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United States Department of Agriculture

Agricultural Research Service

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2013 Annual Report

1a. 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. Develop a Higher Density and More Informative Chicken SNP Panel; 2. Refine the Theoretical and Molecular Aspects of GWMAS 3. Field Assessment of GWMAS; and 4. Further Improve the Chicken Genome Assembly. For this agreement, only Objectives 1, 3, and 4 apply.

1b. Approach (from AD-416):
For Objective #1, to generate the SNPs that we will genotype, we will (1) Divide the latest chicken sequence assembly into ~60K bins of equal size based on chromosomal recombination rates, (2) Assign previously screened SNPs that were validated, have a MAF>0.1, and known to be segregating in one or more lines of interest to the appropriate bin, (3) For bins without a validated marker, identify 3 or more SNPs from either the public databases or our own sequencing efforts, giving preference to those SNPs identified two or more times in the discovery process, and (4) Submit all SNPs and their flanking sequences to the commercial vendor producing the SNP array to determine if a suitable assay can be designed. For Objective #3, DNA from Hendrix Genetics chickens will be extracted, quantified, and the concentration adjusted prior to shipment to our genotyping facility. For Objective #4, the East Lansing and Wageningen University reference families will be genotyped with the 60K SNP chip, and an improved consensus genetic map generated and aligned to the genome sequence to detect discrepancies in genetic marker order.

3. Progress Report:
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.

4. Accomplishments

Last Modified: 05/28/2017
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