Progress 10/01/12 to 09/30/13
Outputs
Progress Report Objectives (from AD-416): As described in grant entitled �Development and field evaluation of genome-wide marker-assisted selection (GWMAS) over multiple generations in commercial poultry,� the consortium that includes members from Wageningen University will: 1. Refine the Theoretical and Molecular Aspects of GWMAS; and 2. Field Assessment of GWMAS Approach (from AD-416): For Objective #1, alternative methods of utilizing GWMAS will also be explored. Principal Component Regression (PCR) and Partial Least Squares (PLS) are two methods that were mainly developed in chemometrics to deal with situations where the number of effects to be estimated greatly exceeds the number of records. The estimation of 10-100,000 haplotype effects from ~1,000 phenotypic records is exactly such a situation, and thus may suit these methods very well. Semi-parametric regression was suggested by Gianola et al. (2006) for the estimation of GW-EBVs. Its main advantages are: (1) it �automatically� deals with non-additivity of gene effects, whereas the other methods, in their basic form, assume additive gene action; and (2) it makes few distributional assumptions about the data, which could make this method more robust in practical situations. A computer simulation study based on data generated by Muir�s (2007) program will be conducted to compare the statistical methods Ridge Regression (RR), BLUP, GRM, BayesB, Xu�s (2003), PCR, PLS, and semi- parametric regression for their computational efficiency and accuracy of predicting breeding values in poultry breeding programs, their bias (over/ under prediction) of true breeding values, their sensitivity to the assumptions that are made about the genetic model, and the number of records and genotypes that are computationally feasible. The simulated genome structure will largely follow that of Meuwissen et al. (2001), but the parameters determining the genome structure, such as effective population size, mutation rates and distribution of mutational effects, gene action (additivity, dominance, epistatic effects), admixture of populations, etc., will be varied, in order to assess the sensitivity of the methods to differences in the structure of the genome. For Objective #2, the expected breeding values (EBV) will be computed for each normalized trait of each animal based on genotypic information and phenotypes (if present). These EBV would in turn be used to determine total merit by use of the appropriate weights given by the companies. The companies will do their own BLUP evaluations based on phenotype as per their usual breeding programs. The goal is to make selection decisions based on the EBVs for each trait and/or total merit the same for each method of selection [BLUP, GWMAS], the only difference being the way in which the EBVs will be estimated. This project is directly linked to project 3635-31320-009-05R titled "Development and Field Evaluation of Genome-Wide Marker-Assisted Selection (GWMAS) Over Multiple Generations in Commercial Poultry." The advantages of genomic selection (GWMAS) are well understood. In summary, GWMAS is most advantageous for traits that are difficult to or costly to measure (carcass quality, feed efficiency) or are collected late in life (full record egg production). This technology is the final realization of genomics, i.e., direct selection on the genotype and can double response to selection per unit of time for some traits. However, we have shown using simulations that a loss in accuracy will result once selection on those markers has commenced. Among the unanswered questions are 1) how rapidly the decline in accuracy will occur and 2) how often the retraining process must occur and how much of the lost accuracy can be regained? To address these questions, we analyzed three traits from two pure lines of broiler type chickens across 5 generations. The complete populations consisted of 183,784 and 164,246 broilers for two lines. The genotyped subsets consisted of 3,284 and 3,098 broilers with 57,636 SNPs. The training population consisted of the first two generations. Following 3 generations of selection based on ssGBLUP (genomic selection), we compared the accuracy with which EBVs could be predicted in those 3 generations based only on the genotypes (ssGBLUP) or pedigree (BLUP). Accuracy was determined as the correlation between predicted breeding value and the phenotype divided by the square root of heritability for that trait. Results showed that the relative accuracy for ssGBLUP remained about 33% higher than BLUP in all generations across both lines. The results differ from the predictions of the simulations from Muir (2007) who used the same set of assumptions as Meuwissen et al (2001). Our conclusion is that the results are characteristic of the infinitesimal model with thousands of quantitative trait locus (QTL) rather than a 100 or fewer as assumed by Meuwissen et al. (2001). There are a number of important conclusions from our results. The infinitesimal model is the most likely the correct approximation to the true biological genetic variation for these traits which implies: 1) ssGBLUP is more accurate than any SNP based method (e.g., BayesA, BayesB, or other such methods, which assume a relatively small number of SNPs whose effects can be estimated and selected for; in contrast, the infinitesimal model implies that genomic selection improves accuracy by increasing the precision with which relationships are estimated), 2) The results also imply that GWAS for these traits will find relatively few large effect QTLs, 3) Missing heritability type issues will result if selection is based on small chip with large effects QTL, i.e. will account for small amount of total genetic variation, 4) The results further imply that for these traits it will be possible to do genotype only selection for several generations before retraining will be necessary. In general these results are very favorable for genomic selection suggesting that such approaches will be useful in commercial breeding programs.
