Genomic breeding value (GEBV) has recently become available in the beef cattle industry. Genomic selection methods are exceptionally valuable for selecting traits, such as marbling, that are difficult to measure until later in life. One method to utilize information from sparse marker panels is the ...
Genomic breeding value (GEBV) has recently become available in the beef cattle industry. Genomic selection methods are exceptionally valuable for selecting traits, such as marbling, that are difficult to measure until later in life. One method to utilize information from sparse marker panels is the Bayesian model selection method with RJMCMC. The accuracy of prediction varies between a multiple SNP model with RJMCMC (0.47 to 0.73) and a least squares method (0.11 to 0.41) when using SNP information, while the accuracy of prediction increases in the multiple SNP (0.56 to 0.90) and least square methods (0.21 to 0.63) when including a polygenic effect. In the multiple SNP model with RJMCMC model selection method, the accuracy ($r^2$) of GEBV for marbling predicted based only on SNP effects was 0.47, while the $r^2$ of GEBV predicted by SNP plus polygenic effect was 0.56. The accuracies of GEBV predicted using only SNP information were 0.62, 0.68 and 0.73 for CWT, EMA and BF, respectively. However, when polygenic effects were included, the accuracies of GEBV were increased to 0.89, 0.90 and 0.89 for CWT, EMA and BF, respectively. Our data demonstrate that SNP information alone is missing genetic variation information that contributes to phenotypes for carcass traits, and that polygenic effects compensate genetic variation that whole genome SNP data do not explain. Overall, the multiple SNP model with the RJMCMC model selection method provides a better prediction of GEBV than does the least squares method (single marker regression).
Genomic breeding value (GEBV) has recently become available in the beef cattle industry. Genomic selection methods are exceptionally valuable for selecting traits, such as marbling, that are difficult to measure until later in life. One method to utilize information from sparse marker panels is the Bayesian model selection method with RJMCMC. The accuracy of prediction varies between a multiple SNP model with RJMCMC (0.47 to 0.73) and a least squares method (0.11 to 0.41) when using SNP information, while the accuracy of prediction increases in the multiple SNP (0.56 to 0.90) and least square methods (0.21 to 0.63) when including a polygenic effect. In the multiple SNP model with RJMCMC model selection method, the accuracy ($r^2$) of GEBV for marbling predicted based only on SNP effects was 0.47, while the $r^2$ of GEBV predicted by SNP plus polygenic effect was 0.56. The accuracies of GEBV predicted using only SNP information were 0.62, 0.68 and 0.73 for CWT, EMA and BF, respectively. However, when polygenic effects were included, the accuracies of GEBV were increased to 0.89, 0.90 and 0.89 for CWT, EMA and BF, respectively. Our data demonstrate that SNP information alone is missing genetic variation information that contributes to phenotypes for carcass traits, and that polygenic effects compensate genetic variation that whole genome SNP data do not explain. Overall, the multiple SNP model with the RJMCMC model selection method provides a better prediction of GEBV than does the least squares method (single marker regression).
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제안 방법
(2008) recently proposed a method to simultaneously analyze whole genome SNP data for association with phenotypes to predict unobserved phenotypes. For prediction of unobserved phenotypes, they applied model selection using RJMCMC to predict genetic value, and the accuracies of unobserved phenotype prediction were higher than for single marker regression. Therefore, the accuracy of the predicted genomic breeding value in our current study suggests that Bayesian model selection using RJMCMC estimates a precise QTL effect for quantitative traits without suffering from the multiple testing problems that commonly occur in the least squares method.
We predicted the BLUP breeding value for the 266 genotyped animals using a numeric relation matrix (NRM) based on pedigree and phenotype from the national progeny test population. For the genomic breeding value, the prediction was performed from a multiple regression analysis using previously selected SNPs from the single SNP analyses with Bayesian model selection (RJMCMC; Fig. 1). To determined how well we predicted GEBV, we used the full set of data for estimation (n=266) and randomly selected animals for prediction and validation (n=100).
Genomic DNA for genotyping assays was extracted from blood samples, and SNP genotyping was performed by SeoLin Bioscience (Seoul, Korea) using the Affymetrix MegAllele GeneChip Bovine Mapping 10K SNP array. Three hundred steers were genotyped for 8,344 SNP, but 34 steers failed to genotype due to low DNA quality from phenol and chloroform contamination.
