Source: IOWA STATE UNIVERSITY submitted to
CROP GENETIC IMPROVEMENT AND ADAPTATION USING GENE DISCOVERY, PHENOTYPIC PREDICTION, AND SYSTEMS ENGINEERING
Sponsoring Institution
National Institute of Food and Agriculture
Project Status
REVISED
Funding Source
HATCH
Reporting Frequency
Annual
Accession No.
1005714
Grant No.
(N/A)
Project No.
IOW04314
Proposal No.
(N/A)
Multistate No.
(N/A)
Program Code
(N/A)
Project Start Date
Jan 1, 2015
Project End Date
Dec 31, 2019
Grant Year
(N/A)
Project Director
Beavis, WI.
Recipient Organization
IOWA STATE UNIVERSITY
2229 Lincoln Way
AMES,IA 50011
Performing Department
Agronomy
Non Technical Summary
Genetic improvement exhibited by many crops during the last 70 years has been due primarily to increased capacity to evaluate larger numbers of potential cultivars through mechanization of planting, cultivation and harvest, logistics of seed transfer between northern and southern hemispheres and computational infrastructure. In the 1940s a breeding program might evaluate hundreds of lines to identify a few superior cultivars over a period of ten to fifteen years. Today commercial maize and soybean breeding programs evaluate millions of progeny annually to identify superior cultivars in half as much time. In the next 30 years human population growth is going to result in demands for genetic improvements at a rate that is 3x current rates. These increased demands will occur at at time of climate change and increased costs associated with expanding resource capacities used in the past.To address this challenge, plant breeders must rapidly deploy genes that will enable plants to adapt to rapidly changing environments while increasing the rate of genetic gain for yield. Plant breeding can no longer be approached as an art based on trial and error. Like the mechanical arts of 100 years ago, plant breeding must become an engineering discipline capable of designing systems in which trade-offs among competing breeding objectives can be quantitatively assessed, so that rational decisions can be made.This umbrella project will demonstrate 1) how to rapidly deploy genes that enable plants to adapt to rapidly changing environments and 2) how to transform plant breeding from an art into an engineering discipline.
Animal Health Component
0%
Research Effort Categories
Basic
25%
Applied
25%
Developmental
50%
Classification

Knowledge Area (KA)Subject of Investigation (SOI)Field of Science (FOS)Percent
2012499108025%
2032499209025%
4022499202025%
2022499108125%
Knowledge Area
202 - Plant Genetic Resources; 203 - Plant Biological Efficiency and Abiotic Stresses Affecting Plants; 201 - Plant Genome, Genetics, and Genetic Mechanisms; 402 - Engineering Systems and Equipment;

Subject Of Investigation
2499 - Plant research, general;

Field Of Science
1080 - Genetics; 2020 - Engineering; 2090 - Statistics, econometrics, and biometrics; 1081 - Breeding;
Goals / Objectives
Two broad goals of this umbrella project include demonstration of 1) how to rapidly deploy adaptive agronomic traits and 2) how to transform plant breeding from an art into an engineering discipline.These will be accomplished by meeting the following specific objectives: 1) discovery of alleles associated with adaptive agronomic traits as represented by reaction norms, 2) development of predictive models for adaptive agronomic traits, 3) development of breeding designs that assure optimal genetic improvement strategies and 4) implementation of accelerated genetic improvement strategies for adaptive agronomic traits.
Project Methods
Approaches. Procedures will depend on the reproductive biology, life-span, public information resources, accessible 'omics' technologies and budgets for each adaptive agronomic trait in each crop species. The specific procedural details will be described in grant proposals to funding agencies. Rather than conjecture about unknown and unpredictable successful grant proposals, herein we describe general approaches that transcend the specifics associated with all combinations of Adaptive Agronomic Trait (AAT) and plant species.a) Approaches for discovering alleles associated with adaptive agronomic traits, as represented by reaction norms, are well established and described in the functional quantitative trait loci (QTL) literature. The only issues that remain include identification of appropriate and fundable AATs, identification of appropriate phenomic technologies for obtaining data for reaction norms associated with each individual line/cultivar/hybrid. Efforts to communicate these identified genomic regions will be throughthrough peer reviewed literature and public information resources, e.g., SoyBASE and MaizeGDB.b) Approaches for development of predictive models for adaptive agronomic traits, will be based primarily on simulation modeling. As noted in our proposal, methods based on additive genetic architectures are incapable of providing accurate predictions for unrelated and new generations of breeding lines. We hypothesize that semi-parametric genome-wide prediction (GP) methods for reaction norms will provide more accurate models. This hypothesis will be investigated using data from simulations as well as empirical data generated by multi-institute collaborations that are available through public web resources. Novel GP methods will be evaluated with respect to objective criteria of accuracy, precision and power.c-i) Approaches for development of designs that assure optimal genetic improvement strategies, through applications of systems engineering. represents an adaptation of established methods from Operations Research (OR) to plant breeding systems. These principles include a) defining the breeding objectives which often include the need for trade-offs b) translating breeding objectives into mathematical objective functions, c) developing algorithms to solve the objective functions and d) implementing the algorithms in computational solvers to obtain optimal solutions. There exist libraries and software to address c) and d). However, a) and b) require education because there are no generic approaches to translating specific project objectives into mathematical functions. Efforts to communicate this transformative approach to treat plant breeding as a system that can be designed and engineered will include joint publications involving plant breeders and systems engineers, as well as development and delivery of a summer short course on use of engineering principles in design of plant breeding projects.c-ii) Approaches for development of designs that assure optimal genetic improvement strategies, through Doubled Haploids (DH) are well understood and have been implemented in maize, barley, and canola. We will investigate genetic sources of haploid induction for soybean, sorghum and sunflower. We will address the challenge to minimize the cost while maximizing the production of DH lines through response surface analyses and OR principles. An important specific outcome will be expansion of the ISU DH facility to provide DH capabilities to crops other than corn and rapid acceleration of cultivar development. The potential of this tecnological innovation will be pursued. Marketing efforts will enable the DH facility to become self sustaining.c-iii) Approaches for development of designs that assure optimal genetic improvement strategies, through In vitro nurseries is currently a concept. As such, there are a large number of fundamental biological discoveries that will be needed before implementation. Design of experiments to discover the underlying mechanisms needed for an in vitro system will be based on Response Surface Designs. If successful, we will utilize OR approaches to integrate the nurseries into genetic improvement programs. Tangible outcomes will be fundamental discoveries on regulation of phase transitions in cellular growth and development. Efforts to communicate these knowledge gaps will be through high impact journals.d) Approaches for implementing accelerated genetic improvement strategies for adaptive agronomic traits will depend on funding. While ISU does not have extensive resources for translational research, the Baker Center for Plant Breeding as well as individual plant breeding programs have some longer term funding for translational research in Corn, Soybean and Sorghum. Results from specific objectives a), b) and c) will establish a foundation for approaching funding agencies with competitive translational grant proposals. For example, two of our members have participated in a successful collaborative translational proposal to accelerate genetic improvements in soybean through application of GP methods. A tangible outcome will be extramural funding of translational research into applied breeding projects.

