Title:

Integrating genomics, phenomics, and data science tools to predict complex traits

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Bio:

Karansher Sandhu (Karan), is a Soybean Product Development Scientist at Bayer Crop Sciences, where he is using his genomics, genetics, breeding, phenomics, data science, and machine learning skills to enhance genetic gain and product development in the North America soybean breeding pipeline. Karan has more than six years of experience in plant breeding, genetics, genomics, and data science, working on multiple crops and projects with collaborators in USA and other countries. Karan is keen on approaching crop improvement from different perspectives and angles. Karan’s research interests include development of improved soybean varieties, breeding strategies for increasing genetic gain, utilization of molecular genetics for crop improvement, implementing high throughput phenotyping techniques, and enablement of increased efficiency in breeding research operations. Prior to this role, Karan worked as a Research Assistant at Washington State University, while pursuing his PhD in a wheat breeding program. Karan have developed various genomic and phenomic based prediction models for complex quantitative traits in wheat. Karan has authored more than 30 research papers and book chapters in different reputed journals and publishers.

Abstract:

The last decade witnessed an unprecedented increase in the adoption of genomic selection (GS) and phenomics tools in plant breeding programs, especially in major cereal crops. GS has demonstrated the potential for selecting superior genotypes with high precision and accelerating the breeding cycle. Phenomics is a rapidly advancing domain to alleviate phenotyping bottlenecks and explores new large-scale phenotyping and data acquisition methods. Analytical methods for complex datasets traditionally used in other disciplines represent an opportunity for improving prediction accuracy in GS. We used the genomic and phenomic datasets to predict different traits using various statistical, machine, and deep learning-based models. Our results indicated the inclusion of secondary traits in the models increases the prediction accuracy. Multi-trait models perform superior to unit-trait models when traits have low heritability and are correlated.

Title:

Explore fruit/vegetable nutrigenomic properties as new target traits to improve phytochemicals and nutrients uptake/health outcomes.

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Bio:

Dr. Massimo Iorizzo is an Associate Professor at North Carolina State University. He is a plant geneticist and his research program focus on comparative structural genomics and genetics of traits associated with enhanced quality characteristics including health properties. Crops of interest in his program includes blueberry, carrot, banana, pineapple, spinach, cranberry and potato. He is the director of the VacCAP project a multi-state and multidisciplinary projects in blueberry and cranberry to advance breeding for improved fruit quality. Through a transdisciplinary research approach he began efforts to understand the relationship among quality traits, bioactive accumulation, and their nutrigenomic properties (bioaccessibility). In the long term, his research will facilitate the selection of new fruit and vegetable cultivars with improved quality and health promoting characteristics.

Dr. Iorizzo is a very active scientific writer and has published 81 peer-reviewed publications and over 246 non-peer reviewed publications. Dr. Iorizzo has active research collaborations with the blueberry team at UF.

Abstract:

Today, a growing body of evidences support the role of phytochemicals from fruits and vegetables (F&V), in meeting nutritional requirements and preventing chronic diseases in the US. To meet these nutritional goals, breeding/genetic strategies for fruit and vegetable crops (F&V) largely focused on phytochemical contents, and made significant genetic gain. However, factors contributing to bioaccessibility, bioavailability and bioactivity (nutrigenomics trait) have not been addressed. For example, the chemical structure of phytochemicals can modulate their ability to be released from the plant matrix and be absorbed, and this trait is independent from total overall phytochemical content. As a results it remain unknown if genetic gain made by breeders to increase nutrient and phytochemicals accumulation translate into higher nutrient density and health benefits. In this seminar Dr. Iorizzo will present the results of interdisciplinary work carried across multiple F&V (blueberry, banana, spinach) that demonstrates the independent relationship between bioactive content and bioaccessbility and identified candidate genes that can significantly enhance bioactive delivery. The research establish a framework to identify nutrigenomic markers as a differentiating trait to effectively select F&V with improve nutritional outcomes. Opportunities and challenges will also be discussed.

Title:

Genomics-assisted breeding of potato.

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Bio:

Jeffrey Endelman is an Associate Professor at the University of Wisconsin-Madison. He trained as a computational scientist (PhD Caltech) before discovering his passion for agriculture and retraining as a plant breeder (PhD Washington State University, Postdoc Cornell). Endelman is an international leader in the application of quantitative genetics to plant breeding and has created several R packages used by hundreds of scientists. A major theme of his research has been the development of methods for genomic analysis and selection in autotetraploid species, which he applies as lead scientist for the University of Wisconsin potato breeding program. Dr. Endelman is also active in the worldwide effort to create diploid, inbred lines of potato, which should improve the efficiency of variety development and gene discovery.

