Publications by authors named "Suchismita Mondal"

26 Publications

  • Page 1 of 1

Harnessing translational research in wheat for climate resilience.

J Exp Bot 2021 Jul;72(14):5134-5157

International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico.

Despite being the world's most widely grown crop, research investments in wheat (Triticum aestivum and Triticum durum) fall behind those in other staple crops. Current yield gains will not meet 2050 needs, and climate stresses compound this challenge. However, there is good evidence that heat and drought resilience can be boosted through translating promising ideas into novel breeding technologies using powerful new tools in genetics and remote sensing, for example. Such technologies can also be applied to identify climate resilience traits from among the vast and largely untapped reserve of wheat genetic resources in collections worldwide. This review describes multi-pronged research opportunities at the focus of the Heat and Drought Wheat Improvement Consortium (coordinated by CIMMYT), which together create a pipeline to boost heat and drought resilience, specifically: improving crop design targets using big data approaches; developing phenomic tools for field-based screening and research; applying genomic technologies to elucidate the bases of climate resilience traits; and applying these outputs in developing next-generation breeding methods. The global impact of these outputs will be validated through the International Wheat Improvement Network, a global germplasm development and testing system that contributes key productivity traits to approximately half of the global wheat-growing area.
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http://dx.doi.org/10.1093/jxb/erab256DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8272565PMC
July 2021

Target Population of Environments for Wheat Breeding in India: Definition, Prediction and Genetic Gains.

Front Plant Sci 2021 24;12:638520. Epub 2021 May 24.

Global Wheat Program, International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico.

In this study, we defined the target population of environments (TPE) for wheat breeding in India, the largest wheat producer in South Asia, and estimated the correlated response to the selection and prediction ability of five selection environments (SEs) in Mexico. We also estimated grain yield (GY) gains in each TPE. Our analysis used meteorological, soil, and GY data from the international Elite Spring Wheat Yield Trials (ESWYT) distributed by the International Maize and Wheat Improvement Center (CIMMYT) from 2001 to 2016. We identified three TPEs: TPE 1, the optimally irrigated Northwestern Plain Zone; TPE 2, the optimally irrigated, heat-stressed North Eastern Plains Zone; and TPE 3, the drought-stressed Central-Peninsular Zone. The correlated response to selection ranged from 0.4 to 0.9 within each TPE. The highest prediction accuracies for GY per TPE were derived using models that included genotype-by-environment interaction and/or meteorological information and their interaction with the lines. The highest prediction accuracies for TPEs 1, 2, and 3 were 0.37, 0.46, and 0.51, respectively, and the respective GY gains were 118, 46, and 123 kg/ha/year. These results can help fine-tune the breeding of elite wheat germplasm with stable yields to reduce farmers' risk from year-to-year environmental variation in India's wheat lands, which cover 30 million ha, account for 100 million tons of grain or more each year, and provide food and livelihoods for hundreds of millions of farmers and consumers in South Asia.
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http://dx.doi.org/10.3389/fpls.2021.638520DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8181127PMC
May 2021

High-resolution spectral information enables phenotyping of leaf epicuticular wax in wheat.

Plant Methods 2021 Jun 7;17(1):58. Epub 2021 Jun 7.

Department of Soil and Crop Sciences, Texas A&M University, College Station, Texas, 77840, USA.

Background: Epicuticular wax (EW) is the first line of defense in plants for protection against biotic and abiotic factors in the environment. In wheat, EW is associated with resilience to heat and drought stress, however, the current limitations on phenotyping EW restrict the integration of this secondary trait into wheat breeding pipelines. In this study we evaluated the use of light reflectance as a proxy for EW load and developed an efficient indirect method for the selection of genotypes with high EW density.

Results: Cuticular waxes affect the light that is reflected, absorbed and transmitted by plants. The narrow spectral regions statistically associated with EW overlap with bands linked to photosynthetic radiation (500 nm), carotenoid absorbance (400 nm) and water content (~ 900 nm) in plants. The narrow spectral indices developed predicted 65% (EWI-13) and 44% (EWI-1) of the variation in this trait utilizing single-leaf reflectance. However, the normalized difference indices EWI-4 and EWI-9 improved the phenotyping efficiency with canopy reflectance across all field experimental trials. Indirect selection for EW with EWI-4 and EWI-9 led to a selection efficiency of 70% compared to phenotyping with the chemical method. The regression model EWM-7 integrated eight narrow wavelengths and accurately predicted 71% of the variation in the EW load (mg·dm) with leaf reflectance, but under field conditions, a single-wavelength model consistently estimated EW with an average RMSE of 1.24 mg·dm utilizing ground and aerial canopy reflectance.

