Source: PURDUE UNIVERSITY submitted to NRP
CONNECTING GENOTYPE AND PHENOTYPE FOR SOYBEAN MATURITY AND SENESCENCE.
Sponsoring Institution
National Institute of Food and Agriculture
Project Status
COMPLETE
Funding Source
HATCH
Reporting Frequency
Annual
Accession No.
1013611
Grant No.
(N/A)
Cumulative Award Amt.
(N/A)
Proposal No.
(N/A)
Multistate No.
(N/A)
Project Start Date
Oct 1, 2017
Project End Date
Sep 30, 2022
Grant Year
(N/A)
Program Code
[(N/A)]- (N/A)
Recipient Organization
PURDUE UNIVERSITY
(N/A)
WEST LAFAYETTE,IN 47907
Performing Department
Agronomy
Non Technical Summary
A low-hanging fruit for genetic improvement of yield potential in crops is the characterization and selection of phenotypes acquired from image analysis. In contrast to point or plant based spectral reflectance, image analysis allows direct measurement of spatial or crop based traits that are known to be valuable, such as canopy coverage and vegetation index, collected from the field with high-throughput platforms such as unmanned aerial systems (UAS) (Cabrera?Bosquet et al. 2012, Liebisch et al. 2015). Offering simplicity and cost-effectiveness over other phonemic approaches, such data are also likely to be more valuable than greenhouse measurements that focus on individual plants and cannot be translated into field applications (White et al. 2012). Currently, the breeding community lacks know-how and computational tools to include such traits in breeding pipelines.
Animal Health Component
40%
Research Effort Categories
Basic
60%
Applied
40%
Developmental
(N/A)
Classification

Knowledge Area (KA)Subject of Investigation (SOI)Field of Science (FOS)Percent
2011820102025%
2011820104025%
2061820102025%
2061820104025%
Knowledge Area
206 - Basic Plant Biology; 201 - Plant Genome, Genetics, and Genetic Mechanisms;

Subject Of Investigation
1820 - Soybean;

Field Of Science
1040 - Molecular biology; 1020 - Physiology;
Goals / Objectives
The overall goal of this project is advance knowledge of the genes involved with soybean maturity and senescence, and that are specifically controlling physiological maturity. Specific objectives are: 1) to develop methodology for high-throughput quantification of field-based senescence and maturity; 2) use the new phenotyping tools to characterize gene function and to identify new loci involved with maturity and senescence.
Project Methods
Objective 1, develop methodology for high-throughput quantification of field-based senescence and maturityWe will use image analysis from aerial and ground platforms of soybean yield plots in the field to quantify senescence and maturity, with complementary ground-truthing to develop prediction models. The phenotype will be based on quantifying yellow color using methodology that is currently being developed by collaborators and is expected by the end of 2017. For a prediction model, we will ground-truth physiological maturity as well as the current pod-based visual estimators and the historic foliar visual estimators. Drone-based imagery and sensor data will be collected at 4 locations in 2107 and 2018, and likely in subsequent years as well. Precision, quantified phenotypes can then be used to investigate genetic variance and genetic architecture, as well as environmental correlations and general relationship to yield for development of new breeding and management strategies.Experiments include panels and populations:multi-environment yield trials of elite material in order to characterize variation in senescence and maturity in elite germplasm, in collaboration with Beck's Hybridsa subset of the SoyNAM populationa phenology-controlled association panel of germplasm accessions of known genotype at certain maturity locia panel of old and new varieties for which we will phenotype physiological maturitysenescence mutants identified by the Hudson labObjective 2, use the new precision phenotypes to characterize gene function and to identify new loci involved with maturity and senescenceWe will integrate the phenotyping data to gain from the objective 1 with available genotyping data (e.g., SNPs detected by 50K or 6K SNP Chip, by re-sequencing, and/or by genotyping-by-sequencing (GBS)) to identify genomic regions associated with physiological maturity and senescence by linkage mapping and association mapping. Once such regions are found, all genes in the region according the Williams 82 reference genome will be compared with known maturity gene loci to predict if potentially novel genes are harbored in the regions or the identified regions are likely to carry known maturity genes. If known maturity genes are reported in the identified regions, the candidate genes will be sequenced and compared with the known genes/alleles. If the identified regions do not carry any known maturity genes, the following two experiments will be performed to further identify candidate genes:Develop mapping/fine-mapping populations for linkage or QTL mapping to identify candidate genes.Knowledge-based prediction and expression analysis of candidate genes. Many maturity genes have functional homologies that are involved in flowering pathways of Arabidopsis, and the Hudson lab has previously identified a set of genes associated with soybean senescence through transcriptomics, which would facilitate this project.Functional validation of candidate genes by disruption of the candidate gene function via CRISPER-Cas9 or by over-expression of the candidate genes via complementation test.

