Non Technical Summary
The overall goal of the proposed project is to increase the efficiency of crop breeding programs by developing and deploying improved genomic selection strategies that rely on improvements in the selection and mating steps.As a consequence of growing populations, changing diets, and the challenges of climate change, agricultural systems must produce more with less. More means greater demand for agricultural products such as food, feed, energy and fiber. Less means reduced agricultural inputs (at least on a per output basis) such as water, fertilizer, pesticides and a reduced environmental footprint, all on less land. A key tool for making agriculture systems more productive, sustainable and resilient is genetic improvement via breeding.Selection based on favorable (phenotypic selection) traits was central to the domestication of crops and has been used successfully in plant breeding for thousands of years. Recurrent selection is a special case of phenotypic selection that refers to a type of population improvement which includes the following key steps: phenotyping (evaluation), selection, and mating.Phenotyping involves measuring trait values of a population, such as grain and biomass yield, flowering time and color, etc. This step is often time-consuming/labor intensive and many phenotypes cannot be accurately scored without replicated tests.Selection involves the identification of those individuals within the population that exhibit the most favorable trait values.Mating occurs following selection and selected lines are typically randomly or arbitrarily mated to create a new population for phenotyping and selection. Little research has been conducted on the effects of other mating strategies.The phenotyping step can be costly, time consuming, logistically challenging, and/or destructive because it often involves a large number of individuals and many factors that can affect the measurements. Genomic selection (GS), which relies on efficient, high-throughput genotyping technologies (to determine the genetic composition of plants), enables breeders to predict the phenotype of plants based on their genotypes using advanced statistical models and a population of plants of known phenotypes used to "train" the prediction model. This new approach has transformed breeding, because it eliminates or reduces the need for phenotyping.Even moderately accurate genomic prediction can result in substantial cost saving and improve the rate of genetic gain in breeding programs. Previous literature has focused on three areas: the first is to improve the accuracy of phenotype prediction, upon which selection is based; the second is to define other quantitative indicators in lieu of the predicted phenotype to reflect the fitness of the lines from the genetics perspective and their likelihood to produce superior progeny; the third is the selection of an optimal training population that maximizes the accuracy of the prediction model when only a limited number of plants can be phenotyped. Although genomic prediction has transformed breeding, the selection and mating steps have not received equal attention and typically remain the same as in traditional phenotypic selection. Optimizing these two steps is a focus of the proposed project.To design optimal selection and mating strategies and to understand their interactions with other aspect of breeding programs we will make use of simulations within a framework of operations research, which deals with the application of advanced analytical methods to help make better decisions. Significantly, we will also develop user-friendly tools to allow breeders to optimize these parameters for their own breeding scenarios. The new selection and mating strategies that we propose will be designed using advanced mathematical programming and optimization techniques, fully tailorable to reflect user preferences with respect to the trade-offs among cost, time, and probability of success.
Animal Health Component
Research Effort Categories
Goals / Objectives
This project will use the framework of operations research to optimize selection and mating parameters for specific types of breeding programs. A sophisticated simulation platform will be developed and used to test hundreds of GS experiments, each with different parameters and breeding goals. In addition, populations will be developed that could be used for empirical validation experiments as part of another project sometime in the future. Finally, a user-friendly interface that will allow breeders to explore and optimize these parameters relative to their own breeding scenarios. The proposed research will contribute to enhancing the efficiency of breeding programs by introducing engineering processes. In addition, this project will train three graduate students at the intersection of operations research and plant breeding, helping to develop "next generation plant breeders".Project Goal 1: Design of Improved Genomic Selection Strategies:We will apply operations research methodology to design and optimize four distinct GS strategies to address various research needs.Project Goal 2: Develop an Exploratory Simulation Platform to Evaluate Novel GS Strategies:An exploratory simulation platform will be developed in Matlab to evaluate the GS strategies developed in Specific Aim 1.Project Goal 3: Evaluate Novel GS Strategies using the Simulation Platform.Project Goal 4: We will create populations that could be used for empirical validation experiments as part of another project sometime in the future.Project Goal 5: Develop a user-friendly interface for our GS simulation platform:A graphical user interface will be designed and implemented to allow breeders to explore alternative GS strategies using the simulation platform developed in Project Goal2.
The proposed project will focus on optimizing selection and mating steps of GS because our preliminary simulation studies have shown that the success of GS is sensitive to the parameters of these steps. In Project Goal 1 we will design and develop GS strategies appropriate for various situations faced by breeders (e.g., short or long-term goals). In Project Goal 2 we will develop a simulation platform that will allow us to evaluate and iteratively optimize in Project Goal 3 the GS strategies developed in Project Goal. In Project Goal 4 we will develop maize populations that will be suitable for empirical testing of simulation platform developed in Project Goal 2 for future studies. Finally, in Project Goal 5 we will develop a user-friendly interface that will allow end-users to use our simulation platform to explore various GS strategies and parameters.This project will train three graduate students at the intersection of operations research and plant breeding, helping to generate "next generation plant breeders". Project progress and availability of the products will be presented in scientific conferences and disseminated via academia seminars and publications. Project Goals 2 and 3 will be used to evaluate the GS strategies built in Project Goal 1 and the data will be presented in annual/final reports and scientific publications.