Conclusions: Overall, the indices EWI-1, EWI-13 and the model EWM-7 are reliable tools for indirect selection for EW based on leaf reflectance, and the indices EWI-4, EWI-9 and the model EWM-1 are reliable for selection based on canopy reflectance. However, further research is needed to define how the background effects and geometry of the canopy impact the accuracy of these phenotyping methods.
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http://dx.doi.org/10.1186/s13007-021-00759-wDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8185930PMC
June 2021

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.

Sci Rep 2021 Mar 4;11(1):5254. Epub 2021 Mar 4.

International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico.

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.
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http://dx.doi.org/10.1038/s41598-021-84308-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7933281PMC
March 2021

Haplotype-Based, Genome-Wide Association Study Reveals Stable Genomic Regions for Grain Yield in CIMMYT Spring Bread Wheat.

Front Genet 2020 3;11:589490. Epub 2020 Dec 3.

International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico.

We untangled key regions of the genetic architecture of grain yield (GY) in CIMMYT spring bread wheat by conducting a haplotype-based, genome-wide association study (GWAS), together with an investigation of epistatic interactions using seven large sets of elite yield trials (EYTs) consisting of a total of 6,461 advanced breeding lines. These lines were phenotyped under irrigated and stress environments in seven growing seasons (2011-2018) and genotyped with genotyping-by-sequencing markers. Genome-wide 519 haplotype blocks were constructed, using a linkage disequilibrium-based approach covering 14,036 Mb in the wheat genome. Haplotype-based GWAS identified 7, 4, 10, and 15 stable (significant in three or more EYTs) associations in irrigated (I), mild drought (MD), severe drought (SD), and heat stress (HS) testing environments, respectively. Considering all EYTs and the four testing environments together, 30 stable associations were deciphered with seven hotspots identified on chromosomes 1A, 1B, 2B, 4A, 5B, 6B, and 7B, where multiple haplotype blocks were associated with GY. Epistatic interactions contributed significantly to the genetic architecture of GY, explaining variation of 3.5-21.1%, 3.7-14.7%, 3.5-20.6%, and 4.4- 23.1% in I, MD, SD, and HS environments, respectively. Our results revealed the intricate genetic architecture of GY, controlled by both main and epistatic effects. The importance of these results for practical applications in the CIMMYT breeding program is discussed.
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http://dx.doi.org/10.3389/fgene.2020.589490DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7737720PMC
December 2020

Improved Accuracy of High-Throughput Phenotyping From Unmanned Aerial Systems by Extracting Traits Directly From Orthorectified Images.

Front Plant Sci 2020 21;11:587093. Epub 2020 Oct 21.

Department of Plant Pathology, Kansas State University, Manhattan, KS, United States.

The development of high-throughput genotyping and phenotyping has provided access to many tools to accelerate plant breeding programs. Unmanned Aerial Systems (UAS)-based remote sensing is being broadly implemented for field-based high-throughput phenotyping due to its low cost and the capacity to rapidly cover large breeding populations. The Structure-from-Motion photogrammetry processes aerial images taken from multiple perspectives over a field to an orthomosaic photo of a complete field experiment, allowing spectral or morphological trait extraction from the canopy surface for each individual field plot. However, some phenotypic information observable in each raw aerial image seems to be lost to the orthomosaic photo, probably due to photogrammetry processes such as pixel merging and blending. To formally assess this, we introduced a set of image processing methods to extract phenotypes from orthorectified raw aerial images and compared them to the negative control of extracting the same traits from processed orthomosaic images. We predict that standard measures of accuracy in terms of the broad-sense heritability of the remote sensing spectral traits will be higher using the orthorectified photos than with the orthomosaic image. Using three case studies, we therefore compared the broad-sense heritability of phenotypes in wheat breeding nurseries including, (1) canopy temperature from thermal imaging, (2) canopy normalized difference vegetation index (NDVI), and (3) early-stage ground cover from multispectral imaging. We evaluated heritability estimates of these phenotypes extracted from multiple orthorectified aerial images via four statistical models and compared the results with heritability estimates of these phenotypes extracted from a single orthomosaic image. Our results indicate that extracting traits directly from multiple orthorectified aerial images yielded increased estimates of heritability for all three phenotypes through proper modeling, compared to estimation using traits extracted from the orthomosaic image. In summary, the image processing methods demonstrated in this study have the potential to improve the quality of the plant trait extracted from high-throughput imaging. This, in turn, can enable breeders to utilize phenomics technologies more effectively for improved selection.
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http://dx.doi.org/10.3389/fpls.2020.587093DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7609415PMC
October 2020

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

Front Plant Sci 2020 15;11:564183. Epub 2020 Sep 15.

Wheat Genetics Resource Center, Department of Plant Pathology, Kansas State University, Manhattan, KS, United States.

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.
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http://dx.doi.org/10.3389/fpls.2020.564183DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7522222PMC
September 2020

Breeder friendly phenotyping.

Plant Sci 2020 Jun 18;295:110396. Epub 2020 Jan 18.

