Mapping crown rust resistance in the oat diploid accession PI 258731 (Avena strigosa).

Oat crown rust, caused by Puccinia coronata Corda f. sp. avenae Eriks. (Pca), is a major biotic impediment to global oat production. Crown rust resistance has been described in oat diploid species A. strigosa accession PI 258731 and resistance from this accession has been successfully introgressed into hexaploid A. sativa germplasm. The current study focuses on 1) mapping the location of QTL containing resistance and evaluating the number of quantitative trait loci (QTL) conditioning resistance in PI 258731; 2) understanding the relationship between the original genomic location in A. strigosa and the location of the introgression in the A. sativa genome; 3) identifying molecular markers tightly linked with PI 258731 resistance loci that could be used for marker assisted selection and detection of this resistance in diverse A. strigosa accessions. To achieve this, A. strigosa accessions, PI 258731 and PI 573582 were crossed to produce 168 F5:6 recombinant inbred lines (RILs) through single seed descent. Parents and RILs were genotyped with the 6K Illumina SNP array which generated 168 segregating SNPs. Seedling reactions to two isolates of Pca (races TTTG, QTRG) were conditioned by two genes (0.6 cM apart) in this population. Linkage mapping placed these two resistant loci to 7.7 (QTRG) to 8 (TTTG) cM region on LG7. Field reaction data was used for QTL analysis and the results of interval mapping (MIM) revealed a major QTL (QPc.FD-AS-AA4) for field resistance. SNP marker assays were developed and tested in 125 diverse A. strigosa accessions that were rated for crown rust resistance in Baton Rouge, LA and Gainesville, FL and as seedlings against races TTTG and QTRG. Our data proposed SNP marker GMI_ES17_c6425_188 as a candidate for use in marker-assisted selection, in addition to the marker GMI_ES02_c37788_255 suggested by Rine's group, which provides an additional tool in facilitating the utilization of this gene in oat breeding programs.

Bearded or smooth? Awns improve yield when wheat experiences heat stress during grain fill in the southeastern United States.

The presence or absence of awns-whether wheat heads are 'bearded' or 'smooth' - is the most visible phenotype distinguishing wheat cultivars. Previous studies suggest that awns may improve yields in heat or water-stressed environments, but the exact contribution of awns to yield differences remains unclear. Here we leverage historical phenotypic, genotypic, and climate data for wheat (Triticum aestivum) to estimate the yield effects of awns under different environmental conditions over a 12-year period in the southeastern USA. Lines were classified as awned or awnless based on sequence data, and observed heading dates were used to associate grain fill periods of each line in each environment with climatic data and grain yield. In most environments, awn suppression was associated with higher yields, but awns were associated with better performance in heat-stressed environments more common at southern locations. Wheat breeders in environments where awns are only beneficial in some years may consider selection for awned lines to reduce year-to-year yield variability, and with an eye towards future climates.

Utilizing genomics and historical data to optimize gene pools for new breeding programs: A case study in winter wheat.

With the rapid generation and preservation of both genomic and phenotypic information for many genotypes within crops and across locations, emerging breeding programs have a valuable opportunity to leverage these resources to 1) establish the most appropriate genetic foundation at program inception and 2) implement robust genomic prediction platforms that can effectively select future breeding lines. Integrating genomics-enabled breeding into cultivar development can save costs and allow resources to be reallocated towards advanced (i.e., later) stages of field evaluation, which can facilitate an increased number of testing locations and replicates within locations. In this context, a reestablished winter wheat breeding program was used as a case study to understand best practices to leverage and tailor existing genomic and phenotypic resources to determine optimal genetics for a specific target population of environments. First, historical multi-environment phenotype data, representing 1,285 advanced breeding lines, were compiled from multi-institutional testing as part of the SunGrains cooperative and used to produce GGE biplots and PCA for yield. Locations were clustered based on highly correlated line performance among the target population of environments into 22 subsets. For each of the subsets generated, EMMs and BLUPs were calculated using linear models with the 'lme4' R package. Second, for each subset, TPs representative of the new SC breeding lines were determined based on genetic relatedness using the 'STPGA' R package. Third, for each TP, phenotypic values and SNP data were incorporated into the 'rrBLUP' mixed models for generation of GEBVs of YLD, TW, HD and PH. Using a five-fold cross-validation strategy, an average accuracy of r = 0.42 was obtained for yield between all TPs. The validation performed with 58 SC elite breeding lines resulted in an accuracy of r = 0.62 when the TP included complete historical data. Lastly, QTL-by-environment interaction for 18 major effect genes across three geographic regions was examined. Lines harboring major QTL in the absence of disease could potentially underperform (e.g., Fhb1 R-gene), whereas it is advantageous to express a major QTL under biotic pressure (e.g., stripe rust R-gene). This study highlights the importance of genomics-enabled breeding and multi-institutional partnerships to accelerate cultivar development.