Progress 10/01/16 to 09/30/17

Outputs
Target Audience:Plant breeders, plant scientists, systems engineers, molecular biologists, bioinformaticists, computational biologists. Changes/Problems:Objective 4 is going to exhibit limited progress until there are funding opportunities that support translational projects in soybean and maize. Despite limited public support, our soybean breeders developed and released new varieties In 2017, although these varieties were developed using traditional genetic improvement designs rather than recently developed optimized genetic improvement designs. In 2017 corn growers and USDA-NIFA agreed to jointly support the Genomes to Fields initiative, which will help support some team members in pursuit of objectives 1, 2 and 3, although it is still not clear if the funds will be available for translational projects described in objective 4. Some members of this CRIS are working on genetic improvement of other crops, eg. Sorghum and Sunflower. However, these crops do not have stakeholders in Iowa and are not supported through check-off systems. What opportunities for training and professional development has the project provided?This project has generated professional development in the form of poster presentations and speaking opportunities for 23 graduate students at an average of twoworkshops or symposia per student. How have the results been disseminated to communities of interest?Results have been disseminated by peer-reviewed publications (see below), PhD dissertations and by making germplasm and software available (see Products Section below). Also, team members have provided an average of three invited and three volunteered presentations to target audiences consisting of farmers, plant breeders, molecular biologists and plant scientists. What do you plan to do during the next reporting period to accomplish the goals?QTL analyses for leaf angle in a second set of segregating Sorghum progeny, collect qRT-PCR data from sorghum accessions varying in leaf angle response to BR inhibitor and characterize the progenies obtained from crosses with the greatest likelihood of pyramiding desirable plant architecture and photosynthetic related alleles Characterize lines demonstrating self-autonomous pollination to determine which traits are contributing to this sunflower phenotype. Identify appropriate markers for marker assisted selection by sunflower breeders. Differential expression studies will also be conducted if suitable candidate genes are identified. Determine the agronomical value of the Rps12 gene by testing the responses of the gene to a large collection of P. sojae isolates. Evaluate the joint effect of PSS30 and GmDS1 in homozygous lines and their individually effects in a separate soybean genetic background. Also the transgenic lines will be evaluated for their responses against an array of SCN races. Identify and conduct functional validation for the candidate gene associated with a plant height QTL (qHT7.1) Experimentally determine optimized training sets for genomic prediction in maize breeding populations. Experimentally determine prediction accuracies for multiple agronomic traits for untested genotypes in untested environments. Determine which of the numerous QTL associated with SDS resistance will be important to improve SDS resistance in soybeans. Also, evaluate the number of copies of of rhg1 in cultivars and germplasm lines that Cianzio has releasedfor inclusion in characterization of ines that will be used for breeding purposes. For application in regional field trials of cultivar development programs, develop a metric for realized genetic gain that minimizes confounding estimates by annual changes of latent environmental factors. Mathematically formulate multi-objective optimization of genomic predictions for agronomic traits in the cost, time and probability of success framework. In particular determine Pareto optimal trade-offs between maintaining genetic diversity, genetic potential and genetic gains for designing efficient and effective cultivar development programs.

Impacts
What was accomplished under these goals? Overall impact: A novel experimental methodology was developed to improve the efficiencies of reverse genetic discovery experiments. (Reverse genetics attempts to connect a given genetic sequence with specific effects.)It employed geometric models and computational algorithms and can be deployed as a smart phone app.Novel methods for predicting agronomic performance within and among environments were developed by integrating genomic prediction methods with crop modeling methods. Designs of genetic improvement projects, based on concepts from Game Theory and Markov Decision Processes, were shown to be at least four times as efficient as designs currently used for marker assisted selection. Objective 1. Discover alleles associated with adaptive traits as represented by reaction norms. Disease resistance. A novel Rps12 gene that confers phytophthora resistance in soybean was mapped.In addition, mixed linear models consisting of random effect covariance structures and fixed effect parameters for single, multiple and epistatic genetic effects were applied to publicly available SNP "chip" data from 466 plant introduction accessions 459 plant introduction accessions that were assayed respectively for Sclerotinia Stem Rot and Charcoal Rot in multiple field and controlled environments. 58 significant main effect and 24 epistatic interactions were associated with SSR resistance. The genomic locations of the loci include candidate genes involved cell wall structure, hormone signaling, and sugar allocation. Candidate genes clustered into salicylic acid, jasmonic acid, and ethylene pathways suggesting a hormonal network is activated by necrotrophic and biotrophic pathogens, supporting the hypothesis that Sclerotinia is a hemibiotrophic pathogen. Five significant genomic regions and putative candidate genes related to abiotic and biotic stress responses were associated with field assays of CR; while eight eight candidate gene families were associated with greenhouse assays of CR. None of the candidate genes for CR were coincident from field and greenhouse assays suggesting resistance to CR in soybean is dependent on environmental signals. Cold stress resistance. 404 sequenced Arabidopsis ecotypes were subjected to continuous cold stress (4oC) for a prolonged period of 30 days after a brief period following sowing of 7 days at 220C. Variability in cold stress was associated with 35 gene variants were identified. Insertions of T-DNA for 25 of the genes provides additional evidence that these genes are involved in adaptation to cold environments. Flowering time and plant height.Segregation improved resolution of a plant height QTL in sorghum (qHT7.1). Predictions of height and flowering time among maize lines based on models built from data collected in previous years were validated in field trials. Specific alleles at cloned genetic loci associated with flowering time and plant height were verified. SNPs associated with self-autonomous pollination in sunflower were discovered based on data analyses of phenotypes obtained in 2013-2015. Plant architecture. QTL analysis of leaf angle measured on multiple leaves from the top and middle canopy of segregation progeny from the cross BTx623 x IS3620C indicated that there is both leaf-level-dependent and leaf-level-independent control of leaf angle. Results indicate that phenotypes based on analyses of a single leaf will not provide adequate characterization of the phenotype. Additionally, a genomic region on chromosome 7 close to Dw3 was identified even when Dw3 was used as a covariate. Characterization of leaf angle response to a Brassinosteroid inhibitor (propiconazole) was conducted on a set of sorghum accessions selected based on contrasting allelic composition at Brassinosteroid candidate genes.Characterization of a gene expression profile in different sorghum tissues associated with seed number per panicle was conducted and complemented by an overexpression study in which the gene was constitutively expressed in transgenic rice. A methodology, based on geometric measures, was applied to digital images of soybean varietal canopies and enabled automated collection, storage and analysis of data from ground and aerial imaging systems. The methodology uses Elliptical Fourier Transformation and Fourier Descriptors, and was validated using canopy images from >450 soybean accessions from 25 different countries. Fourier coefficients/descriptors successfully reconstructed canopy outlines, and were used to quantify morphometric traits. Phenotypic diversity was observed for roundness, while solidity showed the lowest diversity. Principal Component Analysis (PCA) of the metrics was similar to PCA of genetic marker data. Objective 2. Develop predictive models for adaptive agronomic traits A predictive system consisting of image capture→ data storage and curation→ trait extraction→ machine learning/classification→ models/apps was developed for decision support. We illustrated the system for plant stress severity due to iron deficiency chlorosis on a set of canopies representing ~4500 field plots of diverse soybean lines. The best classifier model can be deployed as a smartphone app for rapid and real time severity ratings in the field. A panel of 465 soybean plant introduction accessions was phenotyped for white mold (WM) resistance in replicated field and greenhouse tests. All plant accessions were previously genotyped using the SoySNP50K BeadChip. Cross-validation among environments revealed that prediction models had similar prediction accuracies. Predictive ability did not improve significantly by using more than 5k SNPs, or by increasing the training population size (from 50% to 90% of the total). Models accurately predicted WM resistance between field and greenhouse experiments if either was used as training or validation data. Genome-wide prediction combined with crop modelling can be used to predict patterns hidden in G×E interactions and identify plasticity of hybrid responses across diverse environments. As a result, it is possible to provide performance predictions and forecasts that are "Site-Specific", "In-Season", "On-Target", "Whole-Genome", and "Simple-Straightforward", for many agronomic traits. Objective 3. Develop breeding designs that assure optimal genetic improvement strategies. Introgression of one to many alleles from one to many donors into recipient lines of diploid species was mathematically formulated as a multi-objective optimization challenge in a cost, time and probability of success framework. Pareto optimal plots indicate that backcrossing is almost never the best breeding strategy.Rather it is possible to utilize concepts from Game theory and Markov Decision Processes to identify the best crosses to make among progeny generated by transmission genetics in each generation. Introgression of one to as many as 30 alleles with >95% recovery of the recipient genome can be completed in half as much time for half as much cost as previously suggested. Three methods of representative subset selection from clustering, graphic network analysis, and genetic mating schemes were developed for designing training sets in genomic prediction. With representative subset selection, effective genomic prediction can be accomplished with a training sets that are 2~13% of the size of the whole set. Objective 4. Implement accelerated genetic improvement strategies Two transgenes, PSS30 and GmDS1, have been stacked into F1 soybeans. Both genes confer resistance to Fusarium virguliforme and soybean cyst nematodes (SCN) in transgenetic soybean plants. Based on a gene stacking optimization algorithm 55 genomic regions associated with stem diameter, seed number per panicle, panicle exertion, tiller number and leaf angle in Sorghum are being crossed to create lines with increased photosynthetic capacity, transpiration rate under cold conditions and Fv'/Fm' under non-stress conditions.