International Maize and Wheat Improvement Centre, Mexico.

The word phenotyping can nowadays invoke visions of a drone or phenocart moving swiftly across research plots collecting high-resolution data sets on a wide array of traits. This has been made possible by recent advances in sensor technology and data processing. Nonetheless, more comprehensive often destructive phenotyping still has much to offer in breeding as well as research. This review considers the 'breeder friendliness' of phenotyping within three main domains: (i) the 'minimum data set', where being 'handy' or accessible and easy to collect and use is paramount, visual assessment often being preferred; (ii) the high throughput phenotyping (HTP), relatively new for most breeders, and requiring significantly greater investment with technical hurdles for implementation and a steeper learning curve than the minimum data set; (iii) detailed characterization or 'precision' phenotyping, typically customized for a set of traits associated with a target environment and requiring significant time and resources. While having been the subject of debate in the past, extra investment for phenotyping is becoming more accepted to capitalize on recent developments in crop genomics and prediction models, that can be built from the high-throughput and detailed precision phenotypes. This review considers different contexts for phenotyping, including breeding, exploration of genetic resources, parent building and translational research to deliver other new breeding resources, and how the different categories of phenotyping listed above apply to each. Some of the same tools and rules of thumb apply equally well to phenotyping for genetic analysis of complex traits and gene discovery.
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http://dx.doi.org/10.1016/j.plantsci.2019.110396DOI Listing
June 2020

Regularized selection indices for breeding value prediction using hyper-spectral image data.

Sci Rep 2020 05 18;10(1):8195. Epub 2020 May 18.

Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, MI, USA.

High-throughput phenotyping (HTP) technologies can produce data on thousands of phenotypes per unit being monitored. These data can be used to breed for economically and environmentally relevant traits (e.g., drought tolerance); however, incorporating high-dimensional phenotypes in genetic analyses and in breeding schemes poses important statistical and computational challenges. To address this problem, we developed regularized selection indices; the methodology integrates techniques commonly used in high-dimensional phenotypic regressions (including penalization and rank-reduction approaches) into the selection index (SI) framework. Using extensive data from CIMMYT's (International Maize and Wheat Improvement Center) wheat breeding program we show that regularized SIs derived from hyper-spectral data offer consistently higher accuracy for grain yield than those achieved by standard SIs, and by vegetation indices commonly used to predict agronomic traits. Regularized SIs offer an effective approach to leverage HTP data that is routinely generated in agriculture; the methodology can also be used to conduct genetic studies using high-dimensional phenotypes that are often collected in humans and model organisms including body images and whole-genome gene expression profiles.
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http://dx.doi.org/10.1038/s41598-020-65011-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7235263PMC
May 2020

Grain yield genetic gains and changes in physiological related traits for CIMMYT's High Rainfall Wheat Screening Nursery tested across international environments.

Field Crops Res 2020 Apr;249:107742

Centro Internacional de Mejoramiento de Maíz y Trigo (CIMMYT), Global Wheat Program, Apdo. 0660, Mexico City, Mexico.

The effects of climate change together with the projected future demand represents a huge challenge for wheat production systems worldwide. Wheat breeding can contribute to global food security through the creation of genotypes exhibiting stress tolerance and higher yield potential. The objectives of our study were to () estimate the annual grain yield (GY) genetic gain of High Rainfall Wheat Yield Trials (HRWYT) grown from 2007 (15th HRWYT) to 2016 (24th HRWYT) across international environments, and () determine the changes in physiological traits associated with GY genetic improvement. The GY genetic gains were estimated as genetic progress (GYP) and in terms of local checks (GYLC). In total, 239 international locations were classified into two groups: high- and low-rainfall environments based on climate variables and trial management practices. In the high-rainfall environment, the annual genetic gains for GYP and GYLC were 3.8 and 1.17 % (160 and 65.1 kg ha yr), respectively. In the low-rainfall environment, the genetic gains were 0.93 and 0.73 % (40 and 33.1 kg ha yr), for GYP and GYLC respectively. The GY of the lines included in each nursery showed a significant phenotypic correlation between high- and low-rainfall environments in all the examined years and several of the five best performing lines were common in both environments. The GY progress was mainly associated with increased grain weight (R = 0.35 < 0.001), days to maturity (R = 0.20, < 0.001) and grain filling period (R = 0.06, < 0.05). These results indicate continuous GY genetic progress and yield stability in the HRWYT germplasm developed and distributed by CIMMYT.
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http://dx.doi.org/10.1016/j.fcr.2020.107742DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7079639PMC
April 2020

Incorporating Genome-Wide Association Mapping Results Into Genomic Prediction Models for Grain Yield and Yield Stability in CIMMYT Spring Bread Wheat.

Front Plant Sci 2020 4;11:197. Epub 2020 Mar 4.

Global Wheat Program, International Maize and Wheat Improvement Center, Texcoco, Mexico.

