Theoretical and Applied Genetics最新文献

Key message: A powdery mildew (Pm) resistance locus PmRc1 was identified and transferred from Roegneria ciliaris into wheat. Two compensative translocation lines carrying PmRc1 were developed. Powdery mildew (Pm), caused by the biotrophic fungal pathogen Blumeria graminis f.sp. tritici (Bgt), is a global destructive disease of bread wheat (Triticum aestivum L.). Identifying and utilizing new Pm resistance gene(s) is the most fundamental work for disease control. Roegneria ciliaris (2n = 4 x= 28, genome S^cS^cY^cY^c) is a wild relative species of cultivated wheat. In this work, we evaluated wheat-R. ciliaris disomic chromosome addition lines for Pm resistance in multiple years. The introduction of R. ciliaris chromosome 1S^c into wheat enhanced resistance. The resistance locus on 1S^c was designated as PmRc1. To cytologically map PmRc1, we induced structural rearrangements using ion irradiation and increasing homoeologous chromosomal recombination. The identified 43 1S^c translocation or deletion lines were used to construct 1S^c cytological bin map by marker analysis using 111 molecular markers. Based on the Pm resistance of the characterized structural rearrangement lines, the PmRc1 locus was cytologically mapped to bin 1S^cS-8 of 1S^c short arm, flanked by markers CMH93-2 and CMH114-1. Two compensatory chromosomal translocation lines (T1S^cS $·$ 1BL and T1S^cS-1AS $·$ 1AL) carrying PmRc1 were developed and assessed for their agronomic traits. Translocation chromosome T1S^cS $·$ 1BL had enhanced Pm resistance accompanied by negative effects on grain number and single plant yield. Translocation chromosome T1S^cS-1AS $·$ 1AL had enhanced Pm resistance and increased spikelet number per spike, without any obvious negative effect on other tested traits. Thus, T1S^cS-1AS $·$ 1AL is recommended preferentially used in wheat breeding for Pm resistance.

Flowering time synchronizes reproductive development with favorable environmental conditions to optimize yield. Improved understanding of the genetic control of flowering will help optimize varietal adaptation to future agricultural systems under climate change. Here, we investigate the genetic basis of flowering time in winter wheat (Triticum aestivum L.) using an eight-founder multi-parent advanced generation intercross (MAGIC) population. Flowering time data was collected from field trials across six growing seasons in the United Kingdom, followed by genetic analysis using a combination of linear modelling, simple interval mapping and composite interval mapping, using either single markers or founder haplotype probabilities. We detected 57 quantitative trait loci (QTL) across three growth stages linked to flowering time, of which 17 QTL were identified only when the major photoperiod response locus Ppd-D1 was included as a covariate. Of the 57 loci, ten were identified using all genetic mapping approaches and classified as 'major' QTL, including homoeologous loci on chromosomes 1B and 1D, and 4A and 4B. Additional Earliness per se flowering time QTL were identified, along with growth stage- and year-specific effects. Furthermore, six of the main-effect QTL were found to interact epistatically with Ppd-D1. Finally, we exploited residual heterozygosity in the MAGIC recombinant inbred lines to Mendelize the Earliness per se QTL QFt.niab-5A.03, which was confirmed to modulate flowering time by at least four days. This work provides detailed understanding of the genetic control of phenological variation within varieties relevant to the north-western European wheat genepool, aiding informed manipulation of flowering time in wheat breeding.

Key message: We revealed the neglected genetic relationships of resistance for six major wheat diseases and established a haploblock-based catalogue with novel forms of resistance by multi-trait haplotype characterisation. Genetic potential to improve multiple disease resistance was highlighted through haplotype stacking simulations. Wheat production is threatened by numerous fungal diseases, but the potential to breed for multiple disease resistance (MDR) mechanisms is yet to be explored. Here, significant global genetic correlations and underlying local genomic regions were identified in the Vavilov wheat diversity panel for six major fungal diseases, including biotrophic leaf rust (LR), yellow rust (YR), stem rust (SR), hemibiotrophic crown rot (CR), and necrotrophic tan spot (TS) and Septoria nodorum blotch (SNB). By adopting haplotype-based local genomic estimated breeding values, derived from an integrated set of 34,899 SNP and DArT markers, we established a novel haplotype catalogue for resistance to the six diseases in over 20 field experiments across Australia and Ethiopia. Haploblocks with high variances of haplotype effects in all environments were identified for three rusts, and pleiotropic haploblocks were identified for at least two diseases, with four haploblocks affecting all six diseases. Through simulation, we demonstrated that stacking optimal haplotypes for one disease could improve resistance substantially, but indirectly affected resistance for other five diseases, which varied depending on the genetic correlation with the non-target disease trait. On the other hand, our simulation results combining beneficial haplotypes for all diseases increased resistance to LR, YR, SR, CR, TS, and SNB, by up to 48.1%, 35.2%, 29.1%, 12.8%, 18.8%, and 32.8%, respectively. Overall, our results highlight the genetic potential to improve MDR in wheat. The haploblock-based catalogue with novel forms of resistance provides a useful resource to guide desirable haplotype stacking for breeding future wheat cultivars with MDR.

