Wheat Population Research Articles

Multi-environments Evaluation of Zn and Fe Enhanced Bread Wheat Genotypes in Optimum Areas of Ethiopia

A multi-location experiment was conducted during June, 2020–October, 2021 under rainfed condition at Kulumsa, Asasa, Adet, Holeta, and Sinana Research centers, Ethiopia to evaluate the genotype-by-environment interaction effect and grain yield stability of Zn and Fe enhanced bread wheat genotypes grown. The treatments constituted 21 advanced genotypes and two standard checks were evaluated in an alpha lattice design replicated three times and data analysis was carried using R software. The results showed that genotypes and genotype x environmental interaction had a significant (p<0.001) effect on days to 50% heading, days to 90% maturity, plant height, grain yield, and 1000 kernel weight. The bread wheat lines BW172862, EBW193416, BW172864, and EBW193414 were high-yielding across most test environments, whereas genotype EBW192455 and Hidasse were low-yielding ones. From stability analysis genotypes BW172862, and EBW193416 were identified to be the most adapted bread wheat genotypes. “BW172862 produced 24.43% and 86.38% yield advantage over the standard check (Lemu) and local check (Hidasse), respectively. The second candidate genotype EBW193416 also produced 23.58% and 85.1% yield advantage over the standard check (Lemu) and local check (Hidasse), respectively. EBW193416 and BW172862 have also exhibited lower Yellow and Stem rust severity compared to others across the test environments. Thus, BW172862 and EBW193416 were selected as best parent to recycle for bread wheat population improvement in 2023 for medium agro-ecology of Ethiopia.

Influence of leaf inclination angle and tillering on population transpiration, soil evaporation, and yield in winter wheat near-isogenic lines.

Leaf inclination angle (LIA) and tillering impact the winter wheat (Triticum aestivum L.) population canopy structure. Understanding their effects on water use (WU) parameters and yield can guide water-saving strategies through population control. In this study, six near-isogenic lines (NILs) and their parents were selected as materials. These special materials were characterized by varying tillering at the current sowing density, a similar genetic background, and, particularly, a gradient in mean flag leaf LIA. The investigation focused on the jointing to early grain-filling stage, the peak water requirement period of wheat crops. Population-scale transpiration (PT) and evaporation from the soil surface (E) were partitioned from total evapotranspiration (ET) by the means of micro-lysimeters. The results showed decreased PT, E, and ET with increased population density (PD) within a narrow density range derived from varying tillering across genotypes. Significant correlations existed between PD and ET, E, and PT, especially in the wettest 2017-2018 growing season. Within such narrow PD range, all the correlations between WU parameters and PD were negative, although some correlations were not statistically significant, thereby suggesting the population structure's predominant impact. No significant correlation existed between LIA and both ET and PT within the LIA range of 35°-65°. However, significant correlations occurred between LIA and E in two growing seasons. Genotypes with similar LIA but different PD produced varied ET; while with similar PD, the four pairs of genotypes with different LIA each consumed similar ET, thus highlighting PD's more crucial role in regulating ET. The yield increased with higher LIA, and showed a significant correlation, emphasizing the LIA's significant effect on yield. However, no correlation was observed with PD, indicating the minor effect of tillering at the current sowing density. Therefore these results might offer valuable insights for breeding water-saving cultivars and optimizing population structures for effective field water conservation.

Mapping of main and hidden epistatic QTL effects in spring wheat population using medium parental FHB resistance

Fusarium head blight (FHB) is a devastating disease lowering the yield and quality of wheat. Intensive use of limited allelic pools critically affects wheat resistance, while advancing pathogenic alleles. This study presents new alleles raised from moderate rather than elite crosses. ‘Parshall’ has indigenous moderate resistance that was crossed with the moderate susceptible ‘Reeder’ and developed the 110 RIL population (noted as PR) that was tested across three states and years in the USA, under open-field and greenhouse conditions. Heading- and FHB-related traits of incidence, severity, and index were assessed. A genetic map (1417 cM) of SNP/DArT markers was generated. Composite interval mapping produced 45 QTL. Permutated LOD assessment identified two consistent QTL (4AL and 4BL) of type I and II resistance. Significant markers at the 4A QTL showed correlation with previous FHB resistance, while at the 4B QTL with pathogen responsive regions. The 1- and 2-D genome scans identified 16 QTL and six pairs of interacting markers for resistance, with additive effects between 0.17% and 2.19%; respectively. Additionally, additive × environment interaction was between 0.04% and 3.18% confirming the high environmental effect. Three pairs of QTL with additive × additive effects between 0.15% and 0.30% were revealed by FHB epistasis. However, the additive × additive environmental interaction at these three loci varied between 0.03% and 1%, confirming the G × E effect on FHB resistance. Hidden heritable epistasis was discovered in PR-population for FHB resistance. PR-alleles will help breeders develop lines mitigating epidemic environments and changeable climates, to boost yield facing hanger, food scarce, and secure worldwide penurious economies.

