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Accuracy of genomic selection methods in a standard data set of loblolly pine (Pinus taeda L.).

Resende MF, Muñoz P, Resende MD, Garrick DJ, Fernando RL, Davis JM, Jokela EJ, Martin TA, Peter GF, Kirst M - Genetics (2012)

Bottom Line: Genomic selection is expected to be particularly valuable for traits that are costly to phenotype and expressed late in the life cycle of long-lived species.A limitation of RR-BLUP is the assumption of equal contribution of all markers to the observed variation.However, RR-BLUP B performed equally well as the Bayesian approaches.The genotypic and phenotypic data used in this study are publically available for comparative analysis of genomic selection prediction models.

View Article: PubMed Central - PubMed

Affiliation: Genetics and Genomics Graduate Program, University of Florida, Gainesville, FL 32611, USA.

ABSTRACT
Genomic selection can increase genetic gain per generation through early selection. Genomic selection is expected to be particularly valuable for traits that are costly to phenotype and expressed late in the life cycle of long-lived species. Alternative approaches to genomic selection prediction models may perform differently for traits with distinct genetic properties. Here the performance of four different original methods of genomic selection that differ with respect to assumptions regarding distribution of marker effects, including (i) ridge regression-best linear unbiased prediction (RR-BLUP), (ii) Bayes A, (iii) Bayes Cπ, and (iv) Bayesian LASSO are presented. In addition, a modified RR-BLUP (RR-BLUP B) that utilizes a selected subset of markers was evaluated. The accuracy of these methods was compared across 17 traits with distinct heritabilities and genetic architectures, including growth, development, and disease-resistance properties, measured in a Pinus taeda (loblolly pine) training population of 951 individuals genotyped with 4853 SNPs. The predictive ability of the methods was evaluated using a 10-fold, cross-validation approach, and differed only marginally for most method/trait combinations. Interestingly, for fusiform rust disease-resistance traits, Bayes Cπ, Bayes A, and RR-BLUB B had higher predictive ability than RR-BLUP and Bayesian LASSO. Fusiform rust is controlled by few genes of large effect. A limitation of RR-BLUP is the assumption of equal contribution of all markers to the observed variation. However, RR-BLUP B performed equally well as the Bayesian approaches.The genotypic and phenotypic data used in this study are publically available for comparative analysis of genomic selection prediction models.

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Regression of RR–BLUP predictive ability on narrow-sense heritability for 17 traits (trend line is shown, R2 = 0.79).
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fig1: Regression of RR–BLUP predictive ability on narrow-sense heritability for 17 traits (trend line is shown, R2 = 0.79).

Mentions: For all methods, the ability to predict phenotypes was linearly correlated with trait heritability. The strongest correlation (0.79) was observed for RR–BLUP (Figure 1). The correlation is expected, as traits with lower heritability have phenotypes less reflective of their genetic content, and are expected to be less predictable through genomic selection.


Accuracy of genomic selection methods in a standard data set of loblolly pine (Pinus taeda L.).

Resende MF, Muñoz P, Resende MD, Garrick DJ, Fernando RL, Davis JM, Jokela EJ, Martin TA, Peter GF, Kirst M - Genetics (2012)

Regression of RR–BLUP predictive ability on narrow-sense heritability for 17 traits (trend line is shown, R2 = 0.79).
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3316659&req=5

fig1: Regression of RR–BLUP predictive ability on narrow-sense heritability for 17 traits (trend line is shown, R2 = 0.79).
Mentions: For all methods, the ability to predict phenotypes was linearly correlated with trait heritability. The strongest correlation (0.79) was observed for RR–BLUP (Figure 1). The correlation is expected, as traits with lower heritability have phenotypes less reflective of their genetic content, and are expected to be less predictable through genomic selection.

Bottom Line: Genomic selection is expected to be particularly valuable for traits that are costly to phenotype and expressed late in the life cycle of long-lived species.A limitation of RR-BLUP is the assumption of equal contribution of all markers to the observed variation.However, RR-BLUP B performed equally well as the Bayesian approaches.The genotypic and phenotypic data used in this study are publically available for comparative analysis of genomic selection prediction models.

View Article: PubMed Central - PubMed

Affiliation: Genetics and Genomics Graduate Program, University of Florida, Gainesville, FL 32611, USA.

ABSTRACT
Genomic selection can increase genetic gain per generation through early selection. Genomic selection is expected to be particularly valuable for traits that are costly to phenotype and expressed late in the life cycle of long-lived species. Alternative approaches to genomic selection prediction models may perform differently for traits with distinct genetic properties. Here the performance of four different original methods of genomic selection that differ with respect to assumptions regarding distribution of marker effects, including (i) ridge regression-best linear unbiased prediction (RR-BLUP), (ii) Bayes A, (iii) Bayes Cπ, and (iv) Bayesian LASSO are presented. In addition, a modified RR-BLUP (RR-BLUP B) that utilizes a selected subset of markers was evaluated. The accuracy of these methods was compared across 17 traits with distinct heritabilities and genetic architectures, including growth, development, and disease-resistance properties, measured in a Pinus taeda (loblolly pine) training population of 951 individuals genotyped with 4853 SNPs. The predictive ability of the methods was evaluated using a 10-fold, cross-validation approach, and differed only marginally for most method/trait combinations. Interestingly, for fusiform rust disease-resistance traits, Bayes Cπ, Bayes A, and RR-BLUB B had higher predictive ability than RR-BLUP and Bayesian LASSO. Fusiform rust is controlled by few genes of large effect. A limitation of RR-BLUP is the assumption of equal contribution of all markers to the observed variation. However, RR-BLUP B performed equally well as the Bayesian approaches.The genotypic and phenotypic data used in this study are publically available for comparative analysis of genomic selection prediction models.

Show MeSH
Related in: MedlinePlus