Deep learning methods improve genomic prediction of wheat breeding

In the field of plant breeding, various machine learning models have been developed and studied to evaluate the genomic prediction (GP) accuracy of unseen phenotypes. Deep learning has shown promise. However, most studies on deep learning in plant breeding have been limited to small datasets, and only a few have explored its application in moderate-sized datasets. In this study, we aimed to address this limitation by utilizing a moderately large dataset. We examined the performance of a deep learning (DL) model and compared it with the widely used and powerful best linear unbiased prediction (GBLUP) model. The goal was to assess the GP accuracy in the context of a five-fold cross-validation strategy and when predicting complete environments using the DL model. The results revealed the DL model outperformed the GBLUP model in terms of GP accuracy for two out of the five included traits in the five-fold cross-validation strategy, with similar results in the other traits. This indicates the superiority of the DL model in predicting these specific traits. Furthermore, when predicting complete environments using the leave-one-environment-out (LOEO) approach, the DL model demonstrated competitive performance. It is worth noting that the DL model employed in this study extends a previously proposed multi-modal DL model, which had been primarily applied to image data but with small datasets. By utilizing a moderately large dataset, we were able to evaluate the performance and potential of the DL model in a context with more information and challenging scenario in plant breeding.

Multi-modal deep learning improves grain yield prediction in wheat breeding by fusing genomics and phenomics

MOTIVATION

Developing new crop varieties with superior performance is highly important to ensure robust and sustainable global food security. The speed of variety development is limited by long field cycles and advanced generation selections in plant breeding programs. While methods to predict yield from genotype or phenotype data have been proposed, improved performance and integrated models are needed.

RESULTS

We propose a machine learning model that leverages both genotype and phenotype measurements by fusing genetic variants with multiple data sources collected by unmanned aerial systems. We use a deep multiple instance learning framework with an attention mechanism that sheds light on the importance given to each input during prediction, enhancing interpretability. Our model reaches 0.754 ± 0.024 Pearson correlation coefficient when predicting yield in similar environmental conditions; a 34.8% improvement over the genotype-only linear baseline (0.559 ± 0.050). We further predict yield on new lines in an unseen environment using only genotypes, obtaining a prediction accuracy of 0.386 ± 0.010, a 13.5% improvement over the linear baseline. Our multi-modal deep learning architecture efficiently accounts for plant health and environment, distilling the genetic contribution and providing excellent predictions. Yield prediction algorithms leveraging phenotypic observations during training therefore promise to improve breeding programs, ultimately speeding up delivery of improved varieties.

AVAILABILITY AND IMPLEMENTATION

Available at https://github.com/BorgwardtLab/PheGeMIL (code) and https://doi.org/doi:10.5061/dryad.kprr4xh5p (data).

Mapping QTL for Phenological and Grain-Related Traits in a Mapping Population Derived from High-Zinc-Biofortified Wheat

Genomic regions governing days to heading (DH), days to maturity (DM), plant height (PH), thousand-kernel weight (TKW), and test weight (TW) were investigated in a set of 190 RILs derived from a cross between a widely cultivated wheat-variety, Kachu (DPW-621-50), and a high-zinc variety, Zinc-Shakti. The RIL population was genotyped using 909 DArTseq markers and phenotyped in three environments. The constructed genetic map had a total genetic length of 4665 cM, with an average marker density of 5.13 cM. A total of thirty-seven novel quantitative trait loci (QTL), including twelve for PH, six for DH, five for DM, eight for TKW and six for TW were identified. A set of 20 stable QTLs associated with the expression of DH, DM, PH, TKW, and TW were identified in two or more environments. Three novel pleiotropic genomic-regions harboring co-localized QTLs governing two or more traits were also identified. In silico analysis revealed that the DArTseq markers were located on important putative candidate genes such as MLO-like protein, Phytochrome, Zinc finger and RING-type, Cytochrome P450 and pentatricopeptide repeat, involved in the regulation of pollen maturity, the photoperiodic modulation of flowering-time, abiotic-stress tolerance, grain-filling duration, thousand-kernel weight, seed morphology, and plant growth and development. The identified novel QTLs, particularly stable and co-localized QTLs, will be validated to estimate their effects in different genetic backgrounds for subsequent use in marker-assisted selection (MAS).

