Plant Genome最新文献

Spike length (SL) is one of the major contributors to wheat yield. Uncovering major genetic regions affecting SL is an integral part of elucidating the genetic basis of wheat yield traits and goes further pivotal for marker-assisted selection breeding. A genome-wide meta-quantitative trait locus (MQTL) analysis of wheat SL resulted in the refinement of 48 MQTLs using 227 initial QTLs retrieved from previous studies published over the past decades. The average confidence interval (CI) of these MQTLs amounted to a 5.16-fold reduction compared to the mean CI of the initial QTLs. As many as 2240 putative candidate genes (CGs) were identified from the MQTL intervals using transcriptomics data in silico of wheat, of which 58 CGs were identified based on wheat-rice homology analysis. For the key CG affecting SL, a functional kompetitive allele-specific PCR (KASP) marker, TaPP2C-3B-KASP, was developed to distinguish TaPP2C-3B-Hap I and TaPP2C-3B-Hap II based on the single nucleotide polymorphism at the 272 bp (A/G). The frequency of the elite allelic variation TaPP2C-3B-Hap II with high SL remained relatively stable at about 49.62% from the 1960s to 1990s. Integration of MQTL analysis and in silico transcriptome data led to a significant increase in the reliability of CGs for the genetic regulation of wheat SL, and the haplotype analysis for key CGs TaPP2C-3B of SL provided insights into the biological function of the TaPP2C-3B gene.

Bananas (Musa spp.) are one of the most highly consumed fruits globally, grown in the tropical and sub-tropical regions. We evaluated 856 Musa accessions from the breeding programs of the International Institute of Tropical Agriculture of Nigeria, Tanzania, and Uganda; the National Agricultural Research Organization of Uganda; the Brazilian Agricultural Research Corporation (Embrapa); and the National Research Centre for Banana of India. Accessions from the in vitro gene bank at the International Transit Centre in Belgium were included to provide a baseline of available global diversity. A total of 16,903 informative single nucleotide polymorphism markers were used to estimate and characterize the genetic diversity and population structure and identify overlaps and unique material among the breeding programs. Analysis of molecular variance displayed low genetic variation among accessions and diploids and a higher variation among tetraploids (p < 0.001). Structure analysis revealed two major clusters corresponding to genomic composition. The results indicate that there is potential for the banana breeding programs to increase the diversity in their breeding materials and should exploit this potential for parental improvement and to enhance genetic gains in future breeding efforts.

Powdery mildew, caused by the fungal pathogen Blumeria graminis (DC.) E. O. Speer f. sp. tritici Em. Marchal (Bgt), is a constant threat to global wheat (Triticum aestivum L.) production. Although ∼100 powdery mildew (Pm) resistance genes and alleles have been identified in wheat and its relatives, more is needed to minimize Bgt's fast evolving virulence. In tetraploid wheat (Triticum turgidum L.), wild emmer wheat [T. turgidum ssp. dicoccoides (Körn. ex Asch. & Graebn.) Thell.] accessions from Israel have contributed many Pm resistance genes. However, the diverse genetic reservoirs of cultivated emmer wheat [T. turgidum ssp. dicoccum (Schrank ex Schübl.) Thell.] have not been fully exploited. In the present study, we evaluated a diverse panel of 174 cultivated emmer accessions for their reaction to Bgt isolate OKS(14)-B-3-1 and found that 66% of accessions, particularly those of Ethiopian (30.5%) and Indian (6.3%) origins, exhibited high resistance. To determine the genetic basis of Bgt resistance in the panel, genome-wide association studies were performed using 46,383 single nucleotide polymorphisms (SNPs) from genotype-by-sequencing and 4331 SNPs from the 9K SNP Infinium array. Twenty-five significant SNP markers were identified to be associated with Bgt resistance, of which 21 SNPs are likely novel loci, whereas four possibly represent emmer derived Pm4a, Pm5a, PmG16, and Pm64. Most novel loci exhibited minor effects, whereas three novel loci on chromosome arms 2AS, 3BS, and 5AL had major effect on the phenotypic variance. This study demonstrates cultivated emmer as a rich source of powdery mildew resistance, and the resistant accessions and novel loci found herein can be utilized in wheat breeding programs to enhance Bgt resistance in wheat.