Untangling the genetic architecture of grain yield (GY) and yield stability is an important determining factor to optimize genomics-assisted selection strategies in wheat. We conducted in-depth investigation on the above using a large set of advanced bread wheat lines (4,302), which were genotyped with genotyping-by-sequencing markers and phenotyped under contrasting (irrigated and stress) environments. Haplotypes-based genome-wide-association study (GWAS) identified 58 associations with GY and 15 with superiority index (measure of stability). Sixteen associations with GY were "environment-specific" with two on chromosomes 3B and 6B with the large effects and 8 associations were consistent across environments and trials. For , 8 associations were from chromosomes 4B and 7B, indicating 'hot spot' regions for stability. Epistatic interactions contributed to an additional 5-9% variation on average. We further explored whether integrating consistent and robust associations identified in GWAS as fixed effects in prediction models improves prediction accuracy. For GY, the model accounting for the haplotype-based GWAS loci as fixed effects led to up to 9-10% increase in prediction accuracy, whereas for this approach did not provide any advantage. This is the first report of integrating genetic architecture of GY and yield stability into prediction models in wheat.
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http://dx.doi.org/10.3389/fpls.2020.00197DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7064468PMC
March 2020

Validation of Candidate Gene-Based Markers and Identification of Novel Loci for Thousand-Grain Weight in Spring Bread Wheat.

Front Plant Sci 2019 26;10:1189. Epub 2019 Sep 26.

Department of Bioscience, CIMMYT, Texcoco, Mexico.

Increased thousand-grain weight (TGW) is an important breeding target for indirectly improving grain yield (GY). Fourteen reported candidate genes known to enhance TGW were evaluated in two independent and existing datasets of wheat at CIMMYT, the Elite Yield Trial (EYT) from 2015 to 2016 (EYT2015-16) and the Wheat Association Mapping Initiative (WAMI) panel, to study their allele effects on TGW and to verify their suitability for marker-assisted selection. Of these, significant associations were detected for only one gene () in the EYT2015-16 and two genes ( and ) in WAMI. The reported favorable alleles of and genes decreased TGW in the datasets. A haplotype-based genome wide association study was implemented to identify the genetic determinants of TGW on a large set of CIMMYT germplasm (4,302 lines comprising five EYTs), which identified 15 haplotype blocks to be significantly associated with TGW. Four of them, identified on chromosomes 4A, 6A, and 7A, were associated with TGW in at least three EYTs. The locus on chromosome 6A (Hap-6A-13) had the largest effect on TGW and additionally GY with increases of up to 2.60 g and 258 kg/ha, respectively. Discovery of novel TGW loci described in our study expands the opportunities for developing diagnostic markers and for multi-gene pyramiding to derive new allele combinations for enhanced TGW and GY in CIMMYT wheat.
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http://dx.doi.org/10.3389/fpls.2019.01189DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6775465PMC
September 2019

Improving grain yield, stress resilience and quality of bread wheat using large-scale genomics.

Nat Genet 2019 10 23;51(10):1530-1539. Epub 2019 Sep 23.

International Maize And Wheat Improvement Center (CIMMYT), Texcoco, Mexico.

Bread wheat improvement using genomic tools is essential for accelerating trait genetic gains. Here we report the genomic predictabilities of 35 key traits and demonstrate the potential of genomic selection for wheat end-use quality. We also performed a large genome-wide association study that identified several significant marker-trait associations for 50 traits evaluated in South Asia, Africa and the Americas. Furthermore, we built a reference wheat genotype-phenotype map, explored allele frequency dynamics over time and fingerprinted 44,624 wheat lines for trait-associated markers, generating over 7.6 million data points, which together will provide a valuable resource to the wheat community for enhancing productivity and stress resilience.
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http://dx.doi.org/10.1038/s41588-019-0496-6DOI Listing
October 2019

Hyperspectral Reflectance-Derived Relationship Matrices for Genomic Prediction of Grain Yield in Wheat.

G3 (Bethesda) 2019 04 9;9(4):1231-1247. Epub 2019 Apr 9.

Global Wheat Program, International Maize and Wheat Improvement Center (CIMMYT), Ciudad de México, 06600, México

