Molecular Breeding最新文献

Unraveling the genetic architecture of nitrogen response of development is critical for improving wheat productivity while reducing nitrogen inputs. In this study, hyperspectral imaging (HSI) was applied to wheat grains obtained from nitrogen-deficient and normal conditions, combined with genome-wide association studies (GWAS), to investigate the nitrogen response of development in a diverse wheat panel. The 1,792 i-traits were acquired via hyperspectral imaging system, which reflect detailed phenotypic assessments of wheat development, capturing subtle variations in nitrogen response. A total of 3,556 significant loci and 3,648 candidate genes were identified. Key candidate genes involved in nitrogen uptake and utilization were identified by integrating agronomic traits with i-traits, including TaARE1-7A, TaPTR9-7B, TaNAR2.1, and Rht-B1. This approach underscores the potential of combining HSI on grains with GWAS to dissect complex traits like nitrogen response, offering valuable genetic insights for breeding nitrogen-efficient wheat varieties and enhancing sustainability in crop production.

Supplementary information: The online version contains supplementary material available at 10.1007/s11032-025-01609-6.

Drought is one of the main factors restricting the increase of maize yield. Many genes respond to drought at seedlings have been cloned but less were effective in field. So, more work of understanding the genetic basis of drought response in field experiment need to be done due to its complexity. Herein, we constructed an association panel to carry on genomic wide association mapping for seven important traits under well-watered at whole period and drought at flowering stage. Then, 117 SNPs were identified, 50 SNPs of which were co-located among these traits or treatments or environments, including 50 SNPs identified under drought and 67 SNPs under well-watered. After merging the co-located SNPs, 90 SNPs were obtained. Combining the RNA-seq data of maize inbred line B73 under drought stressed from the public database, 31 differential expressed genes around the associated SNP were considered as drought responsive genes. Through protein interaction analysis and Gene Ontology enrichment analysis, it was shown that these genes are involved in regulating biological processes such as the tricarboxylic acid cycle, glycolysis, cell mitosis, and flowering signaling. And as the aggregation of related favorable allele genes improves the drought tolerance of materials. These results provide some candidate genes for in-depth analyzing the drought resistance mechanism when the drought happened at flowering stage during field experiment.

Supplementary information: The online version contains supplementary material available at 10.1007/s11032-025-01599-5.

To enhance agricultural productivity and resilience in the face of changing climatic conditions, innovative strategies over traditional breeding methods are essential to shorten the breeding cycle for developing new climate-smart crop varieties, thereby supporting food security for a growing global population. Speed breeding (SB) is a promising cutting-edge approach to decrease crop life cycle, enabling accumulation of desirable traits in plants, thereby increasing crop yield and resilience to biotic and abiotic stresses. SB integrates advanced technologies such as marker-assisted selection, genetic engineering, genome editing, and high-throughput plant phenotyping to expedite desired traits incorporation to the plant more precisely. SB technology allows plant breeders to improve selection accuracy, and boost genetic gain, thereby accelerating breeding process for improvement and development of new crop varieties. However, it requires sophisticated infrastructure, intensive management, cost and skilled personnel. This review provides updates of SB, covering its prerequisites, benefits and constraints in applications. Additionally, the synergy of SB with transgenic breeding, high-throughput phenotyping and genome editing for crop improvement is critically discussed. In summary, SB offers a potent strategy for plant breeders to mitigate climate change impacts and ensure food security through efficient agricultural research and production technologies.

The release of the rice reference genome marked the beginning of a genomic era for crops. Over the past decades, the improvements in genome sequencing and assembly techniques, coupled with the continuous decrease in cost, had revolutionized crop research and breeding. In this review, by text mining the literatures published from 2000 to 2024, we summarize the traits, tissues, and methods prioritized by crop scientists during this period. These analyses reveal profound influence of genomic approaches across all the stages of crop research and breeding, and propose a 4D roadmap of crop research, which are decoding, discovery, design and delivery, representing four steps from crop genome sequencing (decoding) to breeding (delivery). The results also highlight a strong bias of crops and traits in the current studies. Finally, a dramatic increase in the frequency of keywords related to artificial intelligence (AI) indicate wider and deeper AI applications in crop science, forecasting the imminent AI era for crops.