Ensuring Nutritional Security in India through Wheat Biofortification: A Review

Undernourishment of nutrients, also known as hidden hunger, affects over 2 billion populace globally. Even though stunting among children below five years of age has decreased in India in the last ten years, India is home to roughly thirty percent of the world's population of stunted pre-schoolers. A significant improvement has been witnessed in the targeted development and deployment of biofortified crops; approximately 20 million farm households from developing counties benefit from cultivating and consuming biofortified crops. There is ample scope for including biofortified varieties in the seed chain, ensuring nutritional security. Wheat is a dietary staple in India, typically consumed as wholemeal flour in the form of flatbreads such as chapatti and roti. Wheat contributes to nearly one fifth of global energy requirements and can also provide better amounts of iron (Fe) and zinc (Zn). As a result, biofortified wheat can serve as a medium for delivery of essential micronutrients such as Fe and Zn to end users. This review discusses wheat biofortification components such as Fe and Zn dynamics, its uptake and movement in plants, the genetics of their buildup, and the inclusion of biofortified wheat varieties in the seed multiplication chain concerning India.

Sparse kernel models provide optimization of training set design for genomic prediction in multiyear wheat breeding data

The success of genomic selection (GS) in breeding schemes relies on its ability to provide accurate predictions of unobserved lines at early stages. Multigeneration data provides opportunities to increase the training data size and thus, the likelihood of extracting useful information from ancestors to improve prediction accuracy. The genomic best linear unbiased predictions (GBLUPs) are performed by borrowing information through kinship relationships between individuals. Multigeneration data usually becomes heterogeneous with complex family relationship patterns that are increasingly entangled with each generation. Under these conditions, historical data may not be optimal for model training as the accuracy could be compromised. The sparse selection index (SSI) is a method for training set (TRN) optimization, in which training individuals provide predictions to some but not all predicted subjects. We added an additional trimming process to the original SSI (trimmed SSI) to remove less important training individuals for prediction. Using a large multigeneration (8 yr) wheat (Triticum aestivum L.) grain yield dataset (n = 68,836), we found increases in accuracy as more years are included in the TRN, with improvements of ∼0.05 in the GBLUP accuracy when using 5 yr of historical data relative to when using only 1 yr. The SSI method showed a small gain over the GBLUP accuracy but with an important reduction on the TRN size. These reduced TRNs were formed with a similar number of subjects from each training generation. Our results suggest that the SSI provides a more stable ranking of genotypes than the GBLUP as the TRN becomes larger.

Genomic approaches for improving grain zinc and iron content in wheat

More than three billion people worldwide suffer from iron deficiency associated anemia and an equal number people suffer from zinc deficiency. These conditions are more prevalent in Sub-Saharan Africa and South Asia. In developing countries, children under the age of five with stunted growth and pregnant or lactating women were found to be at high risk of zinc and iron deficiencies. Biofortification, defined as breeding to develop varieties of staple food crops whose grain contains higher levels of micronutrients such as iron and zinc, are one of the most promising, cost-effective and sustainable ways to improve the health in resource-poor households, particularly in rural areas where families consume some part of what they grow. Biofortification through conventional breeding in wheat, particularly for grain zinc and iron, have made significant contributions, transferring important genes and quantitative trait loci (QTLs) from wild and related species into cultivated wheat. Nonetheless, the quantitative, genetically complex nature of iron and zinc levels in wheat grain limits progress through conventional breeding, making it difficult to attain genetic gain both for yield and grain mineral concentrations. Wheat biofortification can be achieved by enhancing mineral uptake, source-to-sink translocation of minerals and their deposition into grains, and the bioavailability of the minerals. A number of QTLs with major and minor effects for those traits have been detected in wheat; introducing the most effective into breeding lines will increase grain zinc and iron concentrations. New approaches to achieve this include marker assisted selection and genomic selection. Faster breeding approaches need to be combined to simultaneously increase grain mineral content and yield in wheat breeding lines.

New Genomic Regions Identified for Resistance to Spot Blotch and Terminal Heat Stress in an Interspecific Population of Triticum aestivum and T. spelta

Wheat is one of the most widely grown and consumed food crops in the world. Spot blotch and terminal heat stress are the two significant constraints mainly in the Indo-Gangetic plains of South Asia. The study was undertaken using 185 recombinant lines (RILs) derived from the interspecific hybridization of 'Triticum aestivum (HUW234) × T. spelta (H⁺26)' to reveal genomic regions associated with tolerance to combined stress to spot blotch and terminal heat. Different physiological (NDVI, canopy temperature, leaf chlorophyll) and grain traits (TGW, grain size) were observed under stressed (spot blotch, terminal heat) and non-stressed environments. The mean maturity duration of RILs under combined stress was reduced by 12 days, whereas the normalized difference vegetation index (NDVI) was 46.03%. Similarly, the grain size was depleted under combined stress by 32.23% and thousand kernel weight (TKW) by 27.56% due to spot blotch and terminal heat stress, respectively. The genetic analysis using 6734 SNP markers identified 37 significant loci for the area under the disease progress curve (AUDPC) and NDVI. The genome-wide functional annotation of the SNP markers revealed gene functions such as plant chitinases, NB-ARC and NBS-LRR, and the peroxidase superfamily Cytochrome P450 have a positive role in the resistance through a hypersensitive response. Zinc finger domains, cysteine protease coding gene, F-box protein, ubiquitin, and associated proteins, play a substantial role in the combined stress of spot blotch and terminal heat in bread wheat, according to genomic domains ascribed to them. The study also highlights T. speltoides as a source of resistance to spot blotch and terminal heat tolerance.