Hyperspectral reflectance phenotyping and genomic selection are two emerging technologies that have the potential to increase plant breeding efficiency by improving prediction accuracy for grain yield. Hyperspectral cameras quantify canopy reflectance across a wide range of wavelengths that are associated with numerous biophysical and biochemical processes in plants. Genomic selection models utilize genome-wide marker or pedigree information to predict the genetic values of breeding lines. In this study, we propose a multi-kernel GBLUP approach to genomic selection that uses genomic marker-, pedigree-, and hyperspectral reflectance-derived relationship matrices to model the genetic main effects and genotype × environment ( × ) interactions across environments within a bread wheat ( L.) breeding program. We utilized an airplane equipped with a hyperspectral camera to phenotype five differentially managed treatments of the yield trials conducted by the Bread Wheat Improvement Program of the International Maize and Wheat Improvement Center (CIMMYT) at Ciudad Obregón, México over four breeding cycles. We observed that single-kernel models using hyperspectral reflectance-derived relationship matrices performed similarly or superior to marker- and pedigree-based genomic selection models when predicting within and across environments. Multi-kernel models combining marker/pedigree information with hyperspectral reflectance phentoypes had the highest prediction accuracies; however, improvements in accuracy over marker- and pedigree-based models were marginal when correcting for days to heading. Our results demonstrate the potential of using hyperspectral imaging to predict grain yield within a multi-environment context and also support further studies on the integration of hyperspectral reflectance phenotyping into breeding programs.
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http://dx.doi.org/10.1534/g3.118.200856DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6469421PMC
April 2019

High-throughput phenotyping platforms enhance genomic selection for wheat grain yield across populations and cycles in early stage.

Theor Appl Genet 2019 Jun 18;132(6):1705-1720. Epub 2019 Feb 18.

Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853, USA.

Genomic selection (GS) models have been validated for many quantitative traits in wheat (Triticum aestivum L.) breeding. However, those models are mostly constrained within the same growing cycle and the extension of GS to the case of across cycles has been a challenge, mainly due to the low predictive accuracy resulting from two factors: reduced genetic relationships between different families and augmented environmental variances between cycles. Using the data collected from diverse field conditions at the International Wheat and Maize Improvement Center, we evaluated GS for grain yield in three elite yield trials across three wheat growing cycles. The objective of this project was to employ the secondary traits, canopy temperature, and green normalized difference vegetation index, which are closely associated with grain yield from high-throughput phenotyping platforms, to improve prediction accuracy for grain yield. The ability to predict grain yield was evaluated reciprocally across three cycles with or without secondary traits. Our results indicate that prediction accuracy increased by an average of 146% for grain yield across cycles with secondary traits. In addition, our results suggest that secondary traits phenotyped during wheat heading and early grain filling stages were optimal for enhancing the prediction accuracy for grain yield.
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http://dx.doi.org/10.1007/s00122-019-03309-0DOI Listing
June 2019

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

Plant Genome 2018 11;11(3)

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.
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http://dx.doi.org/10.3835/plantgenome2018.03.0017DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7822054PMC
November 2018

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

Theor Appl Genet 2019 Jan 19;132(1):177-194. Epub 2018 Oct 19.

International Maize and Wheat Improvement Center (CIMMYT), Postal 6-641, 06600, Mexico, D.F., Mexico.

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.
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http://dx.doi.org/10.1007/s00122-018-3206-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6320358PMC
January 2019

Combining High-Throughput Phenotyping and Genomic Information to Increase Prediction and Selection Accuracy in Wheat Breeding.

Plant Genome 2018 03;11(1)

Genomics and phenomics have promised to revolutionize the field of plant breeding. The integration of these two fields has just begun and is being driven through big data by advances in next-generation sequencing and developments of field-based high-throughput phenotyping (HTP) platforms. Each year the International Maize and Wheat Improvement Center (CIMMYT) evaluates tens-of-thousands of advanced lines for grain yield across multiple environments. To evaluate how CIMMYT may utilize dynamic HTP data for genomic selection (GS), we evaluated 1170 of these advanced lines in two environments, drought (2014, 2015) and heat (2015). A portable phenotyping system called 'Phenocart' was used to measure normalized difference vegetation index and canopy temperature simultaneously while tagging each data point with precise GPS coordinates. For genomic profiling, genotyping-by-sequencing (GBS) was used for marker discovery and genotyping. Several GS models were evaluated utilizing the 2254 GBS markers along with over 1.1 million phenotypic observations. The physiological measurements collected by HTP, whether used as a response in multivariate models or as a covariate in univariate models, resulted in a range of 33% below to 7% above the standard univariate model. Continued advances in yield prediction models as well as increasing data generating capabilities for both genomic and phenomic data will make these selection strategies tractable for plant breeders to implement increasing the rate of genetic gain.
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http://dx.doi.org/10.3835/plantgenome2017.05.0043DOI Listing
March 2018

Prediction of Multiple-Trait and Multiple-Environment Genomic Data Using Recommender Systems.

G3 (Bethesda) 2018 01 4;8(1):131-147. Epub 2018 Jan 4.

International Maize and Wheat Improvement Center (CIMMYT), Apdo. Postal 6-641, 06600 México City, México.

