The Omics Era - A New Page in Plant Breeding

2018 Keynote Speakers

Aaron Lorenz

Aaron Lorenz

Aaron Lorenz, Ph.D.

Assistant Professor, Agronomy and Plant Genetics

University of Minnesota

Bio: Dr. Aaron Lorenz is an Associate Professor of Soybean Breeding and Genetics in the Department of Agronomy and Plant Genetics at the University of Minnesota. The University of Minnesota Soybean Breeding Program develops specialty, food-type, and general-use soybean varieties adapated to the Upper Midwest. Dr. Lorenz’s research focuses on the application of new technologies to plant breeding, the mining of genetic diversity for cultivar development, and the genetic architecture underlying complex traits. Many of his publications have explored the application and optimization of genomic selection for plant breeding. Dr. Lorenz teaches introductory plant genetics and breeding to undergraduates and advanced plant breeding to graduate students. He received a B.S. from the University of Minnesota in 2002, an M.S. in Plant Breeding from Iowa State in 2005, and a Ph.D. in Plant Breeding and Plant Genetics from the University of Wisconsin in 2008. Following his Ph.D., he was a Postdoctoral Research Associate at Cornell University from 2009 to 2010 and an Assistant Professor at the University of Nebraska from 2010 to 2015. He joined the faculty of the University of Minnesota in 2015.

Exploring genomic prediction for crop improvement: Broadening the reach of evaluation

Abstract: There are countless combinations of factors that could be tested in a breeding program in an effort to identify superior and well-adapted varieties. A variety development pipeline, however, is limited by the resources available for testing performance for complex traits across multiple environments. To circumvent this dilemma, forms of prediction have long been used in plant breeding to help ensure that valuable testing resources are only used on breeding line candidates of high genetic value. Genomic prediction is one of the latest forms of prediction used in plant breeding programs, and holds great potential for widening the reach of testing and evaluation in breeding programs. This seminar will briefly highlight a few examples, followed by an in-depth analysis of genomic prediction for single-cross performance in maize. Opportunity and techniques for model optimization will also be discussed.


Duke Pauli

Duke Pauli

Duke Pauli, Ph.D.

Assistant Professor

University of Arizona

Bio: Dr. Duke Pauli is a new Assistant Professor at the University of Arizona where his lab focuses on elucidating the genetics of abiotic stress tolerance including heat and drought. He attended Montana State University where he received his PhD in Plant Genetics with an emphasis on the application of genomic-assisted breeding for the development of superior malting barley varieties for agricultural production. Upon completion of his PhD, he joined the lab of Dr. Michael Gore at Cornell University where his work centered on the use of field-based, high-throughput phenotyping technologies to investigate stress adaptive traits in cotton as well as the temporal dynamics of QTL expression. His future research is aimed at continued development and application of field-deployable technologies to better understand the physiological response of plants to adverse environmental conditions.

Phenomics: Illuminating the Genetic Basis of Cotton Resiliency

Duke Pauli*, Pedro Andrade-Sanchez†, Greg Ziegler‡, Elodie Gazave*, Andrew N. French§, John Heun†, Ivan Baxter‡, Tim L. Setter††, Kelly R. Thorp§, Jeffrey W. White§ and Michael A. Gore*

*Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
†Department of Agricultural and Biosystems Engineering, University of Arizona, Maricopa Agricultural Center, Maricopa, AZ 85138, USA
‡United States Department of Agriculture–Agricultural Research Service (USDA-ARS), Donald Danforth Plant Science Center, St. Louis, MO 63132, USA
§ United States Department of Agriculture–Agricultural Research Service (USDA-ARS), Arid-Land Agricultural Research Center, Maricopa, AZ 85138, USA
††Soil and Crop Sciences Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA

