A stochastic simulation approach for improving response in genomic selection

<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>

Name thesis:degree_name

Master of Science

Level thesis:degree_level

thesis

Discipline thesis:degree_discipline

Industrial and Manufacturing Systems Engineering

Department dc:contributor.department

Department of Industrial and Manufacturing Systems Engineering

Year dc:date.issued

2018

Author dc:creator

• Moeinizade, Saba

Advisor dc:contributor.advisor

• Guiping Hu

Language dc:language.iso

en

Identifier

archive/lib.dr.iastate.edu/etd/16735/

OAI identifier oai:identifier

oai:dr.lib.iastate.edu:20.500.12876/30918