In genomic-enabled prediction, the task of improving the accuracy of the prediction of lines in environments is difficult because the available information is generally sparse and usually has low correlations between traits. In current genomic selection, although researchers have a large amount of information and appropriate statistical models to process it, there is still limited computing efficiency to do so. Although some statistical models are usually mathematically elegant, many of them are also computationally inefficient, and they are impractical for many traits, lines, environments, and years because they need to sample from huge normal multivariate distributions. For these reasons, this study explores two recommender systems: item-based collaborative filtering (IBCF) and the matrix factorization algorithm (MF) in the context of multiple traits and multiple environments. The IBCF and MF methods were compared with two conventional methods on simulated and real data. Results of the simulated and real data sets show that the IBCF technique was slightly better in terms of prediction accuracy than the two conventional methods and the MF method when the correlation was moderately high. The IBCF technique is very attractive because it produces good predictions when there is high correlation between items (environment-trait combinations) and its implementation is computationally feasible, which can be useful for plant breeders who deal with very large data sets.
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http://dx.doi.org/10.1534/g3.117.300309DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5765342PMC
January 2018

Genetic Yield Gains In CIMMYT's International Elite Spring Wheat Yield Trials By Modeling The Genotype × Environment Interaction.

Crop Sci 2017 Mar-Apr;57:789-801. Epub 2017 Jan 12.

CIMMYT, Global Wheat Program, Apdo. 0660, Mexico City, México.

We calculated the annual genetic gains for grain yield (GY) of wheat ( L.) achieved over 8 yr of international Elite Spring Wheat Yield Trials (ESWYT), from 2006-2007 (27th ESWYT) to 2014-2015 (34th ESWYT). In total, 426 locations were classified within three main megaenvironments (MEs): ME1 (optimally irrigated environments), ME4 (drought-stressed environments), and ME5 (heat-stressed environments). By fitting a factor analytical structure for modeling the genotype environment (G E) interaction, we measured GY gains relative to the widely grown cultivar Attila (GYA) and to the local checks (GYLC). Genetic gains for GYA and GYLC across locations were 1.67 and 0.53% (90.1 and 28.7 kg ha yr), respectively. In ME1, genetic gains were 1.63 and 0.72% (102.7 and 46.65 kg ha yr) for GYA and GYLC, respectively. In ME4, genetic gains were 2.7 and 0.41% (88 and 15.45 kg ha yr) for GYA and GYLC, respectively. In ME5, genetic gains were 0.31 and 1.0% (11.28 and 36.6 kg ha yr) for GYA and GYLC, respectively. The high GYA in ME1 and ME4 can be partially attributed to yellow rust races that affect Attila. When G E interactions were not modeled, genetic gains were lower. Analyses showed that CIMMYT's location at Ciudad Obregon, Mexico, is highly correlated with locations in other countries in ME1. Lines that were top performers in more than one ME and more than one country were identified. CIMMYT's breeding program continues to deliver improved and widely adapted germplasm for target environments.
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http://dx.doi.org/10.2135/cropsci2016.06.0553DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7680939PMC
January 2017

Predicting grain yield using canopy hyperspectral reflectance in wheat breeding data.

Plant Methods 2017 3;13. Epub 2017 Jan 3.

International Maize and Wheat Improvement Center (CIMMYT), Apdo. Postal 6-641, 06600 Mexico City, Mexico.

Background: Modern agriculture uses hyperspectral cameras to obtain hundreds of reflectance data measured at discrete narrow bands to cover the whole visible light spectrum and part of the infrared and ultraviolet light spectra, depending on the camera. This information is used to construct vegetation indices (VI) (e.g., green normalized difference vegetation index or GNDVI, simple ratio or SRa, etc.) which are used for the prediction of primary traits (e.g., biomass). However, these indices only use some bands and are cultivar-specific; therefore they lose considerable information and are not robust for all cultivars.

Results: This study proposes models that use all available bands as predictors to increase prediction accuracy; we compared these approaches with eight conventional vegetation indexes (VIs) constructed using only some bands. The data set we used comes from CIMMYT's global wheat program and comprises 1170 genotypes evaluated for grain yield (ton/ha) in five environments (Drought, Irrigated, EarlyHeat, Melgas and Reduced Irrigated); the reflectance data were measured in 250 discrete narrow bands ranging between 392 and 851 nm. The proposed models for the simultaneous analysis of all the bands were ordinal least square (OLS), Bayes B, principal components with Bayes B, functional B-spline, functional Fourier and functional partial least square. The results of these models were compared with the OLS performed using as predictors each of the eight VIs individually and combined.

Conclusions: We found that using all bands simultaneously increased prediction accuracy more than using VI alone. The Splines and Fourier models had the best prediction accuracy for each of the nine time-points under study. Combining image data collected at different time-points led to a small increase in prediction accuracy relative to models that use data from a single time-point. Also, using bands with heritabilities larger than 0.5 only in Drought as predictor variables showed improvements in prediction accuracy.
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http://dx.doi.org/10.1186/s13007-016-0154-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5209864PMC
January 2017

Harnessing Diversity in Wheat to Enhance Grain Yield, Climate Resilience, Disease and Insect Pest Resistance and Nutrition Through Conventional and Modern Breeding Approaches.