Genetics of Fusarium head blight resistance in soft red winter wheat using a genome-wide association study.

Host resistance is an effective and sustainable approach to manage the negative impact of Fusarium head blight (FHB) on wheat (Triticum aestivum L.) grain yield and quality. The objective of this study was to characterize the phenotypic responses and identify quantitative trait loci (QTL) conditioning different FHB resistance types using a panel of 236 elite soft red winter wheat (SRWW) lines in a genome-wide association study (GWAS). The panel was phenotyped for five FHB and three morphological traits under two field and two greenhouse environments in 2018-2019 and 2019-2020. We identified 160 significant marker-trait associations (MTAs) for FHB traits and 11 MTAs for plant height. Eleven QTL showed major effects and explained >10% phenotypic variation (PV) for FHB resistance. Among these major loci, three QTL were stable and five QTL exhibited a pleiotropic effect. The QTL QFhb-3BL, QFhb-5AS, QFhb-5BL, QFhb-7AS.1, QFhb-7AS.2, and QFhb-7BS are presumed to be novel. Pyramiding multiple resistance alleles from all the major-effect QTL resulted in a significant reduction in FHB incidence, severity, index, deoxynivalenol (DON), and Fusarium-damaged kernel (FDK) by 17, 43, 45, 55, and 25%, respectively. Further validation of these QTL could potentially facilitate successful introgression of these resistance loci in new cultivars for improved FHB resistance in breeding programs.

Multi-trait genomic prediction using in-season physiological parameters increases prediction accuracy of complex traits in US wheat.

BACKGROUND: Recently genomic selection (GS) has emerged as an important tool for plant breeders to select superior genotypes. Multi-trait (MT) prediction model provides an opportunity to improve the predictive ability of expensive and labor-intensive traits. In this study, we assessed the potential use of a MT genomic prediction model by incorporating two physiological traits (canopy temperature, CT and normalized difference vegetation index, NDVI) to predict 5 complex primary traits (harvest index, HI; grain yield, GY; grain number, GN; spike partitioning index, SPI; fruiting efiiciency, FE) using two cross-validation schemes CV1 and CV2. RESULTS: In this study, we evaluated 236 wheat genotypes in two locations in 2 years. The wheat genotypes were genotyped with genotyping by sequencing approach which generated 27,466 SNPs. MT-CV2 (multi-trait cross validation 2) model improved predictive ability by 4.8 to 138.5% compared to ST-CV1(single-trait cross validation 1). However, the predictive ability of MT-CV1 was not significantly different compared to the ST-CV1 model. CONCLUSIONS: The study showed that the genomic prediction of complex traits such as HI, GN, and GY can be improved when correlated secondary traits (cheaper and easier phenotyping) are used. MT genomic selection could accelerate breeding cycles and improve genetic gain for complex traits in wheat and other crops.

Increased Prediction Accuracy Using Combined Genomic Information and Physiological Traits in A Soft Wheat Panel Evaluated in Multi-Environments.

