Dissecting drought tolerance in winter wheat using phenotypic and genetic analyses of agronomic and spectral traits

2015 Summer.To view the abstract, please see the full text of the document

Improving Dual-Purpose Winter Wheat in the Southern Great Plains of the United States

This chapter covers the production and breeding status of winter wheat (Triticum aestivum L.) used for early-season animal grazing and late-season grain production in the Southern Great Plains of the United States. Besides, in the chapter, the current production status and needs, the drawbacks of current cultivars, breeding strategies of the crop, novel genomics tools, and sensor technologies that can be used to improve dual-purpose winter wheat cultivars were presented. We will focus on traits that are, in general, not required by cultivars used for grain-only production but are critical for cool-season forage production

Genomics-Assisted Approaches to Improve Grain Yield And End-Use Quality In Hard Winter Wheat (\u3cem\u3eTriticum Aestivum L.\u3c/em\u3e)

Global wheat production needs to be increased by 60% to meet the future demand of feeding nine billion people by 2050. Simultaneously, it is important to improve the enduse quality to meet the requirements of producers, grain markets, processors, and consumers. Thus, the development of more productive wheat varieties with better enduse quality remains the primary focus for all wheat breeding programs. However, direct phenotypic selection for improving grain yield and end-use quality is difficult as it is highly influenced by environmental factors. This dissertation focuses on harnessing advancements in genomics applications, including genome-wide association studies (GWAS), for the genetic characterization of yield component traits and utilizing it in marker-assisted selection for grain yield. Further, we investigated the efficacy of genomic selection GS and assessed the performance of various statistical models in predicting agronomic and end-use quality traits in the South Dakota hard winter wheat (HWW) breeding program. In the first study, GWAS was used to identify genetic determinants for yieldcomponent traits in HWW, which exhibits higher heritability compared to grain yield per se. We assembled a population of breeding lines and well-adapted cultivars, genotyped using genotyping-by-sequencing (GBS), and evaluated over four environments for phenotypic analysis of spike and kernel traits. GWAS using 8,030 single nucleotide polymorphisms (SNPs) identified 17 significant and multi-environment marker-trait associations (MTAs) for various traits, representing 12 putative quantitative trait loci (QTLs), with five QTLs affecting multiple traits. Further, a highly significant QTL was detected on chromosome 7AS that has not been previously associated with the number of spikelets/spike and putative candidate genes were identified in this region. The allelic frequencies of important QTLs were deduced in a larger set of 1,124 accessions which revealed the importance of identified MTAs in the U.S. HWW breeding programs. In the second strategy, we studied to evaluate the potential of genomic selection in predicting complex traits at earlier stages of the breeding program. Here, we used multi-trait genomic prediction (GP) models to predict multiple agronomic traits using 314 advanced and elite breeding lines of HWW evaluated at ten site-year environments. Extensive data from multi-environment trials was used to cross-validate the multivariate machine learning (ML) models that integrate the analysis of multiple traits and/or include GxE interaction. The multivariate ML models performed better for all traits, with average improvement over the ST-CV1 reaching up to 19%, 71%, 17%, 48%, and 51% for grain yield, grain protein content, test weight, plant height, and days to heading, respectively. Next, we evaluated the efficacy of multivariate GP using a set of advanced breeding lines from 2015-2021 to predict various end-use quality traits that are otherwise difficult to phenotype in earlier generations. The multivariate GP model outperformed the univariate model with up to a two-fold increase in prediction accuracy (PA). For instance, PA was improved from 0.38 to 0.75 for bake absorption and from 0.32 to 0.52 for loaf volume. Further, we compared multi-trait GP models by including different combinations of easyto- score traits as model covariates to predict end-use quality traits and observed that the incorporation of simple traits such as flour protein and flour sedimentation weight value can substantially improve the PA for baking traits. Overall, the findings of these studies elucidate the potential of multivariate GP for agronomic traits when advanced breeding lines are used as training population to predict preliminary breeding lines. The results also showed the application of multivariate GP models in the breeding program can reduce phenotyping costs by facilitating a sparse testing design. Furthermore, we observed that the inclusion of rapid low-cost traits like flour protein and flour sedimentation weight value in MT genomic prediction models can facilitate the use of GS to predict baking traits in earlier generations and provide breeders an opportunity for selection on end-use quality traits by culling inferior lines to increase selection accuracy and genetic gains

