Page Banner

United States Department of Agriculture

Agricultural Research Service

Research Project: BIOINFORMATIC METHODS AND TOOLS TO PREDICT SMALL GRAIN FIELD PERFORMANCE

Location: Plant, Soil and Nutrition Research

Title: Genomic selection in plant breeding: from theory to practice

Authors
item Jannink, Jean-Luc
item Lorenz, Aaron
item Iwata, Hiroyoshi -

Submitted to: Briefings in Functional Genomics and Proteomics
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: January 18, 2010
Publication Date: February 15, 2010
Citation: Jannink, J., Lorenz, A.J., Iwata, H. 2010. Genomic selection in plant breeding: from theory to practice. Briefings in Functional Genomics and Proteomics. 9:166-177.

Interpretive Summary: We intuitively believe that the dramatic drops in the cost of DNA marker information we are currently experiencing should have immediate benefits in accelerating the delivery of crop varieties with improved yield, quality, and biotic and abiotic stress tolerance. But these traits are complex and affected by many genes, each with small effect. Traditional marker assisted selection has been ineffective for such traits. This manuscript provides an introduction to a solution to this problem called “genomic selection” (GS). The manuscript reviews briefly the statistical methods used in GS then reviews the literature on results from simulations using GS, the quantitative and analytical theory addressing problems raised by GS, and results of empirical studies using GS. Rather than seeking to identify individual genetic loci associated with a trait, GS uses all marker data as predictors of performance and consequently delivers more accurate predictions. Selection can be based on GS predictions, potentially leading to more rapid and lower cost gains from breeding. The manuscript also looks forward and considers research needs surrounding methodological questions and the implications of GS for long-term selection.

Technical Abstract: We intuitively believe that the dramatic drops in the cost of DNA marker information we are currently experiencing should have immediate benefits in accelerating the delivery of crop varieties with improved yield, quality, and biotic and abiotic stress tolerance. But these traits are complex and affected by many genes, each with small effect. Traditional marker assisted selection has been ineffective for such traits. The introduction of genomic selection (GS), however, has shifted that paradigm. Rather than seeking to identify individual loci significantly associated with a trait, GS uses all marker data as predictors of performance and consequently delivers more accurate predictions. Selection can be based on GS predictions, potentially leading to more rapid and lower cost gains from breeding. The objectives of this paper are to review essential aspects of GS and summarize the important take-home messages from recent theoretical, simulation, and empirical studies. We then look forward and consider research needs surrounding methodological questions and the implications of GS for long-term selection.

Last Modified: 4/16/2014
Footer Content Back to Top of Page