Seminal root angle (SRA) is an important root architectural trait associated with drought adaptation in cereal crops. To date, all attempts to dissect the genetic architecture of SRA in durum wheat (Triticum durum Desf.) have used large association panels or structured mapping populations. Identifying changes in allele frequency generated by selection provides an alternative genetic mapping approach that can increase the power and precision of QTL detection. This study aimed to map quantitative trait loci (QTL) for SRA by genotyping durum lines created through divergent selection using a combination of marker-assisted selection (MAS) for the major SRA QTL (qSRA-6A) and phenotypic selection for SRA over multiple generations. The created 11 lines (BC₁F_2:5) were genotyped with genome-wide single-nucleotide polymorphism (SNP) markers to map QTL by identifying markers that displayed segregation distortion significantly different from the Mendelian expectation. QTL regions were further assessed in an independent validation population to confirm their associations with SRA. The experiment revealed 14 genomic regions under selection, 12 of which have not previously been reported for SRA. Five regions, including qSRA-6A, were confirmed in the validation population. The genomic regions identified in this study indicate that the genetic control of SRA is more complex than previously anticipated. Our study demonstrates that selection mapping is a powerful approach to complement genome-wide association studies for QTL detection. Moreover, the verification of qSRA-6A in an elite genetic background highlights the potential for MAS, although it is necessary to combine additional QTL to develop new cultivars with extreme SRA phenotypes.

SoySNP50K and SoySNP6K are commonly used for soybean (Glycine max) genotyping. The SoySNP50K assay has been used to genetically analyze the entire USDA Soybean Germplasm Collection, while the SoySNP6K assay, containing a subset of 6000 single-nucleotide polymorphisms (SNPs) from SoySNP50K, has been used for quantitative trait loci mapping of different traits. To meet the needs for genomic selection, selection of parents for crosses, and characterization of breeding populations, especially early selection of ideal offspring from thousands of lines, we developed two assays, SoySNP3K and SoySNP1K, containing 3072 and 1252 SNPs, respectively, based on SoySNP50K and SoySNP6K mark sets. These two assays also contained the trait markers reported or contributed by soybean breeders. The SNPs in the SoySNP3K are a subset from SoySNP6K, while the SNPs in the SoySNP1K are a subset from SoySNP3K. These SNPs were chosen to reduce the SNP number in the large linkage blocks while capturing as much of the haplotype diversity as possible. They are highly polymorphic and of high quality. The mean minor allele frequencies of the SNPs in the southern and northern US elites were 0.25 and 0.27 for SoySNP3K, respectively, and 0.29 and 0.33 for SoySNP1K. The selected SNPs are a valuable source for developing targeted amplicon sequencing assay or beadchip assay in soybean. SoySNP3K and SoySNP1K assays are commercialized by Illumina Inc. and AgriPlex Genomics, respectively. Together with SoySNP50K and SoySNP6K, a series of nested assays with different marker densities will serve as additional low-cost genomic tools for genetic, genomic, and breeding research.

Root metaxylems are phenotypically diverse structures whose function is particularly important under drought stress. Significant research has dissected the genetic machinery underlying metaxylem phenotypes in dicots, but that of monocots are relatively underexplored. In maize (Zea mays), a robust pipeline integrated a genome-wide association study (GWAS) of root metaxylem phenes under well-watered and water-stress conditions with a gene co-expression network to prioritize the strongest gene candidates. We identified 244 candidate genes by GWAS, of which 103 reside in gene co-expression modules most relevant to xylem development. Several candidate genes may be involved in biosynthetic processes related to the cell wall, hormone signaling, oxidative stress responses, and drought responses. Of those, six gene candidates were detected in multiple root metaxylem phenes in both well-watered and water-stress conditions. We posit that candidate genes that are more essential to network function based on gene co-expression (i.e., hubs or bottlenecks) should be prioritized and classify 33 essential genes for further investigation. Our study demonstrates a new strategy for identifying promising gene candidates and presents several gene candidates that may enhance our understanding of vascular development and responses to drought in cereals.