Progress 10/01/19 to 09/30/20

Outputs
Target Audience:Crop research professionals and agronomists. Industry professionals in the seed trade and general agricultural research and development. Policy-makers regarding biotechnology, seed and agriculture. Changes/Problems: Nothing Reported What opportunities for training and professional development has the project provided?Education is key in our objectives ands students who work with us continue to work with soybeans at seed companies, as well as at start-ups and universities. How have the results been disseminated to communities of interest?Yes, through seminars and other presentations,and journal articles. What do you plan to do during the next reporting period to accomplish the goals?Analyze all the 2020 data and plan the 2021 objectives. Publish new journal articles.

Impacts
What was accomplished under these goals? A recent study of historic soybean cultivars from across decades in MG's II, III, and IV showed that the average rate of genetic yield gain in soybean (29 kg ha-1 yr-1) is equal to the rate of on-farm soybean yield increase (also 29 kg ha-1 yr-1), from which it can be inferred that breeding contributes significantly to the U.S. on-farm realized yield increases Our discoveries addresses environmentally and economically sustainable management and yield loss mitigation strategies and technologies for emerging soybean disease, insect pest and weed stresses. This has been addressed in two ways: primarily, through the development of varieties and germplasm with improved yield potential, improved physiological efficiencies, and improved resilience to stressors; secondly, through contributions to soybean phenomics broadly applicable to weed management and detection of stressors. While studying soybean breeding and genetics, we also explore how to breed soybeans using novel UAS phenotypes and genomic data, educate students in soybean genetics and quantitative methods in digital agriculture, and leverage investments in our breeding activities to generate new knowledge of soybean phenomics. We have evidence for success with our breeding activities because we have selected top-ranked lines measured by their performance across regional scales, and recent interest in commercializing our lines.

Publications

  • Type: Journal Articles Status: Published Year Published: 2020 Citation: Moreira Fabiana F., Oliveira Hinayah R., Volenec Jeffrey J., Rainey Katy M., Brito Luiz F. Integrating High-Throughput Phenotyping and Statistical Genomic Methods to Genetically Improve Longitudinal Traits in Crops. 2020. Frontiers in Plant Science, Vol 11; pg. 681. URL=https://www.frontiersin.org/article/10.3389/fpls.2020.00681. DOI=10.3389/fpls.2020.00681
  • Type: Journal Articles Status: Published Year Published: 2020 Citation: B. Lyu, S.D. Smith, X. Yexiang, K.M. RAINEY, K.A. Cherkauer. 2020. Deriving Vegetation Indices from High-throughput Images by Using Unmanned Aerial Systems in Soybean Breeding.Transactions of the ASABE. Status: Accepted Dec 2019, Manuscript number ITSC-13661-2019. Attribution: KMR design the experiment, and was PI for the ground and aerial data collection.
  • Type: Journal Articles Status: Awaiting Publication Year Published: 2020 Citation: Li, S., Wang, X, Clark, C. et al., 2020 Unidirectional Movement of Small RNAs from Shoots to Roots in Interspecific Heterografts. Nature Plants, (in press)


Progress 10/01/18 to 09/30/19

Outputs
Target Audience:Crop reseaerch professionals and agronomists. Industry professionals in the seed trade and general agricultural research and development. Policy-makers regarding biotechnoiligy, seed and agriculture. Changes/Problems: Nothing Reported What opportunities for training and professional development has the project provided?We are educating a graduate student cross-trained in plant breeding and engineering whose responsibilities include data experimentation in large yield trials. We will also teach a workshop for plant breeders on the genetic analysis of longitudinal traits in crops using RRM. March 13-15, 2019, Dr. Rainey contributed toa 2.5 day short course at Purdue "Mixed Models Applied to Genetic Selection in Plant and Livestock Species" (Table 4). Dr. Rainey was nominated by Purdue and selected by NCFAR to present a the "National C-FAR 2019 'Lunch~N~Learn' Hill SeminarSeries" to educate congressional staffers. She presented a talk entitieldSOY-FREE CHICKEN? Role of Soybean Genetic Improvements as Part of U.S. Food Security. How have the results been disseminated to communities of interest?Not yet as they are preliminary. What do you plan to do during the next reporting period to accomplish the goals?Process all of the 2019 field data.

Impacts
What was accomplished under these goals? We have made progress iwth remote sensing of color and temperature. We phenotyped physiological maturity in two special studies.