The photosynthetic phenotype of trees undergoes changes and interactions that reflect their abilities to exploit light energy. Environmental disturbances and genetic factors have been recognized as influencing these changes and interactions, yet our understanding of the underlying biological mechanisms remains limited, particularly in stochastic environments. Here, we developed a high-dimensional stochastic differential framework (HDSD) for the genome-wide mapping of quantitative trait loci (QTLs) that regulate competition or cooperation in environment-dependent phenotypes. The framework incorporates random disturbances into system mapping, a dynamic model that views multiple traits as a system. Not only does this framework describe how QTLs regulate a single phenotype, but also how they regulate multiple phenotypes and how they interact with each other to influence phenotypic variations. To validate the proposed model, we conducted mapping experiments using chlorophyll fluorescence phenotype data from Populus simonii. Through this analysis, we identified several significant QTLs that may play a crucial role in photosynthesis in stochastic environments, in which 76 significant QTLs have already been reported to encode proteins or enzymes involved in photosynthesis through functional annotation. The constructed genetic regulatory network allows for a more comprehensive analysis of the internal genetic interactions of the photosynthesis process by visualizing the relationships between SNPs. This study shows a new way to understand the genetic mechanisms that govern the photosynthetic phenotype of trees, focusing on how environmental stochasticity and genetic variation interact to shape their light energy utilization strategies.

Key message: Genomic resources, alongside the tools and expertise required to leverage them, are essential for the effective improvement of globally significant millet crop species. Millets are essential for global food security and nutrition, particularly in sub-Saharan Africa and South Asia. They are crucial in promoting nutrition, climate resilience, economic development, and cultural heritage. Despite their critical role, millets have historically received less investment in developing genomic resources than major cereals like wheat, maize, and rice. However, recent advancements in genomics, particularly next-generation sequencing technologies, offer unprecedented opportunities for rapid improvement in millet crops. This review paper provides an overview of the status of genomic resources in millets and in harnessing the recent opportunities in artificial intelligence to address challenges in millet crop improvement to boost productivity, nutrition, and end quality. It emphasizes the significance of genomics in tackling global food security issues and underscores the necessity for innovative breeding strategies to translate genomics and AI into effective breeding strategies for millets.

Key message: The gene regulating fruit pedicel length in wax gourd was finely mapped to a 211 kb region on chromosome 8. The major gene, Bch08G017310 (BhGA2ox3), was identified through forward genetics. Fruit pedicel length (FPL) is a crucial trait in wax gourd (Benincasa hispida) that affects fruit development and cultivation management. However, the key regulatory genes and mechanisms of FPL in wax gourds remain poorly understood. In this study, we constructed an F₂ population using wax gourd plants with long fruit pedicels (GF-7-1-1) and short fruit pedicels (YSB-1-1-2) as parents. Through BSA-seq, we initially localised the FPL candidate gene to an 8.4 Mb region on chromosome 8, which was further narrowed down to a 1.1 Mb region via linkage analysis. A large F₂ population of 2163 individuals was used to screen for recombinants, and the locus was ultimately narrowed to within a 211 kb (62,299,856-62,511,174 bp) region. Sequence and expression analyses showed that Bch08G017310 (named BhGA2ox3) is a strong candidate gene for FPL in wax gourds. It encodes gibberellin (GA) 2-beta-dioxygenase, a member of the GA 2-oxidase (GA2ox) family. Cytology showed that GA treatment significantly elongated the fruit pedicels and enlarged the cells in the plants with short fruit pedicels. Ectopic expression of BhGA2ox3 showed that BhGA2ox3 overexpression in Arabidopsis thaliana resulted in significantly shorter fruit pedicels. This study lays a theoretical foundation for the regulatory mechanism of FPL in wax gourds and molecular breeding.

Key message: Two closely linked novel loci, qKRN2-1 and qKRN2-2, associated with kernel row number were fine-mapped on chromosome 2, and a key candidate gene for qKRN2-1 was identified through expression analysis. Kernel row number (KRN) is a crucial factor influencing maize yield and serves as a significant target for maize breeding. The use of wild progenitor species can aid in identifying the essential traits for domestication and breeding. In this study, teosinte (MT1) served as the donor parent, the inbred maize line of Mo17 was used as the recurrent parent, we identified a major quantitative trait locus (QTL) for KRN, designated qKRN2, into two closely linked loci, qKRN2-1 and qKRN2-2. Here, fine mapping was performed to investigate two QTLs, qKRN2-1 and qKRN2-2, within a genomic range of 272 kb and 775 kb, respectively. This was achieved using a progeny test strategy in an advanced backcross population, with the two QTLs explaining 33.49% and 35.30% of the phenotypic variance. Molecular marker-assisted selection resulted in the development of two nearly isogenic lines (NILs), qKRN2-1 and qKRN2-2, which differed only in the segment containing the QTL. Notably, the maize (Mo17) alleles increased the KRN relative to teosinte by approximately 1.4 and 1.2 rows for qKRN2-1 and qKRN2-2, respectively. Zm00001d002989 encodes a cytokinin oxidase/dehydrogenase and its expression in the immature ears exhibited significant differences among the qKRN2-1 NILs. In situ hybridization localized Zm00001d002989 to the primordia of the inflorescence meristem and spikelet pair meristems, is predicted to be the causal gene of qKRN2-1. The findings of this study deepen our understanding of the genetic basis of KRN and hold significant potential for improving maize grain yields.