Phytophthora root rot, caused by oomycete pathogens in the Phytophthora genus, poses a significant threat to soybean productivity. While resistance mechanisms against Phytophthora sojae have been extensively studied in soybean, the molecular basis underlying immune responses to Phytophthora sansomeana remains unclear. In this study, we investigated transcriptomic and epigenetic responses of two resistant (Colfax and NE2701) and two susceptible (Williams 82 and Senaki) soybean lines at four time points (2, 4, 8, and 16 h post inoculation [hpi]) after P. sansomeana inoculation. Comparative transcriptomic analyses revealed a greater number of differentially expressed genes (DEGs) upon pathogen inoculation in resistant lines, particularly at 8 and 16 hpi. These DEGs were predominantly associated with defense response, ethylene, and reactive oxygen species-mediated defense pathways. Moreover, DE transposons were predominantly upregulated after inoculation, and more of them were enriched near genes in Colfax than other soybean lines. Notably, we identified a long non-coding RNA (lncRNA) within the mapped region of the resistance gene that exhibited exclusive upregulation in the resistant lines after inoculation, potentially regulating two flanking LURP-one-related genes. Furthermore, DNA methylation analysis revealed increased CHH (where H = A, T, or C) methylation levels in lncRNAs after inoculation, with delayed responses in Colfax compared to Williams 82. Overall, our results provide comprehensive insights into soybean responses to P. sansomeana, highlighting potential roles of lncRNAs and epigenetic regulation in plant defense.

Helicoverpa armigera (also known as gram pod borer) is a serious threat to chickpea production in the world. A set of 173 chickpea genotypes were evaluated for H. armigera resistance, including mean larval population (MLP), percentage pod damage (PPD), and pest resistance (PR) for 2 consecutive years (year 2020 and 2021). The same core set was also genotyped with 50K Axiom CicerSNP Array. The trait data and 50,000 single nucleotide polymorphism genotypic data were used together to work out marker-trait associations (MTAs) using different genome-wide association studies models. For MLP, a total of 53 MTAs were identified, including 25 MTAs in year 2020 and 28 MTAs in year 2021. A set of three MTAs was found common in both environments. For PPD, two MTAs in year 2020 and five MTAs in year 2021 were identified. A set of two MTAs were common in both environments. Similarly, for PR, only two MTAs common in both environments were identified. Interestingly, a common MTA (Affx_123255526) on chromosome 2 (Ca2) was found to be associated with all the three component traits (MLP, PPD, and PR) of pod borer resistance in chickpea. Further, we report key genes that encode SCAMPs (that facilitates the secretion of defense-related molecules), quinone oxidoreductase (enables the production of reactive oxygen species that promotes diapause of gram pod borer), and NB-LRR proteins that have been implicated in plant defense against H. armigera. The resistant chickpea genotypes, MTAs, and key genes reported in the present study may prove useful in the future for developing pod borer-resistant chickpea varieties.

Sugarcane (Saccharum spp.) plays a crucial role in global sugar production; however, the efficiency of breeding programs has been hindered by its heterozygous polyploid genomes. Considering non-additive genetic effects is essential in genome prediction (GP) models of crops with highly heterozygous polyploid genomes. This study incorporates non-additive genetic effects and pedigree information using machine learning methods to track sugarcane breeding lines and enhance the prediction by assessing the degree of association between genotypes. This study measured the stalk biomass and sugar content of 297 clones from 87 families within a breeding population used in the Japanese sugarcane breeding program. Subsequently, we conducted analyses based on the marker genotypes of 33,149 single-nucleotide polymorphisms. To validate the accuracy of GP in the population, we first predicted the prediction accuracy of the best linear unbiased prediction (BLUP) based on a genomic relationship matrix. Prediction accuracy was assessed using two different cross-validation methods: repeated 10-fold cross-validation and leave-one-family-out cross-validation. The accuracy of GP of the first and second methods ranged from 0.36 to 0.74 and 0.15 to 0.63, respectively. Next, we compared the prediction accuracy of BLUP and two machine learning methods: random forests and simulation annealing ensemble (SAE), a newly developed machine learning method that explicitly models the interaction between variables. Both pedigree and genomic information were utilized as input in these methods. Through repeated 10-fold cross-validation, we found that the accuracy of the machine learning methods consistently surpassed that of BLUP in most cases. In leave-one-family-out cross-validation, SAE demonstrated the highest accuracy among the methods. These results underscore the effectiveness of GP in Japanese sugarcane breeding and highlight the significant potential of machine learning methods.