Genomic selection can accelerate the biofortification of spring wheat

Stable pleotropic loci controlling the accumulation of multiple nutritional elements in wheat

Understanding the genetic basis of nutrient accumulation in wheat is crucial for improving its nutritional content and addressing global food security challenges. Here, we identified stable pleiotropic loci controlling the accumulation of 13 nutritional elements in wheat across diverse environments using a large wheat population of 1470 individuals. Our analysis revealed significant variability in SNP-based heritability values across 13 essential elements. Genetic correlations among elements uncovered complex relations, with positive correlations observed within two distinct groups, where calcium (Ca), cobalt (Co), potassium (K), and sodium (Na) formed one group, and copper (Cu), iron (Fe), magnesium (Mg), manganese (Mn), molybdenum (Mo), nickel (Ni), phosphorus (P), and zinc (Zn) formed the other. Negative correlations were observed among elements across both groups. Through MetaGWAS analysis, we identified stable QTL associated with individual elements and elements with high positive correlations. We identified 67 stable QTL across environments that are independent from grain yield, of which 56 were detected using the MetaGWAS analysis indicating their pleiotropic effect on multiple elements. A major QTL on chromosome 7D that can shift the phenotype up to one standard deviation compared to the mean phenotype in the population exhibited differential effects on multiple elements belonging to both groups. Our findings offer novel insights into the genetic architecture of nutrient accumulation in wheat and have practical implications for breeding programmes aimed at enhancing multiple nutrients simultaneously. By targeting stable QTL, breeders can develop wheat varieties with improved nutritional profiles, contributing to global food security and human health.

Micronutrient Biofortification in Wheat: QTLs, Candidate Genes and Molecular Mechanism

Micronutrient deficiency (hidden hunger) is one of the serious health problems globally, often due to diets dominated by staple foods. Genetic biofortification of a staple like wheat has surfaced as a promising, cost-efficient, and sustainable strategy. Significant genetic diversity exists in wheat and its wild relatives, but the nutritional profile in commercial wheat varieties has inadvertently declined over time, striving for better yield and disease resistance. Substantial efforts have been made to biofortify wheat using conventional and molecular breeding. QTL and genome-wide association studies were conducted, and some of the identified QTLs/marker-trait association (MTAs) for grain micronutrients like Fe have been exploited by MAS. The genetic mechanisms of micronutrient uptake, transport, and storage have also been investigated. Although wheat biofortified varieties are now commercially cultivated in selected regions worldwide, further improvements are needed. This review provides an overview of wheat biofortification, covering breeding efforts, nutritional evaluation methods, nutrient assimilation and bioavailability, and microbial involvement in wheat grain enrichment. Emerging technologies such as non-destructive hyperspectral imaging (HSI)/red, green, and blue (RGB) phenotyping; multi-omics integration; CRISPR-Cas9 alongside genomic selection; and microbial genetics hold promise for advancing biofortification.

Optimising desired gain indices to maximise selection response

Introduction

In plant breeding, we often aim to improve multiple traits at once. However, without knowing the economic value of each trait, it is hard to decide which traits to focus on. This is where "desired gain selection indices" come in handy, which can yield optimal gains in each trait based on the breeder's prioritisation of desired improvements when economic weights are not available. However, they lack the ability to maximise the selection response and determine the correlation between the index and net genetic merit.

Methods

Here, we report the development of an iterative desired gain selection index method that optimises the sampling of the desired gain values to achieve a targeted or a user-specified selection response for multiple traits. This targeted selection response can be constrained or unconstrained for either a subset or all the studied traits.

Results

We tested the method using genomic estimated breeding values (GEBVs) for seven traits in a bread wheat (Triticum aestivum) reference breeding population comprising 3,331 lines and achieved prediction accuracies ranging between 0.29 and 0.47 across the seven traits. The indices were validated using 3,005 double haploid lines that were derived from crosses between parents selected from the reference population. We tested three user-specified response scenarios: a constrained equal weight (INDEX1), a constrained yield dominant weight (INDEX2), and an unconstrained weight (INDEX3). Our method achieved an equivalent response to the user-specified selection response when constraining a set of traits, and this response was much better than the response of the traditional desired gain selection indices method without iteration. Interestingly, when using unconstrained weight, our iterative method maximised the selection response and shifted the average GEBVs of the selection candidates towards the desired direction.

