Source: WASHINGTON STATE UNIVERSITY submitted to
NEXT GENERATION VARIETY DEVELOPMENT AND EDUCATION FOR GRAINS, APPLES, ALTERNATIVE CROPS, AND COOL SEASON LEGUMES
Sponsoring Institution
National Institute of Food and Agriculture
Project Status
EXTENDED
Funding Source
Reporting Frequency
Annual
Accession No.
1008828
Grant No.
2016-68004-24770
Project No.
WNP08532
Proposal No.
2015-08792
Multistate No.
(N/A)
Program Code
A5161
Project Start Date
Feb 15, 2016
Project End Date
Feb 14, 2021
Grant Year
2017
Project Director
Carter, A. H.
Recipient Organization
WASHINGTON STATE UNIVERSITY
240 FRENCH ADMINISTRATION BLDG
PULLMAN,WA 99164-0001
Performing Department
Crop & Soil Sciences
Non Technical Summary
Plant breeding education and applied public variety development efforts are lagging behind promising advances in high-throughput phenotyping, inexpensive and high-density genotyping, and novel statistical genetics methods. This project balances interdisciplinary plant breeding education and professional skill training with research and development efforts necessary to implement next generation phenotyping and statistical genetics techniques in public variety development programs. The eight commercial variety development programs included in this project include an eclectic, but high-impact combination of apple, cool season legumes, camelina, quinoa, barley, spring wheat and winter wheat- synergistically brought together by a common need for innovation in analysis of genotype data, high-throughput phenotyping, and statistical genetic models. Graduate students trained in each program will conduct research to implement next generation breeding technologies in public variety development programs while addressing crop-specific traits of regional, national, and international importance. The long-term goal of this project is to develop platforms that will integrate phenomics and genomics into applied plant breeding programs across a diversity of crops and integrate students into plant breeding programs to ensure they are trained to be successful in future careers.The objectives of this research are:Objective 1)Establish methodologies to integrate recent advances in genotyping, genomic selection, and high-throughput phenotyping into plant breeding programs.Objective 2)Apply novel methodology to select and release cultivars which are regionally adapted, able to resist/tolerate biotic and abiotic stresses, and have enhanced nutritional value, leading to more diverse and resilient production systems.Objective 3)Train a cohort of plant breeders and aspiring young scientists with the skills necessary to be successful in future plant breeding and bioscience careers.A specific aim of this proposal is to develop and use innovative technologies and methods to support the release of new cultivars of several major crops grown in Washington State and the Pacific Northwest. Deliverable products will include well-trained plant breeders and research interns, advanced knowledge in application of high-throughput phenotyping technologies, improved quantitative genetic methods, and cultivars and breeding germplasm with improved resilience to biotic and abiotic stresses, enhanced nutritional value, and ultimately enhanced food security in variable environments.
Animal Health Component
0%
Research Effort Categories
Basic
50%
Applied
50%
Developmental
(N/A)
Classification

Knowledge Area (KA)Subject of Investigation (SOI)Field of Science (FOS)Percent
2011110108110%
2011414108110%
2011550108110%
2011543108110%
2011899108110%
2012299108110%
2017210108120%
2017410108020%
Goals / Objectives
Goals/ObjectivesThe long-term goal of this project is to develop platforms that will integrate phenomics and genomics into applied plant breeding programs across a diversity of crops and integrate students into plant breeding programs to ensure they are trained to be successful in future careers.The objectives of this research are:Objective 1)Establish methodologies to integrate recent advances in genotyping, genomic selection, and high-throughput phenotyping into plant breeding programs.Objective 2)Apply novel methodology to select and release cultivars which are regionally adapted, able to resist/tolerate biotic and abiotic stresses, and have enhanced nutritional value, leading to more diverse and resilient production systems.Objective 3)Train a cohort of plant breeders and aspiring young scientists with the skills necessary to be successful in future plant breeding and bioscience careers.
Project Methods
Obj.1: High-Throughput Phenotyping: Some of the potential sensors currently owned by the investigators include digital RGB cameras (Sony NEX-5, Canon SX-260), multiband cameras (XNiteCanonNDVI, LDP LLC, NJ; MicaSense RedEdge™, Mica-Sense Inc.), thermal cameras (FLIR A655sc & Tau 2 640, FLIR), 3D imaging system (Figure 4; SR4500, Mesa Imaging), 2D laser scanner (SICK LMS511-20100, Olympus Controls), and visible-near infrared (VIS-NIR) spectroradiometer (350-2,500 nm, SVC HR-1024i, Spectra Vista Corporation, NY). The data processing software(s), associated toolboxes, and statistical software such as Matlab and SAS will be used for data processing and analysis. In addition, macros will be developed using Image J (Schneider et al., 2012), a public domain image processing program. Genotyping: The genotyping will be done in the USDA-ARS Western Small Grains Genotyping (WSGGL) lab located at WSU. Total genomic DNA is extracted from plant tissue and purified in an Oktopure workstation with a magnetic bead DNA extraction kits from LGC genomics, Teddington, UK. GBS libraries will be constructed according to the protocol by Mascher et al. (2013). Genomic DNA will be cleaved with both PstI and MspI to enrich for coding DNA. Cleaved products will have adapters ligated to each end. Products will be size selected at 250bp. Template ion sphere particles are enriched on Ion OneTouchTM ES following the protocol from Ion PITM Template OT2 200 Kit V2 User Guide (Thermo Fisher Scientific, Waltham, MA, USA). Libraries with optimum percent of template ion spheres will be sequenced on an Ion ProtonTM Sequencer following protocol from Ion PITM Sequencing 200 Kit V3 User Guide (Thermo Fisher Scientific, Waltham, MA, USA). Samples are loaded on an Ion PITM Chip v2 for sequencing.Genomic Selection: We will develop a new GS method incorporating the SUPER method (sBLUP) to improve accuracy. Pseudo QTNs are estimated by using the SUPER algorithm and used to calculate kinship in the reference and inference populations (Wang et al., 2014). Real phenotype and genotype data from this project will be employed to investigate the prediction accuracy of sBLUP compared with other current methods used to develop GS models through cross validation. A proportion of individuals will be masked for their phenotypes as inference. Only the genotypes are used for genomic prediction. The phenotypes of inference are only used to evaluate the correlation between observed and predicted. Simulated phenotypes will be created from real genotypes to investigate the impact of genetic architecture, such as number of QTNs, QTN effects, and heritability.Obj.2:Apple: Three replicates of approximately 600 trees were planted at the WSU Columbia View orchard in spring 2015. In 2016, three shoots per plant will be challenged with the fire blight bacteria. In 2017, up to 10 shoots will be challenged. Remote sensing will be used to determine if this can be used for a high-throughput phenotyping screen for fire blight. Additionally, results will undergo a GWAS analysis to identify and predict fire blight resistance-influencing loci.Barley: We genotyped 590 advanced barley breeding lines using a Sequenom Assay in the WSGGL. GBS marker data will be added to this population as described above. The GAPIT software and improvements described above will be used for association analysis (Lipka et al. 2012 ; Zhang et al. 2010). GS models will also be developed using developed phenotypic and genotypic models to begin selection of lines with improved malting quality.Quinoa: We will conduct bi-parental genomic analysis for saponin content. We have two distinct bi-parental populations derived from Peruvian altiplano × Chilean lowland germplasm, each with high and low saponin content parental material. Population 1 consists of 96 F8 RILs, and population 2 consists of 240 F4 RILs. GBS will be used for genotyping; linkage maps will be established using Joinmap v 4.0, and QTL analysis will be conducted to identify SNP markers associated with saponin content. These populations will also be phenotyped with high-throughput sensors to identify spectral reflectance traits correlated with heat tolerance and yield potential.Camelina: We will examine the associations between oil content, seed size and stand establishment. Two RIL populations (~300 lines each) have been developed and phenotyping begun. Phenotypic analysis of the traits will include CSR to estimate canopy development. At least one of the two RIL populations, selected for the broadest range of segregating phenotypes, will be saturated with SNP markers using GBS to generate a genetic map and conduct QTL analysis. A population for GWAS will be established that includes selected lines from the two RIL populations, advanced breeding lines and other lines from our germplasm collection.Lentils: We will use two approaches to identify QTL and candidate genes associated with resistance to ARR in lentils. Two existing intraspecific and an interspecific (L. culinaris × L. ervoides) biparental populations will be used for QTL identification. The ICARDA Wild Lens Population and USDA lentil single-plant derived core collection will be used for the GWAS study. Phenotyping will be conducted in field disease nurseries for two years and in pure culture. Genotyping will be accomplished using GBS and GS models will be developed.Spring and Winter Wheat: Each wheat breeding program has identified ~1,500 breeding lines to be analyzed in yield plots grown at Lind and Pullman, WA. Plots are replicated and analyzed using an alpha lattice design, with multiple checks throughout the plots, and change on a yearly basis based on selection. Breeding lines will be genotyped each year by GBS. Agronomic data will be collected on plant emergence, winter survival, heading date, senescence date, plant height, harvest yield, test weight, and protein content. CSR data will be collected throughout the growing season at emergence, tillering, stem elongation, flag leaf emergence, heading, anthesis, and grain fill, as previously described using high-throughput methods. Genomic selection models will be developed and validated to improve efficiency of selection.Obj.3:Week-long Integrative Course: As part of this proposal, a one-week long intensive course on recent developments in quantitative genetics theory and high-throughput phenotyping, with an emphasis on plant breeding applications will be provided. This program will be open to the public and the students associated with this grant will be required to attend. The course will enable students to see how these technologies are implemented in a variety of crops including small grains, legumes, tree fruit, potatoes, grapes, and many others. We expect there to be similar interest both in the public and private sector to be trained on the integration of all three areas and plan to offer the course multiple times.Summer course 'Field Applied Plant Breeding': To address a lack of experience about field operations in plant breeding, we have developed a new course entitled "Field Applied Plant Breeding". Students will be required to spend four hours per week during these months with a different breeding programs and related disciplines. Breeders will plan their visits so that students can see how they do selection within their program, and discussions will be based around selection theory, experimental design, screening for abiotic and biotic stresses, and application of technology. It will deliver hands-on training and application of multiple skills which are often not taught in the classroom.Undergraduate Mentoring: A major focus of this proposal is the education of young people to increase the pool of talented, creative and diverse, scientists adopting the profession of plant breeding both in the private and public sector. Graduate students who are funded by this proposal will act as primary mentors for one student per year.

