Campus Units
Industrial and Manufacturing Systems Engineering, Electrical and Computer Engineering, Plant Sciences Institute
Document Type
Article
Publication Version
Published Version
Publication Date
7-1-2019
Journal or Book Title
G3: Genes, Genomes, Genetics
Volume
9
Issue
7
First Page
2123
Last Page
2133
Research Focus Area(s)
Operations Research
DOI
10.1534/g3.118.200842
Abstract
New genotyping technologies have made large amounts of genotypic data available for plant breeders to use in their efforts to accelerate the rate of genetic gain. Genomic selection (GS) techniques allow breeders to use genotypic data to identify and select, for example, plants predicted to exhibit drought tolerance, thereby saving expensive and limited field-testing resources relative to phenotyping all plants within a population. A major limitation of existing GS approaches is the trade-off between short-term genetic gain and long-term potential. Some approaches focus on achieving short-term genetic gain at the cost of reduced genetic diversity necessary for long-term gains. In contrast, others compromise short-term progress to preserve long-term potential without consideration of the time and resources required to achieve it. Our contribution is to define a new “look-ahead” metric for assessing selection decisions, which evaluates the probability of achieving high genetic gains by a specific time with limited resources. Moreover, we propose a heuristic algorithm to identify optimal selection decisions that maximize the look-ahead metric. Simulation results demonstrate that look-ahead selection outperforms other published selection methods.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright Owner
Moeinizade et al.
Copyright Date
2019
Language
en
File Format
application/pdf
Recommended Citation
Moeinizade, Saba; Hu, Guiping; Wang, Lizhi; and Schnable, Patrick, "Optimizing Selection and Mating in Genomic Selection with a Look-Ahead Approach: An Operations Research Framework" (2019). Industrial and Manufacturing Systems Engineering Publications. 220.
https://lib.dr.iastate.edu/imse_pubs/220
Comments
This article is published as Moeinizade, Saba, Guiping Hu, Lizhi Wang, and Patrick S. Schnable. "Optimizing Selection and Mating in Genomic Selection with a Look-Ahead Approach: An Operations Research Framework." G3: Genes, Genomes, Genetics (2019): g3-200842. DOI: 10.1534/g3.118.200842. Posted with permission.