Theoretical and Applied Genetics最新文献

Key message: Xinong 511, a new wheat-Thinopyrum ponticum variety with excellent fusarium head blight resistance, the QTLs were mapped to the wheat chromosomes 5B and 7A with named QFhb.nwafu-5B and QFhb.nwafu-7A, respectively. Novel Fusarium head blight (FHB) resistance germplasms and genes are valuable for wheat improvement and breeding efforts. Thinopyrum ponticum, a wild relative of common wheat, is a valuable germplasm of disease resistance for wheat improvement and breeding. Xinong 511 (XN511) is a high-quality wheat variety widely cultivated in the Yellow and Huai Rivers Valley of China with stable FHB-resistance. Through analysis of pedigree materials of the wheat cultivar XN511, we found that the genetic material and FHB resistance from Th. ponticum were transmitted to the introgression line, indicating that the FHB resistance in XN511 likely originates from Th. ponticum. To further explore the genetic basis of FHB resistance in XN511, QTL mapping was conducted using the RILs population of XN511 and the susceptible line Aikang 58 (AK58). Survey with makers closely-linked to Fhb1, Fhb2, Fhb4, Fhb5, and Fhb7, indicated that both XN511 and the susceptible lines do not contain these QTL. Using bulked segregant analysis RNA-seq (BSR-Seq) and newly developed allele-specific PCR (AS-PCR) markers, QTLs in XN511 were successfully located on wheat chromosomes 5B and 7A. These findings are significant for further understanding and utilizing FHB resistance genes in wheat improvement.

Key message: Extensive and comprehensive phenotypic data from a maize RIL population under both low- and normal-Pi treatments were used to conduct QTL mapping. Additionally, we integrated parental resequencing data from the RIL population, GWAS results, and transcriptome data to identify candidate genes associated with low-Pi stress in maize. Phosphorus (Pi) is one of the essential nutrients that greatly affect the maize yield. However, the genes underlying the QTL controlling maize low-Pi response remain largely unknown. In this study, a total of 38 traits at both seedling and maturity stages were evaluated under low- and normal-Pi conditions using a RIL population constructed from X178 (tolerant) and 9782 (sensitive), and most traits varied significantly between low- and normal-Pi treatments. Twenty-nine QTLs specific to low-Pi conditions were identified after excluding those with common intervals under both low- and normal-Pi conditions. Furthermore, 45 additional QTLs were identified based on the index value ((Trait_under_LowPi-Trait_under_NormalPi)/Trait_under_NormalPi) of each trait. These 74 QTLs collectively were classified as Pi-dependent QTLs. Additionally, 39 Pi-dependent QTLs were clustered in nine HotspotQTLs. The Pi-dependent QTL interval contained 19,613 unique genes, 6,999 of which exhibited sequence differences with non-synonymous mutation sites between X178 and 9782. Combined with in silico GWAS results, 277 consistent candidate genes were identified, with 124 genes located within the HotspotQTL intervals. The transcriptome analysis revealed that 21 genes, including the Pi transporter ZmPT7 and the strigolactones pathway-related gene ZmPDR1, exhibited consistent low-Pi stress response patterns across various maize inbred lines or tissues. It is noteworthy that ZmPDR1 in maize roots can be sharply up-regulated by low-Pi stress, suggesting its potential importance as a candidate gene for responding to low-Pi stress through the strigolactones pathway.

Key message: Incorporating feature-engineered environmental data into machine learning-based genomic prediction models is an efficient approach to indirectly model genotype-by-environment interactions. Complementing phenotypic traits and molecular markers with high-dimensional data such as climate and soil information is becoming a common practice in breeding programs. This study explored new ways to combine non-genetic information in genomic prediction models using machine learning. Using the multi-environment trial data from the Genomes To Fields initiative, different models to predict maize grain yield were adjusted using various inputs: genetic, environmental, or a combination of both, either in an additive (genetic-and-environmental; G+E) or a multiplicative (genotype-by-environment interaction; GEI) manner. When including environmental data, the mean prediction accuracy of machine learning genomic prediction models increased up to 7% over the well-established Factor Analytic Multiplicative Mixed Model among the three cross-validation scenarios evaluated. Moreover, using the G+E model was more advantageous than the GEI model given the superior, or at least comparable, prediction accuracy, the lower usage of computational memory and time, and the flexibility of accounting for interactions by construction. Our results illustrate the flexibility provided by the ML framework, particularly with feature engineering. We show that the feature engineering stage offers a viable option for envirotyping and generates valuable information for machine learning-based genomic prediction models. Furthermore, we verified that the genotype-by-environment interactions may be considered using tree-based approaches without explicitly including interactions in the model. These findings support the growing interest in merging high-dimensional genotypic and environmental data into predictive modeling.