Progress 02/15/19 to 02/14/20

Outputs
Target Audience:Research efforts have been disemminated to the scientific community, other researchers in genomics and phenomics, stakeholders in the respective commodity crops, the general public, and undergraduate and graduate students. Changes/Problems: Nothing Reported What opportunities for training and professional development has the project provided?Opportunities for training have existed through monthly meeting of project personnel. Graduate students also meet regularly to discuss project, share ideas, and assist each other with data analysis questions. Students (and PI's) have traveled to many scientific conference to present research, many of which have been invited presentations. The students have also been regularly interacting with stakeholders and describing to them their research and how it will impact their cropping systems. This interaction has taught the student many practical communication skills and has broadened their network of individuals. Graduate students have learned how to mentor by working with undergraduate research interns. Collaboration across research disciplines is continuing to expand, and the graduate students funded on this grant are now working with many groups outside this grant funding. How have the results been disseminated to communities of interest?Results have been disseminated through scientific peer-reviewed journal articles, extension articles, conference proceedings, poster presentations, invited lectures, stakeholder meetings, grower field days, and popular press (newspaper, magazine, radio, podcasts, etc). Every avenue available to the researchers has been explored to disseminate information to scientific audiences, the general public, and relevant stakeholders. What do you plan to do during the next reporting period to accomplish the goals?Teaching: Four courses were developed 2016 and have been delivered annually since. Graduate students have taken these courses and new cohorts of students are enrolling in them. Students will continue to be provided opportunities for training and professional development through travel to different meetings where they will present their research and interact with other scientists. Summer support is still available in many programs to ensure undergraduate students work with the graduate students and are exposed to research. Students have met with both Dr. Sankaran and Dr. Zhang on a regular basis to receive training on high-throughput phenotyping and statistical genomics. Students and Post-Docs meet monthly to present project progress and share lessons learned. Students collaborate together when their projects require similar phenotyping methods although crops may be different. Graduate students were able to supervise and work with undergraduate students in various aspects of data collection over the summer and through the academic year. This provided valuable experience in supervision and communication skills, as well as detailed planning and preparation. We are discussing with the WA state Ag Teachers Association to identify ways to train high school students in some of this technology, with the hope that it can be used as a tool to recruit more students with diverse interests into agricultural fields. Protocols: In the areas of phenotyping and genotyping, continued protocol development will take place. Methods have and will be developed to enhance and improve the efficiency of high-throughput phenotyping. Standard protocols have been implemented across crops for data collection, and a single algorithm developed to extract data from images. These protocols are standardized for the different information desired to be collected such as NDVI images and values, ground cover measurements, winter survival, seed size, plant vigor, etc. Refinement of algorithms will continue in the upcoming year. As more data is collected and ground truth measurements are recorded, the algorithms can be updated to get a better idea of how predictive they will be. Groups are working together to collect data on a given trait using the same protocol. We have expanded the number of cameras and sensors we are using, as there is a strong time-demand on these instruments. Genotyping platforms have improved allowing efficient genotyping of panels as well as breeding program material. The results are being analyzed and additional populations and breeding program material has been selected for further testing. Methods in statistical genomics are also being further developed. cBLUP and sBLUP have been implemented in the analysis of many of the projects we are working on in order to enhance the analysis of results. GAPIT version 2 is being implemented by students when doing GWAS. Plant Breeding: For most plant breeding programs, 2020 will be spent finalizing research and data analysis and publishing manuscripts. As with all breeding programs, every year lines are evaluated for release and 2020 will continue to see lines released from different programs. The difference is now genomics and phenomics have become a regular part of the breeding program and selection process. Work is expanding phenotypic selection into all aspects of breeding, including biotic and abiotic stress, end-use quality, post-harvest physiology, and agronomics. Programs are also validating genomic selection protocols as they now have sufficient years of selection to do so. No matter what level of genomic or phenomic selection the plant breeding programs are using, what they do in 2020 with the incorporation of these tools has made each program more efficient and effective.

Impacts
What was accomplished under these goals? Objective 1) Phenotype: The phenomics team continue to work with plant breeding teams. In ongoing work, proximal and remote sensing data were processed to extract apple tree architectural features. The traits extracted include box-counting fractal dimension (DBs), middle branch angle, number of branches, trunk basal diameter, and tree row volume. The results from proximal sensing indicated that there was a significant difference in DBs between two training systems, and correlations between DBs with tree height and total yield per unit area. Moreover, correlations between average or total tree row volume and ground reference data, such as trunk area, total fruit yield per tree, were significant (P < 0.05). In addition to this work, two other research manuscripts have been submitted for publication, which provide protocols for estimating plant height and flower detection. Genotype: Our program provided training and consultation to graduate students and postdoc in the project for gene mapping and prediction of breeding values using genetic markers. The research has generated multiple publications, including the new method of genomic selection, compression BLUP. Objective 2) WSU Quinoa Breeding: 2019 was focused on the completion of research analysis and publication of manuscripts, of which we published 4 on the use of phenomics in Quinoa breeding and cultivar selection. Many Quinoa breeding lines were evaluated under field conditions, and the best selections harvested and advanced to 2020 field trials. WSU Camelina Breeding: We performed a final evaluation of a series of advanced lines that we have developed by recurrent selection for large seed size, high yield and high oil content. The larger seed (1.6-1.7 mg/seed) is roughly 50% larger than most camelina varieties and equivalent or larger than the largest seeded variety in the USDA collection. Two of the lines in our large seed selection program had higher yields than all the check varieties in our trials this year and had among the highest oil contents (40%). We are currently preparing a germplasm release for the two large seeded lines. We also evaluated advanced lines carrying a mutation for low erucic acid content. Selections have been made for agronomic performance, oil content and low erucic acid content. Several high yielding lines are undergoing oil analysis and we expect to release a line for the food industry. We also evaluated the suitability of GBS markers for mapping QTL in two large biparental camelina RIL populations and an AM panel. WSU Apple Breeding: With phenotyping completed previously for resistance to fire blight (Erwinia amylovora) of individuals in the RosBREED apple germplasm reference set (genotyped previously), we spent much of 2019 analyzing data. Multiple QTL associated with resistance/susceptibility to fire blight in apple were detected via analysis of phenotypic data with FlexQTLTM software. Characterization of SNP haplotypes within detected QTL regions has begun and will continue in 2020. In 2019 the cultivar Cosmic Crisp became commercially available. Although not directly selected as part of this grant, it was confirmed to have fire blight resistance using associated phenomics protocols. USDS Pea and Lentil Breeding: The graduate student associated with this project successfully defended in November 2019 and received a job offer at the Plant Materials Center. Her project associated with arsenic uptake in lentils, finding either QTL that are associated with high As or low As concentration in the seeds and vegetative tissues. She completed the development of a RIL population, did the phenotyping of it in +As and -As conditions, quantified As in seeds, and extracted DNA and made GBS libraries. A post-doc has been hired to complete the genotyping and finalize the analysis of the phenotypic data. USDA Western Regional Genotyping Laboratory: We intend to identify several linkage-blocks in the wheat genome associated with adaptation of wheat to Washington and the Pacific Northwest. Marker-assisted selection for multiple adaptation-associated linkage-blocks will allow breeding programs to restore adaptedness quickly. This method will therefore allow breeding programs to more easily incorporate the wealth of genetic diversity from landraces into new wheat varieties. In total, 753 historical varieties (236 spring and 517 winter habit) were genotyped with over 24,000 markers used for analysis. To validate the putative adaptation-associated linkage-blocks, we are developing populations with different combination of adaptation-associated linkage-blocks by crossing adapted spring wheat varieties (Diva, Glee, Kelse, Louise, and Whit) with unadapted landraces. USDA Wheat Breeding: The DNAM population is a nested-association mapping panel comprising 420 lines used to understand the D-genome, how it interacts with the other wheat genomes, and discover new disease resistance and drought tolerance genes. Phenomics has been used for data collection in a three-year drought study conducted at Pullman WA and Ft. Collins CO. CROPSCAN data was collected in Colorado, and NIR images of each plot were taken in Pullman with the help of the Sankaran lab. In addition to those images, drones were used in all three of the 2019 fields for spectral image collection. RGB cameras were used to rate emergence in all fields, and these photos were analyzed using Canopeo software. Multispeq devices were used in Pullman, WA and Fort Collins, CO to collect photosynthesis data during flowering. A subset of the DNAM has been evaluated for temperature-induced adult plant resistance to stripe rust. The DNAM was also screened for Fusarium Crown Rot and Cereal Cyst Nematode resistance in the greenhouse. This screening has identified multiple families with resistance to these stresses. WSU Winter Wheat Breeding: Two graduate students completed their work in 2019. One published two manuscript on the use of carbon-isotope discrimination in wheat and how it can be useful for selection of lines with drought tolerance. The other student published one manuscript on the use of NIR to predict straw decomposition, useful for growers in no-till production systems. A post-doc has also been working on using genomic selection in the breeding program and evaluating its effectiveness in applied selection. Five manuscript were published on the sue of genomic selection for various different traits, including using phenomics data in conjunction with genomic selection. WSU Spring Wheat Breeding: Progress is being made on using genomic selection in the breeding program. Large projects on identification of Hessian fly resistance were phenotyped and genotyped, and genes mapped. This better understanding of Hessian fly resistance has allows us to develop cultivars with very good fly resistance, and looking forward allows us to diversify the genes used in spring wheat resistance. Objective 3) Through leveraged support, 10 graduate students were funded in 2019, along with 6 undergraduate research interns, 2 post-doctoral researchers, and 1 research associate. An additional 3 post-docs and 5 graduate students have completed work and graduated. An additional 6 graduate students working on genomics and phenomics who are not directly funded by this grant assist with various components of research. Students are from the Horticulture, Crop Science, Plant Pathology, and Molecular Plant Sciences degree programs. Each graduate student has had the opportunity to mentor an undergraduate researcher. The undergraduates also get the opportunity to present their research at WSU. We teach four courses at WSU directly related to this grant. Two on using technology in plant breeding efforts, one on genomic selection, and one on phenotyping and sensors. Opportunities for graduate student training and professional development continues.