Genomic and phenomic tools to aid in the utilization of eastern European and central Asian wheat germplasm in U.S. hard winter wheat breeding

2017 Summer.Includes bibliographical references.To view the abstract, please see the full text of the document

Phenomics enabled genetic dissection of complex traits in wheat breeding

Doctor of PhilosophyGenetics Interdepartmental ProgramJesse A. PolandA central question in modern biology is to understand the genotype-to-phenotype (G2P) link, that is, how the genetics of an organism results in specific characteristics. However, prediction of phenotypes from genotypes is a difficult problem due to the complex nature of genomes, the environment, and their interactions. While the recent advancements in genome sequencing technologies have provided almost unlimited access to high-density genetic markers, large-scale rapid and accurate phenotyping of complex plant traits remains a major bottleneck. Here, we demonstrate field-based complex trait assessment approaches using a commercially available light-weight Unmanned Aerial Systems (UAS). By deploying novel data acquisition and processing pipelines, we quantified lodging, ground cover, and crop growth rate of 1745 advanced spring wheat lines at multiple time-points over the course of three field seasons at three field sites in South Asia. High correlations of digital measures to visual estimates and superior broad-sense heritability demonstrate these approaches are amenable for reproducible assessment of complex plant traits in large breeding nurseries. Using these validated high-throughput measurements, we applied genome-wide association and prediction models to assess the underlying genetic architecture and genetic control. Our results suggest a diffuse genetic architecture for lodging and ground cover in wheat, but heritable genetic variation for prediction and selection in breeding programs. The logistic regression-derived parameters of dynamic plant height exhibited strong physiological linkages with several developmental and agronomic traits, suggesting the potential targets of selection and the associated tradeoffs. Taken together, our highly reproducible approaches provide a proof-of-concept application of UAS-based phenomics that is scalable to tens-of-thousands of plots in breeding and genetic studies as will be needed to understand the G2P and increase the rate of gain for complex traits in crop breeding

Pre-breeding of wheat (Triticum aestivum L.) for Biomass allocation and drought tolerance.

