Project Number: 5050-31320-009-07-S
Project Type: Non-Assistance Cooperative Agreement
Start Date: Jun 29, 2009
End Date: Aug 31, 2013
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
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.