Abstract: Heat and drought stress represent two of the most common abiotic stresses that plants encounter in modern agricultural production systems, resulting in significant economic losses. As climate change continues to increase the frequency and severity of these conditions, the development of stress-resilient cultivars becomes pivotal to sustaining crop yields. Central to meeting this challenge is the ability to elucidate the genetic and physiological basis of key stress adaptive and agronomic traits. To investigate these traits, multidimensional phenotypic data are needed that capture the dynamic response of plants to continuously changing conditions over the growing season. In light of this, we implemented high-throughput phenotyping of the plant canopy to map quantitative trait loci (QTL) controlling stress-responsive traits in a cotton population evaluated under contrasting irrigation treatments in a hot, arid environment. The ability of the field-based, mobile phenotyping system to collect data throughout the growing season revealed the temporal patterns of QTL expression in response to environmental conditions. A subset of these identified canopy trait QTL co-localized with those found to control variation for several physiological and agronomic traits, suggesting pleiotropic QTL. To further enhance these results, we investigated how seed ionomic profiles varied by preferential uptake of soil elements in response to drought conditions under high temperature, such as calcium which is critical for regulating stomatal aperture. This was done in combination with analyzing the ion profile of the soil itself to assess the effects of spatial variability on the observed phenotypic data. These combined results demonstrate the value of multidimensional data sets generated from novel phenotyping technologies to help provide insight into the varied physiological responses of plants to abiotic stress.


Sindhuja Sankaran

Sindhuja Sankaran

Sindhuja Sankaran, Ph.D.

Assistant Professor, Biological Systems Engineering

Washington State University

Bio: Dr. Sindhuja Sankaran is an assistant professor in the Department of Biological Systems Engineering, Washington State University. She is working in the Agricultural Automation Engineering research emphasis area of the department since 2013. Her research interests are towards advanced sensing techniques for high-throughput crop phenotyping, with special focus on the development of optical and chemical sensor-based tools for non-invasive, rapid and continuous crop monitoring applications. In 2015, she led a team to organize a conference on ‘Advances in field-based high-throughput phenotyping and data management’.  She currently is leading phenomics aspect as a part of couple ongoing NIFA-AFRI grants, where her focus is on developing high-throughput phenotyping tools for field and postharvest crop trait evaluation in cereal, legume, and specialty crops. Sankaran holds a BS in Zoology, a MS in Environment Science, a MS in Environmental Engineering, and a PhD in Agricultural and Biosystems Engineering. 

Sensors for phenomics: Role of automated image processing and machine learning in high-throughput sensing

Abstract: Sensor advancements to evaluate crop phenotypes has drastically increased in recent years. Multiple sensors at variable scales have been utilized to assess different traits from plant performance field traits to post-harvest crop quality traits. This talk will discuss some of the sensor developments in phenomic research. The talk will focus on the importance of data analytics associated with high-throughput sensing, especially image processing steps to achieve automated image analysis and machine learning approaches to accomplish robust phenotypic predictions. These aspects will be explained using examples from row/field and tree fruit crops. 


Keerti Rathore

Keerti Rathore

Keerti Rathore, Ph.D.

Professor, Soil and Crop Sciences

Texas A&M University

Bio: Dr. Keerti Rathore is a professor in the Department of Soil and Crop Sciences at Texas A&M University. His research focuses on the genetic improvement of crops through the development of protocols for efficient delivery of genes, optimal expression of transgenes, and rapid recovery of transgenic cotton, rice, and sorghum. Projects include regeneration from cell & tissue cultures, use of new reporter and selectable marker genes to understand and improve the transformation process, promoter analysis, enhancement of disease resistance in plants, conferring drought tolerance to crop plants, conferring insect resistance to crop plants, improving nutritional quality of seeds, and production of recombinant antibodies and vaccines in plants. He has recently applied for deregulation of transgenic localized suppressed of Gossypol with cotton seed, making his lab one of a handful of public programs that have released a deregulated transgenic crop. Additionally, his group is working on root rot nematode resistance and development of CRISPR/CAS protocols for cotton. Keerti holds a BS in Zoology/Botany/Chemistry, a MS in Plant Sciences, and a Ph.D. in Plant Physiology.