Doctoral Degree. University of KwaZulu-Natal, Pietermaritzburg.Bread wheat (Triticum aestivum L., 2n=6x=42) is the third most important cereal crop globally after maize and rice. However, its production and productivity is affected by recurrent drought and declining soil fertility. Wheat cultivars with a well-balanced biomass allocation and improved root systems have better water- and nutrient-use efficiency and, hence, increased productivity under dry-land farming systems. The overall objective of this study was to develop breeding populations of wheat with enhanced drought tolerance and biomass allocation under water-limited conditions. The specific objectives of the study were: (i) to evaluate agronomic performance and quantify biomass production and allocation between roots and shoots in selected wheat genotypes in response to different soil water levels to select promising genotypes for breeding for drought tolerance and carbon (C) sequestration, (ii) to determine variance components and heritability of biomass allocation and grain yield related traits among 99 genotypes of bread wheat and triticale (Triticosecale Wittmack) to optimize biomass partitioning for drought tolerance, (iii) to deduce the population structure and genome-wide marker-trait association of yield and biomass allocation traits in wheat to facilitate marker-assisted selection for drought tolerance and C sequestration, and (iv) to estimate the combining ability of selected wheat genotypes and their progenies for agronomic traits, biomass allocation and yield under drought-stressed and non-stressed conditions for future breeding and genetic advancement for drought tolerance and C sequestration. To achieve these objectives, different experiments were conducted. In the first study, 99 wheat genotypes and one triticale accession were evaluated under drought-stressed and non-stressed conditions in the field and greenhouse using a 10×10 alpha lattice design with two replications. Data on the following phenotypic traits were collected: days to heading (DTH), number of productive tillers per plant (NPT), plant height (PH), days to maturity (DTM), spike length (SL), thousand kernel weight (TKW), root and shoot biomass (RB and SB), root to shoot ratio (RS) and grain yield (GY). There was significant (p<0.05) genotypic variation for grain yield and biomass production. The highest grain yield of 247.3 g m-2 was recorded in the genotype LM52 and the least was in genotype Sossognon with 30 g m-2. Shoot biomass ranged from 830g m-2 (genotype Arenza) to 437 g m-2 (LM57), whilst root biomass ranged between 140 g m-2 for LM15 and 603 g m-2 for triticale. Triticale also recorded the highest RS of 1.2, while the least was 0.30 for LM18. Water stress reduced total biomass production by 35% and RS by 14%. Genotypic variation existed for all measured traits useful for improving drought tolerance, while the calculated RS values can improve accuracy in estimating C sequestration potential of wheat. The following genotypes: BW140, BW141, BW152, BW162, LM26, LM47, LM48, LM71, LM70 and LM75 were selected for further development based on their high grain and biomass production, low drought sensitivity and marked genetic diversity. In the second study, data obtained from the above experiment were subjected to analyses of variance to calculate variance components, heritability and genetic correlations. Significant (p≤0.05) genetic and environmental variation were observed for all the traits except for spike length. Drought stress decreased the heritability of RS from 47 to 28% and GY from 55 to 17%. The genetic correlations between RS with PH, NPT, SL, SB and GY were weaker under drought-stress (r ≤ - 0.50; p70%) for RS observed in this population constitute several bottlenecks for improving GY and RS simultaneously. However, indirect selection for DTH, PH, RB, and TKW, could help optimize RS and simultaneously improve drought tolerance and yield under drought-stressed condition. In the third study, the 99 wheat genotypes and one triticale accession were genotyped using 28,356 DArTseq derived single nucleotide polymorphism (SNPs) markers. Phenotypic and genomic data were subjected to genome wide association study (GWAS). Population structure analysis revealed seven clusters with a mean polymorphic information content of 0.42, showing a high degree of diversity. A total of 54 significant marker-trait associations (MTAs) were identified. Twenty-one of the MTAs were detected under drought-stressed condition and 11% were on the genomic loci where quantitative trait loci (QTLs) for GY and RB were previously identified, while the remainder are new events providing information on biomass allocation. There were four genetic markers, two under each water treatment, with pleiotropic genetic effect on RB and SB that may serve as a means for simultaneous selection. Significant MTAs observed in this study will be useful in devising strategies for marker-assisted breeding to improve drought tolerance and to enhance C sequestration capacity of wheat. Lastly, 10 better performing and genetically diverse wheat genotypes selected during the first experiment were crossed using a half diallel mating design to generate F1 families. The parents and crosses were evaluated using a completely randomized block design with 2 replications under a controlled environment condition. Significant (p<0.05) genotype by water regime interaction effects were recorded for RB, SB, RS and GY. Root and shoot biomass were reduced by 48 and 37%, respectively, due to drought stress hindering biomass allocation patterns and hence C sequestration potential of the tested genotypes. Further, drought stress reduced RS and GY by 18 and 28%, respectively compared with the non-stressed treatment. Analysis of variance showed that both general combining ability (GCA) and specific combining ability (SCA) effects were significant (p<0.05) in conditioning the inheritance of grain yield and related traits and biomass allocation. Non-additive gene effects were more important in controlling the inheritance of the measured traits under drought-stressed and non-stressed conditions. Parental genotypes LM47 and BW140 had significant and positive GCA effects for root and shoot biomass and GY under drought-stressed conditions. These are recommended for recurrent selection programs to improve the respective traits. The crosses BW141×LM48 and LM47×LM75 were good specific combiners for biomass allocation and GY under drought stress, while BW141×LM48 and LM48×LM47 were good combiners under non-stressed condition. These families are selected for advanced breeding to develop pure line cultivars. The preliminary results suggest that simultaneous improvement of grain yield and root biomass can be realized to improve drought tolerance and C sequestration ability in wheat. Overall, the study detected marked phenotypic and genetic variation among diverse set of wheat genetic resources and candidate crosses for drought tolerance and biomass allocation through field and greenhouse based experiments and genomic analyses. The selected parents and novel crosses are useful for wheat breeding to enhance drought tolerance, yield and yield components and biomass allocation for C sequestration. This is the first study that evaluated biomass allocation in wheat as a strategy to improve drought tolerance and carbon sequestration