Impacts
(N/A)
Publications
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Progress 10/01/11 to 09/30/12
Outputs
Progress Report Objectives (from AD-416): As described in grant entitled �Development and field evaluation of genome-wide marker-assisted selection (GWMAS) over multiple generations in commercial poultry,� the consortium that includes members from Wageningen University will: 1. Refine the Theoretical and Molecular Aspects of GWMAS; and 2. Field Assessment of GWMAS Approach (from AD-416): For Objective #1, alternative methods of utilizing GWMAS will also be explored. Principal Component Regression (PCR) and Partial Least Squares (PLS) are two methods that were mainly developed in chemometrics to deal with situations where the number of effects to be estimated greatly exceeds the number of records. The estimation of 10-100,000 haplotype effects from ~1,000 phenotypic records is exactly such a situation, and thus may suit these methods very well. Semi-parametric regression was suggested by Gianola et al. (2006) for the estimation of GW-EBVs. Its main advantages are: (1) it �automatically� deals with non-additivity of gene effects, whereas the other methods, in their basic form, assume additive gene action; and (2) it makes few distributional assumptions about the data, which could make this method more robust in practical situations. A computer simulation study based on data generated by Muir�s (2007) program will be conducted to compare the statistical methods Ridge Regression (RR), BLUP, GRM, BayesB, Xu�s (2003), PCR, PLS, and semi- parametric regression for their computational efficiency and accuracy of predicting breeding values in poultry breeding programs, their bias (over/under prediction) of true breeding values, their sensitivity to the assumptions that are made about the genetic model, and the number of records and genotypes that are computationally feasible. The simulated genome structure will largely follow that of Meuwissen et al. (2001), but the parameters determining the genome structure, such as effective population size, mutation rates and distribution of mutational effects, gene action (additivity, dominance, epistatic effects), admixture of populations, etc., will be varied, in order to assess the sensitivity of the methods to differences in the structure of the genome. For Objective #2, the expected breeding values (EBV) will be computed for each normalized trait of each animal based on genotypic information and phenotypes (if present). These EBV would in turn be used to determine total merit by use of the appropriate weights given by the companies. The companies will do their own BLUP evaluations based on phenotype as per their usual breeding programs. The goal is to make selection decisions based on the EBVs for each trait and/or total merit the same for each method of selection [BLUP, GWMAS], the only difference being the way in which the EBVs will be estimated. This project is directly linked to projects 3635-31320-008-37R, and Specific Cooperative Agreements 3635-31320-008-20S and 3635-31320-008-31S titled "Development and Field Evaluation of Genome-Wide Marker-Assisted Selection (GWMAS) Over Multiple Generations in Commercial Poultry." To meet the growing demands of consumers, the poultry industry will need to continue to improve methods of selection in breeding programs for production and associated traits. One possible solution is genome-wide marker-assisted selection (GWMAS). First proposed by one of our team members, GWMAS utilizes markers spanning the entire genome to increase accuracy and efficiency of estimating breeding values (EBV). This new method promises significant benefits, but there are many unanswered questions calling for proof that GWMAS actually works. Retrospective analysis has shown that genome-wide marker-based EBV correlates highly with phenotypic Best Linear Unbiased Prediction (BLUP) EBV. However, there are concerns that these analyses will not reflect reality once implemented because selection may rapidly change variances, allele frequencies, and generate unfavorable linkage disequilibrium (LD), which only becomes apparent after the second round of selection. As planned, two meat-type and three egg-type chicken pure lines are being selected in parallel using either traditional or GWMAS. This year, after completing three rounds of selection, we conclude that in comparison to birds selected in parallel using current state-of-the-art breeding methods, genomic selection is superior for the vast majority of the traits selected including body weight and breast yield. This research strongly suggests that genomic selection is an improved breeding method with accuracies improving by more than 100% depending on the trait and generation. If costs for genetic testing continue to go down, then poultry breeders should be able to economically breed chickens faster using genomic selection and adapt more readily to changing consumer demands. The economic impact could be great since with 1 million meat- type birds processed per hour in the US alone, the net effect of even small improvements are large and worth millions of dollars.