Dissecting the Genetic Architecture of Phenology Affecting Adaptation of Spring Bread Wheat Genotypes to the Major Wheat-Producing Zones in India

Spring bread wheat adaptation to diverse environments is supported by various traits such as phenology and plant architecture. A large-scale genome-wide association study (GWAS) was designed to investigate and dissect the genetic architecture of phenology affecting adaptation. It used 48 datasets from 4,680 spring wheat lines. For 8 years (2014-2021), these lines were evaluated for days to heading (DH) and maturity (DM) at three sites: Jabalpur, Ludhiana, and Samastipur (Pusa), which represent the three major Indian wheat-producing zones: the Central Zone (CZ), North-Western Plain Zone (NWPZ), and North-Eastern Plain Zone (NEPZ), respectively. Ludhiana had the highest mean DH of 103.8 days and DM of 148.6 days, whereas Jabalpur had the lowest mean DH of 77.7 days and DM of 121.6 days. We identified 119 markers significantly associated with DH and DM on chromosomes 5B (76), 2B (18), 7D (10), 4D (8), 5A (1), 6B (4), 7B (1), and 3D (1). Our results clearly indicated the importance of the photoperiod-associated gene (Ppd-B1) for adaptation to the NWPZ and the Vrn-B1 gene for adaptation to the NEPZ and CZ. A maximum variation of 21.1 and 14% was explained by markers 2B_56134146 and 5B_574145576 linked to the Ppd-B1 and Vrn-B1 genes, respectively, indicating their significant role in regulating DH and DM. The results provide important insights into the genomic regions associated with the two phenological traits that influence adaptation to the major wheat-producing zones in India.

Genome-Wide Association Mapping Identifies Key Genomic Regions for Grain Zinc and Iron Biofortification in Bread Wheat

Accelerating breeding efforts for developing biofortified bread wheat varieties necessitates understanding the genetic control of grain zinc concentration (GZnC) and grain iron concentration (GFeC). Hence, the major objective of this study was to perform genome-wide association mapping to identify consistently significant genotyping-by-sequencing markers associated with GZnC and GFeC using a large panel of 5,585 breeding lines from the International Maize and Wheat Improvement Center. These lines were grown between 2018 and 2021 in an optimally irrigated environment at Obregon, Mexico, while some of them were also grown in a water-limiting drought-stressed environment and a space-limiting small plot environment and evaluated for GZnC and GFeC. The lines showed a large and continuous variation for GZnC ranging from 27 to 74.5 ppm and GFeC ranging from 27 to 53.4 ppm. We performed 742,113 marker-traits association tests in 73 datasets and identified 141 markers consistently associated with GZnC and GFeC in three or more datasets, which were located on all wheat chromosomes except 3A and 7D. Among them, 29 markers were associated with both GZnC and GFeC, indicating a shared genetic basis for these micronutrients and the possibility of simultaneously improving both. In addition, several significant GZnC and GFeC associated markers were common across the irrigated, water-limiting drought-stressed, and space-limiting small plots environments, thereby indicating the feasibility of indirect selection for these micronutrients in either of these environments. Moreover, the many significant markers identified had minor effects on GZnC and GFeC, suggesting a quantitative genetic control of these traits. Our findings provide important insights into the complex genetic basis of GZnC and GFeC in bread wheat while implying limited prospects for marker-assisted selection and the need for using genomic selection.