Publications

  • Type: Journal Articles Status: Published Year Published: 2019 Citation: Rhizobial tRNA-derived small RNAs are signal molecules regulating plant nodulation. Bo Ren, Xutong Wang, Jingbo Duan, Jianxin Ma. 2019. Science, Volume 365, 919-922
  • Type: Journal Articles Status: Published Year Published: 2019 Citation: Genomic introgression through interspecific hybridization counteracts genetic bottleneck during soybean domestication. Xutong Wang, Liyang Chen, Jianxin Ma. 2019. Genome biology, Volume 20-1, 22.
  • Type: Journal Articles Status: Published Year Published: 2019 Citation: Moreira, F.F., Hearst, A.A., Cherkauer, K.A. et al. Improving the efficiency of soybean breeding with high-throughput canopy phenotyping. Plant Methods 15, 139 (2019) doi:10.1186/s13007-019-0519-4
  • Type: Journal Articles Status: Published Year Published: 2019 Citation: Lopez, M.A., A. Xavier, and K.M. Rainey. 2019. Phenotypic Variation and Genetic Architecture for Photosynthesis and Water Use Efficiency in Soybean (Glycine max L. Merr). Front. Plant Sci. 10: 680


Progress 10/01/17 to 09/30/18

Outputs
Target Audience:Crop reseaerch professionals and agronomists. Industry professionals in the seed trade and general agricultural research and development. Changes/Problems: Nothing Reported What opportunities for training and professional development has the project provided? K.M. RAINEY. 2017. Drone Phenotyping in Soybean Breeding and Contributions to Gain (starts 1:00) Plant Breeding Innovation Session, American Seed Trade Organization (ASTA), Annual Corn, Sorghum, and Soybean Seed Research Conference & Seed Expo, Dec 7, Chicago, IL. Scope: international; audience: all types of professionals; attendance: 219. 2. J. Ma attended Soy2018 in Athens, GA, and gave a talk entitled "Molecular links underlying pleiotropic traits in soybean". How have the results been disseminated to communities of interest?Not yet as they are preliminary. What do you plan to do during the next reporting period to accomplish the goals?Process all of the 2018 field data.

Impacts
What was accomplished under these goals? Under Objective 1 we progressed with capabilities to measure color from cameras and multispectral sensors. Under Objective 2 we characterized 400 accessions for flowering time and maturity and have identified new QTL for both traits. We also phentoyped physiological maturity on hundreds of samples. In order to identify novel genes controlling semi-determinate stem growth, ~400 soybean landraces (Glycine max) accessions previously phenotype as semi-determinate lines were selected and compared with soybean varieties carrying Dt1 and Dt2 alleles. Based on the phylogenetic relationship of these lines established using the 50K SNP data, as well as the genotypes at the Dt1 and Dt2 loci determined by Dt1- and Dt2-specific markers, ten highly diverged soybean landraces having semi-determinate stems but lacking Dt2 and Dt1 alleles were identified. Each of these 10 landraces, potentially carrying new gene (s) for semi-determinacy, was crossed with an indeterminate soybean variety Williams 82 to generate F1 seeds. Some of the F1 seeds have been planted in greenhouse to generate F2 seeds for inheritance analysis of stem growth habit and gene discovery.

Publications

  • Type: Journal Articles Status: Published Year Published: 2018 Citation: Li, S., Ding, Y., Zhang, D., Wang, X., Tang, X., Dai, D., Jin, H., Lee, S.H., Cai, C. and Ma, J. 2018. Parallel domestication with a broad mutational spectrum of determinate stem growth habit in leguminous crops. Plant Journal doi: 10.1111/tpj.14066.
  • Type: Journal Articles Status: Published Year Published: 2018 Citation: Zeng, A., Chen, P., Korth, K.L., Ping, J., Thomas, J., Wu, C., Srivastava, S., Pereira, A., Hancock, F., Brye, K., and Ma, J. 2018. RNA sequencing analysis of salt tolerance in soybean (Glycine max). Genomics. doi: 10.1016/j.ygeno.2018.03.020.
  • Type: Journal Articles Status: Published Year Published: 2018 Citation: A. Xavier, R. Thapa, W.M. Muir, and K.M. RAINEY*. 2018. Population and Quantitative Genomic Properties of the USDA Soybean Germplasm Collection. Plant Genetic Resources. https://doi.org/10.1017/S1479262118000102 Attribution: AX performed the statistical analysis; AX and RT wrote the manuscript;WM contributed to the theoretical basis of the manuscript and provided
  • Type: Journal Articles Status: Published Year Published: 2018 Citation: A. Xavier, D. Jarquin, R. Howard, V. Ramasubramanian, J. Specht, G. Graef, W. Beavis, B. Diers, Q. Song, P. Cregan, R. Nelson, R. Mian, J. Shannon, L. McHale, D. Wang, W. Schapaugh, A. K.M. Rainey C.V, Lorenz, S. Xu, W. Muir, and K.M. RAINEY. 2018. Genome-wide footprints of grain yield stability and environmental interactions in a multi-parental soybean population. G3: Genes | Genomes | Genetics. DOI: 10.1534/g3.117.300300 Attribution: KMR contributed to the theoretical basis of the manuscript, and operated some of the the multi-environment yield trials.