Plant Genome最新文献

This strategic plan summarizes the major accomplishments achieved in the last quinquennial by the soybean [Glycine max (L.) Merr.] genetics and genomics research community and outlines key priorities for the next 5 years (2024-2028). This work is the result of deliberations among over 50 soybean researchers during a 2-day workshop in St Louis, MO, USA, at the end of 2022. The plan is divided into seven traditional areas/disciplines: Breeding, Biotic Interactions, Physiology and Abiotic Stress, Functional Genomics, Biotechnology, Genomic Resources and Datasets, and Computational Resources. One additional section was added, Training the Next Generation of Soybean Researchers, when it was identified as a pressing issue during the workshop. This installment of the soybean genomics strategic plan provides a snapshot of recent progress while looking at future goals that will improve resources and enable innovation among the community of basic and applied soybean researchers. We hope that this work will inform our community and increase support for soybean research.

Enhancing predictive modeling accuracy in wheat (Triticum aestivum) breeding through the integration of high-throughput phenotyping (HTP) data with genomic information is crucial for maximizing genetic gain. In this study, spanning four locations in the southeastern United States over 3 years, models to predict grain yield (GY) were investigated through different cross-validation approaches. The results demonstrate the superiority of multivariate comprehensive models that incorporate both genomic and HTP data, particularly in accurately predicting GY across diverse locations and years. These HTP-incorporating models achieve prediction accuracies ranging from 0.59 to 0.68, compared to 0.40-0.54 for genomic-only models when tested under different prediction scenarios both across years and locations. The comprehensive models exhibit superior generalization to new environments and achieve the highest accuracy when trained on diverse datasets. Predictive accuracy improves as models incorporate data from multiple years, highlighting the importance of considering temporal dynamics in modeling approaches. The study reveals that multivariate prediction outperformed genomic prediction methods in predicting lines across years and locations. The percentage of top 25% lines selected based on multivariate prediction was higher compared to genomic-only models, indicated by higher specificity, which is the proportion of correctly identified top-yielding lines that matched the observed top 25% performance across different sites and years. Additionally, the study addresses the prediction of untested locations based on other locations within the same year and in new years at previously tested locations. Findings show the comprehensive models effectively extrapolate to new environments, highlighting their potential for guiding breeding strategies.

Foliar fungal diseases are a major limitation in organic naked barley (Hordeum vulgare L.) production. The lack of conventional fungicides in organic systems increases reliance on genetic resistance. We evaluated the severity of barley stripe rust (Puccinia striiformis f. sp. hordei Westend), leaf rust (Puccina hordei sp. hordei), spot blotch (Cochliobolus sativus, anamorph Bipolaris sorokiniana (S. Ito & Kurib.) Drechsler ex Dastur), and scald (Rhynchosporium commune Zaffarano, McDonald and Linde sp. November) on a naked barley diversity panel of 350 genotypes grown in 13 environments to identify quantitative trait loci associated with disease resistance. Genome-wide association analyses across and within environments found 10 marker trait associations for barley stripe rust, four marker trait associations for leaf rust, one marker trait association for scald, and five marker trait associations for spot blotch. Structure analysis identified six Ward groups based on genotypic diversity. Resistance to susceptible allele ratios were high for stripe rust and spot blotch, moderate for leaf rust, and low for scald. Combined phenotypic analysis values for each disease overlayed by a principal component analysis found distinct resistance and susceptibility patterns for barley stripe rust and scald. Most significant marker trait associations were previously identified in the literature, providing confirmation and potential new sources of disease resistance for genetic improvement of naked barley germplasm.

End-use and processing traits in wheat (Triticum aestivum L.) are crucial for varietal development but are often evaluated only in the advanced stages of the breeding program due to the amount of grain needed and the labor-intensive phenotyping assays. Advances in genomic resources have provided new tools to address the selection for these complex traits earlier in the breeding process. We used association mapping to identify key variants underlying various end-use quality traits and evaluate the usefulness of genomic prediction for these traits in hard red spring wheat from the Northern United States. A panel of 383 advanced breeding lines and cultivars representing the diversity of the University of Minnesota wheat breeding program was genotyped using the Illumina 90K single nucleotide polymorphism array and evaluated in multilocation trials using standard assessments of end-use quality. Sixty-three associations for grain or flour characteristics, mixograph, farinograph, and baking traits were identified. The majority of these associations were mapped in the vicinity of glutenin/gliadin or other known loci. In addition, a putative novel multi-trait association was identified on chromosome 6AL, and candidate gene analysis revealed eight genes of interest. Further, genomic prediction had a high predictive ability (PA) for mixograph and farinograph traits, with PA up to 0.62 and 0.50 in cross-validation and forward prediction, respectively. The deployment of 46 markers from GWAS to predict dough-rheology traits yielded low to moderate PA for various traits. The results of this study suggest that genomic prediction for end-use traits in early generations can be effective for mixograph and farinograph assays but not baking assays.