Variation in Grain Zinc and Iron Concentrations, Grain Yield and Associated Traits of Biofortified Bread Wheat Genotypes in Nepal

Wheat (Triticum aestivum L.) is one of the major staples in Nepal providing the bulk of food calories and at least 30% of Fe and Zn intake and 20% of dietary energy and protein consumption; thus, it is essential to improve its nutritional quality. To select high-yielding genotypes with elevated grain zinc and iron concentration, the sixth, seventh, eighth, and ninth HarvestPlus Yield Trials (HPYTs) were conducted across diverse locations in Nepal for four consecutive years: 2015-16, 2016-17, 2017-18, and 2018-19, using 47 biofortified and 3 non-biofortified CIMMYT-bred, bread wheat genotypes: Baj#1, Kachu#1, and WK1204 (local check). Genotypic and spatial variations were found in agro-morphological traits; grain yield and its components; and the grain zinc and iron concentration of tested genotypes. Grain zinc concentration was highest in Khumaltar and lowest in Kabre. Likewise, grain iron concentration was highest in Doti and lowest in Surkhet. Most of the biofortified genotypes were superior for grain yield and for grain zinc and iron concentration to the non-biofortified checks. Combined analyses across environments showed moderate to high heritability for both Zn (0.48-0.81) and Fe (0.46-0.79) except a low heritability for Fe observed for 7th HPYT (0.15). Grain yield was positively correlated with the number of tillers per m², while negatively correlated with days to heading and maturity, grain iron, grain weight per spike, and thousand grain weight. The grain zinc and iron concentration were positively correlated, suggesting that the simultaneous improvement of both micronutrients is possible through wheat breeding. Extensive testing of CIMMYT derived high Zn wheat lines in Nepal led to the release of five biofortified wheat varieties in 2020 with superior yield, better disease resistance, and 30-40% increased grain Zn and adaptable to a range of wheat growing regions in the country - from the hotter lowland, or Terai, regions to the dry mid- and high-elevation areas.

Improving Zinc and Iron Biofortification in Wheat through Genomics Approaches

Globally, about 20% of calories (energy) come from wheat. In some countries, it is more than 70%. More than 2 billion people are at risk for zinc deficiency and even more, people are at risk of iron deficiency, nearly a quarter of all children underage group of 5 are physically and cognitively stunted, and lack of dietary zinc is a major contributing factor. Biofortified wheat with elevated levels of zinc and iron has several potential advantages as a delivery vehicle for micronutrients in the diets of resource-poor consumers who depend on cereal-based diets. The conventional breeding strategies have been successful in the introduction of novel alleles for grain Zn and Fe that led to the release of competitive Zn enriched wheat varieties in South Asia. The major challenge over the next few decades will be to maintain the rates of genetic gains for grain yield along with increased grain Zn/Fe concentration to meet the food and nutritional security challenges. Therefore, to remain competitive, the performance of Zn-enhanced lines/varieties must be equal or superior to that of current non-biofortified elite lines/varieties. Since both yield and Zn content are invisible and quantitatively inherited traits except few intermediate effect QTL regions identified for grain Zn, increased breeding efforts and new approaches are required to combine them at high frequency, ensuring that Zn levels are steadily increased to the required levels across the breeding pipelines. The current review article provides a comprehensive list of genomic regions for enhancing grain Zn and Fe concentrations in wheat including key candidate gene families such NAS, ZIP, VLT, ZIFL, and YSL. Implementing forward breeding by taking advantage of the rapid cycling trait pipeline approaches would simultaneously introgress high Zn and Fe QTL into the high Zn and normal elite lines, further increasing Zn and Fe concentrations.

Sparse testing using genomic prediction improves selection for breeding targets in elite spring wheat

Sparse testing using genomic prediction can be efficiently used to increase the number of testing environments while maintaining selection intensity in the early yield testing stage without increasing the breeding budget. Sparse testing using genomic prediction enables expanded use of selection environments in early-stage yield testing without increasing phenotyping cost. We evaluated different sparse testing strategies in the yield testing stage of a CIMMYT spring wheat breeding pipeline characterized by multiple populations each with small family sizes of 1-9 individuals. Our results indicated that a substantial overlap between lines across environments should be used to achieve optimal prediction accuracy. As sparse testing leverages information generated within and across environments, the genetic correlations between environments and genomic relationships of lines across environments were the main drivers of prediction accuracy in multi-environment yield trials. Including information from previous evaluation years did not consistently improve the prediction performance. Genomic best linear unbiased prediction was found to be the best predictor of true breeding value, and therefore, we propose that it should be used as a selection decision metric in the early yield testing stages. We also propose it as a proxy for assessing prediction performance to mirror breeder's advancement decisions in a breeding program so that it can be readily applied for advancement decisions by breeding programs.