Front Plant Sci 2016 6;7:991. Epub 2016 Jul 6.

International Maize and Wheat Improvement Center Texcoco, Mexico.

Current trends in population growth and consumption patterns continue to increase the demand for wheat, a key cereal for global food security. Further, multiple abiotic challenges due to climate change and evolving pathogen and pests pose a major concern for increasing wheat production globally. Triticeae species comprising of primary, secondary, and tertiary gene pools represent a rich source of genetic diversity in wheat. The conventional breeding strategies of direct hybridization, backcrossing and selection have successfully introgressed a number of desirable traits associated with grain yield, adaptation to abiotic stresses, disease resistance, and bio-fortification of wheat varieties. However, it is time consuming to incorporate genes conferring tolerance/resistance to multiple stresses in a single wheat variety by conventional approaches due to limitations in screening methods and the lower probabilities of combining desirable alleles. Efforts on developing innovative breeding strategies, novel tools and utilizing genetic diversity for new genes/alleles are essential to improve productivity, reduce vulnerability to diseases and pests and enhance nutritional quality. New technologies of high-throughput phenotyping, genome sequencing and genomic selection are promising approaches to maximize progeny screening and selection to accelerate the genetic gains in breeding more productive varieties. Use of cisgenic techniques to transfer beneficial alleles and their combinations within related species also offer great promise especially to achieve durable rust resistance.
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http://dx.doi.org/10.3389/fpls.2016.00991DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4933717PMC
July 2016

Canopy Temperature and Vegetation Indices from High-Throughput Phenotyping Improve Accuracy of Pedigree and Genomic Selection for Grain Yield in Wheat.

G3 (Bethesda) 2016 09 8;6(9):2799-808. Epub 2016 Sep 8.

Global Wheat Program, International Maize and Wheat Improvement Center (CIMMYT), Ciudad de Mexico, 06600, Mexico.

Genomic selection can be applied prior to phenotyping, enabling shorter breeding cycles and greater rates of genetic gain relative to phenotypic selection. Traits measured using high-throughput phenotyping based on proximal or remote sensing could be useful for improving pedigree and genomic prediction model accuracies for traits not yet possible to phenotype directly. We tested if using aerial measurements of canopy temperature, and green and red normalized difference vegetation index as secondary traits in pedigree and genomic best linear unbiased prediction models could increase accuracy for grain yield in wheat, Triticum aestivum L., using 557 lines in five environments. Secondary traits on training and test sets, and grain yield on the training set were modeled as multivariate, and compared to univariate models with grain yield on the training set only. Cross validation accuracies were estimated within and across-environment, with and without replication, and with and without correcting for days to heading. We observed that, within environment, with unreplicated secondary trait data, and without correcting for days to heading, secondary traits increased accuracies for grain yield by 56% in pedigree, and 70% in genomic prediction models, on average. Secondary traits increased accuracy slightly more when replicated, and considerably less when models corrected for days to heading. In across-environment prediction, trends were similar but less consistent. These results show that secondary traits measured in high-throughput could be used in pedigree and genomic prediction to improve accuracy. This approach could improve selection in wheat during early stages if validated in early-generation breeding plots.
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http://dx.doi.org/10.1534/g3.116.032888DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5015937PMC
September 2016

Application of unmanned aerial systems for high throughput phenotyping of large wheat breeding nurseries.

Plant Methods 2016 24;12:35. Epub 2016 Jun 24.

Wheat Genetics Resource Center, Department of Plant Pathology and Department of Agronomy, Kansas State University, Manhattan, KS 66506 USA.

Background: Low cost unmanned aerial systems (UAS) have great potential for rapid proximal measurements of plants in agriculture. In the context of plant breeding and genetics, current approaches for phenotyping a large number of breeding lines under field conditions require substantial investments in time, cost, and labor. For field-based high-throughput phenotyping (HTP), UAS platforms can provide high-resolution measurements for small plot research, while enabling the rapid assessment of tens-of-thousands of field plots. The objective of this study was to complete a baseline assessment of the utility of UAS in assessment field trials as commonly implemented in wheat breeding programs. We developed a semi-automated image-processing pipeline to extract plot level data from UAS imagery. The image dataset was processed using a photogrammetric pipeline based on image orientation and radiometric calibration to produce orthomosaic images. We also examined the relationships between vegetation indices (VIs) extracted from high spatial resolution multispectral imagery collected with two different UAS systems (eBee Ag carrying MultiSpec 4C camera, and IRIS+ quadcopter carrying modified NIR Canon S100) and ground truth spectral data from hand-held spectroradiometer.