The timing of flowering in soybean [Glycine max (L.) Merr.], a key legume crop, is influenced by many factors, including daylight length or photoperiodic sensitivity, that affect crop yield, productivity, and geographical adaptation. Despite its importance, a comprehensive understanding of the local linkage landscape and allelic diversity within regions of the genome influencing flowering and contributing to phenotypic variation in subpopulations has been limited. This study addresses these gaps by conducting an in-depth trait association and linkage analysis coupled with local haplotyping using advanced bioinformatics tools, including crosshap, to characterize genomic variation using a pangenome dataset representing 915 domesticated and wild-type individuals. The association analysis identified eight significant loci on seven chromosomes. Moving beyond traditional association analysis, local haplotyping of targeted regions on chromosomes 6 and 20 identified distinct haplotype structures, variation patterns, and genomic candidates influencing flowering in subpopulations. These results suggest the action of a network of genomic candidates influencing flowering time and an untapped reservoir of genomic variation for this trait in wild germplasm. Notably, GlymaLee.20G147200 on chromosome 20 was identified as a candidate gene that may cause delayed flowering in soybean, potentially through histone modifications of floral repressor loci as seen in Arabidopsis thaliana (L.) Heynh. These findings support future functional validation of haplotype-based alleles for marker-assisted breeding and genomic selection to enhance latitude adaptability of soybean without compromising yield.

Barley (Hordeum vulgare) is a climate-resilient crop widely cultivated in both highly productive and suboptimal agricultural systems, and its ability to adapt to multiple biotic and abiotic stresses has contributed significantly to food security. Greenbug is a destructive insect pest for global barley production, and new greenbug resistance genes are needed to overcome the challenges posed by diverse greenbug biotypes in fields. CI 2458 is a Chinese landrace exhibiting a unique resistance profile to a set of 14 greenbug biotypes, which suggests the presence of a new greenbug resistance gene in CI 2458. A recombinant inbred line population from the cross Weskan × CI 2458 was developed, evaluated for responses to greenbug biotype F, and genotyped using single nucleotide polymorphism (SNP) markers generated by genotyping-by-sequencing. Linkage analysis revealed a single gene, designated Rsg4, conditioning greenbug resistance in CI 2458. Rsg4 was delimited to a 1.14 Mb interval between SNP markers S3H_666512114 and S3H_667651446 in the terminal region of chromosome arm 3HL, with genetic distances of 1.2 cM proximal to S3H_667651446 and 1.1 cM distal to S3H_666512114. Allelism tests confirmed that Rsg4 is a new greenbug resistance gene independent of Rsg1 and Rsg3, which reside in the same chromosome arm. Rsg4 differs from Rsg1 alleles and Rsg3 in its resistance to greenbug biotype TX1, one of the most widely virulent biotypes. The introgression of Rsg4 into locally adapted barley cultivars is of agronomic importance, and kompetitive allele-specific polymerase chain reaction (KASP) markers flanking Rsg4, KASP-Rsg336-1 and KASP-Rsg336-2, enable rapid pyramiding of Rsg4 with other resistance genes to develop durable greenbug-resistant cultivars.

Pea (Pisum sativum L.) is an important temperate legume crop providing plant-based proteins for food and feed worldwide. Pea yield can be limited by several biotic stresses, among which rust represents a major limiting factor in many temperate and subtropical regions. Some efforts have been made to assess the natural variation in pea resistance to rust, but its efficient exploitation in breeding is limited since the resistance loci identified so far are scarce and their responsible gene(s) unknown. To overcome this knowledge gap, a comprehensive genome-wide association study (GWAS) has been performed on pea rust, caused by Uromyces pisi, to uncover genetic loci associated with resistance. Utilizing a diverse collection of 320 pea accessions, we evaluated phenotypic responses to two rust isolates using both traditional methods and advanced image-based phenotyping. We detected 95 significant trait-marker associations using a set of 26,045 Diversity Arrays Technology-sequencing polymorphic markers. Our in silico analysis identified 62 candidate genes putatively involved in rust resistance, grouped into different functional categories such as gene expression regulation, vesicle trafficking, cell wall biosynthesis, and hormonal signaling. This research highlights the potential of GWAS to identify molecular markers associated with resistance and candidate genes against pea rust, offering new targets for precision breeding. By integrating our findings into current breeding programs, we can facilitate the development of pea varieties with improved resistance to rust, contributing to sustainable agricultural practices and food security. This study sets the stage for future functional genomic analyses and the application of genomic selection approaches to enhance disease resistance in peas.