Genome-Wide Association Mapping Indicates Quantitative Genetic Control of Spot Blotch Resistance in Bread Wheat and the Favorable Effects of Some Spot Blotch Loci on Grain Yield

Spot blotch caused by the fungus Bipolaris sorokiniana poses a serious threat to bread wheat production in warm and humid wheat-growing regions of the world. Hence, the major objective of this study was to identify consistent genotyping-by-sequencing (GBS) markers associated with spot blotch resistance using genome-wide association mapping on a large set of 6,736 advanced bread wheat breeding lines from the International Maize and Wheat Improvement Center. These lines were phenotyped as seven panels at Agua Fria, Mexico between the 2013-2014 and 2019-2020 crop cycles. We identified 214 significant spot blotch associated GBS markers in all the panels, among which only 96 were significant in more than one panel, indicating a strong environmental effect on the trait and highlights the need for multiple phenotypic evaluations to identify lines with stable spot blotch resistance. The 96 consistent GBS markers were on chromosomes 1A, 1B, 1D, 2A, 3B, 4A, 5B, 5D, 6B, 7A, 7B, and 7D, including markers possibly linked to the Lr46, Sb1, Sb2 and Sb3 genes. We also report the association of the 2NS translocation from Aegilops ventricosa with spot blotch resistance in some environments. Moreover, the spot blotch favorable alleles at the 2NS translocation and two markers on chromosome 3BS (3B_2280114 and 3B_5601689) were associated with increased grain yield evaluated at several environments in Mexico and India, implying that selection for favorable alleles at these loci could enable simultaneous improvement for high grain yield and spot blotch resistance. Furthermore, a significant relationship between the percentage of favorable alleles in the lines and their spot blotch response was observed, which taken together with the multiple minor effect loci identified to be associated with spot blotch in this study, indicate quantitative genetic control of resistance. Overall, the results presented here have extended our knowledge on the genetic basis of spot blotch resistance in bread wheat and further efforts to improve genetic resistance to the disease are needed for reducing current and future losses under climate change.

Genome-Wide Association Study of Phytic Acid in Wheat Grain Unravels Markers for Improving Biofortification

Biofortification of cereal grains offers a lasting solution to combat micronutrient deficiency in developing countries where it poses developmental risks to children. Breeding efforts thus far have been directed toward increasing the grain concentrations of iron (Fe) and zinc (Zn) ions. Phytic acid (PA) chelates these metal ions, reducing their bioavailability in the digestive tract. We present a high-throughput assay for quantification of PA and its application in screening a breeding population. After extraction in 96-well megatiter plates, PA content was determined from the phosphate released after treatment with a commercially available phytase enzyme. In a set of 330 breeding lines of wheat grown in the field over 3 years as part of a HarvestPlus breeding program for high grain Fe and Zn, our assay unraveled variation for PA that ranged from 0.90 to 1.72% with a mean of 1.24%. PA content was not associated with grain yield. High yielding lines were further screened for low molar PA/Fe and PA/Zn ratios for increased metal ion bioavailability, demonstrating the utility of our assay. Genome-wide association study revealed 21 genetic associations, six of which were consistent across years. Five of these associations mapped to chromosomes 1A, 2A, 2D, 5A, and 7D. Additivity over four of these haplotypes accounted for an ∼10% reduction in PA. Our study demonstrates it is possible to scale up assays to directly select for low grain PA in forward breeding programs.

Direct introgression of untapped diversity into elite wheat lines

The effective utilization of natural variation has become essential in addressing the challenges that climate change and population growth pose to global food security. Currently adopted protracted approaches to introgress exotic alleles into elite cultivars need substantial transformation. Here, through a strategic three-way crossing scheme among diverse exotics and the best historical elites (exotic/elite1//elite2), 2,867 pre-breeding lines were developed, genotyped and screened for multiple agronomic traits in four mega-environments. A meta-genome-wide association study, selective sweeps and haplotype-block-based analyses unveiled selection footprints in the genomes of pre-breeding lines as well as exotic-specific associations with agronomic traits. A simulation with a neutrality assumption demonstrated that many pre-breeding lines had significant exotic contributions despite substantial selection bias towards elite genomes. National breeding programmes worldwide have adopted 95 lines for germplasm enhancement, and 7 additional lines are being advanced in varietal release trials. This study presents a great leap forwards in the mobilization of GenBank variation to the breeding pipelines.

