Genomic prediction using pooled data in a single-step genomic best linear unbiased prediction framework

Authors: BALLER, JOHNNA - University Of Nebraska; KACHMAN, STEPHEN - University Of Nebraska; Kuehn, Larry; SPANGLER, MATTHEW - University Of Nebraska

Submitted to: Journal of Animal Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 6/1/2020
Publication Date: 6/4/2020
Citation: Baller, J.L., Kachman, S.D., Kuehn, L.A., Spangler, M.L. 2020. Genomic prediction using pooled data in a single-step genomic best linear unbiased prediction framework. Journal of Animal Science. 98(6):1-12. https://doi.org/10.1093/jas/skaa184.
DOI: https://doi.org/10.1093/jas/skaa184

Interpretive Summary: National cattle evaluation programs have revolutionized genetic selection progress in the beef cattle industry by helping producers pick out parents most likely to increase profit in future generations. Progress has been improved further through the use of high throughput genotyping arrays that improve the accuracy of predictions from these evaluations. However, this system relies on performance recording in seedstock cattle herds, and, unfortunately, some traits that are economically important for commercial beef production are not easily recorded on seedstock animals (e.g., carcass composition, feedlot performance, cow longevity under commercial conditions). Many of these traits are routinely recorded on groups of cattle in commercial segments of the cattle industry, but relationships to seedstock animals are difficult to establish through either pedigree (hard to track) or genomic (costly) ties. Genotyping pooled DNA from larger animal groups provides a method to link these data to seedstock animals in national cattle evaluation. This manuscript uses simulation to test different pool constructions based on animal similarity (high vs. low variation in pools) and pool size to determine optimal pool constructions to increase prediction accuracy. Results indicate that accuracy was highest when variation within pools was minimized; larger pools did not reduce accuracy when pool variation was minimized. Utilization of DNA pooling could lead to increased genetic improvement of traits that are important to commercial beef production.

Technical Abstract: Economically relevant traits are routinely collected within the commercial segments of the beef industry but are rarely included in genetic evaluations because of unknown pedigrees. Individual relationships could be resurrected with genomics, but this would be costly; therefore, pooling DNA and phenotypic data provide a cost-effective solution. Pedigree, phenotypic, and genomic data were simulated for a beef cattle population consisting of 15 generations. Genotypes mimicked a 50k marker panel (841 quantitative trait loci were located across the genome, approximately once per 3 Mb) and the phenotype was moderately heritable. 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 genomic best linear unbiased prediction model. The effects of pooling strategy (random and minimizing or uniformly maximizing phenotypic variation within pools), 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 there was no gap between genotyped parents and pooled offspring. The EBV accuracies resulting from pools were usually 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. A pool size of 2 was the only scenario that did not significantly decrease EBV accuracy compared with 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 statistical differences in EBV accuracy than individual data when pools were constructed to minimize phenotypic variation (P > 0.05). Largest numerical increases in EBV accuracy resulting from pooling compared with no data from generation 15 were seen with sires with prior low EBV accuracy (those born in generation 14). Pooling of any size led to larger EBV accuracies of the pools than individual data when minimizing phenotypic variation. Resulting EBV for the pools could be used to inform management decisions of those pools. Pooled genotyping to garner commercial-level phenotypes for genetic evaluations seems plausible although differences exist depending on pool size and pool formation strategy.