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Multi-Objective Optimization Approac...

Shrote, Robert Zachary,

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Multi-Objective Optimization Approaches to Plant Breeding /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Multi-Objective Optimization Approaches to Plant Breeding // Robert Zachary Shrote.
Author:	Shrote, Robert Zachary,
Description:	1 electronic resource (484 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 86-05, Section: B.
Contained By:	Dissertations Abstracts International86-05B.
Subject:	Agronomy. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31639705
ISBN:	9798346745327

Multi-Objective Optimization Approaches to Plant Breeding /
Shrote, Robert Zachary,

Multi-Objective Optimization Approaches to Plant Breeding /Robert Zachary Shrote. - 1 electronic resource (484 pages)

Source: Dissertations Abstracts International, Volume: 86-05, Section: B.

Most plant breeding programs must contend with achieving multiple breeding objectives while subject to time and resource constraints. Breeding objectives may include, but are not limited to, yield, standability, adaptation to mechanical harvesting, resistance to abiotic and biotic stressors, flavor and texture quality attributes, environmental adaptation, and genetic diversity. Many times, breeding objectives are conflicting in nature and a balance must be achieved between objectives. Both single- and multi-objective optimization techniques can be used to assist breeders in identifying trade-offs between conflicting objectives and determine a single, optimal solution which satisfies the goals of a breeding program. In this dissertation, we address and tackle several of the challenges faced when balancing multiple breeding objectives. Most of our efforts in addressing these challenges revolve around selection or mate-pairing methodologies. First, we begin by reviewing many of the selection strategies which have been proposed over the years, offer insights into the mathematical nature of several of these selection strategies, and observe attributes of successful selection strategies. Next, we introduce the software package PyBrOpS, a Python package capable of performing single- and multi-objective optimizations and single- and multi-trait simulations for breeding programs. PyBrOpS is unique among other simulation software packages in that it emphasizes multi-objective optimizations for multi-trait breeding scenarios and allows breeders to identify and visualize optimal trade-offs. Next, we introduce two new multi-objective selection strategies that seek to balance genetic gain and genetic diversity. While not the best tested selection strategies, we find that these two new selection strategies are performant, robust, and offer high long-term genetic gain while maintaining a high level of genetic diversity. Next, we introduce two new, multi-trait selection strategies which are customizable variations of marker effect upweighting-based selection strategies. One of these selection strategies dynamically adjusts its upweighting parameter based on the time to a final generation. We find that these upweighting-based selection strategies offer improvements in genetic gain and genetic diversity in ideal scenarios but fail in realistic scenarios unless a low degree of upweighting is specified. Finally, we empirically test the ability to predict the mean and variance of progenies from a diverse set of crosses. Predicting progeny mean and variance is important because it can be used to inform selections and mate-pairings. We developed genomic prediction models using information from a diversity panel, crossed several members of the diversity panel together to create progeny families, and compared predictions of progeny mean and variance using our genomic prediction models to what we observed. We were successful in predicting progeny mean, but largely unable to predict progeny variance.

English

ISBN: 9798346745327Subjects--Topical Terms:

2122783
Agronomy.
Subjects--Index Terms:

Multi-objective optimization

Multi-Objective Optimization Approaches to Plant Breeding /
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520 $a Most plant breeding programs must contend with achieving multiple breeding objectives while subject to time and resource constraints. Breeding objectives may include, but are not limited to, yield, standability, adaptation to mechanical harvesting, resistance to abiotic and biotic stressors, flavor and texture quality attributes, environmental adaptation, and genetic diversity. Many times, breeding objectives are conflicting in nature and a balance must be achieved between objectives. Both single- and multi-objective optimization techniques can be used to assist breeders in identifying trade-offs between conflicting objectives and determine a single, optimal solution which satisfies the goals of a breeding program. In this dissertation, we address and tackle several of the challenges faced when balancing multiple breeding objectives. Most of our efforts in addressing these challenges revolve around selection or mate-pairing methodologies. First, we begin by reviewing many of the selection strategies which have been proposed over the years, offer insights into the mathematical nature of several of these selection strategies, and observe attributes of successful selection strategies. Next, we introduce the software package PyBrOpS, a Python package capable of performing single- and multi-objective optimizations and single- and multi-trait simulations for breeding programs. PyBrOpS is unique among other simulation software packages in that it emphasizes multi-objective optimizations for multi-trait breeding scenarios and allows breeders to identify and visualize optimal trade-offs. Next, we introduce two new multi-objective selection strategies that seek to balance genetic gain and genetic diversity. While not the best tested selection strategies, we find that these two new selection strategies are performant, robust, and offer high long-term genetic gain while maintaining a high level of genetic diversity. Next, we introduce two new, multi-trait selection strategies which are customizable variations of marker effect upweighting-based selection strategies. One of these selection strategies dynamically adjusts its upweighting parameter based on the time to a final generation. We find that these upweighting-based selection strategies offer improvements in genetic gain and genetic diversity in ideal scenarios but fail in realistic scenarios unless a low degree of upweighting is specified. Finally, we empirically test the ability to predict the mean and variance of progenies from a diverse set of crosses. Predicting progeny mean and variance is important because it can be used to inform selections and mate-pairings. We developed genomic prediction models using information from a diversity panel, crossed several members of the diversity panel together to create progeny families, and compared predictions of progeny mean and variance using our genomic prediction models to what we observed. We were successful in predicting progeny mean, but largely unable to predict progeny variance.
546 $a English
590 $a School code: 0128
650 4 $a Agronomy. $3 2122783
650 4 $a Genetics. $3 530508
650 4 $a Computer science. $3 523869
650 4 $a Plant sciences. $3 3173832
650 4 $a Soil sciences. $3 2122699
653 $a Multi-objective optimization
653 $a Plant breeding
653 $a Stochastic simulation
653 $a Optimal solution
690 $a 0285
690 $a 0369
690 $a 0984
690 $a 0479
690 $a 0481
710 2 $a Michigan State University. $b Plant Breeding, Genetics and Biotechnology - Crop and Soil Sciences - Doctor of Philosophy. $e degree granting institution. $3 3815983
720 1 $a Thompson, Addie M. $e degree supervisor.
773 0 $t Dissertations Abstracts International $g 86-05B.
790 $a 0128
791 $a Ph.D.
792 $a 2024
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