Wheat Population Research Articles

Design and Experimental Analysis of an Air-Suction Wheat Precision Hill-Seed Metering Device

The uniformity of the wheat distribution within and between rows has a significant effect on crop population structure, leading to decreased yield as nonuniformity increases. Traditional drills are influenced by soil porosity and flatness in the field, making accurate control of sowing depth and amount challenging and resulting in an uneven spatial distribution of gramineous seedlings. Precision cave-sowing technology effectively enhances wheat population distribution uniformity. However, owing to limitations in existing mechanical precision cave planters, their operational speed is lower than that of drill planters. To address these issues, this study designed an air-suction precision wheat seed dispenser, described its basic structure and working principle, and developed a seed mechanical model. A theoretical analysis was conducted on the working process and key components of the seed feeder. A suitable mould hole diameter was determined to be 1.6~2.0 mm, and the rotation speed range for the seed plate was found to be 65~85 r·min−1. Fluent simulations were used to determine the influence of orifice type on gas chamber flow fields; DEM-CFD-coupled simulation identified an appropriate negative pressure range of 2.6~3.4 kPa for optimal performance during seeding operations. Orthogonal experiments were carried out with mould hole diameter, negative pressure size, and seed plate speed as test factors alongside a qualification index, multiple sowing index, and missed sowing index as response indicators—leading to regression equation establishment, which yielded the optimal parameter combination: mould hole diameter at 1.8 mm; gas chamber negative pressure at 3.2 kPa; and a seed plate speed of 74 r·min−1, with the corresponding forwards speed of the machine being 7 km·h−1—resulting in a qualification index of 91.66%, multiple sowing index of 5.98%, and missed sowing index of 2.36%. This pneumatic suction type wheat precision seeder achieves equivalent operational speeds as traditional drills while enabling precision seeding.

Comparing performances of different statistical models and multiple threshold methods in a nested association mapping population of wheat.

Nested association mapping (NAM) populations emerged as a multi-parental strategy that combines the high statistical power of biparental linkage mapping with greater allelic richness of association mapping. Several statistical models have been developed for marker-trait associations (MTAs) in genome-wide association studies (GWAS), which ranges from simple to increasingly complex models. These statistical models vary in their performance for detecting real association with the avoidance of false positives and false negatives. Furthermore, significant threshold methods play an equally important role for controlling spurious associations. In this study, we compared the performance of seven different statistical models ranging from single to multi-locus models on eight different simulated traits with varied genetic architecture for a NAM population of spring wheat (Triticum aestivum L.). The best identified model was further used to identify MTAs for 11 different agronomic and spectral reflectance traits, which were collected on the NAM population between 2014 and 2016. The "Bayesian information and linkage disequilibrium iteratively nested keyway (BLINK)" model performed better than all other models observed based on QQ plots and detection of real association in a simulated data set. The results from model comparison suggest that BLINK controls both false positives and false negatives under the different genetic architecture of simulated traits. Comparison of multiple significant threshold methods suggests that Bonferroni correction performed superior for controlling false positives and false negatives and complements the performance of GWAS models. BLINK identified 45 MTAs using Bonferroni correction of 0.05 for 11 different phenotypic traits in the NAM population. This study helps identify the best statistical model and significant threshold method for performing association analysis in subsequent NAM population studies.

Vasil Yakovych Yuriev: Life and scientific heritage (on the 150th anniversary of his birth)

The article discusses the main stages of life and scientific activities of the renowned and respected academician-breeder Vasyl Yakovych Yuriev. His name is inextricably linked with the activities of the Ukrainian Research Institute of Plant Breeding, Selection, and Genetics (now Yuriev Plant Production Institute of the National Academy of Agrarian Sciences of Ukraine). It is shown that in his scientific work, Vasyl Yakovych paid special attention to the collection of initial material, the selection of parental forms for crossing, developed methods for testing hybrids and varieties at different stages of the selection process, and demonstrated the advantages of the method of selecting pure lines from local wheat populations compared to the ineffective method of mass selection. Based on this method, under his leadership, many high-yielding valuable varieties of winter and spring wheat, rye, millet, barley, and corn were developed. To study the patterns of inheritance of important valuable traits in wheat, V. Ya. Yuriev used intervarietal and interspecies crosses of the diallel type. Many studies have been conducted in the field of breeding varieties for disease and pest resistance and immunity. In recognition of his scientific achievements, the Academy of Sciences of Ukraine established the Vasyl Yakovych Yuriev Prize, which is awarded for the highest achievements in the field of selection and genetics.