Abstract:

The last decade witnessed an unprecedented increase in the adoption of genomic selection (GS) and phenomics tools in plant breeding programs, especially in major cereal crops. GS has demonstrated the potential for selecting superior genotypes with high precision and accelerating the breeding cycle. Phenomics is a rapidly advancing domain to alleviate phenotyping bottlenecks and explores new large-scale phenotyping and data acquisition methods. Analytical methods for complex datasets traditionally used in other disciplines represent an opportunity for improving prediction accuracy in GS. We used the genomic and phenomic datasets to predict different traits using various statistical, machine, and deep learning-based models. Our results indicated the inclusion of secondary traits in the models increases the prediction accuracy. Multi-trait models perform superior to unit-trait models when traits have low heritability and are correlated.

Title:

Global food security under climate change – lessons from CAM research

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Bio:

Professor Sixue Chen completed his Ph.D. study in plant biochemistry in China, and postdoctoral research in Germany, Denmark and University of Pennsylvania, USA. Dr. Chen is the Chair and Professor of Department of Biology at the University of Mississippi. He is currently a courtesy Professor of Department of Biology, University of Florida. He was a Professor in Department of Biology, UFGI, PMCB, and Director of Proteomics and Mass Spectrometry Facility at University of Florida. Dr. Chen has established three major research projects: plant stomatal disease triangle, glucosinolate metabolism, and photosynthesis transition from C3 to CAM. His lab is known for application of proteomics and mass spectrometry technologies in elucidating plant molecular networks. His research has been continuously funded by different agencies, including NSF, USDA, NIH, University of Mississippi and UF internal fund. In addition to his own research program, Dr. Chen has collaborated extensively with faculty at different institutions worldwide. His research has led to more than 300 publications. Dr. Chen serves as Associate Editors and Board Members of Metabolomics, Frontiers in Plant Proteomics, Journal of Proteomics, Archives of Proteomics and Bioinformatics, and other journals. He is an elected Fellow of the American Association for the Advancement of Science.

Abstract:

Human population is expected to reach 9 billion by 2050, and global crop productivity needs to increase by 70% to feed the growing population. Unfortunately, freshwater shortage and other adverse environmental conditions have posed grand challenges to crop yield and food security. How to increase crop water use efficiency (WUE) is an urgent question for plant biologists to tackle. More than 200 million years ago, nature has evolved a unique photosynthesis mechanism - crassulacean acid metabolism (CAM), which enabled much higher WUE (3-10 times more) than C3 plants. Mesembryanthemum crystallinum (common ice plant) a facultative CAM plant, which can shift from C3 to CAM under stress conditions. Here we identified a three-day transition period and conducted single-cell-type transcriptomics in isolated stomatal guard cells to determine the molecular changes in this key cell type during the transition. Out of 495 differential transcripts, 18 were transcription factors. One of the transcription factors is homeobox 7(McHB7). Using Agrobacterium-mediated transformation, we created McHB7-overexpression (OE) ice plants. Under salt and drought stresses, the growth of OE plants was better than wild type (WT). Proteomics identified 475, 510 and 378 proteins to be significantly changed in the OE under control, salt stress, and drought conditions, respectively. Most increased proteins were involved in Calvin cycle, citric acid cycle, and antioxidant pathways. Some were found to participate in ABA biosynthesis or response. Metabolomics revealed that many metabolites and phytohormones were changed in the OE plants, e.g., ABA was increased in the OE under control and treatment conditions. Yeast one-hybrid analysis revealed that McHB7 can bind to ERD and ABA-related motifs. And protein-protein interaction analysis discovered the candidate proteins that were responsive to stresses and hormones (e.g., ABA). The results suggested that McHB7 may contribute to enhance ice plant stress tolerance through ABA signaling.

Title:

ML Models of Genomic Information in Plants and Microbes.

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Bio:

Justin Gerke is a data science team leader at Corteva Agriscience. He holds a PhD in genomics from the Washington University School of Medicine and continued postdoctoral studies as a Merck Fellow of the Life Sciences Research Foundation at Princeton University. Justin joined Pioneer (now Corteva) in 2011 as a research scientist focused on the use of genomics in plant breeding. Justin is a coauthor of patents and publications on the application of genomic data and analytics to the analysis of quantitative traits and population genetic inference.

Abstract:

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