A stochastic simulation approach for improving response in genomic selection

dc.contributor.advisor Guiping Hu
dc.contributor.author Moeinizade, Saba
dc.contributor.department Industrial and Manufacturing Systems Engineering
dc.date 2019-01-15T09:15:34.000
dc.date.accessioned 2020-06-30T03:13:14Z
dc.date.available 2020-06-30T03:13:14Z
dc.date.copyright Sun Apr 01 00:00:00 UTC 2018
dc.date.embargo 2018-10-13
dc.date.issued 2018-01-01
dc.description.abstract <p>The world population is increasing rapidly and is projected to hit 9.1 billion by 2050.</p> <p>As the demand for food increases, agriculture production will continue to play a significant</p> <p>role. As a method to maintain and increase agriculture production, plant breeding is critical.</p> <p>To improve efficiency in the plant breeding process, an interdisciplinary effort is needed.</p> <p>Operations research as a discipline focuses on decision making and efficient and effective</p> <p>strategy design. In this thesis, operations research tools of simulation, optimization and</p> <p>mathematical modeling are applied to plant breeding, specifically Genomic Selection (GS).</p> <p>GS techniques allow breeders to select the best plants to make crosses by predicting, for</p> <p>example, the heights of the plants using the genotypic data at an early stage of the plant</p> <p>growth cycle, saving both time and cost that would otherwise be necessary to grow the</p> <p>plants to maturity before their heights can be measured. A major limitation of existing GS</p> <p>approaches is the trade-off between short-term genetic gains and long-term growth potential.</p> <p>Some approaches focus on achieving short-term genetic gains at the cost of losing genetic</p> <p>diversity for long-term gains, and others aim to maximize the long-term genetic gains but</p> <p>are unable to achieve it by the breeding deadline. Our contribution is to define a new look</p> <p>ahead method for assessing a selection decision, which evaluates the probability to achieve</p> <p>both genetic diversity and breeding deadline. Moreover, we propose a heuristic algorithm</p> <p>to find an optimal selection decision with respect to the new method. Our new selection</p> <p>method outperforms the other selection methods in the literature.</p>
dc.format.mimetype application/pdf
dc.identifier archive/lib.dr.iastate.edu/etd/16735/
dc.identifier.articleid 7742
dc.identifier.contextkey 13578526
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath etd/16735
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/30918
dc.language.iso en
dc.source.bitstream archive/lib.dr.iastate.edu/etd/16735/Moeinizade_iastate_0097M_17346.pdf|||Fri Jan 14 21:05:13 UTC 2022
dc.subject.disciplines Agriculture
dc.subject.keywords Genetic gain
dc.subject.keywords Genomic selection
dc.subject.keywords Look-ahead selection
dc.subject.keywords Stochastic simulation
dc.title A stochastic simulation approach for improving response in genomic selection
dc.type article
dc.type.genre thesis
dspace.entity.type Publication
relation.isOrgUnitOfPublication 51d8b1a0-5b93-4ee8-990a-a0e04d3501b1
thesis.degree.discipline Industrial and Manufacturing Systems Engineering
thesis.degree.level thesis
thesis.degree.name Master of Science
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