Characterization of Quantitative Trait Loci for Leaf Rust Resistance from CI 13227 in Three Winter Wheat Populations.

Leaf rust is a widespread foliar wheat disease causing substantial yield losses worldwide. Slow rusting is "adult plant" resistance that significantly slows epidemic development and thereby reduces yield loss. Wheat accession CI 13227 was previously characterized as having slow-rusting resistance. To validate the quantitative trait loci (QTLs) and develop diagnostic markers for slow rusting resistance in CI 13227, a new population of recombinant inbred lines of CI 13227 × Everest was evaluated for latent period, final severity, area under the disease progress curve, and infection type in greenhouses and genotyped using genotyping-by-sequencing. Four QTLs were identified on chromosome arms 2BL, 2DS, 3BS, and 7BL, explaining 6.82 to 28.45% of the phenotypic variance for these traits. Seven kompetitive allele-specific polymorphism markers previously reported to be linked to the QTLs in two other CI 13227 populations were validated. In addition, the previously reported QLr.hwwg-7AL was remapped to 2BL (renamed QLr.hwwg-2BL) after adding new markers in this study. Phenotypic data showed that the recombinant inbred lines harboring two or three of the QTLs had a significantly longer latent period. QLr.hwwg-2DS on 2DS showed a major effect on all rust resistance traits and was finely mapped to a 2.7-Mb interval by two newly developed flanking markers from exome capture. Three disease-resistance genes and two transporter genes were identified as the putative candidates for QLr.hwwg-2DS. The validated QTLs can be used as slow-rusting resistance resources, and the markers developed in this study will be useful for marker-assisted selection.

Influence of drought conditions on yield attributing characters and yield of wheat genotypes

Drought stress remains a major environmental factor which decreases the yield and productivity of most cereals growing worldwide. The research was carried out to assess correlation and path coefficient among 20 bread wheat (Triticum aestivum L.) genotypes. The treatments under drought conditions affected all agronomic traits, and the genotypes showed a significant difference. However, the results from the ambient tunnel were satisfactory, and some interesting observations were made. The present experiment was undertaken to estimate the correlation coefficient between grain yield and different yield contributing characters. The experiment was laid-out with two replications using an alpha lattice design. The study aimed to determine the degree of association between yield and yield attributing characters of drought-tolerant wheat populations using correlation and path analyses. The experiment's results demonstrated a highly significant difference in all the attributes examined between the wheat varieties, and water restriction dramatically reduced those traits. Partitioning of correlation coefficients into direct and indirect effects revealed. strong positive direct effects of spike weight on grain yield under drought-stressed conditions. Spike weight expressed the highest positive correlation (0.696) with grain yield, whereas spikelet per spike showed the lowest positive correlation (0.2) with tiller/m2.

Development and characterization of an EMS-mutagenized population of wheat (Triticum aestivum L.) for agronomic trait variation and increased micronutrients content

Development and characterization of an EMS-mutagenized population of wheat (Triticum aestivum L.) for agronomic trait variation and increased micronutrients content

Natural variant of Rht27, a dwarfing gene, enhances yield potential in wheat.