An integration of field-based phenotypic and genomic data can potentially increase the genetic gain in wheat breeding for complex traits such as grain and biomass yield. To validate this hypothesis in empirical field experiments, we compared the prediction accuracy between multi-kernel physiological and genomic best linear unbiased prediction (BLUP) model to a single-kernel physiological or genomic BLUP model for grain yield (GY) using a soft wheat population that was evaluated in four environments. The physiological data including canopy temperature (CT), SPAD chlorophyll content (SPAD), membrane thermostability (MT), rate of senescence (RS), stay green trait (SGT), and NDVI values were collected at four environments (2016, 2017, and 2018 at Citra, FL; 2017 at Quincy, FL). Using a genotyping-by-sequencing (GBS) approach, a total of 19,353 SNPs were generated and used to estimate prediction model accuracy. Prediction accuracies of grain yield evaluated in four environments improved when physiological traits and/or interaction effects (genotype × environment or physiology × environment) were included in the model compared to models with only genomic data. The proposed multi-kernel models that combined physiological and genomic data showed 35 to 169% increase in prediction accuracy compared to models with only genomic data included when heading date was used as a covariate. In general, higher response to selection was captured by the model combing effects of physiological and genotype × environment interaction compared to other models. The results of this study support the integration of field-based physiological data into GY prediction to improve genetic gain from selection in soft wheat under a multi-environment context.

Genetic dissection of heat-responsive physiological traits to improve adaptation and increase yield potential in soft winter wheat.

BACKGROUND: Climate change, including higher temperatures (HT) has a detrimental impact on wheat productivity and modeling studies predict more frequent heat waves in the future. Wheat growth can be impaired by high daytime and nighttime temperature at any developmental stage, especially during the grain filling stage. Leaf chlorophyll content, leaf greenness, cell membrane thermostability, and canopy temperature have been proposed as candidate traits to improve crop adaptation and yield potential of wheat under HT. Nonetheless, a significant gap exists in knowledge of genetic backgrounds associated with these physiological traits. Identifying genetic loci associated with these traits can facilitate physiological breeding for increased yield potential under high temperature stress condition in wheat. RESULTS: We conducted genome-wide association study (GWAS) on a 236 elite soft wheat association mapping panel using 27,466 high quality single nucleotide polymorphism markers. The panel was phenotyped for three years in two locations where heat shock was common. GWAS identified 500 significant marker-trait associations (MTAs) (p ≤ 9.99 × 10- 4). Ten MTAs with pleiotropic effects detected on chromosomes 1D, 2B, 3A, 3B, 6A, 7B, and 7D are potentially important targets for selection. Five MTAs associated with physiological traits had pleiotropic effects on grain yield and yield-related traits. Seventy-five MTAs were consistently expressed over several environments indicating stability and more than half of these stable MTAs were found in genes encoding different types of proteins associated with heat stress. CONCLUSIONS: We identified 500 significant MTAs in soft winter wheat under HT stress. We found several stable loci across environments and pleiotropic markers controlling physiological and agronomic traits. After further validation, these MTAs can be used in marker-assisted selection and breeding to develop varieties with high stability for grain yield under high temperature.

Understanding the Genetic Basis of Spike Fertility to Improve Grain Number, Harvest Index, and Grain Yield in Wheat Under High Temperature Stress Environments.

Moderate heat stress accompanied by short episodes of extreme heat during the post-anthesis stage is common in most US wheat growing areas and causes substantial yield losses. Sink strength (grain number) is a key yield limiting factor in modern wheat varieties. Increasing spike fertility (SF) and improving the partitioning of assimilates can optimize sink strength which is essential to improve wheat yield potential under a hot and humid environment. A genome-wide association study (GWAS) allows identification of novel quantitative trait loci (QTLs) associated with SF and other partitioning traits that can assist in marker assisted breeding. In this study, GWAS was performed on a soft wheat association mapping panel (SWAMP) comprised of 236 elite lines using 27,466 single nucleotide polymorphisms (SNPs). The panel was phenotyped in two heat stress locations over 3 years. GWAS identified 109 significant marker-trait associations (MTAs) (p ≤ 9.99 x 10-5) related to eight phenotypic traits including SF (a major component of grain number) and spike harvest index (SHI, a major component of grain weight). MTAs detected on chromosomes 1B, 3A, 3B, and 5A were associated with multiple traits and are potentially important targets for selection. More than half of the significant MTAs (60 out of 109) were found in genes encoding different types of proteins related to metabolism, disease, and abiotic stress including heat stress. These MTAs could be potential targets for further validation study and may be used in marker-assisted breeding for improving wheat grain yield under post-anthesis heat stress conditions. This is the first study to identify novel QTLs associated with SF and SHI which represent the major components of grain number and grain weight, respectively, in wheat.

