Abstract
This study evaluated the effectiveness of genomic selection (GS) in loblolly pine (Pinus taeda) using a two-generation closed breeding population and a genetically diverse and a large Mainline population. Single-step genomic best linear unbiased prediction (ssGBLUP) models were used to include all phenotypic, genotypic, and pedigree information. Prediction accuracies of genomic estimated breeding values reached up to 0.70 for stem volume and stem straightness. Prediction accuracy showed a strong linear relationship with mean relatedness between training and validation populations (r > 0.92). Scaling the genomic and pedigree relationship matrices improved model stability, increased prediction accuracy, and reduced bias in genomic estimated breeding values. Estimates of heritability and variance components from ssGBLUP were consistent with pedigree-based models, particularly when genomic relationships were properly scaled. Genomic selection had approximately 50% more genetic gain per year relative to conventional selection. Overall, these results demonstrate that GS can be effectively integrated into operational Pinus taeda breeding programs, given sustained investment in large, well-connected training populations with high-quality phenotypic data. We also outline the planned implementation of GS in the North Carolina State University Cooperative Tree Improvement Program to increase genetic gain.
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Claims 8
Claim · model-written summary
Prediction accuracies of genomic estimated breeding values reached up to 0.70 for stem volume and stem straightness.
- Taxon
- loblolly pine
Pinus taedae0 - Outcome
- Prediction accuracies of genomic estimated breeding valuese1
- Value
- up to 0.70e1
- Qualifier
- stem volume and stem straightnesse1
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“in loblolly pine (Pinus taeda)”
e1“Prediction accuracies of genomic estimated breeding values reached up to 0.70 for stem volume and stem straightness”
Claim · model-written summary
Prediction accuracy increased with the mean relatedness between training and validation populations (r > 0.92).
- Taxon
- loblolly pinePinus taedae0
- Treatment
- mean relatedness between training and validation populationse2
- Outcome
- Prediction accuracye2
- Direction
- increaseinferrede2
- Value
- r > 0.92e2
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“in loblolly pine (Pinus taeda)”
e2“Prediction accuracy showed a strong linear relationship with mean relatedness between training and validation populations (r > 0.92)”
Claim · model-written summary
Scaling the genomic and pedigree relationship matrices increased prediction accuracy.
- Taxon
- loblolly pinePinus taedae0
- Treatment
- Scaling the genomic and pedigree relationship matricese3
- Outcome
- prediction accuracye3
- Direction
- increasede3
Not reportedComparator
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“in loblolly pine (Pinus taeda)”
e3“Scaling the genomic and pedigree relationship matrices improved model stability, increased prediction accuracy, and reduced bias in genomic estimated breeding values”
Claim · model-written summary
Scaling the genomic and pedigree relationship matrices reduced bias in genomic estimated breeding values.
- Taxon
- loblolly pinePinus taedae0
- Treatment
- Scaling the genomic and pedigree relationship matricese3
- Outcome
- bias in genomic estimated breeding valuese3
- Direction
- reducede3
Not reportedComparator
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“in loblolly pine (Pinus taeda)”
e3“Scaling the genomic and pedigree relationship matrices improved model stability, increased prediction accuracy, and reduced bias in genomic estimated breeding values”
Claim · model-written summary
Scaling the genomic and pedigree relationship matrices improved model stability.
- Taxon
- loblolly pinePinus taedae0
- Treatment
- Scaling the genomic and pedigree relationship matricese3
- Outcome
- model stabilitye3
- Direction
- improvede3
Not reportedComparator
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“in loblolly pine (Pinus taeda)”
e3“Scaling the genomic and pedigree relationship matrices improved model stability, increased prediction accuracy, and reduced bias in genomic estimated breeding values”
Claim · model-written summary
Heritability and variance component estimates from ssGBLUP were consistent with pedigree-based models, particularly when genomic relationships were scaled.
- Taxon
- loblolly pinePinus taedae0
- Outcome
- Estimates of heritability and variance componentse4
- Qualifier
- particularly when genomic relationships were properly scalede4
- Comparator
- pedigree-based modelse4
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“in loblolly pine (Pinus taeda)”
e4“Estimates of heritability and variance components from ssGBLUP were consistent with pedigree-based models, particularly when genomic relationships were properly scaled”
Claim · model-written summary
Genomic selection gave approximately 50% more genetic gain per year than conventional selection.
- Taxon
- loblolly pinePinus taedae0
- Treatment
- Genomic selectione5
- Outcome
- genetic gain per yeare5
- Direction
- moree5
- Value
- approximately 50%e5
- Comparator
- conventional selectione5
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“in loblolly pine (Pinus taeda)”
e5“Genomic selection had approximately 50% more genetic gain per year relative to conventional selection”
Claim · model-written summary
Genomic selection can be effectively integrated into operational Pinus taeda breeding programs given sustained investment in training populations.
- Taxon
- Pinus taedaPinus taedae6
- Outcome
- effectively integrated into operational Pinus taeda breeding programse6
- Qualifier
- given sustained investment in large, well-connected training populationse6
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“in loblolly pine (Pinus taeda)”
e6“GS can be effectively integrated into operational Pinus taeda breeding programs, given sustained investment in large, well-connected training populations with high-quality phenotypic data”