Discovery of Rht27, a dwarf gene in wheat, showed potential in enhancing grain yield by reducing plant height. Plant height plays a crucial role in crop architecture and grain yield, and semi-dwarf Reduced Height (Rht) alleles contribute to lodging resistance and were important in "Green Revolution." However, the use of these alleles is associated with some negative side effects in some environments, such as reduced coleoptile length, low nitrogen use efficiency, and reduced yield. Therefore, novel dwarf gene resources are needed to pave an alternative route to overcome these side effects. In this study, a super-dwarf mutant rht27 was obtained by the mutagenesis of G1812 (Triticum urartu, the progenitor of the A sub-genome of common wheat). Genetic analysis revealed that the dwarf phenotype was regulated by a single recessive genetic factor. The candidate region for Rht27 was narrowed to a 1.55Mb region on chromosome 3, within which we found two potential candidate genes that showed polymorphisms between the mutant and non-mutagenized G1812. Furthermore, the natural variants and elite haplotypes of the two candidates were investigated in a natural population of common wheat. The results showed that the natural variants affect grain yield components, and the dwarf haplotypes show the potential in improving agronomic traits and grain yield. Although the mutation in Rht27 results in severe dwarf phenotype in T. urartu, the natural variants in common wheat showed desirable phenotype, which suggests that Rht27 has the potential to improve wheat yield by utilizing its weak allelic mutation or fine-tuning its expression level.

Effect of genotyping errors on linkage map construction based on repeated chip analysis of two recombinant inbred line populations in wheat (Triticum aestivum L.)

Linkage maps are essential for genetic mapping of phenotypic traits, gene map-based cloning, and marker-assisted selection in breeding applications. Construction of a high-quality saturated map requires high-quality genotypic data on a large number of molecular markers. Errors in genotyping cannot be completely avoided, no matter what platform is used. When genotyping error reaches a threshold level, it will seriously affect the accuracy of the constructed map and the reliability of consequent genetic studies. In this study, repeated genotyping of two recombinant inbred line (RIL) populations derived from crosses Yangxiaomai × Zhongyou 9507 and Jingshuang 16 × Bainong 64 was used to investigate the effect of genotyping errors on linkage map construction. Inconsistent data points between the two replications were regarded as genotyping errors, which were classified into three types. Genotyping errors were treated as missing values, and therefore the non-erroneous data set was generated. Firstly, linkage maps were constructed using the two replicates as well as the non-erroneous data set. Secondly, error correction methods implemented in software packages QTL IciMapping (EC) and Genotype-Corrector (GC) were applied to the two replicates. Linkage maps were therefore constructed based on the corrected genotypes and then compared with those from the non-erroneous data set. Simulation study was performed by considering different levels of genotyping errors to investigate the impact of errors and the accuracy of error correction methods. Results indicated that map length and marker order differed among the two replicates and the non-erroneous data sets in both RIL populations. For both actual and simulated populations, map length was expanded as the increase in error rate, and the correlation coefficient between linkage and physical maps became lower. Map quality can be improved by repeated genotyping and error correction algorithm. When it is impossible to genotype the whole mapping population repeatedly, 30% would be recommended in repeated genotyping. The EC method had a much lower false positive rate than did the GC method under different error rates. This study systematically expounded the impact of genotyping errors on linkage analysis, providing potential guidelines for improving the accuracy of linkage maps in the presence of genotyping errors.

UAV remote sensing phenotyping of wheat collection for response to water stress and yield prediction using machine learning

Water stress is a significant challenge for global food production. Rainfall pattern is becoming unpredictable due to climate change that causes unprecedent water stress conditions in cereals production including wheat which is one of the important staple food crops. To sustain wheat production under water limiting conditions, there is an urgent need to develop drought-tolerant wheat varieties. For this, screening large numbers of wheat genotype for traits related to growth and yield under water stressed conditions is crucial. In this study, we deployed high-throughput phenotyping approaches, including uncrewed aerial vehicle (UAV)-based multispectral imaging, advanced machine and deep learning regression models. Two separate field experiments, irrigated and rainfed, were conducted comprising 553 wheat genotypes, and collected dataset for traits such as plant height, phenology, grain yield, and timeseries multispectral imaging. UAV-multispectral imagery derived plant height measurements showed a high correlation (R2=0.75) with manual measurements. Vegetation indices derived from multispectral data differentiated growth pattern of genotypes under rainfed and irrigated conditions and were used in yield prediction modeling. Wheat genotypes were effectively ranked, and their response differentiated for water stress tolerance based on yield index, stress susceptibility index, and yield loss%. Importantly, yield prediction in genotypes was computed using four machine learning regression algorithms i.e., linear regression, support vector machine, random forest, and deep learning H2O-3, where H2O-3 was the most accurate model with R2=0.80. Results show that multispectral-driven traits combined with machine learning models effectively phenotyped large wheat population and such approaches can be integrated in crop breeding program to develop varieties tolerant to water stress.

