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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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Example of the two patterns of predictive ability observed among traits, as an increasing number of markers is added to the model. Each marker group is represented by a set of 10 markers. (Left) For DBH, the maximum predictive ability was detected when 380 groups of markers (3800 markers) were included in the model. (Right) For the trait Rust_gall_vol, predictive ability pattern reached a maximum when only 10 groups (100 markers) were added. Lines indicate the predictive ability of RR–BLUP (solid line), Bayes Cπ (dashed line), and RR–BLUP B (dotted line) as reported in Table 1 and Table S6.
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fig2: Example of the two patterns of predictive ability observed among traits, as an increasing number of markers is added to the model. Each marker group is represented by a set of 10 markers. (Left) For DBH, the maximum predictive ability was detected when 380 groups of markers (3800 markers) were included in the model. (Right) For the trait Rust_gall_vol, predictive ability pattern reached a maximum when only 10 groups (100 markers) were added. Lines indicate the predictive ability of RR–BLUP (solid line), Bayes Cπ (dashed line), and RR–BLUP B (dotted line) as reported in Table 1 and Table S6.

Mentions: Prediction of phenotype was also performed with RR–BLUP, but adding increasingly larger marker subsets, until all markers were used jointly in the prediction. The predictive ability was plotted against the size of the subset of markers (Figure 2). The pattern of the prediction accuracy was similar for 13 out 17 traits (Figure 2A), where differences were mainly found in the rate with which the correlation reached the asymptote. In these cases, the size of the subset ranged from 820 to 4790 markers. However, a distinct pattern was detected for disease-resistance-related traits, density, and CWAL (Figure 2B). In these cases, maximum predictive ability was reached with smaller marker subsets (110–590 markers) and decreased with the addition of more markers. An additional RR–BLUP was performed using as covariates only the marker subset for which maximum predictive ability was obtained. For traits where a large number of markers (>600) explain the phenotypic variability, RR–BLUP B was similar to RR–BLUP or Bayesian methods (Table S6). However, for traits where the maximum predictive ability (Density, Rust_bin, Rust_gall_vol) was reached with a smaller number of marker (<600), RR–BLUP B performed significantly better than RR–BLUP. For example, the predictive ability of the trait Rust_gall_vol was 61% higher using RR–BLUP B (0.37) compared to the traditional RR–BLUP (0.23) and also improved relative to BLASSO (0.24), Bayes A (0.28 and Bayes Cπ (0.29).


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)

Example of the two patterns of predictive ability observed among traits, as an increasing number of markers is added to the model. Each marker group is represented by a set of 10 markers. (Left) For DBH, the maximum predictive ability was detected when 380 groups of markers (3800 markers) were included in the model. (Right) For the trait Rust_gall_vol, predictive ability pattern reached a maximum when only 10 groups (100 markers) were added. Lines indicate the predictive ability of RR–BLUP (solid line), Bayes Cπ (dashed line), and RR–BLUP B (dotted line) as reported in Table 1 and Table S6.
© Copyright Policy - open-access
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC3316659&req=5

fig2: Example of the two patterns of predictive ability observed among traits, as an increasing number of markers is added to the model. Each marker group is represented by a set of 10 markers. (Left) For DBH, the maximum predictive ability was detected when 380 groups of markers (3800 markers) were included in the model. (Right) For the trait Rust_gall_vol, predictive ability pattern reached a maximum when only 10 groups (100 markers) were added. Lines indicate the predictive ability of RR–BLUP (solid line), Bayes Cπ (dashed line), and RR–BLUP B (dotted line) as reported in Table 1 and Table S6.
Mentions: Prediction of phenotype was also performed with RR–BLUP, but adding increasingly larger marker subsets, until all markers were used jointly in the prediction. The predictive ability was plotted against the size of the subset of markers (Figure 2). The pattern of the prediction accuracy was similar for 13 out 17 traits (Figure 2A), where differences were mainly found in the rate with which the correlation reached the asymptote. In these cases, the size of the subset ranged from 820 to 4790 markers. However, a distinct pattern was detected for disease-resistance-related traits, density, and CWAL (Figure 2B). In these cases, maximum predictive ability was reached with smaller marker subsets (110–590 markers) and decreased with the addition of more markers. An additional RR–BLUP was performed using as covariates only the marker subset for which maximum predictive ability was obtained. For traits where a large number of markers (>600) explain the phenotypic variability, RR–BLUP B was similar to RR–BLUP or Bayesian methods (Table S6). However, for traits where the maximum predictive ability (Density, Rust_bin, Rust_gall_vol) was reached with a smaller number of marker (<600), RR–BLUP B performed significantly better than RR–BLUP. For example, the predictive ability of the trait Rust_gall_vol was 61% higher using RR–BLUP B (0.37) compared to the traditional RR–BLUP (0.23) and also improved relative to BLASSO (0.24), Bayes A (0.28 and Bayes Cπ (0.29).

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