Skip to main content
  • Facebook
  • Twitter
  • YouTube
  • LinkedIn
  • Google Plus
  • Other GSA Resources
    • Genetics Society of America
    • Genetics
    • Genes to Genomes: The GSA Blog
    • GSA Conferences
    • GeneticsCareers.org

Institution: Massachusetts Inst of Technol MIT Libs

  • Log in
G3: Genes | Genomes | Genetics

Main menu

  • HOME
  • ISSUES
    • Current Issue
    • Early Online
    • Archive
  • ABOUT
    • About the journal
    • Why publish with us?
    • Editorial board
    • Contact us
  • SERIES
    • All Series
    • Genomic Prediction
    • Multiparental Populations
  • ARTICLE TYPES
    • About Article Types
    • Genome Reports
    • Meeting Reports
    • Mutant Screen Reports
    • Software and Data Resources
  • PUBLISH & REVIEW
    • Scope & publication policies
    • Submission & review process
    • Article types
    • Prepare your manuscript
    • Submit your manuscript
    • After acceptance
    • Guidelines for reviewers
  • SUBSCRIBE
    • Email alerts
    • RSS feeds
  • Other GSA Resources
    • Genetics Society of America
    • Genetics
    • Genes to Genomes: The GSA Blog
    • GSA Conferences
    • GeneticsCareers.org

User menu

  • Log out

Search

  • Advanced search
G3: Genes | Genomes | Genetics

Advanced Search

  • HOME
  • ISSUES
    • Current Issue
    • Early Online
    • Archive
  • ABOUT
    • About the journal
    • Why publish with us?
    • Editorial board
    • Contact us
  • SERIES
    • All Series
    • Genomic Prediction
    • Multiparental Populations
  • ARTICLE TYPES
    • About Article Types
    • Genome Reports
    • Meeting Reports
    • Mutant Screen Reports
    • Software and Data Resources
  • PUBLISH & REVIEW
    • Scope & publication policies
    • Submission & review process
    • Article types
    • Prepare your manuscript
    • Submit your manuscript
    • After acceptance
    • Guidelines for reviewers
  • SUBSCRIBE
    • Email alerts
    • RSS feeds
Previous ArticleNext Article

Optimizing Selection and Mating in Genomic Selection with a Look-Ahead Approach: An Operations Research Framework

Saba Moeinizade, Guiping Hu, Lizhi Wang and Patrick S. Schnable
G3: Genes, Genomes, Genetics Early online May 20, 2019; https://doi.org/10.1534/g3.118.200842
Saba Moeinizade
Iowa State University
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: sabamz@iastate.edu
Guiping Hu
Iowa State University
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Lizhi Wang
Iowa State University
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Patrick S. Schnable
Iowa State University
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • Article
  • Info & Metrics
Loading

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.

  • Genetic gain
  • Genomic selection
  • Look-ahead Selection
  • Simulation
  • Optimization
  • Received October 27, 2018.
  • Revision received April 9, 2019.
  • Accepted April 26, 2019.
  • Copyright © 2019, G3: Genes, Genomes, Genetics
Previous ArticleNext Article
Back to top

PUBLICATION INFORMATION

Volume 9 Issue 12, December 2019

G3: Genes|Genomes|Genetics: 9 (12)

ARTICLE CLASSIFICATION

Genomic Prediction
Email

Thank you for sharing this G3: Genes | Genomes | Genetics article.

NOTE: We request your email address only to inform the recipient that it was you who recommended this article, and that it is not junk mail. We do not retain these email addresses.

Enter multiple addresses on separate lines or separate them with commas.
Optimizing Selection and Mating in Genomic Selection with a Look-Ahead Approach: An Operations Research Framework
(Your Name) has forwarded a page to you from G3: Genes | Genomes | Genetics
(Your Name) thought you would be interested in this article in G3: Genes | Genomes | Genetics.
Alerts
Enter your email below to set up alert notifications for new article, or to manage your existing alerts.
SIGN UP OR SIGN IN WITH YOUR EMAIL
View PDF
Share

Optimizing Selection and Mating in Genomic Selection with a Look-Ahead Approach: An Operations Research Framework

Saba Moeinizade, Guiping Hu, Lizhi Wang and Patrick S. Schnable
G3: Genes, Genomes, Genetics Early online May 20, 2019; https://doi.org/10.1534/g3.118.200842
Saba Moeinizade
Iowa State University
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: sabamz@iastate.edu
Guiping Hu
Iowa State University
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Lizhi Wang
Iowa State University
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Patrick S. Schnable
Iowa State University
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
del.icio.us logo Digg logo Reddit logo Twitter logo CiteULike logo Facebook logo Google logo Mendeley logo
Citation

Optimizing Selection and Mating in Genomic Selection with a Look-Ahead Approach: An Operations Research Framework

Saba Moeinizade, Guiping Hu, Lizhi Wang and Patrick S. Schnable
G3: Genes, Genomes, Genetics Early online May 20, 2019; https://doi.org/10.1534/g3.118.200842
Saba Moeinizade
Iowa State University
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: sabamz@iastate.edu
Guiping Hu
Iowa State University
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Lizhi Wang
Iowa State University
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Patrick S. Schnable
Iowa State University
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero

Related Articles

Cited By

More in this TOC Section

  • Efficiency of a Constrained Linear Genomic Selection Index To Predict the Net Genetic Merit in Plants
  • Optimized Genetic Testing for Polledness in Multiple Breeds of Cattle
  • Benchmarking Parametric and Machine Learning Models for Genomic Prediction of Complex Traits
Show more Genomic Prediction
  • Top
  • Article
  • Info & Metrics

GSA

The Genetics Society of America (GSA), founded in 1931, is the professional membership organization for scientific researchers and educators in the field of genetics. Our members work to advance knowledge in the basic mechanisms of inheritance, from the molecular to the population level.

Online ISSN: 2160-1836

  • For Authors
  • For Reviewers
  • For Advertisers
  • Submit a Manuscript
  • Editorial Board
  • Press Releases

SPPA Logo

GET CONNECTED

RSS  Subscribe with RSS.

email  Subscribe via email. Sign up to receive alert notifications of new articles.

  • Facebook
  • Twitter
  • YouTube
  • LinkedIn
  • Google Plus

Copyright © 2019 by the Genetics Society of America

  • About G3
  • Terms of use
  • Permissions
  • Contact us
  • International access