Uncovering the role of transposons in maize variation
Transposable elements (TEs) account for the majority of genome sequence in maize and other crops. Locus-specific and cytogenetic studies suggest that TEs can be highly variable within plant species and account for agronomically important QTL. However, we lack knowledge of the role of TEs in contributing to genomic, epigenomic, transcriptomic and phenotypic diversity in crop plants, in part due to the highly repetitive nature of these sequences, which has, to date, made them recalcitrant to available technologies. In this project we are developing annotation and diversity resources to enable the study of TEs in maize. Within this project these resources will be utilized to study the role of TEs in contributing to phenotypic variation through the use of quantitative genetics and population genetics approaches. These efforts will elucidate the potential to utilize knowledge of TE variation to understand genotype by environment interactions and to improve genotype-phenotype predictions in crop species. The project will monitor how TEs contribute to a dynamic maize genome and identify TEs that are moving in modern maize varieties. The proposed research will monitor the mechanisms through which TE variation can influence phenotype through the analysis of TE influences on chromatin and gene expression. These experiments will shed light upon the role of TE polymorphisms in contributing to variation in the maize epigenome, transcriptome and phenome. This project will provide foundational knowledge of the role of TEs that can be used to enhance maize improvement and responses to abiotic stress.
PI: Nathan Springer; Co-PIs: Candice Hirsch, Jeffrey Ross-Ibarra, Emily Josephs, Shawn Kaeppler
Funding provided by the National Science Foundation
Phenomics Tools for Corn Breeding and Management Decisions
Measuring phenotypes is at the core of corn breeding and management recommendation practices. Due to the time and labor intensive nature of manually phenotyping plants, the data used for these efforts has typically focused only on end of season traits such as plant height, ear height, and yield. Development of robust, rapid, and low cost methods to evaluate key morphological features will allow for measurements throughout the growing season. This is beneficial in a breeding context as it allows breeders to understand variation in environmental responsiveness between varieties and to potentially develop varieties more resilient to increasingly extreme weather events. Likewise, repeated measurements of production fields will allow for more informed management decisions and facilitate precision agriculture management practices. Recent technological advances in drones, sensors, and computational resources are allowing for this to become a reality. We propose to optimize analysis procedures for extracting traits of agronomic importance using unmanned aerial systems. These procedures will be applied to two field settings. The first will be a relatively homogeneous field with variable genotypes, akin to a corn breeding nursery to evaluate plants on a plot basis for variable responses to environmental conditions. The second field setting will be production fields that have variable conditions throughout the field, but are genetically homogeneous. These first two objectives rely on standard RGB imagery for phenotypic evaluation. In our third objective we will use sensors that have high spectral resolution to evaluate plants under a number of stress conditions to identify the unseen signatures of stress that proceed visible phenotypes, allowing for earlier management interventions. Detecting and understanding non-visible early symptoms will also provide valuable information for future physiological and genetic work to understand the specific mechanisms plants use to respond to their environment so that we can create more resilient plants for the future.
PI: Candice Hirsch; Co-PIs: Cory Hirsch and Nathan Springer
Funding providing by the Minnesota Corn Reserach and Promotion Council
Dispensable genes in Maize - Their Role in Heterosis, Specific Combining Ability, and Accuracy of Genomic Prediction of Hybrid Performance
Breeding procedures in maize are designed to efficiently identify inbred line combinations, which form hybrids with improved agronomic performance in target environments. Genomic prediction of hybrid performance has the potential to dramatically increase genetic improvement by both increasing selection intensity and speeding up breeding cycles. No information is yet available of how dispensable genes (i.e., genic copy number and/or present absent genes) and their interaction with the environment affect the accuracy of genomic prediction in maize and what role they play in the expression of heterosis. We hypothesize that the accuracy of genomic hybrid prediction models constructed with single nucleotide polymorphisms (SNPs) tightly linked to known present/absence variation (PAV) and copy number variation (CNV) will be higher than the accuracy of genomic hybrid prediction models built without such SNPs, and that the interaction between dispensable genes and the environment might substantially contribute to the variation of agronomically important traits. We envision that the results obtained in this project will shed light on the contribution of genic copy number variation to heterosis in maize, and ultimately explore genomic selection methodology for improvement of hybrid performance for agronomically important traits, such as grain yield or tolerance to high plant density.
PI: Martin Bohn; Co-PIs: Candice Hirsch, Alex Lipka, Mark Mikel
Funding provided by the United States Department of Agriculture - National Institute of Food and Agriculture
Dissecting Natural Mechanisms for Genome Content Variation and the Impact on Phenotypic Variation
Using maize as a model system, this project will systematically characterize the extent of genome content variation among diverse genotypes in a plant species, identify the genetic mechanisms responsible for this variation in genome content, and measure the impact on phenotypic variation in plants. The research plan integrates genomics, metabolomics, quantitative genetics, and statistical genetics to further our understanding of genome content variation and the role mechanistic origin plays in phenotypic outcomes. Specifically, this project will 1) identify genome content variation between maize inbred lines using a combination of de novo genome assemblies and exome capture using a combination of short- and long-read sequencing technologies, 2) identify mechanistic signatures that elucidate the origin of genome content variation on a genome-wide scale, 3) implement Genome Wide Association Studies to identify genome content variation associated with quantitative, qualitative, essential, and dispensable phenotypic and chemotypic (surface lipid profiles and kernel content) classes of traits in a diverse panel of maize inbred lines, and 4) use statistical genetic approaches to determine if there is a relationship between the mechanisms that create genome content variation and phenotypic outcomes.