Leveraging the potential of big genomic and phenotypic data for genome-wide association mapping in wheat

Genome-wide association mapping studies (GWAS) based on Big Data are a potential approach to improve marker-assisted selection in plant breeding. The number of available phenotypic and genomic data sets in which medium-sized populations of several hundred individuals have been studied is rapidly increasing. Combining these data and using them in GWAS could increase both the power of QTL discovery and the accuracy of estimation of underlying genetic effects, but is hindered by data heterogeneity and lack of interoperability. In this study, we used genomic and phenotypic data sets, focusing on Central European winter wheat populations evaluated for heading date. We explored strategies for integrating these data and subsequently the resulting potential for GWAS. Establishing interoperability between data sets was greatly aided by some overlapping genotypes and a linear relationship between the different phenotyping protocols, resulting in high quality integrated phenotypic data. In this context, genomic prediction proved to be a suitable tool to study relevance of interactions between genotypes and experimental series, which was low in our case. Contrary to expectations, fewer associations between markers and traits were found in the larger combined data than in the individual experimental series. However, the predictive power based on the marker-trait associations of the integrated data set was higher across data sets. Therefore, the results show that the integration of medium-sized to Big Data is an approach to increase the power to detect QTL in GWAS. The results encourage further efforts to standardize and share data in the plant breeding community.

Mutagenesis-Derived Resistance to Black Point in Wheat.

Black point, a severe global wheat disease, necessitates deploying resistant cultivars for effective control. However, susceptibility remains prevalent among most wheat cultivars. Identifying new sources of resistance and understanding their mechanisms are crucial for breeding resistant cultivars. This study pinpointed black point resistance in an ethyl methane sulfonate (EMS)-mutagenized wheat population of Wanyuanbai 1 (WYB) and analyzed resistant mutants using RNA-Seq. The findings revealed the following: (i) wyb-18, among 10,008 EMS-mutagenized lines, exhibited robust resistance with significantly lower black point incidence under artificial Bipolaris sorokiniana inoculation in 2020 and 2021 (average incidence of 5.2% over 2 years), markedly reduced compared with WYB (50.9%). (ii) wyb-18 kernels displayed black point symptoms at 12 days after inoculation (dai), 3 days later than WYB. At 15 dai, wyb-18 kernels had isolated black spots, unlike WYB kernels, where the entire embryo turned black. (iii) wyb-18 showed heightened antioxidant enzyme activity, including peroxidase, catalase, and superoxide dismutase. (iv) Analysis of 543 differentially expressed genes between wyb-18 and WYB at 9 dai identified enrichment in the MAPK signaling pathway through KEGG analysis. Ten genes in this pathway exhibited upregulated expression, while one was downregulated in wyb-18. Among these genes, PR1, WRKY11, SAPK5, and TraesCS1A02G326800 (chitin recognition protein) consistently showed upregulation in wyb-18, making them potential candidates for black point resistance. These results offer valuable germplasm resources for breeding and novel insights into the mechanisms of black point resistance.

Genome-wide association studies and transcriptomics reveal mechanisms explaining the diversity of wheat root responses to nutrient availability.

Nutrient availability profoundly influences plant root system architecture, which critically determines crop productivity. While Arabidopsis has provided important insights into the genetic responses to nutrient deficiency, translating this knowledge to crops, particularly wheat, remains a subject of inquiry. Here, examining a diverse wheat population under varying nitrogen (N), phosphorus (P), potassium (K), and iron (Fe) levels, we uncover a spectrum of root responses, spanning from growth inhibition to stimulation, highlighting genotype-specific strategies. Furthermore, we reveal a nuanced interplay between macronutrient deficiency (N, P, and K) and Fe availability, emphasizing the central role of Fe in modulating root architecture. Through genome-wide association mapping, we identify 11 quantitative trait loci underlying root traits under varying nutrient availabilities, including homologous genes previously validated in Arabidopsis, supporting our findings. In addition, utilizing transcriptomics, ROS imaging, and antioxidant treatment, we uncover that wheat root growth inhibition by nutrient deficiency is attributed to ROS accumulation, akin to the role of ROS in governing Arabidopsis root responses to nutrient deficiency. Therefore, our study reveals the conservation of molecular and physiological mechanisms between Arabidopsis and wheat to adjust root growth to nutrient availability, paving the way for targeted crop improvement strategies aimed at increasing nutrient use efficiency.