Discussion

Our results show that the method is an optimal choice not only when economic weights are unavailable, but also when constraining the selection response is an unfavourable option.

Investigating genomic prediction strategies for grain carotenoid traits in a tropical/subtropical maize panel

Vitamin A deficiency remains prevalent on a global scale, including in regions where maize constitutes a high percentage of human diets. One solution for alleviating this deficiency has been to increase grain concentrations of provitamin A carotenoids in maize (Zea mays ssp. mays L.)-an example of biofortification. The International Maize and Wheat Improvement Center (CIMMYT) developed a Carotenoid Association Mapping panel of 380 inbred lines adapted to tropical and subtropical environments that have varying grain concentrations of provitamin A and other health-beneficial carotenoids. Several major genes have been identified for these traits, 2 of which have particularly been leveraged in marker-assisted selection. This project assesses the predictive ability of several genomic prediction strategies for maize grain carotenoid traits within and between 4 environments in Mexico. Ridge Regression-Best Linear Unbiased Prediction, Elastic Net, and Reproducing Kernel Hilbert Spaces had high predictive abilities for all tested traits (β-carotene, β-cryptoxanthin, provitamin A, lutein, and zeaxanthin) and outperformed Least Absolute Shrinkage and Selection Operator. Furthermore, predictive abilities were higher when using genome-wide markers rather than only the markers proximal to 2 or 13 genes. These findings suggest that genomic prediction models using genome-wide markers (and assuming equal variance of marker effects) are worthwhile for these traits even though key genes have already been identified, especially if breeding for additional grain carotenoid traits alongside β-carotene. Predictive ability was maintained for all traits except lutein in between-environment prediction. The TASSEL (Trait Analysis by aSSociation, Evolution, and Linkage) Genomic Selection plugin performed as well as other more computationally intensive methods for within-environment prediction. The findings observed herein indicate the utility of genomic prediction methods for these traits and could inform their resource-efficient implementation in biofortification breeding programs.

Quantitative trait loci and genomic prediction for grain sugar and mineral concentrations of cowpea [Vigna unguiculata (L.) Walp.]

Development of high yielding cowpea varieties coupled with good taste and rich in essential minerals can promote consumption and thus nutrition and profitability. The sweet taste of cowpea grain is determined by its sugar content, which comprises mainly sucrose and galacto-oligosaccharides (GOS) including raffinose and stachyose. However, GOS are indigestible and their fermentation in the colon can produce excess intestinal gas, causing undesirable bloating and flatulence. In this study, we aimed to examine variation in grain sugar and mineral concentrations, then map quantitative trait loci (QTLs) and estimate genomic-prediction (GP) accuracies for possible application in breeding. Grain samples were collected from a multi-parent advanced generation intercross (MAGIC) population grown in California during 2016-2017. Grain sugars were assayed using high-performance liquid chromatography. Grain minerals were determined by inductively coupled plasma-optical emission spectrometry and combustion. Considerable variation was observed for sucrose (0.6-6.9%) and stachyose (2.3-8.4%). Major QTLs for sucrose (QSuc.vu-1.1), stachyose (QSta.vu-7.1), copper (QCu.vu-1.1) and manganese (QMn.vu-5.1) were identified. Allelic effects of major sugar QTLs were validated using the MAGIC grain samples grown in West Africa in 2017. GP accuracies for minerals were moderate (0.4-0.58). These findings help guide future breeding efforts to develop mineral-rich cowpea varieties with desirable sugar content.

Iron Content of Wheat and Rice in Australia: A Scoping Review

With a shift towards plant-based diets for human and planetary health, monitoring the mineral content of staple crops is important to ensure population nutrient requirements can be met. This review aimed to explore changes in the iron content of unprocessed wheat and rice in Australia over time. A comprehensive systematic search of four electronic databases and the gray literature was conducted. A total of 25 papers published between 1930 and 2023 that measured the iron content of unprocessed wheat and rice were included. Triticum aestivum was the most common wheat type studied, including 26 cultivars; iron content ranged from 40 to 50 µg/g in the 1930s and 1970s and was more variable after this time due to the introduction of modern cultivars, with most values between 25 and 45 µg/g. The iron content of rice (Oryza sativa) was more consistent at 10-15 µg/g between the 1980s and 2020s. Variations over the years may be attributed to environmental, biological, and methodological factors but these were not well documented across all studies, limiting the interpretation of findings. As the number of individuals following plant-based diets continues to rise, the ongoing monitoring of the mineral content in commonly consumed plant-based foods is warranted.