Key message: Optimized phenomic selection in durum wheat uses near-infrared spectra, feature engineering and parameter tuning. Our study reports improvements in predictive ability and emphasizes customized preprocessing for different traits and models. The success of plant breeding programs depends on efficient selection decisions. Phenomic selection has been proposed as a tool to predict phenotype performance based on near-infrared spectra (NIRS) to support selection decisions. In this study, we test the performance of phenomic selection in multi-environmental trials from our durum wheat breeding program for three breeding scenarios and use feature engineering as well as parameter tuning to improve the phenomic prediction ability. In addition, we investigate the influence of genotype and environment on the phenomic prediction ability for agronomic and quality traits. Preprocessing, based on a grid search over the Savitzky-Golay filter parameters based on 756,000 genotype best linear unbiased estimate (BLUE) computations, improved the phenomic prediction ability by up to 1500% (0.02-0.3). Furthermore, we show that preprocessing should be optimized depending on the dataset, trait, and model used for prediction. The phenomic prediction scenarios in our durum breeding program resulted in low-to-moderate prediction abilities with the highest and most stable prediction results when predicting new genotypes in the same environment as used for model training. This is consistent with the finding that NIRS capture both the genotype and genotype-by-environment $(G\times E)$ interaction variance.

Key message: One hundred and fifty-five QTL for trace element concentrations in foxtail millet were identified using a genome-wide association study, and a candidate gene associated with Ni-Co-Cr concentrations was detected. Foxtail millet (Setaria italica) is an important regional crop known for its rich mineral nutrient content, which has beneficial effects on human health. We assessed the concentrations of ten trace elements (Ba, Co, Cr, Cu, Fe, Mn, Ni, Pb, Sr, and Zn) in the grain of 408 foxtail millet accessions. Significant differences in the concentrations of five elements (Ba, Co, Ni, Sr, and Zn) were observed between two subpopulations of spring- and summer-sown foxtail millet varieties. Moreover, 84.4% of the element pairs exhibited significant correlations. To identify the genetic factors influencing trace element accumulation, a comprehensive genome-wide association study was conducted, identifying 155 quantitative trait locus (QTL) for the ten trace elements across three different environments. Among them, ten QTL were consistently detected in multiple environments, including qZn2.1, qZn4.4, qCr4.1, qFe6.3, qFe6.5, qCo6.1, qPb7.3, qPb7.5, qBa9.1, and qNi9.1. Thirteen QTL clusters were detected for multiple elements, which partially explained the correlations between elements. Additionally, the different concentrations of five elements between foxtail millet subpopulations were caused by the different frequencies of high-concentration alleles associated with important marker-trait associations. Haplotype analysis identified a candidate gene SETIT_036676mg associated with Ni accumulation, with the GG haplotype significantly increasing Ni-Co-Cr concentrations in foxtail millet. A cleaved amplified polymorphic sequence marker (cNi6676) based on the two haplotypes of SETIT_036676mg was developed and validated. Results of this study provide valuable reference information for the genetic research and improvement of trace element content in foxtail millet.

Key message: Genetic dissection of a QTL from wild emmer wheat, QGpc.huj.uh-5B.2, introgressed into bread wheat, identified candidate genes associated with tolerance to nitrogen deficiency, and potentially useful for improving nitrogen-use efficiency. Nitrogen (N) is an important macronutrient critical to wheat growth and development; its deficiency is one of the main factors causing reductions in grain yield and quality. N availability is significantly affected by drought or flooding, that are dependent on additional factors including soil type or duration and severity of stress. In a previous study, we identified a high grain protein content QTL (QGpc.huj.uh-5B.2) derived from the 5B chromosome of wild emmer wheat, that showed a higher proportion of explained variation under water-stress conditions. We hypothesized that this QTL is associated with tolerance to N deficiency as a possible mechanism underlying the higher effect under stress. To validate this hypothesis, we introgressed the QTL into the elite bread wheat var. Ruta, and showed that under N-deficient field conditions the introgression IL99 had a 33% increase in GPC (p < 0.05) compared to the recipient parent. Furthermore, evaluation of IL99 response to severe N deficiency (10% N) for 14 days, applied using a semi-hydroponic system under controlled conditions, confirmed its tolerance to N deficiency. Fine-mapping of the QTL resulted in 26 homozygous near-isogenic lines (BC₄F₅) segregating to N-deficiency tolerance. The QTL was delimited from - 28.28 to - 1.29 Mb and included 13 candidate genes, most associated with N-stress response, N transport, and abiotic stress responses. These genes may improve N-use efficiency under severely N-deficient environments. Our study demonstrates the importance of WEW as a source of novel candidate genes for sustainable improvement in tolerance to N deficiency in wheat.