We fitted a linear mixed model with multiple SNPs as the fixed effect and a polygenic effect to account for additive genetic effects not detected by the SNPs. In the additive genetic model, the observations were a linear function of fixed effect, a polygenic term representing the sum of unidentified additive genetic effects, the additive effects due to SNPs associated with QTL and residuals. The linear model can be written as
(2007) is that the data simulating the single marker had very high LD with the QTL, thus the haplotypes accounted for noise in estimating QTL. In this study, single marker regression analysis provides a broad confidence interval compared to the multiple SNP model due to linkage disequilibrium between the QTL and multiple SNPs. In conclusion, a multiple SNP model using RJMCMC estimates a precise QTL position and shows far better accuracy of prediction for genomic breeding value.
In this study, we attempt to identify QTLs associated with carcass traits and applied this information to predict genomic breeding value with whole genome SNP data in Hanwoo cattle. For the whole genome SNP data, a total of 4,525 SNPs were used for estimation of genomic breeding value for carcass traits in the Hanwoo population.
Crude protein (CP) and total digestible nutrients (TDN) of the concentrate were 14-16 and 68-70%, 11-13 and 71-73% and 11% and 72-73% for the growth period, finishing period I and finishing period II, respectively. Phenotypic data in this study included carcass weight (CWT), eye muscle area (EMA), back fat thickness (BFT) and marbling score (MAR). BFT, EMA and MAR were measured at the 12th-13th rib junction after a 24 hour chill.
1. Posterior density of association of SNPs for MAR (B), BF (D), CWT (F) and EMA (H) using the whole genome approach or likelihood ratio of single SNP regression (A, C, E and G) for 4 carcass traits.
To test the association between SNP and QTL, single marker regression analysis was implemented. Markers were assumed to be in LD with QTL in close proximity, and the evaluated effect was additive (QTL allele substitution effect).
대상 데이터
Carcass data and DNA samples were obtained from 266 Hanwoo descending from 66 sires and unrelated dams (2 - 10 progeny number per sire) from two NIAS experimental stations, Dae-Kwan-Ryoung and Nam-Won. The steers received ad libitum intake of a total mixed diet of concentrate and rice straw in total feed with a ratio of approximately 1.
이론/모형
Table 3. Detection of significant SNPs from single marker regression with varying threshold to select significant SNPs and multiple SNPs (RJMCMC method).
Genotypes were tested for Hardy-Weinberg equilibrium (HWE) to identify possible genotyping errors using the Chi-square test in the R/SNPassoc Package (R Development Core Team). SNPs with HWE (p<0.
In this study, we applied a Bayesian model selection termed the Reversible Jump Markov Chain Monte Carlo (RJMCMC) to estimate genomic breeding values for individual Hanwoo using whole genome SNP data.
성능/효과
1)Number of SNPs for multiple SNPs was averaged all significant SNPs detected in every MCMC round (n=1,000 round).
73 for CWT, EMA and BF, respectively. However, when polygenic effects were added, the accuracies of GEBV were increased to 0.89, 0.90 and 0.89 for CWT, EMA and BF, respectively, showing that using only SNP information misses genetic variations that contribute to phenotypes for carcass traits, and that polygenic effects compensate for genetic variation not explained by whole genome SNP data. Overall, the multiple SNP model provided a better prediction than the least squares method (single marker regression).
In this study, single marker regression analysis provides a broad confidence interval compared to the multiple SNP model due to linkage disequilibrium between the QTL and multiple SNPs. In conclusion, a multiple SNP model using RJMCMC estimates a precise QTL position and shows far better accuracy of prediction for genomic breeding value.
The least squares method allows regression of the phenotype on the genotype fitting only one SNP at a time. In this study, the least squares method showed more inaccurate breeding value prediction than the multiple SNP model, which could lead to vast overestimation of some haplotype effects and underestimation of others. In order to escape the overestimation, Meuwissen et al.
The potential QTL detected from the single marker regression analysis and multiple SNP model (RJMCMC) for carcass traits are shown in Table 3. The single SNP regression analysis found 24, 16, 18 and 41 significant SNPs for MAR, CWT, EMA and BF, respectively, at the threshold of 10.83. However, the Bayesian model selection method (RJMCMC) detected the best SNP set for estimation of genomic breeding value.
참고문헌 (12)
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