Publications

  • Type: Journal Articles Status: Published Year Published: 2019 Citation: Hinojosa, L., M. Sanad, D. Jarvis, P. Steel, K. Murphy, A. Smertenko (2019). Impact of heat and drought stress on peroxisome proliferation in quinoa. The Plant Journal 99: 1144-1158. doi.org/10.1111/tpj.14411
  • Type: Journal Articles Status: Published Year Published: 2019 Citation: Hinojosa, L., N. Kumar, K.S. Gill, K. Murphy (2019). Spectral reflectance indices and physiological parameters in quinoa under contrasting irrigation regimes. Crop Science 59: 1927-1944. doi: 10.2135/cropsci2018.11.0711
  • Type: Journal Articles Status: Published Year Published: 2019 Citation: Sankaran, S., C. Zuniga Espinoza, L. Hinojosa, X. Ma, K. Murphy (2019). High-throughput phenotyping to assess irrigation treatment effects in quinoa. Agrosystems, Geosciences & Environment 2: 180063. doi:10.2134/age2018.12.0063
  • Type: Journal Articles Status: Published Year Published: 2019 Citation: Hinojosa, L., J. Matanguihan, K. Murphy (2019). Effect of high temperature on pollen morphology, plant growth and seed yield in quinoa. Journal of Agronomy and Crop Science 205: 33-45.
  • Type: Journal Articles Status: Published Year Published: 2019 Citation: Quir�s, J.J., Zhang, C., Smitchger, J., McGee, R.J., and Sankaran, S. 2019. Phenotyping of plant biomass and performance traits using remote sensing techniques in pea (Pisum sativum, L). Sensors, 19 (9): 2031.
  • Type: Journal Articles Status: Published Year Published: 2019 Citation: Jarolmasjed, S., Sankaran, S., Marzougui, A., Kostick, S., Si, Y., Quir�s, J.J., and Evans, K. 2019. High-Throughput Phenotyping of fire blight disease symptoms using sensing techniques in apple. Frontiers in Plant Science, doi: 10.3389/fpls.2019.00576.
  • Type: Journal Articles Status: Published Year Published: 2019 Citation: Meng Huang, Xiaolei Liu, Yao Zhou, Ryan M. Summers and Zhiwu Zhang*. BLINK: A Package for Next Level of Genome Wide Association Studies with Both Individuals and Markers in the Millions. GigaScience, 2019. https://doi.org/10.1093/gigascience/giy154.
  • Type: Journal Articles Status: Published Year Published: 2018 Citation: Chunpeng Chen and Zhiwu Zhang*. iPat: Intelligent Prediction and Association Tool for Genomic Research. Bioinformatics, 2018, bty015, https://doi.org/10.1093/bioinformatics/bty015.
  • Type: Journal Articles Status: Published Year Published: 2019 Citation: Nielsen N, Stubbs TL, Garland-Campbell K, and Carter AH (2019) Rapid estimation of wheat straw decomposition constituents using near-infrared spectroscopy. Agronomy 9(8), 462 doi.org/10.3390/agronomy9080462
  • Type: Journal Articles Status: Published Year Published: 2019 Citation: Dixon LS, Godoy JV, Carter AH (2019) Evaluating the utility of carbon isotope discrimination as a selection criterion for wheat cultivar development. Plant Phenomics Volume 2019, Article ID 4528719 doi.org/10.34133/2019/4528719
  • Type: Journal Articles Status: Published Year Published: 2019 Citation: Dixon LS and Carter AH (2019) Toward a new use for carbon isotope discrimination in wheat breeding. Agronomy 9(7), 385 doi:10.3390/agronomy9070385
  • Type: Journal Articles Status: Published Year Published: 2019 Citation: Kruse EB, Klos K, Marshall J, Murray TD, Ward BP, Carter AH (2019) Evaluating marker assisted selection in breeding for tolerance to snow mold in winter wheat. Agrosystems, Geosciences, and Environment 2:190059 doi:10.2134/age2019.07.0059
  • Type: Journal Articles Status: Published Year Published: 2019 Citation: Lozada DN, Godoy JV, Murray TD, Ward BP, Carter AH (2019) Genetic dissection of snow mold tolerance in US Pacific Northwest winter wheat through genome-wide association study and genomic selection. Frontiers in Plant Science 29 October 2019 doi.org/10.3389/fpls.2019.01337
  • Type: Journal Articles Status: Published Year Published: 2019 Citation: Lozada DN and Carter AH (2019) Accuracy of single and multi-trait genomic prediction models for grain yield in US Pacific Northwest winter wheat. Crop Breeding, Genetics, and Genomics 1(2):e190012 doi.org/10.20900/cbgg20190012
  • Type: Journal Articles Status: Published Year Published: 2019 Citation: Lozada DN, Godoy JV, Ward BP, Carter AH (2020) Genomic prediction and indirect selection for grain yield in US Pacific Northwest winter wheat using spectral reflectance indices from high-throughput phenotyping. International Journal of Molecular Science 21:165 doi.org/10.3390/ijms21010165
  • Type: Journal Articles Status: Published Year Published: 2019 Citation: Fitria, Ruan H, Fransen SC, Carter AH, Tao H, Yan B (2019) Selecting winter wheat straw for cellulosic ethanol production in Pacific Northwest, USA. Biomass and Bioenergy 123:59-69.
  • Type: Journal Articles Status: Published Year Published: 2019 Citation: Balow K, Shelton G, Burke A, Hagemeyer K, Klarquist E, Froese P, Kruse EB, Carle SW, Roa A, Nielsen N, Carter AH (2019) Registration of the Finch-Eltan Winter Wheat Recombinant Inbred Mapping Population. Journal of Plant Registrations 13:287-293
  • Type: Journal Articles Status: Published Year Published: 2019 Citation: Kostick, S.A., J.L. Norelli, and K.M. Evans. 2019. Novel metrics to classify fire blight resistance of ninety-four apple cultivars. Plant Pathology Volume 68, Issue 5, pgs. 985-996. doi:10.1111/ppa.13012.
  • Type: Other Status: Published Year Published: 2019 Citation: " Kostick, S.A. 2019. Fire blight susceptibility of apple cultivars. Washington State University Tree Fruit Matters. http://treefruit.wsu.edu/article/fire-blight-susceptibility-of-apple-cultivars/
  • Type: Conference Papers and Presentations Status: Published Year Published: 2019 Citation: " Sankaran, S., Z��iga, C.E., Hinojosa, L., and Murphy, K. 2019. High-throughput sensing to evaluate irrigation treatment responses in quinoa varieties. Paper No. 1901360, 2019 ASABE AIM, Boston, MA, 7 July-10 July 2019.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2019 Citation: " Zhang, C., Craine, W., Quir�s, J., McGee, R., Vandemark, G., Davis, J., Brown, J., Hulbert, S., and Sankaran, S. 2019. Trait evaluation of cool-season crops using high-throughput phenotyping technologies. Paper No. 1901201, 2019 ASABE AIM, Boston, MA, 7 July-10 July 2019.