Publications

  • Type: Journal Articles Status: Published Year Published: 2017 Citation: Coser SM, RV Chowda Reddy, J Zhang, DS Mueller, A Mengistu, KA Wise, TW Allen, A Singh, AK Singh. 2017. Genetic Architecture of Charcoal Rot (Macrophomina phaseolina) Resistance in Soybean Revealed Using a Diverse Panel. Frontiers in Plant Science, v8, 1626. https://doi.org/10.3389/fpls.2017.01626.
  • Type: Journal Articles Status: Accepted Year Published: 2017 Citation: Naik HS, J Zhang, A Lofquist, T Assefa, S Sarkar, D Ackerman, A Singh, AK Singh, B Ganapathysubramanian. 2017. A real-time phenotyping framework using machine learning for plant stress severity rating in soybean. Plant Methods 13:23. https://doi.org/10.1186/s13007-017-0173-7
  • Type: Journal Articles Status: Published Year Published: 2017 Citation: Jubery TZ, J Shook, K Parmley, J Zhang, HS Naik., R Higgins, S Sarkar, A Singh, AK Singh, B Ganapathysubramanian. 2017. Deploying Fourier Coefficients to Unravel Soybean Canopy Diversity. Frontiers in Plant Science. v7: 2066. DOI=10.3389/fpls.2016.02066. URL=https://www.frontiersin.org/article/10.3389/fpls.2016.02066
  • Type: Journal Articles Status: Published Year Published: 2017 Citation: de Azevedo Peixoto L, TC Moellers, J Zhang, AJ Lorenz, LL Bhering, WD Beavis, AK Singh. 2017. Leveraging genomic prediction to scan germplasm collection for crop improvement. PLoS ONE 12(6): e0179191. https://doi.org/10.1371/journal.pone.0179191
  • Type: Journal Articles Status: Accepted Year Published: 2017 Citation: Mantilla Perez M.B., Salas Fernandez M.G. 2017. Differential manipulation of leaf angle throughout the canopy: current status and prospects. (Darwin Reviews) J. Exp. Bot. (in press). erx378, https://doi.org/10.1093/jxb/erx378.
  • Type: Journal Articles Status: Accepted Year Published: 2017 Citation: Ortiz D., Hu J., Salas Fernandez M.G. 2017. Genetic architecture of photosynthesis in Sorghum bicolor under non-stress and cold stress conditions. J. Exp. Bot. 68(16): 4545-4557.
  • Type: Journal Articles Status: Published Year Published: 2017 Citation: Salas Fernandez M.G., Bao Y., Tang L., Schnable P.S. 2017. A high-throughput field-based phenotyping technology for tall biomass crops. Plant Physiology Jun 2017, pp.00707.2017; DOI: 10.1104/pp.17.00707.
  • Type: Journal Articles Status: Published Year Published: 2017 Citation: Gage, J.L., D. Jarquin, C. Romay, A. Lorenz, E.S. Buckler, S. Kaeppler, & , J. Yu, and Natalia de Leon. 2017. The effect of artificial section on phenotypic plasticity in maize. Nature Communications 8:1348.
  • Type: Journal Articles Status: Published Year Published: 2017 Citation: Bouchet, S., M.O. Olatoye, S.R. Marla, R. Perumal, T. Tesso, J. Yu, and M. Tuinstra, G.P. Morris. 2017. Increased power to dissect adaptive traits in global sorghum diversity using a nested association mapping population. Genetics 206:573-585.
  • Type: Journal Articles Status: Published Year Published: 2017 Citation: Ignacio Trucillo-Silva, Hari Kishan R. Abbaraju, Lynne P. Fallis, Hongjun Liu, Michael Lee, Kanwarpal S. Dhugga. 2017. Biochemical and genetic analyses of N metabolism in maize testcross seedlings: 1. Leaves. Theoretical and Applied Genetics 130:1453-1466.
  • Type: Journal Articles Status: Published Year Published: 2017 Citation: Hongjun Liu, Lin Zhang, Jiechen Wang, Changsheng Li, Xing Zeng, Shupeng Xie, Yon gzhong Zhang, Sisi Liu, Songlin Hu, Michael Lee, Thomas Lubberstedt, Guangwu Zhao. 2017. Quantitative trait locus analysis for deep- sowing germination ability in the maize IBM Syn10 DH population. Frontiers in Plant Science.
  • Type: Journal Articles Status: Published Year Published: 2017 Citation: Cianzio, S.R., P.R. Arelli, S. Swaminathan, P. Lundeen, G. Gebhart, N. Rivera-Velez, S.R. Guilherme, I.O. Soares, B.W. Diers , H. Knapp, M. Westgate, M.E. Hudson. 2017. Registration of genetically diverse SCN-resistant full-sib soybean germplasm lines AR4SCN, AR5SCN, AR6SCN, AR7SCN, and AR8SCN. J. Plant Reg. doi:10.3198/jpr2017.03.0015crg
  • Type: Journal Articles Status: Published Year Published: 2017 Citation: Luckew, A.S., S. Swamimnathan, L.F. Leandro, J.H. Orf, and S.R. Cianzio. 2017. MN1606SP by Spencer filial soybean population reveals novel quantitative trait loci and interactions among loci conditioning SDS resistance" Theor. Applied Genetics DOI: 10.1007/s00122-017-2947-8.
  • Type: Journal Articles Status: Accepted Year Published: 2017 Citation: Shiming Liu, Pramod Kandoth, Naoufal Lakhssassi, Jingwen Kang, Vincent Colantonio, Robert Heinz, Greg Yeckel, Zhou Zhou, Sadia Bekal, Johannes Dapprich, Bjorn Rotter, Silvia Cianzio, Melissa Mitchum, and Khalid Meksem. 2017. GmSNAP18: One soybean gene underlying two types of resistance to soybean cyst nematode" [Paper #NCOMMS-16-19035C], Nature Communications
  • Type: Journal Articles Status: Published Year Published: 2017 Citation: De La Fuente, G.N., Carstensen, J.M., Edberg, M.A., L�bberstedt, T. (2017) Discrimination of haploid and diploid maize kernels via multispectral imaging. Plant Breeding 136:50-60
  • Type: Journal Articles Status: Published Year Published: 2017 Citation: Liu, Q., Liu, H., Gong, Y., Tao, Y., Jiang, L., Zuo, W., Yang, Q., Ye, J., Lai, J., Wu, J., L�bberstedt, T., Xu, M.L. (2017) An atypical Thioredoxin imparts early resistance to Sugarcane Mosaic Virus in maize. Mol Plant 10:483-497
  • Type: Journal Articles Status: Published Year Published: 2017 Citation: Wu, P., Ren, J., Tian, X., Li, G., Li, W., L�bberstedt, T.,Wang, L., Liu, W., Chen, S. (2017) New insights into the genetics of haploid male fertility. Crop Sci. 57:637-647
  • Type: Journal Articles Status: Published Year Published: 2017 Citation: Jiang, J., Guan, Y., McCormick, S., Juvik, J., L�bberstedt, T., Fei, S.-Z. (2017) Gametophytic self-incompatibility is operative in Miscanthus sinensis (Poaceae) and is affected by pistil age. Crop Sci. 57:1-9