Advances in Wheat Genetics: From Genome to Field: Proceedings of the 12th International Wheat Genetics Symposium

plant genetics; plant genomics; agricultur

High-throughput field phenotyping in cereals and implications in plant ecophysiology

[eng] Global climate change effects on agroecosystems together with increasing world population is already threatening food security and endangering ecosystem stability. Meet global food demand with crops production under climate change scenario is the core challenge in plant research nowadays. Thus, there is an urgent need to better understand the underpinning mechanisms of plant acclimation to stress conditions contributing to obtain resilient crops. Also, it is essential to develop new methods in plant research that permit to better characterize non-destructively plant traits of interest. In this sense, the advance in plant phenotyping research by high throughput systems is key to overcome these challenges, while its verification in the field may clear doubts on its feasibility. To this aim, this thesis focused on wheat and secondarily on maize as study species as they make up the major staple crops worldwide. A large panoply of phenotyping methods was employed in these works, ranging from RGB and hyperspectral sensing to metabolomic characterization, besides of other more conventional traits. All research was performed with trials grown in the field and diverse stressor conditions representative of major constrains for plant growth and production were studied: water stress, nitrogen deficiency and disease stress. Our results demonstrated the great potential of leave-to-canopy color traits captured by RGB sensors for in-field phenotyping, as they were accurate and robust indicators of grain yield in wheat and maize under disease and nitrogen deficiency conditions and of leaf nitrogen concentration in maize. On the other hand, the characterization of the metabolome of wheat tissues contributed to elucidate the metabolic mechanisms triggered by water stress and their relationship with high yielding performance, providing some potential biomarkers for higher yields and stress adaptation. Spectroscopic studies in wheat highlighted that leaf dorsoventrality may affect more than water stress on the reflected spectrum and consequently the performance of the multispectral/hyperspectral approaches to assess yield or any other relevant phenotypic trait. Anatomy, pigments and water changes were responsible of reflectance differences and the existence of leaf-side-specific responses were discussed. Finally, the use of spectroscopy for the estimation of the metabolite profiles of wheat organs showed promising for many metabolites which could pave the way for a new generation phenotyping. We concluded that future phenotyping may benefit from these findings in both the low-cost and straightforward methods and the more complex and frontier technologies.[cat] Els efectes del canvi climàtic sobre els agro-ecosistemes i l’increment de la població mundial posa en risc la seguretat alimentària i l’estabilitat dels ecosistemes. Actualment, satisfer les demandes de producció d’aliments sota l’escenari del canvi climàtic és el repte central a la Biologia Vegetal. Per això, és indispensable entendre els mecanismes subjacents de l’aclimatació a l’estrès que permeten obtenir cultius resilients. També és precís desenvolupar nou mètodes de recerca que permetin caracteritzar de manera no destructiva els trets d’interès. L’avenç del fenotipat vegetal amb sistemes d’alt rendiment és clau per abordar aquests reptes. La present tesi s’enfoca en el blat i secundàriament en el panís com a espècies d’estudi ja que constitueixen els cultius bàsics arreu del món. Un ampli ventall de mètodes de fenotipat s’han utilitzat, des sensors RGB a híper-espectrals fins a la caracterització metabolòmica. La recerca s’ha dut a terme en assajos de camp i s’han avaluat diversos tipus d’estrès representatius de les majors limitacions pel creixement i producció vegetal: estrès hídric i biòtic i deficiència de nitrogen. Els resultats demostraren el gran potencial dels trets del color RGB (des de la planta a la capçada) pel fenotipat de camp, ja que foren indicadors precisos del rendiment a blat i panís sota condicions de malaltia i deficiència de nitrogen i de la concentració de nitrogen foliar a panís. La caracterització metabolòmica de teixits de blat contribuí a esbrinar els processos metabòlics endegats per l’estrès hídric i la seva relació amb comportament genotípic, proporcionant bio-marcadors potencials per rendiments més alts i l’adaptació a l’estrès. Estudis espectroscòpics en blat van demostrar que la dorsoventralitat pot afectar més que l’estrès hídric sobre l’espectre de reflectància i consegüentment sobre el comportament de les aproximacions multi/híper-espectrals per avaluar el rendiment i d’altres trets fenotípics com anatòmics i contingut de pigments. Finalment, l’ús de l’espectroscòpia per l’estimació del contingut metabòlic als teixits de blat resulta prometedor per molts metabòlits, la qual cosa obre les portes per a un fenotipat de nova generació. El fenotipat pot beneficiar-se d’aquestes troballes, tant en els mètodes de baix cost com de les tecnologies més sofisticades i d’avantguarda

