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ARS Home » Plains Area » Clay Center, Nebraska » U.S. Meat Animal Research Center » Genetics and Animal Breeding » Research » Publications at this Location » Publication #373767
Research Project: Developing a Systems Biology Approach to Enhance Efficiency and Sustainability of Beef and Lamb Production

Location: Genetics and Animal Breeding

Title: Using pooled data for single-step genomic prediction: Impact of within-pool variance and size

Author
item BALLER, JOHNNA - University Of Nebraska
item KACHMAN, STEPHEN - University Of Nebraska
item Kuehn, Larry
item SPANGLER, MATTHEW - University Of Nebraska

Submitted to: Journal of Animal Science
Publication Type: Abstract Only
Publication Acceptance Date: 5/15/2020
Publication Date: 11/30/2020
Citation: Baller, J.L., Kachman, S.D., Kuehn, L.A., Spangler, M.L. 2020. Using pooled data for single-step genomic prediction: Impact of within-pool variance and size [abstract]. Journal of Animal Science. 98(Supplement 4):9. https://doi.org/10.1093/jas/skaa278.017.
DOI: https://doi.org/10.1093/jas/skaa278.017

Interpretive Summary:

Technical Abstract: Economically relevant traits (ERT) are routinely collected within commercial segments of the beef industry but are rarely included in genetic evaluations because of unknown pedigrees. Individual relationships could be resurrected with genomics, which would be costly; therefore, pooling DNA and phenotypic data provides a cost-effective solution. A simulated beef cattle population consisting of 15 generations was genotyped with approximately 50k markers (841 quantitative trait loci were located across the genome) and phenotyped for a moderately heritable trait. Individuals from generation 15 were included in pools (observed genotype and phenotype were mean values of a group). Estimated breeding values (EBV) were generated from a single-step GBLUP model. The effects of pooling strategy (random and minimizing or uniformly maximizing phenotypic variation), pool size (1, 2, 10, 20, 50, 100, or no data from generation 15), and generational gaps of genotyping on EBV accuracy (correlation of EBV with true breeding values) were quantified. Greatest EBV accuracies of sires and dams were observed when no gap between genotyped parents and pooled offspring occurred. The EBV accuracies resulting from pools were greater than no data from generation 15 regardless of sire or dam genotyping. Minimizing phenotypic variation increased EBV accuracy by 8% and 9% over random pooling and uniformly maximizing phenotypic variation, respectively. Pool size of 2 was the only scenario that did not significantly decrease EBV accuracy compared to individual data when pools were formed randomly or by uniformly maximizing phenotypic variation (P > 0.05). Pool sizes of 2, 10, 20, or 50 did not generally lead to EBV accuracies that were statistically different than individual data when pools were constructed to minimize phenotypic variation (P > 0.05). Pooled genotyping to garner commercial-level phenotypes for genetic evaluations seems plausible although differences exist depending on pool size and pool formation strategy.