Comparative metabolomic profiling in the roots and leaves in contrasting genotypes reveals complex mechanisms involved in post-anthesis drought tolerance in wheat.

Understanding the contrasting biochemical changes in different plant parts in response to drought can help to formulate smart strategies to develop drought tolerant genotypes. The current study used metabolomics and physiological approaches to understand the differential biochemical changes coupled with physiological adjustments in leaves and roots to cope with drought stress in two wheat genotypes, LA754 (drought tolerant) and AGS2038 (drought sensitive). The gas chromatography-mass spectrometry (GC-MS) analysis and physiological trait estimation were performed in the roots and leaves after drought imposition. Drought induced reduction was observed in all physiological and yield related traits. In LA754, higher numbers of metabolites were altered in leaves (45) compared to roots (20) which indicates that plants allocated more resources to leaves in tolerant genotype. In addition, the metabolic components of the root were less affected by the stress which supports the idea that the roots are more drought tolerant than the leaf or shoot. In AGS2038, thirty and twenty eight metabolites were altered in the leaves and roots, respectively. This indicates that the sensitive genotype compromised resource allocation to leaves, rather allocated more towards roots. Tryptophan, valine, citric acid, fumaric acid, and malic acid showed higher accumulation in leaf in LA754, but decreased in the root, while glyceric acid was highly accumulated in the root, but not in the leaf. The results demonstrated that the roots and shoots have a different metabolic composition, and shoot metabolome is more variable than the root metabolome. Though the present study demonstrated that the metabolic response of shoots to drought contrasts with that of roots, some growth metabolites (protein, sugar, etc) showed a mirror increase in both parts. Protein synthesis and energy cycle was active in both organs, and the organs were metabolically activated to enhance water uptake and maintain growth to mitigate the effect of drought.

Metabolic and physiological changes induced by plant growth regulators and plant growth promoting rhizobacteria and their impact on drought tolerance in Cicer arietinum L.

Plant growth regulators (PGRs) and plant growth promoting rhizobacteria (PGPRs) play an important role in mitigating abiotic stresses. However, little is known about the parallel changes in physiological processes coupled with metabolic changes induced by PGRs and PGPRs that help to cope with drought stress in chickpeas. The present investigation was carried out to study the integrative effects of PGRs and PGPRs on the physiological and metabolic changes, and their association with drought tolerance in two chickpea genotypes. Inoculated seeds of two chickpea genotypes, Punjab Noor-2009 (drought sensitive) and 93127 (drought tolerance), were planted in greenhouse condition at the University of Florida. Prior to sowing, seeds of two chickpea varieties were soaked for 3 h in 24 h old cultures of PGPRs (Bacillus subtilis, Bacillus thuringiensis, and Bacillus megaterium), whereas, some of the seeds were soaked in distilled water for the same period of time and were treated as control. Plant growth regulators, salicylic acid (SA) and putrescine (Put), were applied on 25 days old seedlings just prior to the induction of drought stress. Drought stress was imposed by withholding the supply of water on 25-day-old seedlings (at the three-leaf stage) and continued for the next 25 days until the soil water content reached 14%. Ultrahigh-performance liquid chromatography-high resolution mass spectrometry (UPLC-HRMS) analysis concomitant with physiological parameters were carried out in chickpea leaves at two-time points i.e. 14 and 25 d after imposition of drought stress. The results showed that both genotypes, treated with PGRs and PGPRs (consortium), performed significantly better under drought condition through enhanced leaf relative water content (RWC), greater biomass of shoot and root, higher Fv/FM ratio and higher accumulation of protein, sugar and phenolic compounds. The sensitive genotype was more responsive than tolerant one. The results revealed that the accumulation of succinate, leucine, disaccharide, saccharic acid and glyceric acid was consistently higher in both genotypes at both time points due to PGRs and PGPRs treatment. Significant accumulation of malonate, 5-oxo-L-proline, and trans-cinnamate occurred at both time points only in the tolerant genotype following the consortium treatment. Aminoacyl-tRNA, primary and secondary metabolite biosynthesis, amino acid metabolism or synthesis pathways, and energy cycle were significantly altered due to PGRs and PGPRs treatment. It is inferred that changes in different physiological and metabolic parameters induced by PGRs and PGPRs treatment could confer drought tolerance in chickpeas.