Progress 02/15/18 to 02/14/19

Outputs
Target Audience:Research efforts have been disemminated to the scientific community, other researchers in genomics and phenomics, stakeholders in the respective commodity crops, the general public, and undergraduate and graduate students. Changes/Problems: Nothing Reported What opportunities for training and professional development has the project provided?Opportunities have existed through monthy meetings of project personnel, travel to local and international professional meetings to present research, interaction with stakeholders at field days, interviews with popular press for newspaper and radio pieces, in-field training of plant breeding, professional networking opportunities, poster presentations, supervision of undergradaute students, and collaborations across research disciplines. How have the results been disseminated to communities of interest?Results have been disseminated through journal articles, extension articles, conference proceedings, poster presentations, stakeholder meetings, field days, popular press (newspaper, magazine, radio, podcasts, etc), and invited lectures. What do you plan to do during the next reporting period to accomplish the goals?Teaching: Four courses were developed 2016 and have been delivered in both 2017 and 2018. Graduate students associated with this grant have either taken these courses either in 2016 or 2017, with a new cohort of students taking the classes in 2018. These courses were also made available to the general student population, and due to popularity are being regularly taught. The four courses developed will again be taught in 2019. Students will continue to be provided opportunities for training and professional development through travel to different meetings where they will present their research and interact with other scientists. Summer support is still available in many programs to ensure undergraduate students work with the graduate students and are exposed to research. Students have met with both Dr. Sankaran and Dr. Zhang on a regular basis to receive training on high-throughput phenotyping and statistical genomics. Students and Post-Docs meet monthly to present project progress and share lessons learned. Students collaborate together when their projects require similar phenotyping methods although crops may be different. The grant has provided funding for students and PI's to travel to over 20 different regional, national, and international meetings to present results. Graduate students were able to supervise and work with undergraduate students in various aspects of data collection over the summer and through the academic year. This provided valuable experience in supervision and communication skills, as well as detailed planning and preparation. We are discussing with the WA state Ag Teachers Association to identify ways to train high school students in some of this technology, with the hope that it can be used as a tool to recruit more students with diverse interests into agricultural fields. Protocols: In the areas of phenotyping and genotyping, continued protocol development will take place. Methods have and will be developed to enhance and improve the efficiency of high-throughput phenotyping. Standard protocols have been implemented across crops for data collection, and a single algorithm developed to extract data from images. These protocols are standardized for the different information desired to be collected such as NDVI images and values, ground cover measurements, winter survival, seed size, plant vigor, etc. Refinement of algorithms will continue in the upcoming year. As more data is collected and ground truth measurements are recorded, the algorithms can be updated to get a better idea of how predictive they will be. Coordination with each program has identified traits of interest and where overlaps occur. Groups are working together to collect data on a given trait using the same protocol. We have expanded the number of cameras and sensors we are using, as there is a strong time-demand on these instruments. Genotyping platforms have improved allowing efficient genotyping of panels as well as breeding program material. The results are being analyzed and additional populations and breeding program material has been selected for further testing. Methods in statistical genomics are also being further developed. cBLUP and sBLUP have been implemented in the analysis of many of the projects we are working on in order to enhance the analysis of results. GAPIT version 2 is being implemented by students when doing GWAS. Plant Breeding: 2018 was the third year many of the programs were collecting data. With established protocols, all programs will be completing phenotyping in 2019 and will be finalizing data analysis. Most programs are now moving from working with genetic populations and moving into using these methods within their breeding programs for selection. Validation in the breeding programs will be an essential key to evaluate the usefulness of methods in applied programs.

Impacts
What was accomplished under these goals? Objective 1) Phenotype: In regard to the work with apple on fire blight resistance evaluation, multiple sensing tools were utilized to assess fire blight symptoms. The features extracted from RGB images and multispectral images demonstrated potential in evaluation of disease rating. In regard to quinoa field assessment, handheld multispectral radiometer, proximal sensing system using ground platform, and remote sensing with unmanned aerial system were used to assess the performance of eight quinoa varieties under two irrigation treatments. Multiple features extracted from sensing systems, remote sensing thermal data and water band index were consistently significantly correlated with plant height and yield data. Image processing pipeline for analyzing data from UAS sensing system is being established to assess days to flowering and plant height across multiple crops. Genotype: Recent research has replaced the relationship based on all markers in gBLUP with the relationship based on selected markers in SUPER for genomic prediction of phenotypes and breeding values, named sBLUP. Simulations and analyses on real traits demonstrated that sBLUP is superior to gBLUP, especially for traits controlled by a finite number of genes with varied effects. We developed compressed BLUP (cBLUP) which addresses the most challenging situation when heritability is low. In situations where individuals' phenotypes poorly reflect their true breeding values, we clustered individuals into groups according their relationships based on either pedigree, all genetic markers from gBLUP, or selected markers from sBLUP. Objective 2) USDA Wheat Breeding: The DNAM population is a nested-association mapping panel comprising 420 lines. High throughput phenotyping is being used in a three-year drought study conducted at Pullman WA and Ft. Collins CO. CROPSCAN data, NIR images, RGB cameras were used to collect various spectral and agronomic data. Multispeq devices were used to collect photosynthesis data during flowering. In 2018, a subset of the DNAM was evaluated for temperature-induced adult plant resistance to stripe rust and screened for Fusarium Crown Rot and Cereal Cyst Nematode resistance. WSU Winter Wheat Breeding: Phenotyping was completed on two diversity panels and the breeding program. We are tracking lines selected in the breeding program through spectral selection to see how they compare with historical selection methods to evaluate how HTP can be used in applied breeding. Phenotyping data was collected for carbon isotope discrimination, transpiration, and photosynthesis to estimate tolerance to drought stress in the field. Prediction models are also being developed for spectral indices, cold tolerance, disease resistance, winter dormancy, and end-use quality. WSU Apple Breeding: Final phenotypic data collection for levels of resistance to fire blight (Erwinia amylovora) of individuals in the RosBREED apple germplasm reference set (genotyped previously) was completed. Spectral data correlates well with visual ratings. To identify loci that influence fire blight resistance/susceptibility in apple, analysis of phenotypic data with FlexQTLTM software has begun and will continue in 2019. Spectral data can now be used routinely in the program for selection, with the hopes of adding genomic selections as well. WSU Camelina Breeding: We grew and evaluated two biparental camelina RIL populations and an AM panel in replicated plots at two locations. We genotyped the populations using GBS and have begun data analysis. We calibrated an instrument for measuring oil content, protein and oil composition and have begun collecting data from field samples. Image analysis was used to assay seed size, seedling emergence, early growth rates and canopy development in the field. We released one herbicide tolerant camelina variety that performs well in the Pacific Northwest and is compatible with herbicides commonly used in cereal and legume production. One EMS induced mutant line was found to have low levels of erucic acids in the oil seed, and after backcrossing this mutant line to our herbicide tolerant line we evaluated breeding lines in the field last summer. USDA Pea and Lentil Breeding: During 2018, we grew a lentil RIL population and phenotyped it for As uptake in two environments. Using a split plot design with two treatments (+As and 0 As), the RIL population, derived from a cross between cvs 'Red Chief' and 'Pardina', was evaluated. Morphological data were collected during growth and at physiological maturity plants were harvested, divided into shoots and seeds/pods and dry weight recorded. DNA is extracted and we have started preparation of the GBS library. WSU Barley and Quinoa Breeding: For phenotyping of quinoa for heat tolerance, we utilized multiple advanced sensing platforms for traits correlated to heat tolerance. We conducted the second year of a field trial where we grew eight genotypes in irrigated and non-irrigated conditions in two locations. We also tested subsets of these genotypes under controlled greenhouse and growth chamber conditions. Nutritional value, and in particular protein quality and amino acid content, is particularly valuable in quinoa. We began the process of testing the feasibility and accuracy of conducting high-throughput analysis of amino acid content in quinoa using near infrared (NIR) technology and are calibrating it to allow for high throughput analysis of beta-glucan in barley. WSU Spring Wheat Breeding: In 2018 we explored genomic selection and genome wide association studies for several key traits in our breeding germplasm. Resistance to Fusarium head blight was evaluated as a potential application for genomic selection. We found that disease incidence had higher heritability, higher correlation across years and locations, and higher prediction accuracy than disease severity or deoxynivalenol concentration. We used Finlay-Wilkinson regression to classify environmentally stable responses and 50,208 genetic markers to identify influential resistance loci for harnessing environmentally stable stripe rust resistance. We reported on efforts to use spectral reflectance for indirect selection and genome-wide association analyses of agronomic traits and drought tolerance in spring wheat. USDA Western Regional Genotyping Laboratory: Historical US cultivars (n = 780) have been genotyped using the Illumina 90k SNP array. R Scripts have been developed to calculate Gene Diversity, Jukes Cantor Distance and Hamming Distance. Hierarchical clustering and principal component analysis will be used for data exploration. The objective is to identify linkage blocks in wheat cultivars that are consistently selected in breeding programs to see if they hold the key to local adaptation for these varieties. Spring and winter cultivars in the study were grown in the summer of 2018 and stripe rust, heading dates, awn-type and plant height were recorded. Objective 3) Through leveraged support, 12 graduate students and 3 post-docs are currently funded. And additional 2 post-docs and 3 graduate students have completed work and graduated. An additional 8 graduate students working on phenotyping and genomics who are not directly funded by this project, assist with various components. Students are from the Horticulture, Crop Science, Plant Pathology, and Molecular Plant Sciences degree programs. Each graduate student is also working with at least one-undergraduate research, with a total of 15 being funded in 2018. Five of these undergraduate students completed research internships for course credit, and will be presenting their research at an undergraduate research fair at the university. We have taught four courses in 2018 to graduate students associated with this grant. Online resources are available for most of these training opportunities. Opportunities for training and professional development for students continues.