Identifying QTL for grain protein content independent from grain yield-related traits in durum wheat

Grain protein content (GPC) is a crucial quality trait determining the nutritional, rheological, and end-use properties characteristics of wheat. Breeding programs for GPC were hindered by complex genetic control, the strong influence of environmental factors, and the negative correlation between GPC and grain yield. To identify stable quantitative trait loci (QTL) associated with increased GPC without decreasing grain yield, a recombinant inbred lines population of durum wheat was genotyped with a 25K SNP array and evaluated for GPC and yield-related traits in four field trials. Six QTL for GPC were identified on chromosomes 1B, 2B (two loci), 4B, 5A, and 6A, consistently expressed across environments, four of which had antagonistic effects on grain yield per spike (GYS). Two QTL (QGpc.mgb-5A and QGpc/TKW.mgb-2B.2) on 2B and 5A chromosomes were independent of GYS variations and could be used in marker-assisted selection (MAS) for GPC improvement. Identifying and utilizing beneficial QTL/genes for wheat improvement requires careful consideration of the inverse relationship between GPC and yield-related traits, and phenotypic data collected across multiple environments. MAS or genomic selection techniques can effectively target favorable alleles for GPC enhancement while minimizing the impact on grain yield.

Genetic mapping of deoxynivalenol and fusarium damaged kernel resistance in an adapted durum wheat population

BackgroundFusarium head blight (FHB) infection results in Fusarium damaged kernels (FDK) and deoxynivalenol (DON) contamination that are downgrading factors at the Canadian elevators. Durum wheat (Triticum turgidum L. var. durum Desf.) is particularly susceptible to FHB and most of the adapted Canadian durum wheat cultivars are susceptible to moderately susceptible to this disease. However, the durum line DT696 is less susceptible to FHB than commercially grown cultivars. Little is known about genetic variation for durum wheat ability to resist FDK infection and DON accumulation. This study was undertaken to map genetic loci conferring resistance to DON and FDK resistance using a SNP high-density genetic map of a DT707/DT696 DH population and to identify SNP markers useful in marker-assisted breeding. One hundred twenty lines were grown in corn spawn inoculated nurseries near Morden, MB in 2015, 2016 and 2017 and the harvested seeds were evaluated for DON. The genetic map of the population was used in quantitative trait locus analysis performed with MapQTL.6® software.ResultsFour DON accumulation resistance QTL detected in two of the three years were identified on chromosomes 1 A, 5 A (2 loci) and 7 A and two FDK resistance QTL were identified on chromosomes 5 and 7 A in single environments. Although not declared significant due to marginal LOD values, the QTL for FDK on the 5 and 7 A were showing in other years suggesting their effects were real. DT696 contributed the favourable alleles for low DON and FDK on all the chromosomes. Although no resistance loci contributed by DT707, transgressive segregant lines were identified resulting in greater resistance than DT696. Breeder-friendly KASP markers were developed for two of the DON and FDK QTL detected on chromosomes 5 and 7 A. Markers flanking each QTL were physically mapped against the durum wheat reference sequence and candidate genes which might be involved in FDK and DON resistance were identified within the QTL intervals.ConclusionsThe DH lines harboring the desired resistance QTL will serve as useful resources in breeding for FDK and DON resistance in durum wheat. Furthermore, breeder-friendly KASP markers developed during this study will be useful for the selection of durum wheat varieties with low FDK and DON levels in durum wheat breeding programs.

