Abstract
Improving the prediction accuracy of a complex trait of interest is key to performing genomic selection (GS) for crop breeding. For the complex trait measured in multiple environments, this paper proposes a two-stage method to solve a linear model that jointly models the genetic effects and the genotype × environment interaction ( G × E ) effects. In the first stage, the least absolute shrinkage and selection operator (LASSO) penalized method was utilized to identify quantitative trait loci (QTL). Then, the ordinary least squares (OLS) approach was used in the second stage to reestimate the QTL effects. As a case study, this approach was used to improve the prediction accuracies of flowering time (FT), oil content (OC), and seed yield per plant (SY) in Brassica napus ( B. napus ). The results showed that the G × E effects reduced the mean squared error (MSE) significantly. Numerous QTL were environment-specific and presented minor effects. On average, the two-stage method, named OLS post-LASSO, offers the highest prediction accuracies (correlations are 0.8789, 0.9045, and 0.5507 for FT, OC, and SY, respectively). It was followed by the marker × environment interaction ( M × E ) genomic best linear unbiased prediction (GBLUP) model (correlations are 0.8347, 0.8205, and 0.4005 for FT, OC, and SY, respectively), the LASSO method (correlations are 0.7583, 0.7755, and 0.2718 for FT, OC, and SY, respectively), and the stratified GBLUP model (correlations are 0.6789, 0.6361, and 0.2860 for FT, OC, and SY, respectively). The two-stage method showed an obvious improvement in the prediction accuracy, and this study will provide methods and reference to improve GS of breeding.
Abstract from the paper’s OpenAlex record; open copy licensed cc-by. Read at the source ↗
The text the model read, with the source record’s inline markup (italics, sub- and superscripts) rendered and its character entities decoded; quote offsets refer to the raw text.
Claims 5
Claim · model-written summary
Genotype by environment effects significantly reduced prediction mean squared error.
- Taxon
- Brassica napus
_Brassica_napus_-_Habit.jpg?width=60)
Brassica napuse0 - Outcome
- mean squared error (MSE)e1
- Direction
- reducede1
- Qualifier
- G × E effectse1
Not reportedComparatorValue
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
_Brassica_napus_-_Habit.jpg?width=500)
Verbatim
e0“in Brassica napus ( B. napus )”
e1“the G × E effects reduced the mean squared error (MSE) significantly”
Claim · model-written summary
Numerous QTL were environment specific and had minor effects.
- Taxon
- Brassica napusBrassica napuse0
- Gene
- QTLe1
- Outcome
- environment-specific and presented minor effectse1
Not reportedDirection
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“in Brassica napus ( B. napus )”
e1“Numerous QTL were environment-specific and presented minor effects”
Claim · model-written summary
OLS post-LASSO attained a flowering time prediction correlation of 0.8789, the highest among tested models.
- Taxon
- Brassica napusBrassica napuse0
- Treatment
- OLS post-LASSOe1
- Outcome
- prediction accuraciese1
- Direction
- higheste1
- Value
- 0.8789e1
- Qualifier
- FTe1
Not reportedOrgan / stage
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“in Brassica napus ( B. napus )”
e1“the two-stage method, named OLS post-LASSO, offers the highest prediction accuracies (correlations are 0.8789, 0.9045, and 0.5507 for FT, OC, and SY, respectively)”
Claim · model-written summary
OLS post-LASSO attained an oil content prediction correlation of 0.9045, the highest among tested models.
- Taxon
- Brassica napusBrassica napuse0
- Treatment
- OLS post-LASSOe1
- Outcome
- prediction accuraciese1
- Direction
- higheste1
- Value
- 0.9045e1
- Qualifier
- OCe1
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“in Brassica napus ( B. napus )”
e1“the two-stage method, named OLS post-LASSO, offers the highest prediction accuracies (correlations are 0.8789, 0.9045, and 0.5507 for FT, OC, and SY, respectively)”
Claim · model-written summary
OLS post-LASSO attained a seed yield prediction correlation of 0.5507, the highest among tested models.
- Taxon
- Brassica napusBrassica napuse0
- Treatment
- OLS post-LASSOe1
- Outcome
- prediction accuraciese1
- Direction
- higheste1
- Value
- 0.5507e1
- Qualifier
- SYe1
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“in Brassica napus ( B. napus )”
e1“the two-stage method, named OLS post-LASSO, offers the highest prediction accuracies (correlations are 0.8789, 0.9045, and 0.5507 for FT, OC, and SY, respectively)”