Publications

  • Type: Conference Papers and Presentations Status: Published Year Published: 2018 Citation: 1. Zhang, C., Pumphrey, M., Zhou, J., Zhang, Q., and Sankaran, S. 2018. Development of automated high-throughput phenotyping system for wheat evaluation in controlled environment, Transactions of the ASABE, Accepted, November, 2018.
  • Type: Journal Articles Status: Published Year Published: 2018 Citation: 2. Zhang, C., Si, Y., Lamkey, J., Boydston, R.A., Garland-Campbell, K.A., and Sankaran, S. 2018. High-throughput phenotyping of seed/seedling evaluation using digital image analysis. Agronomy, 8, DOI: 10.3390/agronomy8050063.
  • Type: Journal Articles Status: Published Year Published: 2018 Citation: 3. Jarolmasjed, S., Khot, L.R., and Sankaran, S. 2018. Hyperspectral imaging and spectrometry-derived spectral features for bitter pit detection in storage apples. Sensors, 18 (5), 1561; DOI: https://doi.org/10.3390/s18051561.
  • Type: Journal Articles Status: Published Year Published: 2018 Citation: Hulbert, S.H., Craine, W. Pan, W.L. (2018) Registration of WA-HT1, a Camelina Line with Resistance to Residual levels of ALS Inhibitor Herbicides. Journal of Plant Registrations. 12: 2: 253-256 doi:10.3198/jpr2017.09.0054crg.
  • Type: Journal Articles Status: Published Year Published: 2018 Citation: Jiabo Wang, Zhengkui Zhou, Zhe Zhang, Hui Li, Di Liu, Qin Zhang, Peter J. Bradbury, Edward S. Buckler & Zhiwu Zhang. Expanding the BLUP alphabet for genomic prediction adaptable to the genetic architectures of complex traits. Heredity, 2018, https://doi.org/10.1038/s41437-018-0075-0.
  • Type: Journal Articles Status: Published Year Published: 2018 Citation: Haixiao Dong, Rui Wang, Yaping Yuan, James Anderson, Michael O Pumphrey, Zhiwu Zhang and Jianli Chen. Evaluation of the Potential for Genomic Selection to Improve Spring Wheat Resistance to Fusarium Head Blight in the Pacific Northwest. Frontiers in Plant Science, 2018, https://www.frontiersin.org/articles/10.3389/fpls.2018.00911/abstract.
  • Type: Journal Articles Status: Published Year Published: 2018 Citation: Hinojosa, L, J.A. Gonzalez, F.H. Barrios-Masias, F. Fuentes, K. Murphy (2018). Quinoa abiotic stress responses: A review. Plants 7: 106.
  • Type: Journal Articles Status: Published Year Published: 2018 Citation: Hinojosa, L., J. Matanguihan, K. Murphy (2019). Effect of high temperature on pollen morphology, plant growth and seed yield in quinoa. Journal of Agronomy and Crop Science 205: 33-45. doi.org/10.1111/jac.12302.
  • Type: Journal Articles Status: Published Year Published: 2018 Citation: Dong, H. R. Wang, Y. Yuan, J. Anderson, M. Pumphrey, Z. Zhang, and J. Chen. 2018. Evaluation of the Potential for Genomic Selection to Improve Spring Wheat Resistance to Fusarium Head Blight in the Pacific Northwest. Frontiers in Plant Science. 9: 911.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2108 Citation: Zhou J, Zhang C, Sankaran S, Khot LR, Pumphrey MO, Carter AH (2015) Crop height estimation in wheat using proximal sensing techniques. ASABE Conference Proceedings doi: 10.13031/aim.20152188566
  • Type: Journal Articles Status: Published Year Published: 2018 Citation: Gizaw SA, Godoy J, Garland-Campbell K, Carter AH (2018) Genome-wide association study of yield and component traits in Pacific Northwest winter wheat (Triticum aestivum L.). Crop Science 58:2315-2330
  • Type: Journal Articles Status: Published Year Published: 2018 Citation: Gizaw SA, Godoy JG, Pumphrey MO, Carter AH (2018) Spectral reflectance for indirect selection and genome-wide association analyses of grain yield and drought tolerance in North American spring wheat (Triticum aestivum L.). Crop Science 58:1-13 doi: 10.2135/cropsci 2017.11.0690
  • Type: Journal Articles Status: Published Year Published: 2018 Citation: Godoy J, Gizaw S, Chao S, Blake N, Carter A, Cuthbert R, Dubcovsky J, Hucl P, Kephart K, Pozniak C, Prasad PVV, Pumphrey M, Talbert L (2018) Genome-wide association study (GWAS) of agronomic traits in a spring planted North American elite hard red spring wheat panel. Crop Science 58:1838-1852
  • Type: Journal Articles Status: Published Year Published: 2018 Citation: Gizaw SA, Godoy JGV, Garland-Campbell K, Carter AH (2018) Using spectral reflectance as proxy phenotypes for genome-wide association studies of yield and yield stability in Pacific Northwest winter wheat. Crop Science 58:1232-1241.
  • Type: Journal Articles Status: Accepted Year Published: 2019 Citation: Kostick, S.A., J.L. Norelli, and K.M. Evans. Novel metrics to classify fire blight resistance of ninety-four apple cultivars. Accepted by Plant Pathology 1/16/2019.
  • Type: Theses/Dissertations Status: Published Year Published: 2018 Citation: Leonardo Hinojosa, Ph.D. in Crop Science, Graduated Fall 2018 Dissertation: Effect of Heat and Drought Stress in Quinoa (Chenopodium quinoa Willd.)
  • Type: Conference Papers and Presentations Status: Published Year Published: 2018 Citation: Zhang, C., Craine, W., Davis, J. B., Khot, L. R., Marzougui, A., Brown, J., Hulbert, S. H., and Sankaran, S. 2018. Detection of canola flowering using proximal and aerial remote sensing techniques. Proceeding SPIE 10664, Autonomous air and ground sensing systems for agricultural optimization and phenotyping (III).