Optimization of Multi-Generation Multi-location Genomic Prediction Models for Recurrent Genomic Selection in an Upland Rice Population

Genomic selection is a worthy breeding method to improve genetic gain in recurrent selection breeding schemes. The integration of multi-generation and multi-location information could significantly improve genomic prediction models in the context of shuttle breeding. The Cirad-CIAT upland rice breeding program applies recurrent genomic selection and seeks to optimize the scheme to increase genetic gain while reducing phenotyping efforts. We used a synthetic population (PCT27) of which S0 plants were all genotyped and advanced by selfing and bulk seed harvest to the S0:2, S0:3, and S0:4 generations. The PCT27 was then divided into two sets. The S0:2 and S0:3 progenies for PCT27A and the S0:4 progenies for PCT27B were phenotyped in two locations: Santa Rosa the target selection location, within the upland rice growing area, and Palmira, the surrogate location, far from the upland rice growing area but easier for experimentation. While the calibration used either one of the two sets phenotyped in one or two locations, the validation population was only the PCT27B phenotyped in Santa Rosa. Five scenarios of genomic prediction and 24 models were performed and compared. Training the prediction model with the PCT27B phenotyped in Santa Rosa resulted in predictive abilities ranging from 0.19 for grain zinc concentration to 0.30 for grain yield. Expanding the training set with the inclusion of the PCT27A resulted in greater predictive abilities for all traits but grain yield, with increases from 5% for plant height to 61% for grain zinc concentration. Models with the PCT27B phenotyped in two locations resulted in higher prediction accuracy when the models assumed no genotype-by-environment (G × E) interaction for flowering (0.38) and grain zinc concentration (0.27). For plant height, the model assuming a single G × E variance provided higher accuracy (0.28). The gain in predictive ability for grain yield was the greatest (0.25) when environment-specific variance deviation effect for G × E was considered. While the best scenario was specific to each trait, the results indicated that the gain in predictive ability provided by the multi-location and multi-generation calibration was low. Yet, this approach could lead to increased selection intensity, acceleration of the breeding cycle, and a sizable economic advantage for the program.

Genome-wide association mapping and genomic prediction of agronomical traits and breeding values in Iranian wheat under rain-fed and well-watered conditions

BACKGROUND

The markers detected by genome-wide association study (GWAS) make it possible to dissect genetic structure and diversity at many loci. This can enable a wheat breeder to reveal and used genomic loci controlling drought tolerance. This study was focused on determining the population structure of Iranian 208 wheat landraces and 90 cultivars via genotyping-by-sequencing (GBS) and also on detecting marker-trait associations (MTAs) by GWAS and genomic prediction (GS) of wheat agronomic traits for drought-tolerance breeding. GWASs were conducted using both the original phenotypes (pGWAS) and estimated breeding values (eGWAS). The bayesian ridge regression (BRR), genomic best linear unbiased prediction (gBLUP), and ridge regression-best linear unbiased prediction (rrBLUP) approaches were used to estimate breeding values and estimate prediction accuracies in genomic selection.

RESULTS

Population structure analysis using 2,174,975 SNPs revealed four genetically distinct sub-populations from wheat accessions. D-Genome harbored the lowest number of significant marker pairs and the highest linkage disequilibrium (LD), reflecting different evolutionary histories of wheat genomes. From pGWAS, BRR, gBLUP, and rrBLUP, 284, 363, 359 and 295 significant MTAs were found under normal and 195, 365, 362 and 302 under stress conditions, respectively. The gBLUP with the most similarity (80.98 and 71.28% in well-watered and rain-fed environments, correspondingly) with the pGWAS method in the terms of discovered significant SNPs, suggesting the potential of gBLUP in uncovering SNPs. Results from gene ontology revealed that 29 and 30 SNPs in the imputed dataset were located in protein-coding regions for well-watered and rain-fed conditions, respectively. gBLUP model revealed genetic effects better than other models, suggesting a suitable tool for genome selection in wheat.

CONCLUSION

We illustrate that Iranian landraces of bread wheat contain novel alleles that are adaptive to drought stress environments. gBLUP model can be helpful for fine mapping and cloning of the relevant QTLs and genes, and for carrying out trait introgression and marker-assisted selection in both normal and drought environments in wheat collections.