Cotton hybrids offer significant advantages, the application of male sterile lines in cotton hybrid breeding can reduce the cost of artificial castration and ensure hybrid seed purity. Pollen and anther development are a crucial aspect of plant fertility, sporopollenin synthesis provides the major component of the outer walls in pollen (exines) for preserving pollen grains activity, mutations in the genes involved in sporopollenin synthesis affect pollen development and fertility formation. The differentially expressed genes (DEGs) between the developing anthers of genic male sterile mutant (ms1) and its genetic background Coker 312 were identified, the genes related to pollen exine and anther cutin biosynthesis were screened from the DEGs. GhCYP704B1 (Gh_D12G2768) was the DEGs with a significantly down-regulated expression level in ms1 anthers, kept very low expression level in ms1 developing anthers. At the same time, we also screened 20 homologies of GhCYP704B1 from DEGs data, and the results showed that only GhCYP704B1 was predominantly expressed in cotton anthers, while other homologies did not show significant expression changes. We used VIGS technology the expression level of GhCYP704B1 in cotton C312, resulting in disrupted callose formation during the tetrad formation of microspore development, partial defect of the pollen exine, weakened pollen activity, low pollen germination rate, and poor plant fertility. The expression levels of genes related to pollen exine and anther cutin synthesis changed significantly, the composition and content of cutin monomers in cotton anthers were significantly reduced in GhCYP704B1-silenced lines. Abnormalities in callose caused blockage of sporopollenin synthesis and failure to synthesize the pollen exine properly. The findings indicate that GhCYP704B1 affects cotton fertility and is involved in pollen exine biosynthesis, thus providing a candidate gene for creating new male sterile lines in G. hirsutum.

Supplementary information: The online version contains supplementary material available at 10.1007/s11032-025-01608-7.

The identification of germplasm with high phosphorus efficiency is helpful to the genetic improvement of wheat. In this study, a doubled haploid (DH) population was used to investigate the traits related to phosphorus efficiency and map relevant loci under different conditions. On this basis, the association panel was used to verify mapping results. The results showed that shoot phosphorus concentration (SPC) and shoot phosphorus uptake per plant (SPUP) decreased, while shoot phosphorus utilization efficiency (SPUE) increased under low phosphorus. Correlation analysis showed that seedling biomass and root diameter could provide reference for identification of phosphorus efficiency. Twenty-one stable loci related to phosphorus efficiency were detected by linkage analysis. Among these, 11 loci including QRC-4D, QSpue.7A.2, and QSpup.7A.2 haven't been reported yet. The physical interval of QRC-4D was detected by three seedling phosphorus efficiency indexes, along with five seedling morphological indexes and five adult agronomic traits, which explained phenotypic variation up to 31.18%. In the association panel, QSpue.7A.2 associated with SPUE was also detected by genome-wide association study. Gene analysis revealed two candidate genes related to phosphorus within QRC-4D and QSpue.7A.2. These results provide valuable insights into genetic improvement and gene mining aimed at improving high phosphorus efficiency in wheat.

Supplementary information: The online version contains supplementary material available at 10.1007/s11032-025-01596-8.