Genomic insights into glume pubescence in durum wheat: GWAS and haplotype analysis implicates TdELD1-1A as a candidate gene

Glume pubescence is an important morphological trait for the characterization of wheat cultivars. It shows tolerance to biotic and abiotic stresses to some extent. Hg1 (formerly named Hg) locus on chromosome 1AS controls glume pubescence in wheat. Its genetic analysis, fine-mapping and candidate gene analysis have been widely studied recently, however, the cloning of Hg1 has not yet been reported. Here, we conducted a GWAS between a dense panel of 171,103 SNPs and glume pubescence (Gp) in a durum wheat population of 145 lines, and further analyzed the candidate genes of Hg1 combined with the gene expression, functional annotation, and haplotype analysis. As a results, TRITD0Uv1G104670 (TdELD1-1A), encoding glycosyltransferase-like ELD1/KOBITO 1, was detected as the most promising candidate gene of Hg1 for glume pubescence in durum wheat. Our findings not only contribute to a deeper understanding of its cloning and functional validation but also underscore the significance of accurate genome sequences and annotations. Additionally, our study highlights the relevance of unanchored sequences in chrUn and the application of bioinformatics analysis for gene discovery in durum wheat.

Variation of TaMyb10 and their function on grain color and pre-harvest sprouting resistance of wheat.

Pre-harvest sprouting (PHS) is a significant threat to global food security due to its association with losses in both yield and quality. Among the genes involved in PHS resistance in wheat, PHS-3D (TaMyb10-D) plays a crucial role. Here, we characterized the sequence variations of TaMyb10 genes in 416 bread wheat and 302 Aegilops tauschii accessions. Within TaMyb10-A sequences, we identified a deletion ranging from 214 to 305 bp in the signal and amino acid coding region, present in 61.3% of the accessions. Similarly, 79.3% of the TaMyb10-B sequences within the third exon region exhibited a 19 bp deletion. Additionally, 40.8% of the accessions lacked the 2.4 Mb fragment (in/del mutations) on Chr3D, where TaMyb10-D/PHS-3D was located. Interestingly, the geographical distribution of accessions showed little correlation with the divergence of TaMyb10. TaMyb10-A-IIIDele, TaMyb10-B-IVDele, and TaMyb10-D-VDele genotypes were prevalent in wheat populations across continents. Despite their structural variations, the five distinct protein types exhibited comparable ability to bind the promoters of downstream genes in the flavonoid and ABA pathways, such as CHS, DFR, and NCED. Furthermore, the combination of TaMyb10 homologs was significantly associated with grain color and germination percentages. Accessions exclusively harboring TaMyb10-D displayed red seed color and reduced germination percentages, indicating the predominant role of TaMyb10-D compared to TaMyb10-A and TaMyb10-B. This comprehensive investigation enhances our understanding of the structural variations and functional divergence of TaMyb10, providing valuable insights and resources for improving PHS resistance in wheat.

Beyond pathways: Accelerated flavonoids candidate identification and novel exploration of enzymatic properties using combined mapping populations of wheat.

Although forward-genetics-metabolomics methods such as mGWAS and mQTL have proven effective in providing myriad loci affecting metabolite contents, they are somehow constrained by their respective constitutional flaws such as the hidden population structure for GWAS and insufficient recombinant rate for QTL. Here, the combination of mGWAS and mQTL was performed, conveying an improved statistical power to investigate the flavonoid pathways in common wheat. A total of 941 and 289 loci were, respectively, generated from mGWAS and mQTL, within which 13 of them were co-mapped using both approaches. Subsequently, the mGWAS or mQTL outputs alone and their combination were, respectively, utilized to delineate the metabolic routes. Using this approach, we identified two MYB transcription factor encoding genes and five structural genes, and the flavonoid pathway in wheat was accordingly updated. Moreover, we have discovered some rare-activity-exhibiting flavonoid glycosyl- and methyl-transferases, which may possess unique biological significance, and harnessing these novel catalytic capabilities provides potentially new breeding directions. Collectively, we propose our survey illustrates that the forward-genetics-metabolomics approaches including multiple populations with high density markers could be more frequently applied for delineating metabolic pathways in common wheat, which will ultimately contribute to metabolomics-assisted wheat crop improvement.