Progress 02/15/17 to 02/14/18

Outputs
Target Audience:The audiences targeted by this project include researchers in the target crops of spring and winter wheat, barley, quinoa, apples, peas, lentils, and camelina, along with researchers in the field of plant breeding. Furthermore, researchers in the field of high-throughput phenotyping and genotyping, as well as statistical genomics have been reached. Our efforts directly reach the stakeholders of each commodity group we are working with, including growers, industry, and commodity commissions. Private breeding companies have also been a focused audience as we often share germplasm and high-throughput phenotyping protocols with them. Results of this project have been presented to researchers and stakeholders in scientific meetings, journal articles, popular press, and commodity meetings. These have varied from regional meetings to international audiences. Results are also being formatted to be posted on a webpage dedicated to this grant. In the areas of education, we have targeted graduate and undergraduate students to disseminate information to them. They have taken part in courses developed as part of this grant, and have collaborated with students directly funded from this grant. This collaboration has allowed the funded graduate students to share ideas and methodologies about plant breeding, phenotyping, and statistical analysis to students from many disciplines. Changes/Problems: Nothing Reported What opportunities for training and professional development has the project provided?Four courses were developed 2016 and delivered again in 2017 as part of this project. Graduate students associated with this grant have either taken these courses either in 2016 or 2017. These courses were also made available to the general student population, and due to popularity will be regularly taught. Students have met with both Dr. Sankaran and Dr. Zhang on a regular basis to receive training on high-throughput phenotyping and statistical genomics. Students and Post-Docs meet monthly to present project progress and share lessons learned. Students collaborate together when their projects require similar phenotyping methods although crops may be different. The grant has provided funding for students and PI's to travel to over 15 different regional, national, and international meetings to present results. Graduate students were able to supervise and work with undergraduate students in various aspects of data collection over the summer. This provided valuable experience in supervision and communication skills, as well as detailed planning and preparation. We have begun discussions with the WA state Ag Teachers Association to try and identify ways to begin training high school students on some of this technology, with the hope that they can use this as a tool to recruit more students with diverse interests into agricultural fields. How have the results been disseminated to communities of interest?During the second year of this grant, there has been 10 peer-reviewed journal articles published or in press, one popular press articles, 36 oral presentations, 16 poster presentations, one workshop, five new germplasms, and four courses taught. Popular press articles were disseminated to a national audience. Oral presentations varied from local groups to international organizations. Many of the oral presentations were invited requests where the PI's were speaking on how they are using high-throughput genotyping and phenotyping in their plant breeding programs. The software iPat for GWAS and GS analysis was developed and has been disseminated through the website http://ZZLab.net/iPat. Information has been disseminated to four different commodity commissions through personal communication during annual meetings. Results also include new protocols in high-throughput phenotyping which are disseminated to other research and plant breeding groups through workshops and personal communication. Finally, information is disseminated to our stakeholders through field days and grower meetings. What do you plan to do during the next reporting period to accomplish the goals?In the areas of phenotyping and genotyping, continued protocol development will take place. Methods will be developed to enhance and improve the efficiency of high-throughput phenotyping. Standard protocols will be implemented across crops for data collection, and a single algorithm will be developed to extract data from images. These protocols will be standardized for the different information desired to be collected such as NDVI images and values, ground cover measurements, winter survival, seed size, plant vigor, etc. Refinement of algorithms will continue in the upcoming year. As more data is collected and ground truth measurements are recorded, the algorithms can be updated to get a better idea of how predictive they will be. Coordination with each program has identified traits of interest and where overlaps occur. Groups are working together to collect data on a given trait using the same protocol. Because of this, we are also expanding the number of cameras and sensors we are using, as there is now a strong time-demand on these instruments. Genotyping platforms have been trialed in order to spike molecular markers associated with specific traits of interest into general genotyping-by-sequencing. The results are being analyzed and additional populations and breeding program material has been selected for further testing. Methods in statistical genomics are also being further developed. cBLUP and sBLUP will be implemented in the analysis of many of the projects we are working on in order to enhance the analysis of results. GAPIT version 2 is being implemented by students when doing GWAS. Methods to impute missing data in genotyping-by-sequencing will be further refined, which will provide useful analysis to the plant breeders when looking at genotyping data. In the areas of plant breeding, 2017 was the second year many of the programs were collecting data. With protocols now in place, all programs will be repeating field and greenhouse trials in 2018 and collecting data in the same way it was done in 2017. Programs are now starting to do some investigative analysis of data, while waiting for the 2018 data set. Additional data will make results more robust. Many programs are also starting to look for validation populations to identify the level of usefulness results will have in breeding programs. In the area of education, the four courses developed will again be taught in 2018. Students will be provided additional opportunities for training and professional development through travel to different meetings where they will present their research and interact with other scientists. Additionally, summer support is still available in many programs to ensure undergraduate students work with the graduate students and are exposed to research. In 2018, the students have asked for workshops in statistical analysis using their collected data. This will be provided, allowing students to work through their data sets in real time with Dr. Zhang.

Impacts
What was accomplished under these goals? Objective 1) Phenotype: The phenomics team is closely working with plant breeding teams on research objectives for evaluating fire blight disease resistance in apples; emergence and canopy vigor evaluation in wheat and camelina; days to flowering evaluation in camelina, lentil, peas, and chickpeas; and seed quality evaluation in quinoa, lentils, and camelina. Several experiments have been conducted for validating the phenomics data with standard ground-truthing methods (e.g. seed size, emergence rating, etc.). Customized algorithms have been developed to extract phenotypes from the images in an automated fashion. For example, customized algorithm developed for emergence and canopy vigor estimation performs several steps (radiometric calibration with reference panel, background removal using thresholding, and estimating canopy cover and NDVI) on each image from a series of images and exports the data in excel in an automated manner. Similar results could be found for seed analysis. In more challenging fire blight resistance evaluation, the data processing and algorithm development is underway. Genotype: In 2016, we enhanced our GAPIT software into version 2. We also developed a new method, named FarmCPU, Both GAPIT and FarmCPU have to be run under command line interface. It requires users to have substantial programming skills, which limits the usage from plant breeders. To solve this problem, we develop a user-friendly software package in 2017, named iPat, and has a Graphical User Interface (http://www.zzlab.net/iPat). It allows users to perform gene mapping and predict breeding values by simply dragging data files and clicking graphical icons. iPat not only implemented the methods of GAPIT and FarmCPU, but also other packages of genomic selection, such as rrBLUP and BGLR. The software provides comprehensive graphical outputs to help users interpret the analysis results. Objective 2) USDA Wheat Breeding: The DNAM population is a nested-association mapping panel comprising 420 lines with only the D-genome segregating. Advanced phenotyping technology is being used for data collection in a three year (2017-2019) drought study conducted at two locations, Pullman WA and Ft. Collins CO. In 2017, the DNAM was screened for seedling stripe rust in the field and the greenhouse and for cereal cyst nematode resistance in the greenhouse. Image analysis was used to distinguish soft white club kernels from soft white kernels in a mixed sample. Image analysis was also used to calculate coleoptile length in conjunction with the Sankharan lab. WSU Winter Wheat Breeding: Genotyping was completed in 2017 on two diversity panels and the breeding program. Additional phenotyping data was collected on these panels and the breeding program using various advanced phenotyping methods to estimate tolerance to drought stress in the field. We are investigating cold tolerance, disease resistance, winter dormancy, and end-use quality of wheat. Genomic prediction models were developed for end-use quality, and we are in the process of developing these models for the other listed traits. WSU Apple Breeding: Year 2 data was collected for levels of resistance to fire blight (Erwinia amylovora) of individuals in the RosBREED apple germplasm reference set (genotyped previously). Data was taken on 1650 trees (556 individuals with approximately three replicate trees per individual). Disease severity was quantified by proportion of healthy tissue, calculated from shoot and lesion length measurements. In addition, each shoot was rated on a scale of zero to four, based on the age of the wood infected. A portion of the orchard was screened by the Sankaran group using sensor technologies. In order to identify loci that influence fire blight resistance/susceptibility, analysis of data collected in years 1 and 2 with FlexQTLTM software has begun and will continue in 2018. WSU Camelina Breeding: We grew and evaluated two large (>250 lines each) biparental camelina RIL populations at three dryland locations and an AM panel at two locations. We genotyped the two RIL populations using GBS. We have begun calibrating an NIR machine for measuring oil content and composition and determining which other biochemical properties we can predict. We have developed image analysis protocols for estimating seed size in the lab and seedling emergence and growth rates in the field. We have started optimizing image analysis protocols for estimating flowering time and maturity in the field for both canola and camelina. We released one herbicide tolerant camelina variety that performs very well in the Pacific Northwest and is compatible with herbicides commonly used in cereal and legume production in the PNW. USDA Pea and Lentil Breeding: An RIL population segregating for Arsenic accumulation levels was completed, and is in the process of being genotyped using sequencing approaches. Phenotyping for Arsenic accumulation in the greenhouse will begin in the spring of 2018. WSU Barley and Quinoa Breeding: For phenotyping of quinoa for heat tolerance, we utilized multiple advanced sensing platforms for traits correlated to heat tolerance. We conducted the second year of a field trial where we grew eight genotypes in irrigated and non-irrigated conditions in two locations. We also tested subsets of these genotypes under controlled greenhouse and growth chamber conditions. Nutritional value, and in particular protein quality and amino acid content, is particularly valuable in quinoa. We began the process of testing the feasibility and accuracy of conducting high-throughput analysis of amino acid content in quinoa using near infrared (NIR) technology. We are currently in the calibration process where we have testing 100 to 200 genotypes using our NIR instrument, and these genotypes will next be submitted for testing in wet chemistry laboratory. We are also in the final process of calibrating our NIR to allow for high throughput analysis of beta-glucan in barley. WSU Spring Wheat Breeding: Building upon the results of 2016 where we found multiple pleiotropic QTL regions for WUE, plant water status, and yield on chromosomes 1A, 1B, 3A, 3D, 4A, 4B, 6B, and 7D, we have now begun developing genomic selection models on the same traits of interest. The spring wheat breeding program can utilize genomic selection to better select cultivars with increased yield potential and higher WUE. USDA Western Regional Genotyping Laboratory: The study intends to identify several linkage-blocks in the wheat genome responsible for adaptation of wheat to Washington and the PNW. To date, genomic DNA has been extracted from 913 varieties (244 spring and 669 winter), 176 of which are from the PNW states of Idaho, Oregon and Washington. Of these, 380 varieties are being genotyped at the USDA Central Small Grain Genotyping Lab in Fargo, ND using the Illumina 90K SNP Array. Of the 176 PNW varieties only 19 were released after 2000. Yet, at least 66 varieties from the public breeding programs of Idaho, Oregon and Washington have obtained Plant Variety Protection certificate since 2000. We are in the process of obtaining seeds of these new varieties, growing them and extracting genomic DNA. Objective 3) Through leveraged support, 10 graduate students and four post-docs have been funded. We also have an additional 6 graduate students working on phenotyping and genomics which are not directly funded by this project, but who assist with various components. Students are from the Horticulture, Crop Science, Plant Pathology, and Molecular Plant Sciences degree programs. Each graduate student is also working with at least one-undergraduate research, with a total of 12 being funded in 2017. We have taught four courses in 2017 to graduate students associated with this grant. Online resources are available for most of these training opportunities. Opportunities for training and professional development for these students are listed previously.