Heterotic grouping of wheat hybrids based on general and specific combining ability from line × tester analysis.

The most important step in plant breeding is the correct selection of parents, and it would be wise to use heterotic groups for this. The purpose of this study is to analyse yield and its components as well as genetic diversity in line ×tester wheat populations. It also seeks to present a coherent framework for the isolation of early superior families and the development of heterotic groups in bread wheat. F1 and F2 generations of 51 genotypes, including 36 combinations between 12 lines and three testers and 15 parents, were evaluated for yield and its components in a three-replication experiment according to the randomized block design. Line × tester analysis of variance, general and specific combining abilities, heterosis, heterobeltiosis and inbreeding depression were calculated. Heterotic groups created based on general and specific combining abilities were compared with each other. The results showed that there was sufficient genetic variation in the population and that further genetic calculations could be made. The selections made based on general and specific combining abilities, heterosis values and average performance of genotypes without heterotic grouping indicated different genotypes for each feature. The creation of heterotic groups made it possible to select genotypes that were superior in terms of all the criteria listed. It was concluded that heterotic groups created based on specific combining abilities may be more useful for breeding studies.

Integrating Hyperspectral Reflectance-Based Phenotyping and SSR Marker-Based Genotyping for Assessing the Salt Tolerance of Wheat Genotypes under Real Field Conditions.

Wheat breeding programs are currently focusing on using non-destructive and cost-effective hyperspectral sensing tools to expeditiously and accurately phenotype large collections of genotypes. This approach is expected to accelerate the development of the abiotic stress tolerance of genotypes in breeding programs. This study aimed to assess salt tolerance in wheat genotypes using non-destructive canopy spectral reflectance measurements as an alternative to direct laborious and time-consuming phenological selection criteria. Eight wheat genotypes and sixteen F8 RILs were tested under 150 mM NaCl in real field conditions for two years. Fourteen spectral reflectance indices (SRIs) were calculated from the spectral data, including vegetation SRIs and water SRIs. The effectiveness of these indices in assessing salt tolerance was compared with four morpho-physiological traits using genetic parameters, SSR markers, the Mantel test, hierarchical clustering heatmaps, stepwise multiple linear regression, and principal component analysis (PCA). The results showed significant differences (p ≤ 0.001) among RILs/cultivars for both traits and SRIs. The heritability, genetic gain, and genotypic and phenotypic coefficients of variability for most SRIs were comparable to those of measured traits. The SRIs effectively differentiated between salt-tolerant and sensitive genotypes and exhibited strong correlations with SSR markers (R2 = 0.56-0.89), similar to the measured traits and allelic data of 34 SSRs. A strong correlation (r = 0.27, p < 0.0001) was found between the similarity coefficients of SRIs and SSR data, which was higher than that between measured traits and SSR data (r = 0.20, p < 0.0003) based on the Mantel test. The PCA indicated that all vegetation SRIs and most water SRIs were grouped with measured traits in a positive direction and effectively identified the salt-tolerant RILs/cultivars. The PLSR models, which were based on all SRIs, accurately and robustly estimated the various morpho-physiological traits compared to using individual SRIs. The study suggests that various SRIs can be integrated with PLSR in wheat breeding programs as a cost-effective and non-destructive tool for phenotyping and screening large wheat populations for salt tolerance in a short time frame. This approach can replace the need for traditional morpho-physiological traits and accelerate the development of salt-tolerant wheat genotypes.

QTL analysis of native Fusarium head blight and deoxynivalenol resistance in ‘D8006W’/’Superior’, soft white winter wheat population

BackgroundFusarium head blight (FHB), caused by Fusarium graminearum, is a major disease of wheat in North America. FHB infection causes fusarium damaged kernels (FDKs), accumulation of deoxynivalenol (DON) in the grain, and a reduction in quality and grain yield. Inheritance of FHB resistance is complex and involves multiple genes. The objective of this research was to identify QTL associated with native FHB and DON resistance in a ‘D8006W’/’Superior’, soft white winter wheat population.ResultsPhenotyping was conducted in replicated FHB field disease nurseries across multiple environments and included assessments of morphological and FHB related traits. Parental lines had moderate FHB resistance, however, the population showed transgressive segregation. A 1913.2 cM linkage map for the population was developed with SNP markers from the wheat 90 K Infinium iSelect SNP array. QTL analysis detected major FHB resistance QTL on chromosomes 2D, 4B, 5A, and 7A across multiple environments, with resistance from both parents. Trait specific unique QTL were detected on chromosomes 1A (visual traits), 5D (FDK), 6B (FDK and DON), and 7D (DON). The plant height and days to anthesis QTL on chromosome 2D coincided with Ppd-D1 and were linked with FHB traits. The plant height QTL on chromosome 4B was also linked with FHB traits; however, the Rht-B1 locus did not segregate in the population.ConclusionsThis study identified several QTL, including on chromosome 2D linked with Ppd-D1, for FHB resistance in a native winter wheat germplasm.