Comparative Physiological and Metabolic Analysis Reveals a Complex Mechanism Involved in Drought Tolerance in Chickpea (Cicer arietinum L.) Induced by PGPR and PGRs.

The plant growth promoting rhizobacteria (PGPR) and plant growth regulators (PGRs) can be applied to improve the growth and productivity of plants, with potential to be used for genetic improvement of drought tolerance. However, for genetic improvement to be achieved, a solid understanding of the physiological and biochemical changes in plants induced by PGPR and PGR is required. The present study was carried out to investigate the role of PGPR and PGRs on the physiology and biochemical changes in chickpea grown under drought stress conditions and their association with drought tolerance. The PGPR, isolated from the rhizosphere of chickpea, were characterized on the basis of colony morphology and biochemical characters. They were also screened for the production of indole-3-acetic acid (IAA), hydrogen cyanide (HCN), ammonia (NH3), and exopolysaccharides (EPS) production. The isolated PGPR strains, named P1, P2, and P3, were identified by 16S-rRNA gene sequencing as Bacillus subtilis, Bacillus thuringiensis, and Bacillus megaterium, respectively. The seeds of two chickpea varieties, Punjab Noor-2009 (drought sensitive) and 93127 (drought tolerant) were soaked for 2-3 h prior to sowing in 24 h old cultures of isolates. The salicylic acid (SA) and putrescine (Put) were sprayed (150 mg/L) on 25 day old chickpea seedlings. The results showed that chickpea plants treated with a consortium of PGPR and PGRs significantly enhanced the chlorophyll, protein, and sugar contents compared to irrigated and drought conditions. Leaf proline content, lipid peroxidation, and activities of antioxidant enzymes (CAT, APOX, POD, and SOD) all increased in response to drought stress but decreased due to the PGPR and PGRs treatment. An ultrahigh performance liquid chromatography-high resolution mass spectrometry (UPLC-HRMS) analysis was carried out for metabolic profiling of chickpea leaves planted under controlled (well-irrigated), drought, and consortium (drought plus PGPR and PGRs) conditions. Proline, L-arginine, L-histidine, L-isoleucine, and tryptophan were accumulated in the leaves of chickpea exposed to drought stress. Consortium of PGPR and PGRs induced significant accumulation of riboflavin, L-asparagine, aspartate, glycerol, nicotinamide, and 3-hydroxy-3-methyglutarate in the leaves of chickpea. The drought sensitive chickpea variety showed significant accumulation of nicotinamide and 4-hydroxy-methylglycine in PGPR and PGR treated plants at both time points (44 and 60 days) as compared to non-inoculated drought plants. Additionally, arginine accumulation was also enhanced in the leaves of the sensitive variety under drought conditions. Metabolic changes as a result of drought and consortium conditions highlighted pools of metabolites that affect the metabolic and physiological adjustments in chickpea that reduce drought impacts.

UPLC-HRMS-based untargeted metabolic profiling reveals changes in chickpea (Cicer arietinum) metabolome following long-term drought stress.

