Plant Genome最新文献

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.

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. nov) 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.

Genomic selection is revolutionizing both plant and animal breeding, with its practical application depending critically on high prediction accuracy. In this study, we aimed to enhance prediction accuracy by exploring the use of graph models within a linear mixed model framework. Our investigation revealed that incorporating the graph constructed with line connections alone resulted in decreased prediction accuracy compared to conventional methods that consider only genotype effects. However, integrating both genotype effects and the graph structure led to slightly improved results over considering genotype effects alone. These findings were validated across 14 datasets commonly used in plant breeding research.

Starch is the main component of wheat (Triticum aestivum L.) flour, and its quality directly affects the processing quality of the final product. To investigate the genetic basis of starch, this study assessed the starch quality traits of 341 winter wheat varieties/lines grown in Emin and Qitai during the years 2019-2020 and 2020-2021. A genome-wide association study was conducted with the genotype data obtained from wheat 40K breeding chips using the mixed linear model. Wheat starch quality traits exhibited coefficients of variation ranging from 1.43% to 23.66% and broad-sense heritabilities between 0.37 and 0.87. All traits followed an approximately normal distribution, except for T. There were highly significant correlations among starch quality traits, with the strongest correlation observed between final viscosity (FV) and trough viscosity (TV) (r = 0.748), followed by peak viscosity and breakdown (BD) (r = 0.679). Thirty-four single-nucleotide polymorphism markers significantly and stably associated with starch quality traits were identified, clustering in 31 genetic loci. These included one locus for TV, six loci for BD, three loci for FV, two loci for peak time (PT), 12 loci for T, five loci for falling number, and two loci for damaged starch. One PT-related block of 410 kb was identified in the region of 596 Mb on chromosome 5A, where significant phenotypic differences were observed between different haplotypes. One Kompetitive allele-specific PCR (KASP) marker for T was developed on chromosome 7B, and two KASP markers for BD were developed on chromosome 7A. Four candidate genes possibly affecting BD during grain development were identified on chromosome 7A, including TraesCS7A02G225100.1, TraesCS7A02G225900.1, TraesCS7A02G226400.1, and TraesCS7A02G257100.1. The results have significant implications for utilizing marker-assisted selection in breeding to improve wheat starch quality.

With every 1°C rise in temperature, yields are predicted to decrease by 5%-6% for both cool and warm season crops, threatening food production, which should double by 2050 to meet the global demand. While high night-time temperature (HNT) stress is expected to increase due to climate change, limited information is available on the genetic control of the trait, especially in wheat (Triticum aestivum L.). To identify genes controlling the HNT trait, we evaluated a doubled haploid (DH) population developed from a cross between an HNT tolerant line KSG1203 and KSG0057, a selection out of a mega variety PBW343 from South East Asia that turned out to be HNT susceptible. The population, along with the parents, were evaluated under 30°C night-time (HNT stress) keeping the daytime temperature to normal 22°C. The same daytime and 16°C night-time temperature were used as a control. The HNT treatment negatively impacted all agronomic traits under evaluation, with a percentage reduction of 0.5%-35% for the tolerant parent, 8%-75% for the susceptible parent, and 8%-50% for the DH population. Performed using sequencing-based genotyping, quantitative trait locus (QTL) mapping identified 19 QTLs on 13 wheat chromosomes explaining 9.72%-28.81% of cumulative phenotypic variance for HNT stress tolerance, along with 13 that were for traits under normal growing conditions. The size of QTL intervals ranged between 0.021 and 97.48 Mb, with the number of genes ranging between 2 and 867. A candidate gene analysis for the smallest six QTL intervals identified eight putative candidates for night-time heat stress tolerance.

Strawberries (Fragaria sp.) are cherished for their organoleptic properties and nutritional value. However, breeding new cultivars involves the simultaneous selection of many agronomic and fruit quality traits, including fruit firmness and extended postharvest life. The strawberry germplasm collection here studied exhibited extensive phenotypic variation in 26 agronomic and fruit quality traits across three consecutive seasons. Phenotypic correlations and principal component analysis revealed relationships among traits and accessions, emphasizing the impact of plant breeding on fruit weight and firmness to the detriment of sugar or vitamin C content. Genetic diversity analysis on 124 accessions using 44,408 markers denoted a population structure divided into six subpopulations still retaining considerable diversity. Genome-wide association studies for the 26 traits unveiled 121 significant marker-trait associations distributed across 95 quantitative trait loci (QTLs). Multiple associations were detected for fruit firmness, a key breeding target, including a prominent locus on chromosome 6A. The candidate gene FaPG1, controlling fruit softening and postharvest shelf life, was identified within this QTL region. Differential expression of FaPG1 confirmed its role as the primary contributor to natural variation in fruit firmness. A kompetitive allele-specific PCR assay based on the single nucleotide polymorphism (SNP) AX-184242253, associated with the 6A QTL, predicts a substantial increase in fruit firmness, validating its utility for marker-assisted selection. In essence, this comprehensive study provides insights into the phenotypic and genetic landscape of the strawberry collection and lays a robust foundation for propelling the development of superior strawberry cultivars through precision breeding.

Groundnut (Arachis hypogaea L.) is one of the most important climate-resilient oil crops in sub-Saharan Africa. There is a significant yield gap for groundnut in Africa because of poor soil fertility, low agricultural inputs, biotic and abiotic stresses. Cross-country evaluations of promising breeding lines can facilitate the varietal development process. The objective of our study was to characterize popular test environments in Uganda (Serere and Nakabango) and Malawi (Chitala and Chitedze) and identify genotypes with stable superior yields for potential future release. Phenotypic data were generated for 192 breeding lines for yield-related traits, while genotypic data were generated using skim-sequencing. We observed significant variation (p < 0.001; p < 0.01; p < 0.05) across genotypes for all yield-related traits: days to flowering (DTF), pod yield (PY), shelling percentage, 100-seed weight, and grain yield within and across locations. Nakabango, Chitedze, and Serere were clustered as one mega-environment with the top five most stable genotypes being ICGV-SM 01709, ICGV-SM 15575, ICGV-SM 90704, ICGV-SM 15576, and ICGV-SM 03710, all Virginia types. Population structure analysis clustered the genotypes in three distinct groups based on market classes. Eight and four marker-trait associations (MTAs) were recorded for DTF and PY, respectively. One of the MTAs for DTF was co-localized within an uncharacterized protein on chromosome 13, while another one (TRv2Chr.11_3476885) was consistent across the two countries. Future studies will need to further characterize the candidate genes as well as confirm the stability of superior genotypes across seasons before recommending them for release.

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.

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.

Exploration of novel alleles from ex situ collection is still limited in modern plant breeding as these alleles exist in genetic backgrounds of landraces that are not adapted to modern production environments. The practice of backcross breeding results in preservation of the adapted background of elite parents but leaves little room for novel alleles from landraces to be incorporated. Selection of adaptation-associated linkage blocks instead of the entire adapted background may allow breeders to incorporate more of the landrace's genetic background and to observe and evaluate novel alleles. Important adaptation-associated linkage blocks would have been selected over multiple cycles of breeding and hence are likely to exhibit signatures of positive selection or selective sweeps. We conducted genome-wide scan for candidate selective sweeps (CSS) using F_st, Rsb, and xpEHH in state, regional, spring, winter, and market-class population pairs and reported 446 CSS in 19 population pairs over time and 1033 CSS in 44 population pairs across geography and class. Further validation of these CSS in specific breeding programs may lead to identification of sets of loci that can be selected to restore population-specific adaptation in pre-breeding germplasms.