Publications

  • Type: Journal Articles Status: Published Year Published: 2016 Citation: Liu X, Huang M, Fan B, Buckler ES, Zhang Z (2016) Iterative usage of fixed and random effect models for powerful and efficient genome-wide association studies. Plos Genetics 12(3): e1005957.
  • Type: Journal Articles Status: Published Year Published: 2018 Citation: Chen CJ, Zhang Z (2018) iPat: intelligent prediction and association tool for genomic research. Bioinformatics doi: 10.1093/bioinformatics/bty015
  • Type: Journal Articles Status: Awaiting Publication Year Published: 2018 Citation: Hulbert SH, Craine W, Pan WL (2018) Registration of WA-HT1, a Camelina Line with Resistance to Residual levels of ALS Inhibitor Herbicides. In Press.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2017 Citation: Osroosh Y, Sankaran S (2017) Development of a flexible phenomic field platform for high-throughput phenotyping in Palouse region of Washington State, Paper No. 1700407, 2017 ASABE Annual International Meeting, Spokane, WA.
  • Type: Journal Articles Status: Accepted Year Published: 2018 Citation: Jernigan KL, Godoy J, Huang M, Zhou Y, Morris CF, Garland-Campbell KA, Zhang Z, Carter AH (2018) Association mapping for end-use quality in Pacific Northwest adapted soft white winter wheat. Frontiers in Plant Science
  • Type: Journal Articles Status: Awaiting Publication Year Published: 2018 Citation: Lewien MJ, Murray TD, Jernigan KL, Garland-Campbell KA, Carter AH (2018) Genome-wide association mapping for eyespot disease in US Pacific Northwest winter wheat. PLOS One
  • Type: Journal Articles Status: Awaiting Publication Year Published: 2018 Citation: Gizaw SA, Godoy JG, Garland-Campbell K, Carter AH (2018) Using spectral reflectance for genome wide association study of yield and yield stability in Pacific Northwest winter wheat (Triticum aestivum L.). Crop Science
  • Type: Journal Articles Status: Awaiting Publication Year Published: 2018 Citation: Godoy J, Gizaw S, Chao S, Blake N, Carter A, Cuthbert R, Dubcovsky J, Hucl P, Kephart K, Pozniak C, Prasad PVV, Pumphrey M, Talbert L (2018) Genome-wide association study (GWAS) of agronomic traits in a spring planted North American elite hard red spring wheat panel. Crop Science
  • Type: Journal Articles Status: Awaiting Publication Year Published: 2018 Citation: Gizaw SA, Godoy JG, Pumphrey MO, Carter AH (2018) Use of spectral reflectance indices in indirection selection and genome wide association studies of drought resistance and yield potential in North American spring wheat (Triticum aestivum L.). Crop Science
  • Type: Journal Articles Status: Awaiting Publication Year Published: 2018 Citation: Martinez SA, Godoy J, Huang M, Zhang Z, Carter AH, Garland-Campbell K, Steber CM (2018) Genome-wide association mapping for tolerance to preharvest sprouting and low falling numbers in wheat. Frontiers in Plant Science
  • Type: Journal Articles Status: Awaiting Publication Year Published: 2018 Citation: Weizhen L, Naruoka Y, Miller K, Garland-Campbell K, Carter AH (2018) Characterizing and validating stripe rust resistance loci in US Pacific Northwest winter wheat accessions (Triticum aestivum L.) by genome-wide association and linkage mapping. The Plant Genome


Progress 02/15/16 to 02/14/17

Outputs
Target Audience:The audiences targeted by this project include researchers in the target crops of spring and winter wheat, barley, quinoa, apples, peas, lentils, and camelina, along with researchers in the field of plant breeding. Furthermore, researchers in the field of high-throughput phenotyping and genotyping, as well as statistical genomics have been reached. Our efforts directly reach the stakeholders of each commodity group we are working with, including growers, industry, and commodity commissions. Private breeding companies have also been a focused audience as we often share germplasm and high-throughput phenotyping protocols with them. Results of this project have been presented to researchers and stakeholders in scientific meetings, journal articles, popular press, and commodity meetings. These have varied from regional meetings to international audiences. Results are also being formatted to be posted on a webpage dedicated to this grant. In the areas of education, we have targeted graduate and undergraduate students to disseminate information to them. They have taken part in courses developed as part of this grant, and have collaborated with students directly funded from this grant. This collaboration has allowed the funded graduate students to share ideas and methodologies about plant breeding, phenotyping, and statistical analysis to students from many disciplines. Changes/Problems:The USDA Pea and Lentil Breeding Program, under the directions of Dr. McGee, was originally going to investigate Aphanomyces resistance as part of her breeding objects. Early in 2016, it was found that another research group, using the same population, had already done this and was preparing their results for publication. At the same time, The Pea and Lentil Commission approached Dr. McGee about developing winter peas of food grade quality. Thus, the breeding program is now investigating using high-throughput phenotyping to identify food quality winter peas with cold tolerance. To date, only food quality spring peas have been developed. This change has not set their research program back, as this change was identified shortly after funding. What opportunities for training and professional development has the project provided?Four courses havebeen developed and delivered as part of this project. Graduate students associated with this grant have either taken these courses or will be taking these courses infuture academic semesters. These courses were also made available to the general student population. Students have met with both Dr. Sankaran and Dr. Zhang on a regular basis to receive trainingon high-throughput phenotyping and statistical genomics. Students and Post-Docs meet monthly to present project progress and share lessons learned. Students collaborate together when their projects require similar phenotyping methods although crops may be different. The grant has provided funding for students and PI's to travel to 12 different regional, national, and international meetings to present results. Graduate students were able to supervise and work with undergraduate students in various aspects of data collection over the summer. This provided valuable experience in supervision and communication skills, as well as detailed planning and preparation. Selected students were also invited to attend a two-day visit to Monsanto where they interacted with scientists, presented research findings, and learned about the soft skills needed to be successful in future careers. How have the results been disseminated to communities of interest?During the first year of this grant, there has been one peer-reviewed journal article published, two popular press articles, 15oral presentations, two poster presentations, one workshop, four courses, and one website. Popular press articles were disseminated to a national audience. Oral presentations varied from local groups to international organizations. Many of the oral presentations were invited requests where the PI's were speaking on how they are using high-throughput genotyping and phenotyping in their plant breeding programs. The software iPat for GWAS and GS analysis was developed and has been disseminated through the website http://ZZLab.net/iPat. Information has been disseminated to four different commodity commissions through personal communication during annual meetings. Results also includenew protocols in high-throughput phenotyping which are disseminated to other research and plant breeding groups through workshops and personal communication. What do you plan to do during the next reporting period to accomplish the goals?In the areas of phenotyping and genotyping, continued protocol development will take place. Methods will be developed toenhance andimprove the efficiency of high-throughput phenotyping. Standard protocols will be implemented across crops for data collection, and a single algorithm will be developed to extract data from images. These protocols will be standardized for the different information desired to be collected such as NDVI images and values, ground cover measurements, winter survival, seed size, plant vigor, etc. Coordination with each program has identified traits of interest and where overlaps occur. Groups will then work together to establish one protocol for the trait, regardless of the crop involved. Genotyping platforms are being investigated in order to spike molecular markers associated with specific traits of interest into general genotyping-by-sequencing. These platforms will be tested in the upcoming year in order to facilitate a more informative data set which can be used for genomic selection models. Methods in statistical genomics are also being further developed. cBLUP and sBLUP will be implemented in the analysis of many of the projects we are working on in order to enhance the analysis of results. A new version of GAPIT will be implemented by students when doing GWAS. Methods to impute missing data in genotyping-by-sequencing will be further refined, which will provide useful analysis to the plant breeders when looking at genotyping data. In the areas of plant breeding, 2016 was the first year many of the programs were collecting data. With protocols now in place, all programs will be repeating field and greenhouse trials in 2017 and collecting data in the same way it was done in 2016. Each breeding program associated with this grant is in a different levelof genotyping and phenotyping implementation. Some programs are researching effective methods and concurrently using them in the breeding program. Other programs have already developed these methods and are now using them in the breeding programs routinely for variety selection (namely the wheat breeding programs through the past efforts of the TCAP project). For each breeding program, 2017 will be a year of continued data collection and validation of previous methods. Statistical analysis will begin in the fall as more data becomes available. In the area of education, the four courses developed and taught in 2016 will be available for the graduate student cohort in future academic semesters. Students will be provided additional opportunities for training and professional development through travel to different meetings where they will present their research and interact with other scientists. Additionally, students will receive summer interns to assist them with their work, allowingopportunities to further enhance their communication and leadership skills.In 2017, we also have plans to develop two workshops; one on genotyping and one on phenotyping. Both will provide students information on the technologies and methods currently available. The second part of the workshop will provide students opportunities to handle data and learn how to organize, extract, and analyze appropriately.