  • Type: Journal Articles Status: Published Year Published: 2017 Citation: Vanous, K., Vanous, A., Frei, U.K., L�bberstedt, T. (2017) Generation of maize (Zea mays) doubled haploids via traditional methods. Current Protocols in Plant Biology 2: 147-157
  • Type: Journal Articles Status: Published Year Published: 2017 Citation: Hu, S., Sanchez, D., Wang, C., Lipka, A. E., Yin, Y., Blanco, M., L�bberstedt, T. (2017) Gibberellins promote brassinosteroids and both increase heterosis for plant height in maize (Zea mays L.). Frontiers in Plant Science 8: 1039
  • Type: Journal Articles Status: Published Year Published: 2017 Citation: Wang, C., Hu, S., Gardner, C., L�bberstedt, T. (2017) Emerging avenues for utilization of exotic germplasm. TIPS 22: 624-637
  • Type: Journal Articles Status: Published Year Published: 2017 Citation: Ren, J., Wu, P., Tian, X., L�bberstedt, T., Chen, S. (2017) Fine mapping of quantitative trait locus qhmf4 causing haploid male fertility in maize based on segregation distortion. Theor. Appl. Genet. 130:13491359
  • Type: Journal Articles Status: Published Year Published: 2017 Citation: Leng, P., Ji, Q., Asp, T., Frei, U.K., Ingvardsen, C., Xing, Y., Studer, B., Redinbaugh, M., Jones, M., Ye, J., Liu, S., Pan, G., Xu, M.L., L�bberstedt, T. (2017) Auxin Binding Protein 1 reinforces resistance to Sugarcane Mosaic Virus in maize. Mol. Plant (in press); https://doi.org/10.1016/j.molp.2017.07.013
  • Type: Journal Articles Status: Published Year Published: 2017 Citation: Howard, R, A. Xavier, D. Jarquin, V. Ramasubramanian, J. Specht, G. Graef, WD Beavis, B. Diers, Q. Song, P. Cregan, R. Nelson, R. Mian, G. Shannon, L. McHale, D. Wang, W. Schapaugh, A. Lorenz, S. Xu, W. Muir, K. Rainey, (2017). Genome-wide analysis of grain yield stability and environmental interactions in a multi-parental soybean population. G3:(accepted 11-21-2017)
  • Type: Journal Articles Status: Published Year Published: 2017 Citation: Byrum, J, WD Beavis, C Davis, G Doonan, T Doubler, V Kaster, R Mowers and S Parry, (2017). Genetic Gain Performance Metric Accelerates Agricultural Productivity. Interfaces 47: 47:442-453. https://doi.org/10.1287/inte.2017.0909 (finalist for the Daniel H Wagner Prize for Excellence in Operations Research)
  • Type: Journal Articles Status: Published Year Published: 2017 Citation: Cameron, J.N., Y. Han; L. Wang; W.D. Beavis (2017). Systematic Design for Trait Introgression Projects. Theor. Apl Gen. 130: 1993-2004 doi: 10.1007/s00122-017-2938-9
  • Type: Journal Articles Status: Published Year Published: 2017 Citation: Howard, R, AL Carriquiry, WD Beavis 2017. Response Surface Methodology in Genomic Selection. G3: Genes, Genomes, Genetics 7:3103-3113. doi.org/10.1534/g3.117.044453.
  • Type: Journal Articles Status: Published Year Published: 2017 Citation: Peixoto, L.deA., T.C Moellers, J Zhang, A.J. Lorenz, LL Bhering, W.D. Beavis, A.K. Singh. (2017). Leveraging genomic prediction to scan germplasm collection for crop improvement. PLOS1: doi.org/10.1371/journal.pone.0179191
  • Type: Journal Articles Status: Published Year Published: 2017 Citation: Song, Q., L. Yan, C. Quigley, B.D. Jordan, E. Fickus, S. Schroeder, B-H Song, Y-Q.C. An, D. Hyten, R. Nelson, K. Rainey, W.D. Beavis, J. Specht, B. Diers, and P. Cregan (2017). Genetic characterization of Soybean Nested Association Mapping population. The Plant Genome doi: 10.3835/plantgenome2016.10.0109
  • Type: Journal Articles Status: Published Year Published: 2017 Citation: Han, Y, JN Cameron, L Wang, WD Beavis (2017). The predicted cross value for genetic introgression of multiple alleles. Genetics 205:1409-1423, doi: 10.1534/genetics.116.197095)
  • Type: Journal Articles Status: Published Year Published: 2017 Citation: Kim, B and WD Beavis (2017). Numericware-i: Identity by state calculator. Evolutionary Bioinformatics 13: doi: 10.1177/1176934316688663
  • Type: Journal Articles Status: Published Year Published: 2017 Citation: Sandhu, D., Baumbach, J., Ghosh, J., Johnson, C., Srivastava, S.K., Baumert, E., Cina, T., Grant, D., Palmer, R., Bhattacharyya, M.K. (2017) The endogenous transposable element Tgm9 is suitable for generating knockout mutants for functional analyses of soybean genes and genetic improvement in soybean. PloS One, 12(8):e0180732. https://doi.org/10.1371/journal.pone.0180732
  • Type: Journal Articles Status: Published Year Published: 2017 Citation: Hu, S., Sanchez, D., Wang, C., Lipka, A. E., Yin, Y., Gardner, C.A, L�bberstedt, T. (2017) Brassinosteroid and Gibberellin control of seedling traits in maize (Zea mays L.). Plant Science 263: 132-141
  • Type: Journal Articles Status: Published Year Published: 2017 Citation: Li, H. and T. L�bberstedt. (2017) Molecular mechanisms controlling seed set in cereal crop species. J. Integrative Agriculture 16: 60345-7
  • Type: Theses/Dissertations Status: Published Year Published: 2017 Citation: Fang, Ze. Genome Wide Association Study of Seed and Seedling Root Traits in Sunflower. Iowa State University. Ames, Iowa.
  • Type: Theses/Dissertations Status: Published Year Published: 2017 Citation: John N Cameron. The optimization of introgression projects for plant genetic improvement. Iowa State University. Ames, Iowa.
  • Type: Theses/Dissertations Status: Published Year Published: 2017 Citation: do Canto, Javier. Genetic studies on self-fertility in perennial ryegrass (Lolium perenne L.) with implications for hybrid breeding in allogamous grasses. Iowa State University. Ames, Iowa.
  • Type: Journal Articles Status: Published Year Published: 2017 Citation: Sahoo, D., Abeysekara, N., Cianzio, S., and Robertson, A.E., Bhattacharyya, M.K. (2017) A novel Phytophthora resistance gene, Rps12 mapped tightly to the Rps4/6 region in soybean. PLoS One, 12:e0169950. http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0169950
  • Type: Theses/Dissertations Status: Published Year Published: 2017 Citation: Sanchez, Darlene. Molecular and phenotypic characterization of doubled haploid exotic introgression lines for nitrogen use efficiency in maize. . Iowa State University. Ames, Iowa.