Genome-wide analysis for root and leaf architecture traits associated with drought tolerance at the seedling stage in a highly ecologically diverse wheat population

Drought stress occurred at early growth stages in wheat affecting the following growth stages. Therefore, selecting promising drought-tolerant genotypes with highly adapted traits at the seedling stage is an important task for wheat breeders and geneticists. Few research efforts were conducted on the genetic control for drought-adaptive traits at the seedling stage in wheat. In this study, a set of 146 highly diverse spring wheat core collections representing 28 different countries was evaluated under drought stress at the seedling stage. All genotypes were exposed to drought stress for 13 days by water withholding. Leaf traits including seedling length, leaf wilting, days to wilting, leaf area, and leaf rolling were scored. Moreover, root traits such as root length, maximum width, emergence angle, tip angle, and number of roots were scored. Considerable significant genetic variation was found among all genotypes tested in these experiments. The heritability estimates ranged from 0.74 (leaf witling) to 0.99 (root tip angle). A set of nine genotypes were selected and considered drought-tolerant genotypes. Among all leaf traits, shoot length had significant correlations with all root traits under drought stress. The 146 genotypes were genotyped using the Infinium Wheat 15 K single nucleotide polymorphism (SNP) array and diversity arrays technology (DArT) marker platform. The result of genotyping revealed 12,999 SNPs and 2150 DArT markers which were used to run a genome-wide association study (GWAS). The results of GWAS revealed 169 markers associated with leaf and root traits under drought stress. Out of the 169 markers, 82 were considered major quantitative trait loci (QTL). The GWAS revealed 95 candidate genes were identified with 53 genes showing evidence for drought tolerance in wheat, while the remaining candidate genes were considered novel. No shared markers were found between leaf and root traits. The results of the study provided mapping novel markers associated with new root traits at the seedling stage. Also, the selected genotypes from different countries could be employed in future wheat breeding programs not only for improving adaptive drought-tolerant traits but also for expanding genetic diversity.

A method for small-sized wheat seedlings detection: from annotation mode to model construction

The number of seedlings is an important indicator that reflects the size of the wheat population during the seedling stage. Researchers increasingly use deep learning to detect and count wheat seedlings from unmanned aerial vehicle (UAV) images. However, due to the small size and diverse postures of wheat seedlings, it can be challenging to estimate their numbers accurately during the seedling stage. In most related works in wheat seedling detection, they label the whole plant, often resulting in a higher proportion of soil background within the annotated bounding boxes. This imbalance between wheat seedlings and soil background in the annotated bounding boxes decreases the detection performance. This study proposes a wheat seedling detection method based on a local annotation instead of a global annotation. Moreover, the detection model is also improved by replacing convolutional and pooling layers with the Space-to-depth Conv module and adding a micro-scale detection layer in the YOLOv5 head network to better extract small-scale features in these small annotation boxes. The optimization of the detection model can reduce the number of error detections caused by leaf occlusion between wheat seedlings and the small size of wheat seedlings. The results show that the proposed method achieves a detection accuracy of 90.1%, outperforming other state-of-the-art detection methods. The proposed method provides a reference for future wheat seedling detection and yield prediction.

Genetic analysis of late-maturity α-amylase in twelve wheat populations

Main conclusionGenetic loci, particularly those with an effect in the independent panel, could be utilised to further reduce LMA expression when used with favourable combinations of genes known to affect LMA.Late maturity α-amylase (LMA) is a grain quality defect involving elevated α-amylase within the aleurone of wheat (Triticum aestivum L.) grains. The genes known to affect expression are the reduced height genes Rht-B1 (chromosome 4B) and Rht-D1 (chromosome 4D), and an ent-copalyl diphosphate synthase gene (LMA-1) on chromosome 7B. Other minor effect loci have been reported, but these are poorly characterised and further genetic understanding is needed. In this study, twelve F4-derived populations were created through single seed descent, genotyped and evaluated for LMA. LMA-1 haplotype C and the Rht-D1b allele substantially reduced LMA expression. The alternative dwarfing genes Rht13 and Rht18 had no significant effect on LMA expression. Additional quantitative trait loci (QTL) were mapped at 16 positions in the wheat genome. Effects on LMA expression were detected for four of these QTL in a large independent panel of Australian wheat lines. The QTL detected in mapping populations and confirmed in the large independent panel provide further opportunity for selection against LMA, especially if combined with Rht-D1b and/or favourable haplotypes of LMA-1.