Progress 11/01/16 to 09/30/17
Target Audience:PI Gore was an instructor at the Tucson Plant Breeding Institute (Tucson, Arizona) for the two modules: Introduction to Plant Quantitative Genetics and Advanced Statistical Plant Breeding. Classroom teaching examples included goals, background information, and preliminary results from this Hatch project and the 50 students in attendance were representing public institutions, private companies, and non-profit organizations from NY, the US and several other countries. While teaching at Cornell in 2017, Gore presented the project's phenotyping research efforts in several lectures to undergraduate and graduate students that included those from local farming communities and the New York metropolitan area over the semester in High-Throughput Plant Phenotyping (PLBRG 4110). It was the first introduction for many of them to high-throughput plant phenotyping and its role in accelerating the genetic improvement of crops important to New York State and beyond. In addition, PI Gore presented preliminary results from this Hatch project at several scientific conferences and seminars at the national and international levels. Changes/Problems:There are no major problems, changes to objectives, or delays to report. There have been only minor modifications to the research procedure and none of which will cause significant deviations from goals and deliverables. Because of adverse weather conditions at planting, it was only possible to have a single planting date. What opportunities for training and professional development has the project provided?The Hatch project has been central to training the next-generation of plant breeders in the Gore lab and helped to address the paucity of women and racial and ethnic minorities in agriculture. PI Gore (field and computational), Technician III Nicholas Kaczmar (field), and Research Support Specialist III James Clohessy (computational) provided training in field (pollinations and phenotyping), lab (ear phenotyping), and computational (mobile applications, programming, and data analysis) research to Brazilian (1 male), Chinese (1 female), and American (1 female) graduate students and a postdoctoral associate (Chinese, female). In the past year, the Hatch project involved four undergraduate students that participated in field, lab, and computational research and of which two were women. How have the results been disseminated to communities of interest?As an instructor at the Tucson Plant Breeding Institute (two modules: Introduction to Plant Quantitative Genetics and Advanced Statistical Plant Breeding) over the past year in Arizona (January), PI Gore included classroom teaching examples on the Hatch project's sweetcorn high-throughput phenotyping research efforts to the more than 50 students in attendance from public institutions, private companies, and non-profit organizations. Furthermore, collectively, these students represented several countries. While teaching at Cornell in 2017, PI Gore presented the project's sweet corn high-throughput phenotyping research efforts and in general, the importance of germplasm in several lectures to undergraduate and graduate students over the semester in High-Throughput Plant Phenotyping (PLBRG 4110), as well as a lecture on high-throughput phenotyping to undergraduate and graduate students in Methods of Plant Breeding Laboratory (PLBR 4060). The Ph.D. student (Matt Baseggio) conducting research on the project presented Hatch project high-throughput phenotyping results at the International Sweet Corn Development Association in Chicago, IL in December 2016. What do you plan to do during the next reporting period to accomplish the goals?
What was accomplished under these goals?
An experimental field trial comprised of 660 sweet corn plots was planted in two replications on June 9, 2017 at the Cornell University Musgrave Research Farm in Aurora, NY. Each replicate contained 134 sweet corn inbred accessions provided by the USDA-ARS Germplasm Resources Information Network (GRIN), along with 430 sweet corn inbred accessions from our Sweet Corn Diversity panel developed by Cornell University (PI Gore and Co-PI Smith) and University of Wisconsin (collaborator William Tracy), and 4 check lines. The experimental field trail was arranged as an incomplete block design with two replications. Each replicate was divided into 3 superblocks based on average plant height of each accession in order to minimize shading effects. Each accession was planted as a single plot, and the order of the accession within the incomplete block was randomized. Stand counts, days to anthesis, and days to silking were measured for each plots. Plant height was collected from 5 plants from each plot on a weekly basis to monitor plant growth throughout the season. An unmanned aerial vehicle (DJI Matrice 600) outfitted with a RGB camera (Sony alpha A6000) and 5 band multispectral camera (MicaSense Rededge) was flown over the plots on a weekly basis for aerial collection of phenotypic image data. Digital elevation models are being developed from images taken with the RGB camera to determine plant height for each flight. The multispectral camera collects images from 5 spectral bands (blue, green, red, red edge, and near infrared) to calculate several vegetative health indices for each plot such as Normalized Difference Vegetation Index (NDVI), Normalized Difference Red Edge (NDRE), Color Infrared (CIR), and Optimized Soil-Adjusted Vegetation Index (OSAVI). Leaf relative chlorophyll content and photosynthetic parameters that are known to be related to the productivity and health of plants were evaluated at flowering using a non-invasive, handheld and high-throughput phenotyping device (called MultispeQ) in the field. Photosynthetic traits included photosystem II quantum yield (ΦII, qP and qL), non-photochemical quenching (ΦNPQ, NPQt and ΦNO) and light-driven proton translocation (linear electron flow, LEF). Primary ear leaves were measured in two plants within each plot. Because photosynthetic traits are highly influenced by environmental factors, light intensity, ambient temperature and ambient humidity were recorded by MultispeQ at the time of measuring photosynthetic parameters. A series of statistical analyses were performed on the multiple photosynthetic traits collected in the SAS, ASReml-R and RStudio software. Leaf relative chlorophyll content had negligible correlations with all measured environmental factors. Photosynthetic traits except ΦNO were highly correlated with light intensity, while having negligible correlations with the other environmental factors. Additionally, light intensity accounted for substantial variance (R2) of the measured photosynthetic traits except ΦNO when conducting simple linear regression. Therefore, light intensity was included as a fixed effect when fitting mixed linear models for the following analyses. For individual traits, box-cox transformation, outlier removal and model fitting were conducted sequentially to estimate the best linear unbiased prediction (BLUP) of each accession across two replications. Broad-sense heritability was calculated using the same model, with the heritabilities of photosynthetic traits ranging from 0.33 to 0.59 and 0.74 for relative chlorophyll content. The sweet corn diversity panel was genotyped using genotyping-by-sequencing. Single-nucleotide polymorphism (SNP) markers were called using the Tassel 5 production pipeline. After filtering markers, a total of 223,800 high quality SNPs was retained for genome-wide association studies (GWAS) and whole-genome prediction (WGP).Currently, we are working on GWAS and WGP analyses using the single year BLUP data. GWAS results showed that 9, 6, 6, 3, 1, 5, 10 and 8 SNPs were significantly associated (false discovery rate < 5%) with chlorophyll content, ΦII, ΦNO, ΦNPQ, nPQt, LEF, qL and qP, respectively. These measurements and analyses allow for the deeper understanding of maize photosynthesis responses in field conditions, dissecting the genetic architecture of these traits, and identifying genetic markers associated with photosynthetic related traits. Eventually, it will help plant breeders to develop new sweet corn germplasm with high performance under stress environments to meet increasing demand for food production.
Conference Papers and Presentations
Baseggio, M., Murray, M., Kaczmar, N., Magallanes-Lundback, M., Buckler, E. S., DellaPenna, D., Tracy, W., Smith, M. E., and Gore, M. A. Genetic analysis of kernel nutritional quality and agronomic traits in sweet corn. Talk presented at the International Sweet Corn Development Association (ISCDA) Meeting in Chicago, IL. December 5-6, 2016.