Progress 10/01/15 to 09/30/16

Outputs
Target Audience:Soybean breeders (public and private); seed companies; farmers in Iowa and other states; researchers in academia, undergraduate and graduate students, colleagues within discipline and outside within ISU, and other national and international research institutions. Changes/Problems:Objective 4 is going to have limited progress. Plant scientists and breeders at Iowa State University are expected to work on genetic discovery and improvement of corn and soybeans, both of which are extremely profitable to the commercial seed and commodity sectors. While there exist competitive funding opportunities for fundamental discoveries in the evolution and development of these two species, the commercial seed sector has clearly communicated to funding agencies and commodity groups that there is no reason to commit limited public resources to genetic improvement of these two crops. This has been reflected in the lack of USDA extramural funding for genetic improvement of these crops. Also there has been a lack of funding for genetic improvement by the corn growers association and soybean stakeholders at USB, ISA and the NCSRP are reducing their support for genetic improvement of soybeans. The 2016 reductions in funding, primarily from the commodity sector, seriously affected Cianzio's breeding program; a program that has been successful as measured by many criteria over decades and will likely adversely affect Singh's program in the near future. Some members of this project are working on genetic improvement, of other crops, e.g. sorghum and sunflower. However, these crops do not have stakeholders in Iowa and thus also are not being supported through check-off systems. A tangible consequence is that the Bill and Melinda Gates Foundation has decided to provide financial support to other universities that have genetic improvement programs for multiple crops. What opportunities for training and professional development has the project provided?Through NIFA support for Cianzio 1 minority undergraduate student from Tuskegee University; 1 minority student from a high-school at Ames, IA; 5 Puerto Rican minority undergraduate students from the Univ. of Puerto Rico; 1 minority undergraduate student from ISU; graduated 2 graduate students (1 with MS degree; 1 with a PhD degree). Through NIFA support for Lubberstedt, a short course on DH technology was offered as part of the Summer Institute for Advanced Topics in Plant Breeding and will be offered annually. Lubberstedt also provided online education in Molecular Plant Breeding for (17?) professional plant breeders Through NIFA support for Beavis, a short course on applications of Operations Research in Plant Breeding as part of the Summer Institute for Advanced Topics in Plant Breeding and will be offered annually. Beavis also provided online education modules in Quantitative Genetics that were delivered to (10) professional plant breeders. How have the results been disseminated to communities of interest?Results have been disseminated by peer-reviewed publications (see below), PhD dissertations and by making germplasm and software available (see Products Section below). Also, presentations have been made at stakeholder meetings, conferences and universities. Cianzio presented three talks to Iowa farmers on yield advances in soybean (attendants in each consisted of about 150 farmers) one invited seminar at the Univ. of Minnesota on breeding for resistance to SDS; one invited talk at a Symposium organized by the American Phytopathological Society on merging molecular and phenotyping techniques to increase breeding efficiency; published 6 research papers on recognized journals; presented one poster at the SCN conference in Tampa, Florida; 18 Material and Transfer Agreements have been signed for use of Cianzio's genetics, users are public soybean breeders and private seed companies. What do you plan to do during the next reporting period to accomplish the goals?Cianzio and Singh will continue to develop populations and conduct phenotypic analyses in greenhouse, growth chamber and field experiments. Training of minority students continues, as well as eh training of graduate students. Singh will develop physio-genetic models to predict reaction norms of growth and development in soybean and validate candidate IDC tolerance genes using virus induced gene silencing. Lubberstedt will continue to pursue DH technology related research, e.g., inducer development in maize and orphan species, utilization of SHGD and in vitro nursery. He is also working on grant proposals for USDA, NSF, SCRI, OREI, and Foundations and to transfer DH knowledge to commercial and public breeding programs. Salas Fernandez will characterize progenies' genotypes from optimal crosses for purposes of generating the next generation of crosses most likely to affect plant architecture and photosynthetic related traits. Differential expression studies, currently conducted to validate BR genes controlling plant architecture and leaf angle, will be completed under controlled environmental conditions. Yu will 1) Identify the gene underlying the plant height QTL (qHT7.1) and conduct functional validation of the identified gene. 2) Design and test additional sampling algorithms to build the optimized training sets for genomic prediction for genebank collections, development of inbred lines, hybrids, and environment sampling. 3) Finalize the joint genomic regression analysis for flowering time to demonstrate that reliable performance prediction can be made for untested genotypes under untested environments. Examine the potential of this framework for other adaptive agronomic traits. 4) Work with breeders to initiate empirical breeding experiments to test the optimal design for hybrid prediction in sorghum and wheat. Beavis will work with Lubberstedt and the department head in a strategic assessment of Objective 4 and the long term potential for active plant genetic improvement projects at ISU. He will continue to provide leadership in transforming plant breeding through application of operations research to design genetic improvement projects through publications, short courses and workshops.

