Related Experiment Video
Updated: May 12, 2026
07:01
Processing the Loblolly Pine PtGen2 cDNA Microarray
Published on: March 20, 2009
Genomic selection validated across two generations of loblolly pine breeding
Fikret Isik1, Mohammad Nasir Shalizi1, Trevor Walker1
1Department of Forestry and Environmental Resources, Cooperative Tree Improvement Program, North Carolina State University, Raleigh, NC 27695, United States.
G3 (Bethesda, Md.)
|May 11, 2026
Summary
Genomic selection (GS) significantly boosts genetic gain in loblolly pine breeding by 50% annually. Effective GS implementation requires large, connected training populations with quality data for improved tree breeding.
Area of Science:
- Forestry and Plant Breeding
- Quantitative Genetics
Background:
- Loblolly pine (Pinus taeda) breeding programs aim to accelerate genetic gain.
- Genomic selection (GS) offers a potential method to enhance breeding efficiency.
Purpose of the Study:
- To evaluate the effectiveness of GS in loblolly pine using single-step GBLUP.
- To assess prediction accuracy and genetic gain under GS.
Main Methods:
- Utilized single-step genomic best linear unbiased prediction (ssGBLUP) models.
- Incorporated phenotypic, genotypic, and pedigree data from breeding populations.
- Analyzed prediction accuracy and relatedness, and scaled relationship matrices.
Main Results:
- Achieved prediction accuracies up to 0.70 for stem volume and straightness.
- Demonstrated a strong linear relationship between prediction accuracy and population relatedness (r > 0.92).
- GS increased annual genetic gain by approximately 50% compared to conventional selection.
Conclusions:
- GS is effective for operational loblolly pine breeding programs.
- Sustained investment in large, high-quality training populations is crucial for GS success.
- GS implementation in operational programs can significantly increase genetic gain.
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