Results: We found good correlation between the VIs obtained from UAS platforms and ground-truth measurements and observed high broad-sense heritability for VIs. We determined radiometric calibration methods developed for satellite imagery significantly improved the precision of VIs from the UAS. We observed VIs extracted from calibrated images of Canon S100 had a significantly higher correlation to the spectroradiometer (r = 0.76) than VIs from the MultiSpec 4C camera (r = 0.64). Their correlation to spectroradiometer readings was as high as or higher than repeated measurements with the spectroradiometer per se.

Conclusion: The approaches described here for UAS imaging and extraction of proximal sensing data enable collection of HTP measurements on the scale and with the precision needed for powerful selection tools in plant breeding. Low-cost UAS platforms have great potential for use as a selection tool in plant breeding programs. In the scope of tools development, the pipeline developed in this study can be effectively employed for other UAS and also other crops planted in breeding nurseries.
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http://dx.doi.org/10.1186/s13007-016-0134-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4921008PMC
June 2016

Functionality of gliadin proteins in wheat flour tortillas.

J Agric Food Chem 2009 Feb;57(4):1600-5

Department of Soil and Crop Sciences, Texas A&M University, College Station, Texas 77843, USA.

Gliadins are monomeric proteins that are encoded by the genes at the loci Gli 1 and Gli 2 present on the short arm of homologous wheat chromosomes 1 and 6, respectively. Studies have suggested that gliadins may play an important role in determining the functional properties of wheat flour. The main objective of this study was to understand the functionality of gliadins with respect to tortilla quality. The important tortilla quality attributes are diameter, opacity, and shelf stability, designated here as rollability or the ability to roll or fold the tortilla without cracking. In this study gliadin functionality in tortilla quality was studied using near-isogenic wheat lines that have deletions in either Gli A1, Gli D1, Gli A2, or Gli D2 gliadin loci. The deletion lines are designated by the same abbreviations. Dough and tortillas were prepared from the parent line used to derive these deletion lines, each individual deletion line, and a control commercial tortilla flour. Quantitative and qualitative evaluations were performed on the dough and tortillas derived from the flour from each of these lines. None of the deletions in the gliadin loci altered the shelf stability versus that found for the parent to the deletion lines or control tortilla flour. However, deletions in the Gli 2 loci, in particular Gli A2 reduced the relative proportion of alpha- and beta-gliadins with a greater cysteine amino acid content and gluten cross-link function versus the chain-terminating omega-gliadins in Gli 1, which were still present. As such, the dough and gluten matrix appeared to have greater extensibility, which improved the diameter and overall quality of the tortillas while not altering the rollability. Deletions in the Gli 1 loci had the opposite result with increased cross-linking of alpha- and beta-gliadins, polymeric protein content, and a stronger dough that decreased the diameter and overall quality of the tortillas. The data suggest that altering certain Gli 2 loci through null alleles could be a viable strategy to develop cultivars improved for the specific functionality requirements needed for the rapidly growing tortilla market.
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http://dx.doi.org/10.1021/jf802105eDOI Listing
February 2009

Use of near-isogenic wheat lines to determine the glutenin composition and functionality requirements for flour tortillas.

J Agric Food Chem 2008 Jan 12;56(1):179-84. Epub 2007 Dec 12.

Department of Soil and Crop Sciences, Texas A&M University, College Station, TX 77843, USA.

In wheat ( Triticum aestivum L), the synthesis of high molecular weight (HMW) glutenins (GS) is controlled by three heterologous genetic loci present on the long arms of group 1 wheat chromosomes. The loci Glu-A1, Glu-B1, and Glu-D1 and their allelic variants play important roles in the functional properties of wheat flour. This study focused on understanding the functionality of these protein subunits on tortilla quality. Near-isogenic wheat lines in which one or more of these loci were absent or deleted were used. Tortillas were prepared from each deletion line and the parent lines. The elimination of certain HMW-GS alleles alter distinct but critical aspects of tortilla quality such as diameter, shelf stability, and overall quality. Two deletion lines possessing HMW-GS 17 + 18 at Glu-B1 and deletions in Glu-A1 and Glu-D1 had significantly larger tortilla diameters, yet tortilla shelf life was compromised or unchanged from the parent lines used to develop the deletion lines or the commercial tortilla flour used as a control. Alternatively, a deletion line possessing Glu-A1 and Glu-D1 (HMW-GS 1, 5 + 10) and a deletion in Glu-B1 also significantly improved tortilla diameters. Whereas the increase in diameter was less than the line possessing only HMW-GS 17 + 18 at Glu-B1, the stability of the tortillas were, however, maintained and improved as compared to the parent lines containing a full compliment of HMW-GS. Thus, the presence of subunits 5 + 10 at Glu-D1 alone or in combination with subunit 1 at Glu-A1 appears to provide a compromise of improvement in dough extensibility for improved tortilla diameters while also providing sufficient gluten strength to maintain ideal shelf stability.
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http://dx.doi.org/10.1021/jf071831sDOI Listing
January 2008