Genetic improvement for drought tolerance in chickpea requires a solid understanding of biochemical processes involved with different physiological mechanisms. The objective of this study is to demonstrate genetic variations in altered metabolic levels in chickpea varieties (tolerant and sensitive) grown under contrasting water regimes through ultrahigh-performance liquid chromatography/high-resolution mass spectrometry-based untargeted metabolomic profiling. Chickpea plants were exposed to drought stress at the 3-leaf stage for 25 days, and the leaves were harvested at 14 and 25 days after the imposition of drought stress. Stress produced significant reduction in chlorophyll content, Fv /Fm , relative water content, and shoot and root dry weight. Twenty known metabolites were identified as most important by 2 different methods including significant analysis of metabolites and partial least squares discriminant analysis. The most pronounced increase in accumulation due to drought stress was demonstrated for allantoin, l-proline, l-arginine, l-histidine, l-isoleucine, and tryptophan. Metabolites that showed a decreased level of accumulation under drought conditions were choline, phenylalanine, gamma-aminobutyric acid, alanine, phenylalanine, tyrosine, glucosamine, guanine, and aspartic acid. Aminoacyl-tRNA and plant secondary metabolite biosynthesis and amino acid metabolism or synthesis pathways were involved in producing genetic variation under drought conditions. Metabolic changes in light of drought conditions highlighted pools of metabolites that affect the metabolic and physiological adjustment in chickpea that reduced drought impacts.

Comparative physiological and metabolomics analysis of wheat (Triticum aestivum L.) following post-anthesis heat stress.

Genetic improvement for stress tolerance requires a solid understanding of biochemical processes involved with different physiological mechanisms and their relationships with different traits. The objective of this study was to demonstrate genetic variability in altered metabolic levels in a panel of six wheat genotypes in contrasting temperature regimes, and to quantify the correlation between those metabolites with different traits. In a controlled environment experiment, heat stress (35:28 ± 0.08°C) was initiated 10 days after anthesis. Flag leaves were collected 10 days after heat treatment to employ an untargeted metabolomics profiling using LC-HRMS based technique called IROA. High temperature stress produced significant genetic variations for cell and thylakoid membrane damage, and yield related traits. 64 known metabolites accumulated 1.5 fold of higher or lower due to high temperature stress. In general, metabolites that increased the most under heat stress (L-tryptophan, pipecolate) showed negative correlation with different traits. Contrary, the metabolites that decreased the most under heat stress (drummondol, anthranilate) showed positive correlation with the traits. Aminoacyl-tRNA biosysnthesis and plant secondary metabolite biosynthesis pathways were most impacted by high temperature stress. The robustness of metabolic change and their relationship with phenotypes renders those metabolites as potential bio-markers for genetic improvement.

Mapping QTL for the traits associated with heat tolerance in wheat (Triticum aestivum L.).

BACKGROUND: High temperature (heat) stress during grain filling is a major problem in most of the wheat growing areas. Developing heat tolerant cultivars has become a principal breeding goal in the Southern and Central Great Plain areas of the USA. Traits associated with high temperature tolerance can be used to develop heat tolerant cultivars in wheat. The present study was conducted to identify chromosomal regions associated with thylakoid membrane damage (TMD), plasmamembrane damage (PMD), and SPAD chlorophyll content (SCC), which are indicative of high temperature tolerance. RESULTS: In this study we have reported one of the first linkage maps in wheat using genotype by sequencing SNP (GBS-SNP) markers to extreme response to post anthesis heat stress conditions. The linkage map was comprised of 972 molecular markers (538 Bin, 258 AFLPs, 175 SSRs, and an EST). The genotypes of the RIL population showed strong variation for TMD, SCC and PMD in both generations (F10 and F9). Composite interval mapping identified five QTL regions significantly associated with response to heat stress. Associations were identified for PMD on chromosomes 7A, 2B and 1D, SCC on 6A, 7A, 1B and 1D and TMD on 6A, 7A and 1D. The variability (R(2)) explained by these QTL ranged from 11.9 to 30.6% for TMD, 11.4 to 30.8% for SCC, and 10.5 to 33.5% for PMD. Molecular markers Xbarc113 and AFLP AGCTCG-347 on chromosome 6A, Xbarc121 and Xbarc49 on 7A, gwm18 and Bin1130 on 1B, Bin178 and Bin81 on 2B and Bin747 and Bin1546 on 1D were associated with these QTL. CONCLUSION: The identified QTL can be used for marker assisted selection in breeding wheat for improved heat tolerance in Ventnor or Karl 92 genetic background.