Identification of Genetic Loci and Candidate Genes Related to Grain Zinc and Iron Concentration Using a Zinc-Enriched Wheat 'Zinc-Shakti'

The development of nutritionally enhanced wheat (Triticum aestivum L.) with higher levels of grain iron (Fe) and zinc (Zn) offers a sustainable solution to micronutrient deficiency among resource-poor wheat consumers. One hundred and ninety recombinant inbred lines (RILs) from 'Kachu' × 'Zinc-Shakti' cross were phenotyped for grain Fe and Zn concentrations and phenological and agronomically important traits at Ciudad Obregon, Mexico in the 2017-2018, 2018-2019, and 2019-2020 growing seasons and Diversity Arrays Technology (DArT) molecular marker data were used to determine genomic regions controlling grain micronutrients and agronomic traits. We identified seven new pleiotropic quantitative trait loci (QTL) for grain Zn and Fe on chromosomes 1B, 1D, 2B, 6A, and 7D. The stable pleiotropic QTL identified have expanded the diversity of QTL that could be used in breeding for wheat biofortification. Nine RILs with the best combination of pleiotropic QTL for Zn and Fe have been identified to be used in future crossing programs and to be screened in elite yield trials before releasing as biofortified varieties. In silico analysis revealed several candidate genes underlying QTL, including those belonging to the families of the transporters and kinases known to transport small peptides and minerals (thus assisting mineral uptake) and catalyzing phosphorylation processes, respectively.

Elucidating the genetics of grain yield and stress-resilience in bread wheat using a large-scale genome-wide association mapping study with 55,568 lines

Wheat grain yield (GY) improvement using genomic tools is important for achieving yield breakthroughs. To dissect the genetic architecture of wheat GY potential and stress-resilience, we have designed this large-scale genome-wide association study using 100 datasets, comprising 105,000 GY observations from 55,568 wheat lines evaluated between 2003 and 2019 by the International Maize and Wheat Improvement Center and national partners. We report 801 GY-associated genotyping-by-sequencing markers significant in more than one dataset and the highest number of them were on chromosomes 2A, 6B, 6A, 5B, 1B and 7B. We then used the linkage disequilibrium (LD) between the consistently significant markers to designate 214 GY-associated LD-blocks and observed that 84.5% of the 58 GY-associated LD-blocks in severe-drought, 100% of the 48 GY-associated LD-blocks in early-heat and 85.9% of the 71 GY-associated LD-blocks in late-heat, overlapped with the GY-associated LD-blocks in the irrigated-bed planting environment, substantiating that simultaneous improvement for GY potential and stress-resilience is feasible. Furthermore, we generated the GY-associated marker profiles and analyzed the GY favorable allele frequencies for a large panel of 73,142 wheat lines, resulting in 44.5 million datapoints. Overall, the extensive resources presented in this study provide great opportunities to accelerate breeding for high-yielding and stress-resilient wheat varieties.

Genomic Selection for Grain Yield in the CIMMYT Wheat Breeding Program-Status and Perspectives

Genomic breeding technologies offer new opportunities for grain yield (GY) improvement in common wheat. In this study, we have evaluated the potential of genomic selection (GS) in breeding for GY in wheat by modeling a large dataset of 48,562 GY observations from the International Maize and Wheat Improvement Center (CIMMYT), including 36 yield trials evaluated between 2012 and 2019 in Obregón, Sonora, Mexico. Our key objective was to determine the value that GS can add to the current three-stage yield testing strategy at CIMMYT, and we draw inferences from predictive modeling of GY using 420 different populations, environments, cycles, and model combinations. First, we evaluated the potential of genomic predictions for minimizing the number of replications and lines tested within a site and year and obtained mean prediction accuracies (PAs) of 0.56, 0.5, and 0.42 in Stages 1, 2, and 3 of yield testing, respectively. However, these PAs were similar to the mean pedigree-based PAs indicating that genomic relationships added no value to pedigree relationships in the yield testing stages, characterized by small family-sizes. Second, we evaluated genomic predictions for minimizing GY testing across stages/years in Obregón and observed mean PAs of 0.41, 0.31, and 0.37, respectively when GY in the full irrigation bed planting (FI BP), drought stress (DS), and late-sown heat stress environments were predicted across years using genotype × environment (G × E) interaction models. Third, we evaluated genomic predictions for minimizing the number of yield testing environments and observed that in Stage 2, the FI BP, full irrigation flat planting and early-sown heat stress environments (mean PA of 0.37 ± 0.12) and the reduced irrigation and DS environments (mean PA of 0.45 ± 0.07) had moderate predictabilities among them. However, in both predictions across years and environments, the PAs were inconsistent across years and the G × E models had no advantage over the baseline model with environment and line effects. Overall, our results provide excellent insights into the predictability of a quantitative trait like GY and will have important implications on the future design of GS for GY in wheat breeding programs globally.

