Skip to main content
ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Animal Genomics and Improvement Laboratory » Research » Publications at this Location » Publication #354693

Research Project: Improving Dairy Animals by Increasing Accuracy of Genomic Prediction, Evaluating New Traits, and Redefining Selection Goals

Location: Animal Genomics and Improvement Laboratory

Title: Modeling pedigree accuracy and uncertain parentage in single-step genomic evaluations of simulated and US Holstein datasets

item BRADFORD, HEATHER - University Of Georgia
item MASUDA, YUTAKA - University Of Georgia
item Cole, John
item MISZTAL, IGNACY - University Of Georgia
item Vanraden, Paul

Submitted to: Journal of Dairy Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 11/14/2018
Publication Date: 3/1/2019
Citation: Bradford, H.L., Masuda, Y., Cole, J.B., Misztal, I., Van Raden, P.M. 2019. Modeling pedigree accuracy and uncertain parentage in single-step genomic evaluations of simulated and US Holstein datasets. Journal of Dairy Science. 102(3):2308–2318.

Interpretive Summary: Genotypes were used to confirm and correct pedigree errors. Selective genotyping of superior females creates variation in pedigree accuracy across sires, and these pedigree accuracy differences were modeled with uncertain parentage. Pedigree accuracy differences could bias genetic predictions and prevent fair comparisons of all bulls. Genetic predictions had almost the same accuracy when modeling and not modeling pedigree accuracy. Differences in pedigree accuracy were not biasing genetic predictions for Holstein bulls.

Technical Abstract: The objective was to use uncertain parentage to model differences in pedigree accuracy caused by selective genotyping. Pedigrees were confirmed or even discovered with genomic information, and genotyping was more common in elite herds and elite animals. Elite bulls were initially used in elite herds resulting in more of their first daughters genotyped. These genotyped daughters had accurate sire pedigree relationships that could prevent accurate comparisons with other bulls that had more daughter pedigree errors. This problem was addressed through simulation and Holstein data. Data were simulated to be representative of the dairy industry with heterogeneous pedigree depth, pedigree accuracy, and genotyping. Holstein data were obtained from the official evaluation for milk, fat, and protein. Two models were compared: the traditional approach assuming accurate pedigrees and uncertain parentage where each animal could have more than 2 possible parents. The uncertain parentage model included 2 possible sires (dams) when the sire (dam) could not be confirmed with genotypes. The 2 sires (dams) were the sire (dam) on record with probability 0.90 (0.95) and the unknown parent group for the birth year of the sire (dam) with probability 0.10 (0.05). In simulation, small bias differences occurred between models based on pedigree accuracy and genotype status. Rank correlations were strong between traditional and uncertain parentage models in simulation (=> 0.99) and in Holstein (=> 0.96). For Holsteins, estimated breeding value differences between models were small for most animals. Thus, traditional models can continue to be used for dairy genomic prediction despite using genotypes to improve pedigree accuracy. Those genotypes can also be used to discover maternal parentage, specifically maternal grandsires and great grandsires when the dam is not known. More research is needed to understand how to use discovered maternal pedigrees in genetic prediction.