Wheat Biofortification: Utilizing Natural Genetic Diversity, Genome-Wide Association Mapping, Genomic Selection, and Genome Editing Technologies

Alleviating micronutrients associated problems in children below five years and women of childbearing age, remains a significant challenge, especially in resource-poor nations. One of the most important staple food crops, wheat attracts the highest global research priority for micronutrient (Fe, Zn, Se, and Ca) biofortification. Wild relatives and cultivated species of wheat possess significant natural genetic variability for these micronutrients, which has successfully been utilized for breeding micronutrient dense wheat varieties. This has enabled the release of 40 biofortified wheat cultivars for commercial cultivation in different countries, including India, Bangladesh, Pakistan, Bolivia, Mexico and Nepal. In this review, we have systematically analyzed the current understanding of availability and utilization of natural genetic variations for grain micronutrients among cultivated and wild relatives, QTLs/genes and different genomic regions regulating the accumulation of micronutrients, and the status of micronutrient biofortified wheat varieties released for commercial cultivation across the globe. In addition, we have also discussed the potential implications of emerging technologies such as genome editing to improve the micronutrient content and their bioavailability in wheat.

Identification of genomic regions conferring rust resistance and enhanced mineral accumulation in a HarvestPlus Association Mapping Panel of wheat

New genomic regions for high accumulation of 10 minerals were identified. The 1B:1R and 2NS translocations enhanced concentrations of four and two minerals, respectively, in addition to disease resistance. Puccinia species, the causal agents of rust diseases of wheat, have the potential to cause total crop failures due their high evolutionary ability to acquire virulence for resistance genes deployed in commercial cultivars. Hence, the discovery of genetically diverse sources of rust resistance is essential. On the other hand, biofortification of wheat for essential nutrients, such as zinc (Zn) and iron (Fe), is also an objective in wheat improvement programs to tackle micronutrient deficiency. The development of rust-resistant and nutrient-concentrated wheat cultivars would be important for sustainable production and the fight against malnutrition. The HarvestPlus association mapping panel (HPAMP) that included nutrient-dense sources from diverse genetic backgrounds was genotyped using a 90 K Infinium SNP array and 13 markers linked with rust resistance genes. The HPAMP was used for genome-wide association mapping to identify genomic regions underpinning rust resistance and mineral accumulation. Twelve QTL for rust resistance and 53 for concentrations of 10 minerals were identified. Comparison of results from this study with the published QTL information revealed the detection of already known and some putatively new genes/QTL underpinning stripe rust and leaf rust resistance in this panel. Thirty-six new QTL for mineral concentration were identified on 17 chromosomes. Accessions carrying the 1B:1R translocation accumulated higher concentrations of Zn, Fe, Copper (Cu) and sulphur (S). The 2NS segment showed enhanced accumulation of grain Fe and Cu. Fifteen rust-resistant and biofortified accessions were identified for use as donor sources in breeding programs.

Genomic Selection: A Tool for Accelerating the Efficiency of Molecular Breeding for Development of Climate-Resilient Crops

Since the inception of the theory and conceptual framework of genomic selection (GS), extensive research has been done on evaluating its efficiency for utilization in crop improvement. Though, the marker-assisted selection has proven its potential for improvement of qualitative traits controlled by one to few genes with large effects. Its role in improving quantitative traits controlled by several genes with small effects is limited. In this regard, GS that utilizes genomic-estimated breeding values of individuals obtained from genome-wide markers to choose candidates for the next breeding cycle is a powerful approach to improve quantitative traits. In the last two decades, GS has been widely adopted in animal breeding programs globally because of its potential to improve selection accuracy, minimize phenotyping, reduce cycle time, and increase genetic gains. In addition, given the promising initial evaluation outcomes of GS for the improvement of yield, biotic and abiotic stress tolerance, and quality in cereal crops like wheat, maize, and rice, prospects of integrating it in breeding crops are also being explored. Improved statistical models that leverage the genomic information to increase the prediction accuracies are critical for the effectiveness of GS-enabled breeding programs. Study on genetic architecture under drought and heat stress helps in developing production markers that can significantly accelerate the development of stress-resilient crop varieties through GS. This review focuses on the transition from traditional selection methods to GS, underlying statistical methods and tools used for this purpose, current status of GS studies in crop plants, and perspectives for its successful implementation in the development of climate-resilient crops.