Economic benefits of blast-resistant biofortified wheat in Bangladesh: The case of BARI Gom 33

The first occurrence of wheat blast in 2016 threatened Bangladesh's already precarious food security situation. The Bangladesh Agricultural Research Institute (BARI), together with the International Maize and Wheat Improvement Center (CIMMYT) developed and released the wheat variety BARI Gom 33 that is resistant to wheat blast and other common diseases. The new variety provides a 5-8% yield gain over the available popular varieties, as well as being zinc enriched. This study examines the potential economic benefits of BARI Gom 33 in Bangladesh. First, applying a climate analogue model, this study identified that more than 55% of the total wheat-growing area in Bangladesh (across 45 districts) is vulnerable to wheat blast. Second, applying an ex-ante impact assessment framework, this study shows that with an assumed cumulative adoption starting from 2019-20 and increasing to 30% by 2027-28, the potential economic benefits of the newly developed wheat variety far exceeds its dissemination cost by 2029-30. Even if dissemination of the new wheat variety is limited to only the ten currently blast-affected districts, the yearly average net benefits could amount to USD 0.23-1.6 million. Based on the findings, international funder agencies are urged to support the national system in scaling out the new wheat variety and wheat research in general to ensure overall food security in Bangladesh and South Asia.

Integrating genomic-enabled prediction and high-throughput phenotyping in breeding for climate-resilient bread wheat

Genomic selection and high-throughput phenotyping (HTP) are promising tools to accelerate breeding gains for high-yielding and climate-resilient wheat varieties. Hence, our objective was to evaluate them for predicting grain yield (GY) in drought-stressed (DS) and late-sown heat-stressed (HS) environments of the International maize and wheat improvement center's elite yield trial nurseries. We observed that the average genomic prediction accuracies using fivefold cross-validations were 0.50 and 0.51 in the DS and HS environments, respectively. However, when a different nursery/year was used to predict another nursery/year, the average genomic prediction accuracies in the DS and HS environments decreased to 0.18 and 0.23, respectively. While genomic predictions clearly outperformed pedigree-based predictions across nurseries, they were similar to pedigree-based predictions within nurseries due to small family sizes. In populations with some full-sibs in the training population, the genomic and pedigree-based prediction accuracies were on average 0.27 and 0.35 higher than the accuracies in populations with only one progeny per cross, indicating the importance of genetic relatedness between the training and validation populations for good predictions. We also evaluated the item-based collaborative filtering approach for multivariate prediction of GY using the green normalized difference vegetation index from HTP. This approach proved to be the best strategy for across-nursery predictions, with average accuracies of 0.56 and 0.62 in the DS and HS environments, respectively. We conclude that GY is a challenging trait for across-year predictions, but GS and HTP can be integrated in increasing the size of populations screened and evaluating unphenotyped large nurseries for stress-resilience within years.

Prospects and Challenges of Applied Genomic Selection-A New Paradigm in Breeding for Grain Yield in Bread Wheat

Genomic selection (GS) has been promising for increasing genetic gains in several species. Therefore, we evaluated the potential integration of GS for grain yield (GY) in bread wheat ( L.) in CIMMYT's elite yield trial nurseries. We observed that the genomic prediction accuracies within nurseries (0.44 and 0.35) were substantially higher than across-nursery accuracies (0.15 and 0.05) for GY evaluated in the bed and flat planting systems, respectively. The accuracies from using only a subset of 251 genotyping-by-sequencing markers were comparable to the accuracies using all 2038 markers. We also used the item-based collaborative filtering approach for incorporating other related traits in predicting GY and observed that it outperformed genomic predictions across nurseries, but was less predictive when trait correlations with GY were low. Furthermore, we compared GS and phenotypic selections (PS) and observed that at a selection intensity of 0.5, GS could select a maximum of 70.9 and 61.5% of the top lines and discard 71.5 and 60.5% of the poor lines selected or discarded by PS within and across nurseries, respectively. Comparisons of GS and pedigree-based predictions revealed that the advantage of GS over the pedigree was moderate in populations without full-sibs. However, GS was less advantageous for within-family selections in elite families with few full-sibs and minimal Mendelian sampling variance. Overall, our results demonstrate the importance of applying GS for GY at the appropriate stage of the breeding cycle, and we speculate that gains can be maximized if it is implemented in early-generation within-family selections.