Impacts
What was accomplished under these goals? IMPACT: We discovered genetic bases of several adaptive agronomic traits, developed mathematical models and computational algorithms to predict performance and incorporated operations research in design of efficient and effective breeding projects. The Bhattacharyya lab was the first to elucidate signaling for iron deficiency chlorosis (IDC) in plant species. The Beavis lab was the first to publish metrics and objective criteria for evaluating and designing breeding systems that translate genetic discoveries into genetic improvements. Objective 1: Several developmental and physiological traits were evaluated using ground sensors at several time-points during growth stages of soybean using five site-year replicated field trials. The sites provided distinctive growing conditions and the germplasm was sampled from a range of maturity groups, growth habits. Traits included leaf area index, mean branch angle, intercepted photosynthetically active radiation, chlorophyll content, canopy temperature, indices provided by hyperspectral sensors, plant height, maturity and yield. We recorded vegetation indices representing physiological traits at multiple reproductive stages in four SoyNAM RIL families grown at three IA locations. Quantitative trait loci (QTL) were identified. Several of the hyperspectral indices QTL were coincident with yield QTL while others represented unique index-specific QTL. Analyses of reaction norms for IDC revealed that the expression of DNA replication genes travels from shoot to root, while the expression of defense and iron uptake genes travels form root to shoot, suggesting a complex iron stress response previously undetected in any plant species. 243 Sorghum RILs derived from the cross Tx430 x P898012 were used to identify QTL that differentially control leaf angle throughout the canopy. The identified QTL for leaf angle at the top canopy do not co-localize with chromosomal segments that control the same trait on lower leaves. A QTL responsible for a large amount of the variability for high spontaneous haploid genome doubling was discovered and a project to fine map, characterize and clone this QTL was initiated. Characterization of the sorghum candidate gene that controls seed number per panicle was conducted to discover its expression profile and the effect of overexpression on transgenic rice. New alleles for resistance to pathogens (soybean cyst nematode, Fusarium virguliforme and Phytophthora sojae) as well as tolerance to IDC were discovered. A genome-wide association study identified 61 and 91 nucleotide polymorphisms (SNPs) associated with IDC field tolerance in 2014 and 2015 respectively. Many of these showed evidence of epistatic interactions with SNPs on GmChr03. Studies to find resistance alleles to Phialophora gregata, the causal agent of Brown Stem Rot (BSR), and Asian Soybean Rust (ASR) were initiated. Objective 2: We developed a proof-of-concept for integrating Genomic Estimated Breeding Values into the germplasm evaluation process involving 7.4 million plant accessions in germplasm banks (Yu et al 2016; Sukumaran, et al, 2016; Adeyanju, et al, 2016). A set of 962 sorghum accessions were genetically characterized with 340,496 SNPs. A set of 299 accessions were used as a training set for biomass yield and other related traits. Cross-validation indicated high prediction accuracy. Detailed analyses on prediction reliability provided insights into strategy optimization and suggested that a global, cost-effective strategy could be designed to assess the potential value of archived germplasm. With emergence of GBS technologies is it not unusual for Genomic Prediction methods to use realized numerator relationship matrices that are based on assays of hundreds of thousands to millions of SNP loci. Calculating these matrices is computationally time consuming and can require massive allocations of memory. We developed an algorithm that enables these computations to be conducted on laptop computers (Kim and Beavis). A comparison with TASSEL and SPAGeDi resulted in correlations of .997 with TASSEL and .05 with SPAGeDi for data sets consisting of tens of thousands of SNP loci. A comparison using a public data set consisting of 500 accessions and 10 million SNP assays revealed that numeric-i was completed in ~ 6 hours, whereas SPAGeDi and TASSEL failed to produce the 500x500 numerator relationship matrix. Gene-mining techniques are being used to identify alleles responsible for adaptation. To take advantage of this information, a novel metric for predicting performance of breeding crosses was developed and evaluated in the context of transferring seven to 20 adaptive alleles from a source to a recipient (Han et al, 2017). The metric determines the probability that progeny from a cross will produce gametes with all of the desirable alleles. In other words, the predicted cross value is a prediction about the potential of possible progeny from each cross in two future generations of gametes. A genomic prediction model for white mold resistance was applied to 19,000 G. max accessions. A study panel of 500 genetically diverse accessions were evaluated with field and greenhouse testing. They were next split into five-fold cross-validation subsets. The resulting GP model was then used to accurately predict WM-resistant accessions that were not included in the study panel. Objective 3: Representative subset selection applied to training set design enhanced prediction accuracies of hybrids. Specifically, a metric for maximizing connectedness and diversity (MaxCD) was developed for mating schemes. MaxCD utilizes realized genomic relationships and phenotypic variations and was coupled with partitioning around medoids and fast representative subset selection from cluster analysis and graphic network analysis. These training set designs outperformed random sampling in prediction accuracy across three traits evaluated for a set of 276 maize hybrids. Similarly, analyses with 2,556 wheat hybrids from an early-stage hybrid breeding system and 1,439 rice hybrids from an established hybrid breeding system demonstrated that with representative subset selection, effective genomic prediction models can be established with a training set of ~13% of the size of the whole set. Experimental assessment of implemented genetic improvement strategies requires evaluation of annual genetic gains from field trials. Evaluations need to remove non-genetic sources of variability. An expectation maximization algorithm was developed to estimate the non-genetic effects and remove them from annual estimated phenotypes. Beginning in 2010 the algorithm was applied to three levels of Syngenta soybean field trials. A retrospective analysis of genetic gains for the time period 2006 - 2016 revealed that the Syngenta soybean variety development program experienced erratic genetic gains prior to 2010 and subsequently experienced consistent genetic gains in five of ten maturity zones Objective 4. A genotype with high spontaneous haploid genome doubling (SHGD) ability has been developed and the causal allele is being introduced into exotic and elite germplasm. A comparison of DH lines developed with SHGD versus traditional colchicine treatment was more efficient in exotic population BS39. An optimization algorithm was used to find the best crossing scheme for accumulating 108 SNPs representing 55 genomic regions associated with stem diameter, seed number per panicle, panicle exertion, tiller number and leaf angle in Sorghum. Additionally, a best crossing scheme was designed for 36 SNPs representing five regions to increase photosynthetic capacity, transpiration rate under cold conditions and Fv'/Fm' under non-stress conditions. The first set of parents were crossed and progeny are under evaluation to identify F1 plants for the next generation of crossing.

Publications

  • Type: Journal Articles Status: Published Year Published: 2016 Citation: Yu, X., X. Li, T. Guo, C. Zhu, Y. Wu, S.E. Mitchell, K.L. Roozeboom, D. Wang, M.L. Wang, G.A. Pederson, T.T. Tesso, P.S. Schnable, R. Bernardo, and J. Yu. 2016. Genomic prediction contributing to a promising global strategy to turbocharge genebanks. Nature Plants 2:16150.
  • Type: Journal Articles Status: Published Year Published: 2016 Citation: Sukumaran, S., Xin Li, Xianran Li, C. Zhu, G. Bai, R. Perumal, M.R. Tuinstra, P.V.V. Prasad, S.E. Mitchell, T.T. Tesso, and J. Yu. 2016. QTL mapping for grain yield, flowering time, and stay-green traits in sorghum using genotyping-by-sequencing markers. Crop Science 56:1429-1442.
  • Type: Journal Articles Status: Published Year Published: 2016 Citation: Adeyanju, A., J. Yu, C. Little, W. Rooney, P. Klein, J.J. Burke, and T. Tesso. 2016. Sorghum recombinant inbred lines segregating for stay-green QTLs and leaf dhurrin content show differential reaction to stalk rot diseases. Crop Science 56:2895-2903.
  • Type: Journal Articles Status: Published Year Published: 2016 Citation: Jeffrey, B., Kuzhiyil, N., de Leon, N., L�bberstedt, T. (2016) Genetic and quantitative trait locus analysis for bio-oil compounds after fast pyrolysis in maize cobs. PLoS ONE 11(1): e0145845
  • Type: Journal Articles Status: Published Year Published: 2016 Citation: Lukman, R., Afifudin, A., L�bberstedt, T. (2016) Tracing the signature of Peronosclerospora maydis on maize seeds. Australasian Plant Pathology 45:73-82
  • Type: Journal Articles Status: Published Year Published: 2016 Citation: Hu, S., L�bberstedt, T., Zhao, G., Lee, M. (2016) QTL mapping of low-temperature germination ability in the maize IBM Syn4 RIL population. PLoS ONE 11(3): e0152795
  • Type: Journal Articles Status: Published Year Published: 2016 Citation: Liu, Z., Ren, J., Trampe, B., Frei, U.K., L�bberstedt, T. (2016) Doubled Haploids: From obscure phenomenon to key technology of current maize breeding programs. Plant Breeding Reviews 40: 123-166.
  • Type: Journal Articles Status: Published Year Published: 2016 Citation: Irani, S., Knapp, A., L�bberstedt, T., Frei, U.K., Askari, E. (2016) Cause for reduced seed viability of maize haploid inducing lines and counter-measure. Crop Sci. 56: 1940-1947.
  • Type: Journal Articles Status: Published Year Published: 2016 Citation: Boote, B.W., Freppon, D.J., De La Fuente, G.N., L�bberstedt, T., Nikolau, B., Smith, E.A. (2016) Haploid differentiation in maize kernels based on fluorescence imaging. Plant Breeding 135: 439-445.
  • Type: Journal Articles Status: Published Year Published: 2016 Citation: Richard, C., Osiru, D.S., Mwala, M.S., L�bberstedt, T. (2016) Heterotic grouping of a core set of Southern African and temperate maize inbred lines based on SNP markers. Maydica 61: M3.
  • Type: Journal Articles Status: Published Year Published: 2016 Citation: Abdel-Ghani, A., Hu, S., Kumar, B., Chen, Y., Blanco, M., Brenner, E., L�bberstedt, T. (2016) Phenotypically selected introgression families: a case study on plant height in maize. Plant Breeding 135: 429-438.
  • Type: Journal Articles Status: Published Year Published: 2016 Citation: Abdel-Ghani, A., Kumar, B., Sanchez, D., L�bberstedt, T. (2016) Paper roll culture and assessment of root parameters. Bio-Protocols 6:18.
  • Type: Journal Articles Status: Published Year Published: 2016 Citation: Smelser, A., Blanco, M., L�bberstedt, T., Frei, U.K., Gardner, C. (2016) Germplasm Enhancement of Maize: A look into haploid induction of adapted tropical sources. Plant Breeding 135: 593-597.
  • Type: Journal Articles Status: Published Year Published: 2016 Citation: Do Canto, J., Studer, B., L�bberstedt, T. (2016) Self-fertility: key for hybrid breeding of allogamous grasses. Theor. Appl. Genet. 126: 1815-1829.
  • Type: Journal Articles Status: Published Year Published: 2016 Citation: Begheyn, R., L�bberstedt, T., Studer, B. (2016) Haploid and doubled haploid techniques in perennial ryegrass (Lolium perenne L.) to advance research and breeding. Agronomie 6: 60.
  • Type: Journal Articles Status: Published Year Published: 2016 Citation: Cianzio, S.R. ,P. Lundeen, M. K. Bhattacharyya, S. Swaminathan, G. Gebhart, and N. Rivera-Velez. 2016. Registration of AR11SDS Soybean Germplasm Resistant to Sudden Death Syndrome, Soybean Cyst Nematode and with adequate Iron Deficiency Chlorosis. Journal of Plant Registrations 10: 177-188 DOI:10.3198?jpr2015.02.0010crg.
  • Type: Journal Articles Status: Published Year Published: 2016 Citation: Samarah, N., R. Mullen, S.R. Cianzio, R. Gladon, and S. Goggi. 2016. Ethylene evolution from soybean seeds podded and depodded related to seed desiccation tolerance during maturation. Seed Science and Technology 44: 53-63.
  • Type: Journal Articles Status: Published Year Published: 2016 Citation: Min L., S. Li, S. Swaminathan, B.B. Sahu, L.F. Leandro, A.J. Cardinal, M.K Bhattacharyya, Q. Song, D.R. Walker, and S.R. Cianzio. 2016. Identification to a soybean rust resistance gene in PI 567104B. Theor. Applied Genetics 129: 863-877.
  • Type: Journal Articles Status: Published Year Published: 2016 Citation: McCabe Ch., A. King, L. F. Leandro, S.R. Cianzio and M.A. Graham. 2016. Identifying New Sources of resistance to Brown Stem Rot in Soybean. Crop Sci. 56: 2287-2296.
  • Type: Journal Articles Status: Published Year Published: 2016 Citation: Abesekara, N.S., R. L. Mathiesen, S.R. Cianzio, M.K. Bhattacharyya, and A.E. Robertson. 2016. Novel sources of partial resistance against Phtyophthora sojae in soybean PI 399067. Crop Sci. 56: 1-14.
  • Type: Theses/Dissertations Status: Published Year Published: 2016 Citation: Howard, Reka. Evaluation of parametric and nonparametric statistical methods in genomic prediction. Ph.D. Dissertation. Iowa State University.
  • Type: Theses/Dissertations Status: Published Year Published: 2016 Citation: Doubler, Tracy William. The use of genetic information to predict relative maturity in soybean. Ph.D. Dissertation. Iowa State University.
  • Type: Journal Articles Status: Published Year Published: 2016 Citation: Zhao J., Mantilla Perez M.B., Hu J., and Salas Fernandez M.G. 2016. Genome-wide association study for nine plant architecture traits in Sorghum bicolor. The Plant Genome 9 (2):1-14. doi: 10.3835/plantgenome2015.06.0044