With the rapid development of sequencing technology, the application of genomic prediction has become more and more common in breeding schemes of livestocks and crops. Selecting an appropriate statistical model is of central importance to achieve high prediction accuracy. Recently, machine learning models have been expected to upgrade genomic prediction into a new era. However, the perspective still suffers from lack of evidence that machine learning models can generally outperform the traditional ones on empirical data sets. In this study, we compared two machine learning models based on artificial neural network (ANN) and convolutional neural network (CNN) with four traditional models, including genomic best linear unbiased prediction (GBLUP), Bayesian ridge regression (BRR), BayesA and BayesB, using three published data sets for grain yield in wheat. For each model, we considered two variants: modeling and ignoring the genotype-by-environment ([Formula: see text]) interaction. In the comparison, we considered two strategies of cross-validation: predicting genotypes that have not been evaluated in any environment (CV1) and predicting genotypes that have been tested in other environments (CV2). Our results showed that traditional Bayesian models (BayesA, BayesB, and BRR) outperformed GBLUP, ANN and CNN when considering [Formula: see text] interaction. The accuracies of ANN and CNN were higher than traditional models only in CV1 and when [Formula: see text] interaction was ignored. It was also found that the performance of the two machine learning models was significantly affected by the interaction between the CV strategy and the way of treating the [Formula: see text] interaction, while that of the four traditional models was only influenced by whether the [Formula: see text] interaction was considered or not. Thus, machine learning models can be a powerful complementary to the traditional ones and their superiority may depend on the prediction scenario. Among the two machine learning models, we observed that the accuracy of ANN was higher than CNN in most cases, indicating that it is still challenging to adapt complex machine learning models such as CNN to genomic prediction.

Zucchini (Cucurbita pepo subsp. pepo) stands as an economically vital crop in China. In zucchini breeding, plant architectural patterns and fruit morphological characteristics serve as pivotal traits. In this study, we employed quantitative trait locus (QTL) analysis using recombinant inbred lines (RILs) derived from two distinct inbred lines, JinGL (subsp. ovifera) and HM-S2 (subsp. pepo), in conjunction with a high-density genetic map. Our investigation focused on ten QTLs associated with six horticulturally significant traits, including hypocotyl length (HL), plant height (PH), and four fruit-related traits: fruit length (FL), fruit diameter (FD), fruit shape index (FSI), and fruit weight (FW). The QTLs governing HL and PH were mapped to Chr03/LG10 and named qhl3.1 and qph3.1, respectively. The candidate gene Cp4.1LG10g05910/CpDw for qph3.1 was successfully identified. Additionally, three novel QTLs related to fruit size and shape were discovered. Among them, qfsi8.1/qfl8.1, demarcated by Marker238258 and Marker240069 on Chromosome 08/Linkage group 17 (Chr08/LG17), is a new major QTL regulating the fruit shape of zucchini. Through genomic insertion-deletion (InDel) and qRT-PCR analyses, we predicted genes within the qfsi8.1/qfl8.1 candidate interval, uncovering Cp4.1LG17g02030/CpIAA12 and Cp4.1LG17g02010/CpCalB as potential candidate genes. We developed molecular markers tightly linked to qph3.1 and qfl8.1 and validated them in 171 and 224 Cucurbita pepo germplasms, achieving accuracy rates of 96% and 100%, respectively. This study deepens our understanding of the genetic basis of key traits and provides valuable references for molecular breeding in Cucurbita pepo.

Supplementary information: The online version contains supplementary material available at 10.1007/s11032-025-01592-y.

Tiller number is an essential agronomic characteristic that influences barley morphology and yield. A barley low number of tillers mutant CIHO 11,530 exhibits few tillers and in this study, we conducted a genetic analysis of the barley low number of tillers 2 (lnt2) locus. Linkage analysis showed that lnt2 was mapped in an interval of 3.39 cM on chromosome 6HS between the flanking markers SNP1765 and SNP526, explaining 53.06% of the phenotypic variance. The genetic effect of lnt2 was further verified in two other genetic backgrounds, explaining variances of 86.43% and 91.01% in tiller numbers between lines carrying the lnt2 mutant and wild-type alleles, respectively. Furthermore, we constructed a large F₂ population and fine-mapped lnt2. Finally, lnt2 was mapped within a 0.19 cM genetic interval delimited by the tightly linked KASP markers KASP6359 and KASP365, and the physical interval was located at 40.57-42.35 Mb. In this interval, three genes were highly likely lnt2 based on gene annotations, sequence and gene expression analyses. Our research provides valuable information for the map-based cloning of lnt2.

Supplementary information: The online version contains supplementary material available at 10.1007/s11032-025-01589-7.