Impacts
(N/A)
Publications
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Progress 10/01/10 to 09/30/11
Outputs
Progress Report Objectives (from AD-416) As described in grant entitled �Development and field evaluation of genome-wide marker-assisted selection (GWMAS) over multiple generations in commercial poultry,� the consortium that includes members from Wageningen University will: 1. Refine the Theoretical and Molecular Aspects of GWMAS; and 2. Field Assessment of GWMAS Approach (from AD-416) For Objective #1, alternative methods of utilizing GWMAS will also be explored. Principal Component Regression (PCR) and Partial Least Squares (PLS) are two methods that were mainly developed in chemometrics to deal with situations where the number of effects to be estimated greatly exceeds the number of records. The estimation of 10-100,000 haplotype effects from ~1,000 phenotypic records is exactly such a situation, and thus may suit these methods very well. Semi-parametric regression was suggested by Gianola et al. (2006) for the estimation of GW-EBVs. Its main advantages are: (1) it �automatically� deals with non-additivity of gene effects, whereas the other methods, in their basic form, assume additive gene action; and (2) it makes few distributional assumptions about the data, which could make this method more robust in practical situations. A computer simulation study based on data generated by Muir�s (2007) program will be conducted to compare the statistical methods Ridge Regression (RR), BLUP, GRM, BayesB, Xu�s (2003), PCR, PLS, and semi- parametric regression for their computational efficiency and accuracy of predicting breeding values in poultry breeding programs, their bias (over/under prediction) of true breeding values, their sensitivity to the assumptions that are made about the genetic model, and the number of records and genotypes that are computationally feasible. The simulated genome structure will largely follow that of Meuwissen et al. (2001), but the parameters determining the genome structure, such as effective population size, mutation rates and distribution of mutational effects, gene action (additivity, dominance, epistatic effects), admixture of populations, etc., will be varied, in order to assess the sensitivity of the methods to differences in the structure of the genome. For Objective #2, the expected breeding values (EBV) will be computed for each normalized trait of each animal based on genotypic information and phenotypes (if present). These EBV would in turn be used to determine total merit by use of the appropriate weights given by the companies. The companies will do their own BLUP evaluations based on phenotype as per their usual breeding programs. The goal is to make selection decisions based on the EBVs for each trait and/or total merit the same for each method of selection [BLUP, GWMAS], the only difference being the way in which the EBVs will be estimated. This project is directly linked to projects 3635-31320-008-37R, Specific Cooperative Agreements 3635-31320-008-20S, 25S and 31S, titled �Development and Field Evaluation of Genome-Wide Marker-Assisted Selection (GWMAS) Over Multiple Generations in Commercial Poultry." To meet the growing demands of consumers, the poultry industry will need to continue to improve methods of selection in breeding programs for production and associated traits. One possible solution is genome-wide marker-assisted selection (GWMAS). First proposed by one of our team members, GWMAS utilizes markers spanning the entire genome to increase accuracy and efficiency of estimating breeding values (EBV). This new method promises significant benefits, but there are many unanswered questions calling for proof that GWMAS actually works. Retrospective analysis has shown genome-wide marker-based EBV correlate highly with phenotypic Best Linear Unbiased Prediction (BLUP) EBV. However, there are concerns that these analyses will not reflect reality once implemented because selection may rapidly change variances, allele frequencies, and generate unfavorable linkage disequilibrium (LD) which only becomes apparent after the second round of selection. As planned, two meat-type and three egg-type chicken pure lines are being selected in parallel using either traditional or GWMAS. This year, after completing two rounds of selection, we conclude that compared to birds selected in parallel using current state-of-the-art breeding methods, genomic selection is superior for the vast majority of the traits selected including body weight and breast yield. This research strongly suggests that genomic selection is an improved breeding method. If costs for genetic testing continue to go down, then poultry breeders should be able to economically breed chickens faster using genomic selection and adapt more readily to changing consumer demands. The economic impact could be great since with 1 million meat-type birds processed per hour in the US alone, the net effect of even small improvements are large and worth millions of dollars. This project is monitored by weekly e-mail and telephone calls between the parties, a monthly teleconference and, when possible, direct interactions at scientific meetings.