From Conception to(wards) Deregulation of Ultra-low Gossypol Cottonseed - A 21-year-long Odyssey

Abstract: The amount of cottonseed (a highly undervalued byproduct of lint production), produced worldwide contains enough protein to meet the basic requirements of 500 - 600 million people per year at a rate of 50 g protein/day. However, gossypol, a noxious compound present in the seed glands, renders cottonseed unfit as food for human consumption or even as feed for non-ruminant animals.  We have used seed-specific RNAi to silence δ-cadinene synthase gene(s), thus blocking the first committed step in the biosynthesis of gossypol. The gossypol levels in the seed have been reduced from ~10,000 ppm to well below 450 ppm, a level considered safe for human consumption. The rest of the plant maintains normal levels of this defensive terpenoid for protection against insects and diseases. This Ultra-low Gossypol Cottonseed (ULGCS) trait, when commercialized has the potential to make cottonseed as valuable as the lint, thus providing additional benefits to the cotton producers.

The project began in 1996 and after overcoming many hurdles and setbacks, the proof-of-concept was established in 2005/2006. The breakthrough was finally achieved with the availability and use of right tools and technologies, persistence, and teamwork. Most other labs would have moved on after publishing one or two papers based on this achievement. However, considering the potential impact of developing this technology into a viable product, it would have been unconscionable not to pursue it further. So, in the second phase of the work, several hundred new ULGCS lines were generated, screened and characterized at biochemical/molecular level. We conducted eight, multi-location, multi-year, regulatory field trials to confirm the stability and heritability of the ULGCS trait. Based on the performance and proof of substantial equivalence (with the exception of the desired, ultra-low gossypol levels in the seed) to the parental variety, a petition to deregulate a ULGCS event has been submitted to USDA-APHIS and a dossier to US-FDA. This ULGCS event is one of only five genetically engineered new plant varieties created by a public institution to seek pre-market approvals in the 25-year history of agricultural biotechnology. 

The potential and limitations of genome-editing technologies, such as the recently discovered CRISPR/Cas9 system, in engineering a trait such as the ULGCS will also be discussed.


John VAn Hemert

John VAn Hemert

John Van Hemert, Ph.D.

DuPont Pioneer

Bio:  Formally trained in Computational Biology, Multivariate Statistics, and Machine Learning, I’ve worked in Discovery R&D at DuPont Pioneer since 2011.  Projects are balanced between developing computational methods that enrich the discovery pipeline, and characterize its products-- some of which appear in scientific and IP literature.  I’ve led statisticians, computational biologists, and data scientists around the world, working on everything from experiment design, to drone imagery analysis, to molecular biotechnology development.  Before that, I was a Staff Scientist at Iowa State University in the Crop Genome Informatics Laboratory where I led modernization of the Plant Expression DataBase (PlExDB).  Prior, I received my PhD (2010) from the Electrical and Computer Engineering Department at Iowa State University in Bioinformatics and Computational Biology, where I worked on Systems Biology of the Grapevine for an international consortium of researchers and published several papers in biological network analysis and mining.  I also hold Bachelor’s degrees (2006) from the University of Northern Iowa, by the Computer Science Department and Business College.

From Satellites to Sequencing: How Omics Fits Into the New Industrial Digital Agriculture

Abstract: In 1905, when Edward Jenkins hired onto the scientific staff at the Connecticut Experiment Station Donald F. Jones, he said, “Young man, what can you do to improve corn?  Connecticut needs a lot of milk.  To get the milk we need lots of corn for our cows, and we lack good land to grow it.”  Jones soon published the double-cross hybrid technique in 1918, which was adopted by Henry A. Wallace to found Pioneer in 1926.  Today we continue to improve crop yield, stress tolerance, and more—not just to feed cows in Connecticut, but to feed a growing world human population.  During this talk, I will describe some of the Precision Agriculture activities at DuPont Pioneer, ranging from satellite-based remote sensing to advanced plant imaging indoors to molecular characterization of the transcriptome and metabolome.  Specifically, I will describe how we constructed data cubes of metabolomics and hyperspectral images to predict nitrogen response and yield in the field.  I will also share how meta-analyses of many omics datasets have introduced new variation into the breeding and selection process.