Climate change represents a significant challenge to global food security by altering environmental conditions critical to crop growth. Plant breeders can play a key role in mitigating these challenges by developing more resilient crop varieties; however, these efforts require significant investments in resources and time. In response, it is imperative to use current technologies that assimilate large biological and environmental datasets into predictive models to accelerate the research, development, and release of new improved varieties that can be more resilient to the increasingly variable climatic conditions. Leveraging large and diverse datasets can improve the characterization of phenotypic responses due to environmental stimuli and genomic pulses. A better characterization of these signals holds the potential to enhance our ability to predict trait performance under changes in weather and/or soil conditions with high precision. This paper introduces characterization and integration of driven omics (CHiDO), an easy-to-use, no-code platform designed to integrate diverse omics datasets and effectively model their interactions. With its flexibility to integrate and process datasets, CHiDO's intuitive interface allows users to explore historical data, formulate hypotheses, and optimize data collection strategies for future scenarios. The platform's mission emphasizes global accessibility, democratizing statistical solutions for situations where professional ability in data processing and data analysis is not available.

Lettuce (Lactuca sativa L.) is a source of beneficial compounds though they are generally present in low quantities. We used 40K Axiom and 9K Infinium SNP (single nucleotide polymorphism) arrays to (i) explore the genetic variability in 21 varieties and (ii) carry out genome-wide association studies (GWAS) of vitamin C content in21 varieties and a population of 205 plants from the richest variety in vitamin C ('Lechuga del Pirineo'). Structure and phylogenetic analyses showed that the group formed mainly by traditional varieties was the most diverse, whereas the red commercial varieties clustered together and very distinguishably apart from the rest. GWAS consistently detected, in a region of chromosome 2, several SNPs related to dehydroascorbic acid (a form of vitamin C) content using three different methods to assess population structure, subpopulation membership coefficients, multidimensional scaling, and principal component analysis. The latter detected the highest number of SNPs (17) and the most significantly associated, 12 of them showing a high linkage disequilibrium with the lead SNP. Among the 84 genes in the region, some have been reported to be related to vitamin C content or antioxidant status in other crops either directly, like those encoding long non-coding RNA, several F-box proteins, and a pectinesterase/pectinesterase inhibitor, or indirectly, like extensin-1-like protein and endoglucanase 2 genes. The involvement of other genes identified within the region in vitamin C levels needs to be further studied. Understanding the genetic control of such an important quality trait in lettuce becomes very relevant from a breeding perspective.

Synthetic genetic circuits in plants could be the next technological horizon in plant breeding, showcasing potential for precise patterned control over expression. Nevertheless, uncertainty in metabolic environments prevents robust scaling of traditional genetic circuits for agricultural use, and studies show that a deterministic system is at odds with biological randomness. We analyze the necessary requirements for assuring Boolean logic gate sequences can function in unpredictable intracellular conditions, followed by interpreted pathways by which a mathematical representation of probabilistic circuits can be translated to biological implementation. This pathway is utilized through translation of a probabilistic circuit model presented by Pervaiz that works through a series of bits; each composed of a weighted matrix that reads inputs from the environment and a random number generator that takes the matrix as bias and outputs a positive or negative signal. The weighted matrix can be biologically represented as the regulatory elements that affect transcription near promotors, allowing for an electrical bit to biological bit translation that can be refined through tuning using invertible logic prediction of the input to output relationship of a genetic response. Failsafe mechanisms should be introduced, possibly through the use of self-eliminating CRISPR-Cas9, dosage compensation, or cybernetic modeling (where CRISPR is clustered regularly interspaced short palindromic repeats and Cas9 is clustered regularly interspaced short palindromic repeat-associated protein 9). These safety measures are needed for all biological circuits, and their implementation is needed alongside work with this specific model. With applied responses to external factors, these circuits could allow fine-tuning of organism adaptation to stress while providing a framework for faster complex expression design in the field.