Key message: Approaches for constructing training sets in genomic selection are proposed to efficiently identify top-performing genotypes from a breeding population. Identifying superior genotypes from a candidate population is a key objective in plant breeding programs. This study evaluates various methods for the training set optimization in genomic selection, with the goal of enhancing efficiency in discovering top-performing genotypes from a breeding population. Additionally, two approaches, inspired by classical optimal design criteria, are proposed to expand the search space for the best genotypes and compared with methods focusing on maximizing accuracy in breeding value prediction. Evaluation metrics such as normalized discounted cumulative gain, Spearman's rank correlation, and Pearson's correlation are employed to assess performance in both simulation studies and real trait analyses. Overall, for candidate populations lacking a strong subpopulation structure, a ridge regression-based method, referred to as ${\text{MSPE}}^{\text{Ridge}},$ is recommended. For candidate populations with a strong subpopulation structure, a heuristic-based version of generalized coefficient of determination $\left({\text{CD}}_{\text{mean}\left(\text{v}2\right)}\right)$ and a D-optimality-like method that maximizes overall genomic variation $\left({\text{GV}}_{\text{overall}}\right)$ are preferred approaches for the primary objective of plant breeding. For populations with a large number of candidates, a proposed ranking method ( ${\text{GV}}_{\text{average}}$ ) can first be used to down-scale the candidate population, after which a heuristic-based method is employed to identify the best genotypes. Notably, the proposed ${\text{CD}}_{\text{mean}\left(\text{v}2\right)}$ has been verified to be equivalent to the original version, known as ${\text{CD}}_{\text{mean}}$ , but its implementation is much more computationally efficient.

Key message: A major locus for spike compactness and length was mapped on chromosome 7H and its pleiotropic effects, candidate genes and transcriptional regulatory network were analyzed. Spike compactness (SC) and length (SL) are important traits of barley (Hordeum vulgare L.) due to their close association with grain yield. In this study, a major SC and SL locus QSc/Sl.cib-7H was primarily identified on chromosome 7H by bulked segregant analysis, and further fine mapped to a recombination cold spot expanding 244.36-388.09 Mb by developing a secondary population using residual heterozygous lines. This region is much more accurate than previously reported spike compactness loci on chromosome 7H. The strong effects of QSc/Sl.cib-7H on SL and SC were validated in two pair of near isogenic lines (NILs) and diverse genetic backgrounds. QSc/Sl.cib-7H exhibited pleiotropic effects on plant height (PH), thousand grain weight and grain length, and did not significantly influence the spikelet number of main spike (SMS) and grain width. Transcriptome analysis based on NILs showed that regulation of SC and SL might be related to the plant circadian rhythm pathway. The candidate genes were mined by analyzing variants and expression patterns of genes in the target region employing multiple genome and transcriptome data. This study takes a further step towards cloning of QSc/Sl.cib-7H, and the data obtained and the developed molecular markers will facilitate its utilization in barley breeding.

Key message: Key message A major quantitative trait locus (qGW12) for grain shape and weight has been isolated in rice, corresponding to LOC_Os12g17900/OsPUB23, and its encoded protein interacts with OsMADS1. Grain shape in rice is an important trait that influences both yield and quality. The primary determinants of grain shape are quantitative trait loci (QTLs) inherited from natural variation in crops. In recent years, much attention has been paid to the molecular role of QTLs in regulating grain shape and weight. In this study, we report the cloning and characterization of qGW12, a major QTL regulating grain shape and weight in rice, using a series of chromosome fragment substitution lines (CSSLs) derived from Oryza sativa indica cultivar 9311 (acceptor) and Oryza rufipogon Griff (donor). One CSSL line, Q187, harboring the introgression of qGW12, exhibited a significant decrease in grain-shape-related traits (including grain length and width) and thousand-grain weight compared to the cultivar 9311. Subsequent backcrossing of Q187 with 9311 resulted in the generation of secondary segregating populations, which were used to fine-map qGW12 to a 24-kb region between markers Seq-44 and Seq-48. Our data indicated that qGW12 encodes a previously unreported U-box type E3 ubiquitin ligase, designated OsPUB23, which exhibited E3 ubiquitin ligase activity. Overexpression of OsPUB23 in rice resulted in higher plant yield than the wild type due to an increase in grain size and weight. Conversely, loss of OsPUB23 function resulted in the opposite tendency. Yeast two-hybrid screening and split luciferase complementation assays revealed that OsPUB23 interacts with OsMADS1. The functional characterization of OsPUB23 provides new genetic resources for improving of grain yield and quality in crops.