Impacts
(N/A)
Publications
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Progress 10/01/09 to 09/30/10
Outputs
Progress Report Objectives (from AD-416) As described in grant entitled �Development and field evaluation of genome-wide marker-assisted selection (GWMAS) over multiple generations in commercial poultry,� the consortium that includes members from Wageningen University will: 1. Refine the Theoretical and Molecular Aspects of GWMAS; and 2. Field Assessment of GWMAS Approach (from AD-416) For Objective #1, alternative methods of utilizing GWMAS will also be explored. Principal Component Regression (PCR) and Partial Least Squares (PLS) are two methods that were mainly developed in chemometrics to deal with situations where the number of effects to be estimated greatly exceeds the number of records. The estimation of 10-100,000 haplotype effects from ~1,000 phenotypic records is exactly such a situation, and thus may suit these methods very well. Semi-parametric regression was suggested by Gianola et al. (2006) for the estimation of GW-EBVs. Its main advantages are: (1) it �automatically� deals with non-additivity of gene effects, whereas the other methods, in their basic form, assume additive gene action; and (2) it makes few distributional assumptions about the data, which could make this method more robust in practical situations. A computer simulation study based on data generated by Muir�s (2007) program will be conducted to compare the statistical methods Ridge Regression (RR), BLUP, GRM, BayesB, Xu�s (2003), PCR, PLS, and semi- parametric regression for their computational efficiency and accuracy of predicting breeding values in poultry breeding programs, their bias (over/under prediction) of true breeding values, their sensitivity to the assumptions that are made about the genetic model, and the number of records and genotypes that are computationally feasible. The simulated genome structure will largely follow that of Meuwissen et al. (2001), but the parameters determining the genome structure, such as effective population size, mutation rates and distribution of mutational effects, gene action (additivity, dominance, epistatic effects), admixture of populations, etc., will be varied, in order to assess the sensitivity of the methods to differences in the structure of the genome. For Objective #2, the expected breeding values (EBV) will be computed for each normalized trait of each animal based on genotypic information and phenotypes (if present). These EBV would in turn be used to determine total merit by use of the appropriate weights given by the companies. The companies will do their own BLUP evaluations based on phenotype as per their usual breeding programs. The goal is to make selection decisions based on the EBVs for each trait and/or total merit the same for each method of selection [BLUP, GWMAS], the only difference being the way in which the EBVs will be estimated. This project is directly linked to projects 3635-31320-008-19R, 3635- 31320-008-37R, Specific Cooperative Agreemets 3635-31320-008-20S, 25S and 31S, titled �Development and Field Evaluation of Genome-Wide Marker- Assisted Selection (GWMAS) Over Multiple Generations in Commercial Poultry." To meet the growing demands of consumers, the poultry industry will need to continue to improve methods of selection in breeding programs for production and associated traits. One possible solution is genome-wide marker-assisted selection (GWMAS). First proposed by one of us, evenly-spaced genetic markers spanning the entire genome are genotyped (scored) on individuals to estimate their breeding value, which in theory could substantially increase the rate of genetic gain compared to traditional selection methods. To test the power of GWMAS, meat-type and egg-type chicken lines are being selected in parallel using either traditional or GWMAS. This year, after completing two rounds of selection, we conclude that compared to birds selected in parallel using current state-of-the-art breeding methods, genomic selection is superior for the vast majority of the traits selected including body weight and breast yield. This research strongly suggests that genomic selection is an improved breeding method. If costs for genetic testing continue to go down, then poultry breeders should be able to economically breed chickens faster using genomic selection and adapt more readily to changing consumer demands. The economic impact could be great with 1 million meat- type birds being processed per hour in the U.S. alone, the net effect of even small improvements are large and worth millions of dollars. This project is monitored by weekly e-mail and telephone calls between the two parties, a monthly teleconference and, when possible, direct interactions at scientific meetings.