Intellectual Merit: Variation in genome content is becoming more evident across the biosphere, and genome content variation in maize is rampant. Despite the documentation of extensive genome content variation between individuals within species, little is known about the origin of this variation or the impacts it has on phenotypic variation. Understanding how natural mechanistic origins of genome content variation impact phenotypes can provide valuable insights into mechanisms that can be adapted to generate artificial genome content variation through various genome editing and transgenic approaches, which could make a significant contribution to expanding field of synthetic biology. Thus, the knowledge gained from this project can be directly used to maximize outcomes from synthetic biology in maize and other economically important plant species. Moreover, understanding natural mechanisms that drive genome content variation and the impact mechanistic origin has on phenotypic variation will provide fundamental knowledge concerning the relationship between genotype, chemotype, and phenotype, and will improve our ability to dissect the genetic basis of phenotypic variation. Additionally, fundamental insights into natural mechanisms that drive genome content variation and phenotypic impact can be leveraged in traditional plant breeding approaches through improved genomic prediction methods.
Broader Impacts: There is a severe underrepresentation of women, particularly minority women, in STEM fields. Increasing the representation of women in STEM can help improve income gender gaps among the many other societal benefits. Mentorship and training opportunities for young women who express an interest in STEM is an important step toward increasing representation of women in STEM careers. Our team of three female PIs has a passion for engaging females in STEM fields. We propose three synergistic activities aimed at providing young women with both mentoring and valuable training opportunities in genomics, metabolomics, and statistical genetics. The specific proposed activities are 1) summer research experiences for female teachers and high school and undergraduate students including activities centered at building a sense of community among female participants, 2) public engagement through the SCIENCE BOUND and “Taking the Road Less Traveled” programs at ISU with an emphasis on females in STEM, and 3) generating a database of mentorship and training opportunities for females in STEM.
PI: Candice Hirsch; Co-PIs: Marna Yandeau-Nelson, Suzanne McGaugh
Funding provided by the National Science Foundation
Genetic Resources to Reduce Acrylamide in Corn Chips
Acrylamide accumulation in chips is the product of a reaction between free amino acids (i.e. asparagine) and reducing sugars (i.e. glucose, fructose, and sucrose) during high-temperature cooking and processing. The level of acrylamide in chips is highly regulated because acrylamide is a carcinogen. Due to the method in which corn chips are made, an enzyme can be added to the masa to break down reducing sugars and limit this reaction. Our goal, however, is to determine if there is natural variation for these compositional traits that can be incorporated into food grade corn breeding programs, and to identify key genes controlling variation for these compositional traits that are involved in the reaction that produces acrylamide.
PI: Candice Hirsch; CoPI: George Annor
Funding provided by PepsiCo
Foundational Genetic Platform for Improving Food Grade Corn
The corn chip industry relies on the development of food grade corn hybrids that meet quality specifications necessary for the post-harvest processing chain. However, breeding efforts targeted at breeding food grade corn hybrids, particularly white food grade corn hybrids, have been relatively limited. Currently, large scale evaluation of hybrids in pilot plants is required to determine which hybrids can be managed through a given processing chain, as the specific traits and parameters necessary for a hybrid to be successful are not well understood. This project seeks to identify traits that are effective proxies to determine kernel processing success, evaluate the effective environment on these traits, and identify markers associated with these traits that can packaged as a breeders toolkit for improving food grade corn hybrids for the corn chip processing industry.
PI: Candice Hirsch; CoPI: George Annor
Funding provided by PepsiCo
Genomes to Fields Initiative
The overall objective of this project is to leverage genomic information with phenotypic and environmental data to enable working knowledge and prediction of plant performance under variable growing conditions. G2F is an umbrella initiative to support translation of maize genomic information for the benefit of growers, consumers and society. This public-private partnership is building on publicly funded corn genome sequencing projects to develop approaches to understand the functions of corn genes and specific alleles across environments. Ultimately this information will be used to enable accurate prediction of the phenotypes of corn plants in diverse environments. There are many dimensions to the over-arching goal of understanding genotype-by-environment (GxE) interactions, including which genes impact which traits and trait components, how genes interact among themselves (GxG), the relevance of specific genes under different growing conditions, and how these genes influence plant growth during various stages of development. For more information visit www.genomes2fields.org.
Funding providing by the Minnesota Corn Research and Promotion Council and the Iowa Corn Growers Association
