incorporating G × E (M3 and M4) further improved predictive ability and reduced residual and environmental variances. The inclusion of significant markers and G × E was more advantageous in CV1 and CV00 scenarios, demonstrating that this strategy is especially useful when phenotypic data for the target genotypes is limited or unavailable. Across the CV schemes, predictive ability was higher in CV2, suggesting that including additional information on the performance of genotypes in known environments can increase the accuracy of selecting superior genotypes in breeding programs. Integrating significant markers and modeling G × E in GP models could be an effective approach in groundnut breeding programs to accelerate genetic gains.

The loss of agricultural biodiversity will compromise societal ability to proof the food system against abiotic and biotic perturbations. The steady decrease in planted area of sorghum [Sorghum bicolor (L.) Moench] in the United States is alarming. Recent studies attributed this decline to a lower rate of genetic gain in sorghum relative to maize due to the lower investment in grain sorghum breeding. While this is a reasonable interpretation, it is also plausible that sorghum breeding has reached a peak in the adaptation landscape for drought within the genetic and physiological boundaries imposed by the germplasm currently used by breeders. To test this hypothesis, we have conducted a breeding gap analysis. CERES-Sorghum was used to run a simulation experiment comprised of ∼1 billion genotype × environment × management combinations for the US sorghum belt. We estimated the 0.99 quantile of the response of yield to evapotranspiration (ET); this boundary defines the biophysical limits to yield based on water availability. We then projected data from multienvironment trials onto this yield-trait space. When trials were conducted in managed stress environments in the absence of water deficit at flowering time, we observed that modern sorghum hybrids reached the biophysical boundary. This result can explain the observed lack of genetic gain, which could be reverted by increasing investments in breeding efforts that harness novel sources of genetic diversity, phenomics, and genome-to-phenome technologies. We hypothesize that there are transfer learning opportunities to inform sorghum breeding strategies that can shift the yield-ET production front from successful crop improvement pathways identified in maize.

Understanding the physiological, metabolic, and genetic mechanisms underlying salt tolerance is essential for improving crop resilience and productivity, yet their complex interactions remain poorly defined. We compared physiological and metabolic responses to salinity between two contrasting maize (Zea mays L.) inbred lines: the salt-sensitive C68 and the salt-tolerant NC326. The sensitivity of C68 was characterized by reduced shoot and root dry weights and plant height, high tissue accumulation of Na and Cl but low K, and lower leaf proline accumulation compared to the salt-tolerant NC326. Untargeted metabolomics identified 56 metabolites categorized as constitutively upregulated or salt-responsive. In NC326, constitutive accumulation of flavonoids, including schaftoside, tricin, and kaempferol-related compounds in leaves, suggests adaptive priming against oxidative stress, while constitutively higher lipids and fatty acids in roots may enhance membrane stability. Salt-responsive metabolites, notably antioxidants and lanosterol, highlighted inducible oxidative-stress mitigation and membrane-stabilization strategies. By integrating metabolomic and genetic analyses, we identified 10 candidate genes involved in the biosynthesis of key metabolites. These findings establish a comprehensive platform for functional validation of metabolites and candidate genes for developing maize varieties with improved resilience to soil salinity through targeted breeding or biotechnological strategies.

Crop growth rate is a critical physiological trait for forage and bioenergy crops like sorghum [Sorghum bicolor (L.) Moench], influencing overall crop productivity, particularly in photoperiod-sensitive (PS) types. Crop growth rate studies focus on either a physiological approach utilizing a few genotypes to analyze biomass accumulation or a genetic approach characterizing easily scorable proxy traits in larger populations. Thus, the genetic control of crop growth rate in terms of biomass accumulation is poorly understood in PS sorghum. In this study, we monitored biomass accumulation in a diverse panel comprising 269 PS sorghum accessions in two growing seasons. We performed sequential samplings at 11 timepoints, separating leaves from stems. For the total biomass and each fraction, we applied the beta growth function to determine the maximum crop growth rate (c_m), maximum biomass accumulation (w_max), and time to c_m (t_m). Significant genetic variability was observed for all three parameters. Our analysis identified a practical window for c_m assessment through accumulated biomass at 60-70 days after planting. Genome-wide association analysis suggested distinct and independent genetic controls of leaf and stem biomass accumulation, both physically and temporally. Common genomic regions were discovered controlling w_max and c_m of stem and total biomass. These results provide new insights into the genetic control of crop growth rate, highlighting promising genomic regions for functional validation. This research also offers practical applications for plant breeding programs demonstrating the feasibility of selecting superior genotypes for both early and late biomass accumulation to enhance crop productivity.

Peroxidase (POD) is one of the key factors affecting the wheat flour quality. Characterization and development of functional markers, as well as expression analysis of POD genes, will help in breeding wheat cultivars and advanced lines with better flour quality. Here, we cloned a POD gene, TaPod-A3, on chromosome 7AL and developed its functional marker in common wheat (Triticum aestivum L). Based on single nucleotide polymorphisms (SNPs) and Indel between TaPod-A3 allele sequences, functional markers POD-7A1, POD-7A2, and POD-7A3 were developed, amplifying 216, 882, and 156 bp fragments in wheat cultivars and advanced lines with lower, middle, and higher POD activities, respectively. The analysis of variance of 228 wheat cultivars and advanced lines showed that the mean POD activity (668.6 U min^-1 g^-1) of 113 wheat cultivars and advanced lines supplemented with TaPod-A3a was lower than 17 wheat cultivars and advanced lines supplemented with TaPod-A3b (679.7 U min^-1 g^-1) and the 98 wheat cultivars and advanced lines supplemented with TaPod-A3c (731.2 U min^-1 g^-1). A total of 228 wheat cultivars and advanced lines were found using the functional markers of TaPod-A1, TaPod-D1, and TaPod-A3 genes located on chromosomes 3A, 7D, and 7AL of the functional markers developed in this study. The wheat cultivars and advanced lines with favorable allele combination of TaPod-A1b/TaPod-A3c/TaPod-D1b had higher POD activity (mean POD activity 780.6 U min^-1 g^-1) than those with alleles TaPod-A1a/TaPod-A3a/TaPod-D1a (625.7 U min^-1 g^-1). Six wheat cultivars and advanced lines with the same genotype and phenotype were selected for quantitative real-time polymerase chain reaction, and we found that the expression level of F49-70 in wheat cultivars and advanced lines with high POD activity was significantly higher than that in Wanmai 29 with low POD activity at each stage after flowering (p < 0.05). Based on correction analyses on the TaPod-A3 gene expression, the expression level was positively correlated with POD activity. This study provides useful information on the POD genes in bread wheat, insight into the TaPod-A3 gene structure and functional markers, as well as valuable resources for improving the quality of wheat flour.