Impacts
(N/A)
Publications
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Progress 10/01/08 to 09/30/09
Outputs
Progress Report Objectives (from AD-416) As described in grant entitled �Development and field evaluation of genome-wide marker-assisted selection (GWMAS) over multiple generations in commercial poultry,� the consortium that includes members from Wageningen University will: 1. Refine the Theoretical and Molecular Aspects of GWMAS; and 2. Field Assessment of GWMAS Approach (from AD-416) For Objective #1, alternative methods of utilizing GWMAS will also be explored. Principal Component Regression (PCR) and Partial Least Squares (PLS) are two methods that were mainly developed in chemometrics to deal with situations where the number of effects to be estimated greatly exceeds the number of records. The estimation of 10-100,000 haplotype effects from ~1,000 phenotypic records is exactly such a situation, and thus may suit these methods very well. Semi-parametric regression was suggested by Gianola et al. (2006) for the estimation of GW-EBVs. Its main advantages are: (1) it �automatically� deals with non-additivity of gene effects, whereas the other methods, in their basic form, assume additive gene action; and (2) it makes few distributional assumptions about the data, which could make this method more robust in practical situations. A computer simulation study based on data generated by Muir�s (2007) program will be conducted to compare the statistical methods Ridge Regression (RR), BLUP, GRM, BayesB, Xu�s (2003), PCR, PLS, and semi- parametric regression for their computational efficiency and accuracy of predicting breeding values in poultry breeding programs, their bias (over/under prediction) of true breeding values, their sensitivity to the assumptions that are made about the genetic model, and the number of records and genotypes that are computationally feasible. The simulated genome structure will largely follow that of Meuwissen et al. (2001), but the parameters determining the genome structure, such as effective population size, mutation rates and distribution of mutational effects, gene action (additivity, dominance, epistatic effects), admixture of populations, etc., will be varied, in order to assess the sensitivity of the methods to differences in the structure of the genome. For Objective #2, the expected breeding values (EBV) will be computed for each normalized trait of each animal based on genotypic information and phenotypes (if present). These EBV would in turn be used to determine total merit by use of the appropriate weights given by the companies. The companies will do their own BLUP evaluations based on phenotype as per their usual breeding programs. The goal is to make selection decisions based on the EBVs for each trait and/or total merit the same for each method of selection [BLUP, GWMAS], the only difference being the way in which the EBVs will be estimated. Significant Activities that Support Special Target Populations This project is directly linked to projects 3635-31320-008-19R, 20S, and 25S �Development and Field Evaluation of Genome-Wide Marker-Assisted Selection (GWMAS) Over Multiple Generations in Commercial Poultry.� Evenly spaced genetic markers spanning the entire genome are genotyped (scored) on individuals to estimate their breeding value, which in theory could substantially increase the rate of genetic gain compared to traditional selection methods. Efforts are underway to increase the power of the genome-wide marker- assisted selection (GWMAS) algorithms and use them in our actual experiments to evaluate the real-world power of this promising technology. This project is monitored by weekly e-mail and telephone calls between the two parties, a monthly teleconference and, when possible, direct interactions at scientific meetings.
Impacts
(N/A)
Publications
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