Using crop-pathogen modeling to identify plant traits to control Zymoseptoria tritici epidemics on wheat

Diversification in pathogen control methods to reduce the severity of economically important foliar diseases such as Zymoseptoria tritici on wheat is needed. One way is to identify plant physiological and architectural traits that influence disease development and that can be selected in the process of crop breeding. Such traits may be used for improving tolerance or disease escape. Traits favoring disease escape, the focus of our work, may significantly decrease crop epidemics (Robert et al., 2018). However, understanding the role of such traits in crop-pathogen interactions is a daunting task because the interactions are multiple and dynamic in time. To characterize and quantify crop-pathogen interactions, an innovative trait-based and resource-based modeling framework was developed (Precigout et al., 2017). In this framework, the pathosystem is assumed to respond dynamically to both architecture and physiological status of the host canopy. A canopy consists of plenty of small patches, i.e. small functional and infectable units of leaf tissue. Production of new patches, for canopy growth and renewal of photosynthetically active plant tissues, is a function of the available resources produced by the other patches. Pathogen spores can contaminate nearby healthy patches. The definition of patch proximity depends on dispersal abilities of the pathogen and canopy architecture. We used and adapted this modeling framework to quantify the effects of several plant traits on Zymoseptoria tritici epidemics for varied climate scenarios. The complex infection cycle of Z. tritici characterized by a long symptomless incubation period was implemented in the model. We studied plant architectural traits such as leaf size or stem height, and plant physiological traits such as leaf lifespan or leaf metabolite contents. In our simulations, these traits impacted the epidemics dynamics though their effects on pathogen dispersal and on the amount of resources available for the pathogen. Sensitivity analyses showed how disease severity depended on plant traits and pathogen virulence. The importance of several plant and pathogen traits could be linked to the pathogen’s ability to manage the race for the colonization of the canopy in the face of canopy growth. Playing on host traits also made it possible to simulate different wheat varieties - with contrasted heights, pathogen resistance or precocity - to characterize the behavior of the pathosystem of interest for different host ideotypes. We argue that this kind of trait-based modeling approach is a valuable tool to identify plant traits promoting more resilient agroecosystems in particular for crop breeding in a context of innovative and sustainable crop protection