Impacts
What was accomplished under these goals? Objective 1) Phenotype: Automated seed sizing (legumes, camelina) using digital imaging and crop vigor evaluation using multispectral imaging were investigated. A unified protocol to enable automated extraction of phenotypic features from the images (crop cover) for multiple crops (camelina, wheat, dry pea) is under development. The protocol will allow rapid image acquisition and processing of data (images) into features (phenotypes). The first year data will further assist in optimizing the process for automated phenotyping. In addition to the algorithms that are developed for feature (phenotype) extraction from the images, a simple, portable, easy-to-use field phenomic platform is being developed that can be adapted to different planting systems in this project. Genotype: Hold and instant accuracy were compared to evaluate accuracy of genomic prediction; hold accuracy was underestimated compared to instant accuracy. Instant accuracy was biased downward when the test population was small. An improved version of instant correlation coefficients that is near unbiased in all cases was developed (Zhou et. al. 2016). Genomic prediction by using kinship derived from genomic markers was optimized. Two new gBLUP methods were developed, the first by compressing individuals into groups was named compression BLUP (cBLUP). The next used the SUPER method (developed by Zhang Lab) and the method was named SUPER BLUP (sBLUP). The sBLUP method is superior for traits controlled by few genes and cBLUP is superior for traits with low heritability. These methods have been implemented in an R package GAPIT (Tang et. al. 2016). An integrated Prediction and association tool (iPat) software was written in JAVA with interface with R language. The Graphic User Interface created from Java provides a friendly environment that will allow Genome-Wide Association Studies and Genomic Selection analyses without programming. The user manualand executable program are available online at http://ZZLab.net/iPat. Objective 2) USDA Wheat Breeding: The objectives of the studies on the DNAM population (NAM population with variation in the D genome) are to increase knowledge of the D-genome in wheat, to assess the potential of Aegilops tauschii as a germplasm resource, and to continue to optimize high throughput phenotyping technology. The DNAM population was planted in Pullman in 2016 and in Pullman and Ft. Collins, CO for 2017. Traits associated with increased yield in diverse environments, including the Palouse region and drought stressed Great Plains are being discovered. In the 2016 field season, spectral reflectance data was collected, as well as plant height, maturity, grain yield, and test weight. Canopy reflectance measurements will be correlated with agronomic performance. The phenotyping tractor will be used to collect photosynthetic activity data in 2017. WSU Winter Wheat Breeding: Genotyping (GBS) of two diversity panels and the breeding program was completed in 2016. Data for over 40 traits for agronomics, spectral reflectance, disease resistance, and end-use quality was collected. Genomic selection models are being developed and will be applied to 2017 trials. The graduate student associated with this grant is investigating cold tolerance in winter wheat. Using an NDVI camera, digital images were collected 8 weeks after planting to estimate biomass and ground cover at locations in WA and OR. Images will be collected frequently in the spring of 2017 in order to evaluate dormancy and spring growth. WSU Apple Breeding: Data was collected for levels of resistance to fire blight (Erwinia amylovora) of the RosBREED apple germplasm reference set. Data was collected from 1603 trees with approximately three replicate trees of each of 552 genotypes in 2016. Disease severity was quantified by the proportion of healthy tissue and each shoot was also aged by where the infection had spread. Preliminary screening of some sections of the orchard using sensor technologies was completed. Data using sensor technologies will again be collected in 2017, as well as through visual observations. WSU Camelina Breeding: Two RIL populations of approximately 250 lines were grown at two sites. Maturity traits and yield measurements are complete, with seed size and oil content under progress. Dr. Sankaran developed an image analysis technique to measure seed size using a digital image and processing system. A greenhouse experiment to determine how stand density affects seed size was performed to account for genetic control of seed size and how it affects oil content. Three camelina accessions were collected that are described as 'winter types' requiring some vernalization, and planted at two planting dates along with winter hardy spring types. The same varieties were planted for greenhouse production to test vernalization requirements. USDA Pea and Lentil Breeding: Two previously developed lentil RIL populations are being phenotyped and genotyped for cold tolerance. Protocols to quantify response to hardening, challenge, and recovery were developed in 2016. An RGB camera will be used to compare with visual ratings to develop an automated algorithm. The RIL populations were also autumn-sown in Pullman, WA in 2016. Emergence, winter survival, above-ground damage, growth habit and flowering date will be recorded. Images will be collected in the spring in order to estimate levels of winter survival and spring growth. WSU Barley and Quinoa Breeding: Two elite quinoa variety trials were evaluated in 2016, plus a multi-generation quinoa breeding trial, consisting of over 800 plots. These plots were used to identify varieties which are adapted to PNW growing conditions. Greenhouse and field trials testing high-throughput phenotyping methods to identify heat tolerance characteristics in quinoa genotypes were initiated. Trials were also conducted under controlled conditions in the WSU Phenomics facility. Spectral reflectance traits associated with heat tolerance are the focus. Once these methods are validated, variety trials will be characterized to aid in selection. WSU Spring Wheat Breeding: To dissect and understand the genetic architecture controlling water use efficiency (WUE) in spring wheat and facilitate breeding efforts, a genome-wide nested association mapping study was conducted for a second year in 2016. Using joint-inclusive composite interval mapping on population of 750 recombinant inbred spring wheat lines phenotyped in field plots with high-throughput instruments and traditional yield and yield component traits, we identified seven QTL for WUE and more than 30 QTL for traits associated with plant water status and health across years. Multiple pleiotropic QTL regions for WUE, plant water status and yield were identified on chromosomes 1A, 1B, 3A, 3D, 4A, 4B, 6B, and 7D. USDA-ARS Western Regional Genotyping Laboratory: In 2016,1075 wheat accessionsfrom the National Small Grains Collection in Aberdeen, Idaho were grown and genotyped. Accessions from 40 out of the 50 states are represented at least once. Kansas is the most represented state with 87 accessions, followed by Washington (66), California (56), Indiana (55) and Colorado (49). The Pacific Northwest (Idaho, Oregon and Washington) is represented by 147 accessions (18%). It is predicted that certain genetic loci are needed and are essential components of successful cultivars in the PNW. Analysis of this population hopes to identify these loci. Objective 3) Eight graduate students and two post-docs were initially committed, but we have been able to fund 10 graduate students and four post-docs. Students are from the Horticulture, Crop Science, Plant Pathology, and Molecular Plant Sciences degree programs. We developed 4 courses taught in 2016 for this grant. One workshop was also developed and presented, with resources available online. Opportunities for training and professional development for these students are listed previously.

Publications

  • Type: Journal Articles Status: Published Year Published: 2016 Citation: Zhou, Y., Vales, M.I., Wang, A., and Zhang, Z. (2016) Systematic bias of correlation coefficient may explain negative accuracy of genomic prediction. Briefings in Bioinformatics doi: 10.1093/bib/bbw064.