Progress 01/01/15 to 09/30/15

Outputs
Target Audience:Plant geneticists, statistical geneticists, plant breeders, systems engineers, students, teachers, and researchers of both public and private sectors. Changes/Problems: Nothing Reported What opportunities for training and professional development has the project provided?Opportunities for training and professional development have been provided to various graduate students, postdocs, undergraduate students, and visiting scholars. Importantly, interdisciplinary training has been provided in collaboration with faculty in electrical engineering, plant biology, bioinformatics and biostatistics. How have the results been disseminated to communities of interest?Peer-reviewed journal articles, and invited meeting presentations (described in detail under products). What do you plan to do during the next reporting period to accomplish the goals?In the next reporting period, differential expression studies will be completed under controlled environment conditions, to characterize and validate the sorghum BR genes associated with variation in leaf angle. Genotypic data will be collected for the F2 populations evaluated under field conditions in summer 2015 and a subset of selected lines will be advanced to F3 and field phenotyped again for leaf angle and seed number per panicle during summer 2016. Transgenic rice lines over-expressing the sorghum candidate gene that controls seed number per panicle will be generated to validate our results as part of a complementation study, with the ultimate goal to clone the gene and discover the functional polymorphism/s. In soybean, field evaluation and data analyses for various traits will be conducted in the above mentioned mapping populations. We will evaluate the set of over 1,135 Arabidopsis ecotypes to a set of growing conditions. We will develop maize inducers with higher induction rate, utilize DH process in conjunction with spontaneous doubling for population improvement in maize, establish automated haploid-diploid selection procedures and initiate research on in vitro nurseries and additional DH orphan crops. The PD is on sabbatical for purposes of learning linear programming and evolutionary dynamics of complex systems models. All (Co-) PDs will emphasize publication and dissemination of their research.

Impacts
What was accomplished under these goals? In the reporting period representing the first nine months of this 5-year project, progress has been made and published regarding discovery of alleles affecting adaptive agronomic traits. Predictive models for adaptive agronomic traits have been developed and evaluated in experimental cases studies. Important foundations have been laid towards optimal breeding designs, both in terms of pioneering work to frame plant breeding research as operation research challenge, which is commonly used in engineering, and with regard to method development for accelerating plant breeding, specifically in relation to doubled haploid technology. Research has been initiated to implement accelerated genetic improvement strategies in sorghum. Objective 1) discovery of alleles associated with adaptive agronomic traits as represented by reaction norms. Flowering time and plant height are two adaptive agronomic traits in sorghum, both for adaption to environment and agronomic harvesting practice. Alleles associated with plant height have been delineated and a pair of loci in repulsion linkage on sorghum chromosome 7 identified. The genetic contribution of these two loci to sorghum plant height provide a detailed example of heterosis. A reaction norm study of flowering time is being conducted together with genetic mapping of crop growth parameters and systems biology analysis through RNA-sequencing data. Novel genomic regions associated with variation in number of tillers, stem circumference, number of internodes, leaf angle and seed number were identified in sorghum. Several F2 populations were phenotyped in Ames, IA, during summer 2015 to fine map two genomic regions on chromosome 7 and 6 controlling leaf angle and seed number, respectively. We are validating our discoveries of brassinosteroid (BR) genes that control plant architecture traits by differential expression analysis and their response to BR inhibitors. In soybean, we conducted a genome wide association study (GWAS) data analyses on a dozen agronomic traits evaluated with 5600 Recombinant Inbred Lines derived from 40 crosses involving a reference parent, IA3023. All parental lines were adapted to Maturity Zone 3 so that agronomic traits would be evaluated under adapted conditions. We discovered a handful of genomic regions that were associated with variability in traits across all families. Estimated genetic effects included favorable alleles from both parents. Moreover, we performed seed increase of five recombinant inbred line (RIL) populations of the SoyNAM population, and conducted limited phenotypic assessment of the RILs for physiological traits of interest. In 2016, we will perform genetic mapping of predictive yield traits at critical temporal points to identify useful alleles associated with yield in contrasting genetic background and growth habits. An effort has been initiated to identify adaptation genes to improve environmental adaption of crop plants to adverse weather conditions that may arise from global warming. Seeds of a set of 1,135 Arabidopsis thaliana ecotypes have been collected for evaluating their growth responses to a range of growing conditions. Genomes of these ecotypes have been sequenced. We are in the process of developing miniature growth chambers for evaluating the Arabidopsis ecotypes. The first chamber is being evaluated for growing Arabidopsis plants. Finally, a software to capture root phenotypes was developed and employed to identify polymorphisms in candidate genes and across the genome controlling root development, an important adaptive trait under abiotic stress conditions, using a subset of 384 inbred lines of the Ames panel in maize for GWAS. Objective 2) development of predictive models for adaptive agronomic traits. In addition to generating genomic estimated breeding values (GEBV) and checking prediction accuracy, recent research started looking into variance associated with each GEBV (e.g., prediction error variance (PEV), coefficient of determination (CD), predictability, and reliability). Our research team has examined the effect of different predictive models on variance of GEBV for adaptive agronomic traits. Our findings indicate the capacity of different predictive models varies when the relationship between prediction set and training set fluctuates. We employed Response Surface Methods to determine the sets of conditions under which differences among accuracies of estimated breeding values are maximized. At least seven combinations of conditions (sample size, sample structure, genomic organization, number of sampled markers, genetic architecture of the trait, heritability) were investigated. The maximum differences among prediction accuracies for parametric and non-parametric methods are obtained when at least 600 RIL or DH progeny are evaluated, heritability is 1 and genetic architecture is completely epistatic. In soybean, several important agronomic and physiological traits were collected using ground sensors at several time-points during the vegetative and reproductive growth stages. Analysis will be performed to help determine the critical growth stages and traits, and to develop predictive models for seed yield. White mold (Sclerotinia sclerotiorum) (WM) is a fungal disease that causes yield losses in soybean. We tested several methods to generate GEBV and tested the prediction accuracy from field and greenhouse tests. In maize, the 384 inbred line subset of the Ames panel was used as training population to successfully predict inbred line root phenotypes in the complete Ames panel consisting of 2800 genotyped inbred lines. Objective 3) development of breeding designs that assure optimal genetic improvement strategies. We have framed the challenge of genetic improvement as an Operation Research challenge. Using this framework we addressed several aspects of genetic improvement including optimization of trait introgression, gene stacking, while minimizing winter nursery and field plot costs. In maize, novel procedures for optimizing doubled haploid (DH) line production have been established, both for haploid - diploid discrimination (based on weight and fluorescence), haploid induction (novel inducers), and genome doubling (identification of genotypes with high spontaneous genome doubling potential). Research on establishing DH procedures in DH orphan species has been initiated. Objective 4) implementation of accelerated genetic improvement strategies for adaptive agronomic traits. We developed a template to rapidly clearly define breeding objectives and translate these into models that can be optimized. The template is being used for a breeding project to increase biomass in sorghum.