36-year study reveals stability of a wild wheat population across microhabitats.

Long-term genetic studies of wild populations are very scarce, but are essential for connecting ecological and population genetics models, and for understanding the dynamics of biodiversity. We present a study of a wild wheat population sampled over a 36-year period at high spatial resolution. We genotyped 832 individuals from regular sampling along transects during the course of the experiment. Genotypes were clustered into ecological microhabitats over scales of tens of metres, and this clustering was remarkably stable over the 36 generations of the study. Simulations show that it is difficult to determine whether this spatial and temporal stability reflects extremely limited dispersal or fine-scale local adaptation to ecological parameters. Using a common-garden experiment, we showed that the genotypes found in distinct microhabitats differ phenotypically. Our results provide a rare insight into the population genetics of a natural population over a long monitoring period.

Mapping quantitative trait loci for seminal root angle in a selected durum wheat population.

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 (BC1F2:5) 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.

Virulence and Molecular Characterization Reveal Signs of Sexual Genetic Recombination of Puccinia striiformis f. sp. tritici and Puccinia striiformis f. sp. hordei in Tibet.

Stripe rust of wheat and barley is caused by different formae speciales, Puccinia striiformis f. sp. tritici (Pst) and P. striiformis f. sp. hordei (Psh), respectively. To understand the relationship between the populations of the two formae speciales, a total of 260 P. striiformis isolates, including 140 from barley and 120 from wheat collected from Linzhi, Tibet, China, from 2018 to 2020, were tested on 18 barley and 13 wheat genotypes and genotyped with 26 single-nucleotide polymorphism (SNP)-based Kompetitive allele-specific PCR (KASP) markers. As a result, 260 isolates were identified as 83 virulence phenotypes (VPs), 115 of which as 9 VPs and could infect only wheat (wheat population), 111 as 54 VPs and could infect only barley (barley population), and 34 belonged to 20 VPs that could infect both wheat and barley (mixed population). Of the 149 multilocus genotypes (MLGs) that were identified, 92 were from wheat, 56 from barley, and 1 from both wheat and barley. Phenotypic and genotypic diversity was high in the populations from wheat and barley. Low linkage disequilibrium was found in most of the sampling sites of both crops, indicating strong signs of sexual reproduction (|r̄d| = 0.022 to 0.393, P = 0.004 to 0.847), whereas it was not observed in the overall population (wheat and barley sources) and the wheat, barley, and mixed populations, which may be because of the complex composition of isolates. Population structure analyses based on phenotyping and SNP-KASP genotypes supported the separation of the two formae speciales. However, MLGs and clusters containing isolates from both wheat and barley obviously indicated sexual genetic recombination between the two formae speciales. The results of the study provided an insight into evolution of Pst and Psh and showed the importance of management strategies for stripe rust of wheat and barley in regions where both crops are grown.

Integrating high-throughput phenotyping and genome-wide association studies for enhanced drought resistance and yield prediction in wheat.

Drought, especially terminal drought, severely limits wheat growth and yield. Understanding the complex mechanisms behind the drought response in wheat is essential for developing drought-resistant varieties. This study aimed to dissect the genetic architecture and high-yielding wheat ideotypes under terminal drought. An automated high-throughput phenotyping platform was used to examine 28 392 image-based digital traits (i-traits) under different drought conditions during the flowering stage of a natural wheat population. Of the i-traits examined, 17 073 were identified as drought-related. A genome-wide association study (GWAS) identified 5320 drought-related significant single-nucleotide polymorphisms (SNPs) and 27 SNP clusters. A notable hotspot region controlling wheat drought tolerance was discovered, in which TaPP2C6 was shown to be an important negative regulator of the drought response. The tapp2c6 knockout lines exhibited enhanced drought resistance without a yield penalty. A haplotype analysis revealed a favored allele of TaPP2C6 that was significantly correlated with drought resistance, affirming its potential value in wheat breeding programs. We developed an advanced prediction model for wheat yield and drought resistance using 24 i-traits analyzed by machine learning. In summary, this study provides comprehensive insights into the high-yielding ideotype and an approach for the rapid breeding of drought-resistant wheat.

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&lt;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.