Key message: Phenotypical, physiological and genetic characterization was carried out on the hybrid necrosis gene from Haynaldia villosa, and the related gene Ne-V was mapped to chromosome arm 2VL. Introducing genetic variation from wild relatives into common wheat through wide crosses is a vital strategy for enriching genetic diversity and promoting wheat breeding. However, hybrid necrosis, a genetic autoimmunity syndrome, often occurs in the offspring of interspecific or intraspecific crosses, restricting both the selection of hybrid parents and the pyramiding of beneficial genes. To utilize the germplasms of Haynaldia villosa (2n = 2x = 14, VV), we conducted wide hybridization between durum wheat (2n = 4x = 28, AABB) and multiple H. villosa accessions to synthesize the amphiploids (2n = 6x = 42, AABBVV). This study revealed that 61.5% of amphiploids derived from the above crosses exhibited hybrid necrosis, with some amphiploids even dying before reaching maturity. However, the initiation time and severity of necrosis varied dramatically among the progenies, suggesting that there were multiple genetic loci or multiple alleles in the same genetic locus conferring to hybrid necrosis in H. villosa accessions. Genetic analysis was performed on the F₂ and derived F_2:3 populations, which were constructed between amphiploid STH59-1 with normal leaves and amphiploid STH59-2 with necrotic leaves. A semidominant hybrid necrosis-related gene, Ne-V, was mapped to an 11.8-cM genetic interval on the long arm of chromosome 2V, representing a novel genetic locus identified in Triticum-related species. In addition, the hybrid necrosis was correlated with enhanced H₂O₂ accumulation and cell death, and it was influenced by the temperature and light. Our findings provide a foundation for cloning the Ne-V gene and exploring its molecular mechanism.

Phytotoxic soil salinity is a global problem, and in the northern Great Plains and western Canada, salt accumulates on the surface of marine sediment soils with high water tables under annual crop cover, particularly near wetlands. Crop production can overcome saline-affected soils using crop species and cultivars with salinity tolerance along with changes in management practices. This research seeks to improve our understanding of sunflower (Helianthus annuus) genetic tolerance to high salinity soils. Genome-wide association was conducted using the Sunflower Association Mapping panel grown for two years in naturally occurring saline soils (2016 and 2017, near Indian Head, Saskatchewan, Canada), and six phenotypes were measured: days to bloom, height, leaf area, leaf mass, oil percentage, and yield. Plot level soil salinity was determined by grid sampling of soil followed by kriging. Three estimates of sunflower performance were calculated: (1) under low soil salinity (< 4 dS/m), (2) under high soil salinity (> 4 dS/m), and (3) plasticity (regression coefficient between phenotype and soil salinity). Fourteen loci were significant, with one instance of co-localization between a leaf area and a leaf mass locus. Some genomic regions identified as significant in this study were also significant in a recent greenhouse salinity experiment using the same panel. Also, some candidate genes underlying significant QTL have been identified in other plant species as having a role in salinity response. This research identifies alleles for cultivar improvement and for genetic studies to further elucidate salinity tolerance pathways.

Polyploidy played an important role in the evolution of the three most important crops: wheat, maize and rice, each of them providing a unique model for studying allopolyploidy, segmental alloploidy or paleopolyploidy. However, its genetic and evolutionary role is still vague. The undelying mechanisms and consequences of polyploidy remain fundamental objectives in the study of eukaryotes. Maize is one of the underutilized crops at the polyploid level. This species has no stable natural polyploids, the existing ones being artificially obtained. From the experimental polyploid series of maize, only the tetraploid forms (4n = 40) are of interest. They are characterized by some valuable morphological, physiological and biochemical features, superior to the diploid forms from which they originated, but also by some drawbacks such as: reduced fertility, slower development, longer vegetation period, low productivity and adaptedness. Due to these barriers to using tetraploids in field production, maize tetraploids primarily found utility in scientific studies regarding genetic variability, inbreeding, heterosis and gene dosage effect. Since the first mention of a triploid maize plant to present, many scientists and schools, devoted their efforts to capitalize on the use of polyploidy in maize. Despite its common disadvantages as a crop, significant progress in developing tetraploid maize with good agronomic performance was achieved leading to registered tetraploid maize varieties. In this review we summarize and discuss the different aspects of polyploidy in maize, such as evolutionary context, methods of induction, morphology, fertility issue, inheritance patterns, gene expression and potential use.

Key message: The exploration and dissection of a set of QTLs and candidate genes for gray leaf spot disease resistance using two fully assembled parental genomes may help expedite maize resistance breeding. The fungal disease of maize known as gray leaf spot (GLS), caused by Cercospora zeae-maydis and Cercospora zeina, is a significant concern in China, Southern Africa, and the USA. Resistance to GLS is governed by multiple genes with an additive effect and is influenced by both genotype and environment. The most effective way to reduce the cost of production is to develop resistant hybrids. In this study, we utilized the IBM Syn 10 Doubled Haploid (IBM Syn10 DH) population to identify quantitative trait loci (QTLs) associated with resistance to gray leaf spot (GLS) in multiple locations. Analysis of seven distinct environments revealed a total of 58 QTLs, 49 of which formed 12 discrete clusters distributed across chromosomes 1, 2, 3, 4, 8 and 10. By comparing these findings with published research, we identified colocalized QTLs or GWAS loci within eleven clustering intervals. By integrating transcriptome data with genomic structural variations between parental individuals, we identified a total of 110 genes that exhibit both robust disparities in gene expression and structural alterations. Further analysis revealed 19 potential candidate genes encoding conserved resistance gene domains, including putative leucine-rich repeat receptors, NLP transcription factors, fucosyltransferases, and putative xyloglucan galactosyltransferases. Our results provide a valuable resource and linked loci for GLS marker resistance selection breeding in maize.