Publications

  • Type: Theses/Dissertations Status: Published Year Published: 2015 Citation: Gibson, D. 2015. Genetic and Physiologic Analyses of Soybean Grain Yields in Water Limited Environments. MS Thesis, Graduate College, Iowa State University. Ames, Iowa.
  • Type: Journal Articles Status: Published Year Published: 2015 Citation: Abdel-Ghani, A.H., Kumar, B.T.N., Jansen, C., Gonzalez-Portilla, P., Reyes-Matamoros, J., San Martin, J.P., Lee, M., L�bberstedt, T. (2015) Association analysis of genes involved in maize root development with seedling and agronomic traits under contrasting nitrogen levels. Plant Molecular Biology 88:133-147.
  • Type: Journal Articles Status: Published Year Published: 2015 Citation: Adeyanju, A., C. Little, J. Yu, and T. Tesso. 2015. Genome-wide association study on resistance to stalk rot diseases in grain sorghum. G3: Genes, Genomes, Genetics 5:1165-1175.
  • Type: Journal Articles Status: Published Year Published: 2015 Citation: Chen, Y., Pan, G., L�bberstedt, T. (2015) Haploid strategies for functional validation of plant genes. Trends in Biotechnology 33:611-620.
  • Type: Journal Articles Status: Published Year Published: 2015 Citation: Li, X., M.J. Scanlon, and J. Yu. 2015. Evolutionary patterns of DNA base composition and correlation to polymorphisms in DNA repair systems. Nucleic Acids Research 43:3614-3625.
  • Type: Journal Articles Status: Published Year Published: 2015 Citation: Li, Xin, Xianran Li, E. Fridman, T.T. Tesso, and J. Yu. 2015. Dissecting repulsion linkage in the drawfing gene Dw3 region for sorghum plant height provides insights into heterosis. PNAS 112:11823-11828.
  • Type: Journal Articles Status: Published Year Published: 2015 Citation: Lipka, A.E., C.B. Kandianis, M.E. Hudson, J. Yu, J. Drnevich, P.J. Bradbury, and M.A. Gore. 2015. From association to prediction: statistical methods for the dissection and selection of complex traits in plants. Current Opinion in Plant Biology 24:110-118.
  • Type: Journal Articles Status: Published Year Published: 2015 Citation: Pace, J., Gardner, C., Romay, C., Ganapathsybrumanian, B., L�bberstedt, T. (2015) Genome-wide association analysis of seedling root development in maize. BMC Genomics 16:47.
  • Type: Book Chapters Status: Published Year Published: 2015 Citation: Wu, Y., Frei, U.K., Liu, H., De La Fuente, G., Huang, K., Wei, Y., L�bberstedt, T. (2015) Combining genomic selection and doubled haploid technology increases efficiency of maize breeding. In: Recent Developments in Biotechnology Vol. 2: Plant Biotechnology, Studium Press, J.N. Govil (ed.) 215-237.
  • Type: Journal Articles Status: Published Year Published: 2015 Citation: Xu, Z., J Yu, RJ Kohel, RG Percy, WD Beavis, D Main, ZY John. 2015. Distribution and evolution of cotton fiber development genes in the fiberless Gossypium raimondii genome. Genomics 106:61-69.
  • Type: Conference Papers and Presentations Status: Other Year Published: 2015 Citation: Beavis, WD (2015) Transforming Breeding to an Engineering Discipline. Animal Science Seminar Series. 17 February. Ames, IA.
  • Type: Conference Papers and Presentations Status: Other Year Published: 2015 Citation: Beavis, WD (2015) Optimal Use of Genomic Prediction in Plant Breeding. Soybean Breeders and Physiologists Workshop. St Louis. 16 February. St. Louis, MO.
  • Type: Conference Papers and Presentations Status: Other Year Published: 2015 Citation: Beavis, WD (2015) Transforming Breeding into an Engineering Discipline. Plant Science Seminar Series. 20 April. Columbia, MO.
  • Type: Conference Papers and Presentations Status: Other Year Published: 2015 Citation: Beavis, WD (2015) Transforming Breeding into an Engineering Discipline. Graduate Plant Science Seminar Series. 22 October. Athens, GA.
  • Type: Conference Papers and Presentations Status: Other Year Published: 2015 Citation: Kim, Bongsong and WD Beavis (2015) Trait-associated markers increase the prediction accuracy in ridge regression best linear unbiased prediction. Soybean Breeders' Workshop, St. Louis, MO.
  • Type: Conference Papers and Presentations Status: Other Year Published: 2015 Citation: Salas Fernandez M.G. Genomics, physiology and high-throughput phenotyping strategies to improve sorghum for biofuel production. Early Career Awardee presentation. National Association of Plant Breeders Annual Meeting, Pullman, WA. July 29, 2015.
  • Type: Journal Articles Status: Published Year Published: 2015 Citation: Pace, J., Yu, X., L�bberstedt, T. (2015) Genomic prediction of seedling root length in maize (Zea mays L.): A model approach. Plant J. 83:903-912.
  • Type: Journal Articles Status: Published Year Published: 2015 Citation: Thompson, A.M., J. Yu, M.C.P. Timmermans, P. Schnable, J.C. Crants, M.J. Scanlon, G.J. Muehlbauer. 2015. Diversity of maize shoot apical meristem architecture and its relationship to plant morphology. G3: Genes, Genomes, Genetics 5:819-827.