Harnessing genetic potential of wheat germplasm banks through impact-oriented-prebreeding for future food and nutritional security

The value of exotic wheat genetic resources for accelerating grain yield gains is largely unproven and unrealized. We used next-generation sequencing, together with multi-environment phenotyping, to study the contribution of exotic genomes to 984 three-way-cross-derived (exotic/elite1//elite2) pre-breeding lines (PBLs). Genomic characterization of these lines with haplotype map-based and SNP marker approaches revealed exotic specific imprints of 16.1 to 25.1%, which compares to theoretical expectation of 25%. A rare and favorable haplotype (GT) with 0.4% frequency in gene bank identified on chromosome 6D minimized grain yield (GY) loss under heat stress without GY penalty under irrigated conditions. More specifically, the ‘T’ allele of the haplotype GT originated in Aegilops tauschii and was absent in all elite lines used in study. In silico analysis of the SNP showed hits with a candidate gene coding for isoflavone reductase IRL-like protein in Ae. tauschii. Rare haplotypes were also identified on chromosomes 1A, 6A and 2B effective against abiotic/biotic stresses. Results demonstrate positive contributions of exotic germplasm to PBLs derived from crosses of exotics with CIMMYT’s best elite lines. This is a major impact-oriented pre-breeding effort at CIMMYT, resulting in large-scale development of PBLs for deployment in breeding programs addressing food security under climate change scenarios.

QTL Mapping of Grain Zn and Fe Concentrations in Two Hexaploid Wheat RIL Populations with Ample Transgressive Segregation

More than 50% of undernourished children live in Asia and more than 25% live in Africa. Coupled with an inadequate food supply, mineral deficiencies are widespread in these populations; particularly zinc (Zn) and iron (Fe) deficiencies that lead to retarded growth, adverse effects on both the immune system and an individual's cognitive abilities. Biofortification is one solution aimed at reducing the incidence of these deficiencies. To efficiently breed a biofortified wheat variety, it is important to generate knowledge of the genomic regions associated with grain Zn (GZn) and Fe (GFe) concentration. This allows for the introgression of favorable alleles into elite germplasm. In this study we evaluated two bi-parental populations of 188 recombinant inbred lines (RILs) displaying a significant range of transgressive segregation for GZn and GFe during three crop cycles in CIMMYT, Mexico. Parents of the RILs were derived from Triticum spelta L. and synthetic hexaploid wheat crosses. QTL analysis identified a number of significant QTL with a region denominated as QGZn.cimmyt-7B_1P2 on chromosome 7B explaining the largest (32.7%) proportion of phenotypic variance (PVE) for GZn and leading to an average additive effect of -1.3. The QTL with the largest average additive effect for GFe (-0.161) was found on chromosome 4A (QGFe.cimmyt-4A_P2), with 21.14% of the PVE. The region QGZn.cimmyt-7B_1P2 co-localized closest to the region QGZn.cimmyt-7B_1P1 in a consensus map built from the linkage maps of both populations. Pleiotropic or tightly linked QTL were also found on chromosome 3B, however of minor effects and PVE between 4.3 and 10.9%. Further efforts are required to utilize the QTL information in marker assisted backcrossing schemes for wheat biofortification. A strategy to follow is to intercross the transgressive individuals from both populations and then utilize them as sources in biofortification breeding pipelines.

The emergence of Ug99 races of the stem rust fungus is a threat to world wheat production

Race Ug99 of the fungus Puccinia graminis tritici that causes stem or black rust disease on wheat was first detected in Uganda in 1998. Seven races belonging to the Ug99 lineage are now known and have spread to various wheat-growing countries in the eastern African highlands, as well as Zimbabwe, South Africa, Sudan, Yemen, and Iran. Because of the susceptibility of 90% of the wheat varieties grown worldwide, the Ug99 group of races was recognized as a major threat to wheat production and food security. Its spread, either wind-mediated or human-aided, to other countries in Africa, Asia, and beyond is evident. Screening in Kenya and Ethiopia has identified a low frequency of resistant wheat varieties and breeding materials. Identification and transfer of new sources of race-specific resistance from various wheat relatives is underway to enhance the diversity of resistance. Although new Ug99-resistant varieties that yield more than current popular varieties are being released and promoted, major efforts are required to displace current Ug99 susceptible varieties with varieties that have diverse race-specific or durable resistance and mitigate the Ug99 threat.

Limitations on nonlinear pulse propagation in coupled-resonator slow-light waveguides

Under the constraint of fixed pulse distortion, we study the bandwidth-delay product and nonlinear phase shift performance of coupled-resonator slow-light waveguides with designs that produce maximally-flat transmission and maximally-flat group delay responses. Even though improvement in bandwidth-delay product can be obtained with increasing number of resonators, the nonlinear response fails to improve beyond a certain number of resonators due to the increased filter bandwidth necessary to maintain a fixed pulse distortion. However, as expected, the nonlinear response improves with resonator finesse and degrades with resonator loss.