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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Animal Genomics and Improvement Laboratory » Research » Publications at this Location » Publication #359450

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 missing pedigree in single-step genomic BLUP

item BRADFORD, HEATHER - University Of Georgia
item MASUDA, YATAKA - University Of Georgia
item Vanraden, Paul
item LEGARRA, A - University Of Toulouse
item MISZTAL, IGNACY - University Of Georgia

Submitted to: Journal of Dairy Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 11/12/2018
Publication Date: 3/1/2019
Citation: Bradford, H.L., Masuda, Y., Van Raden, P.M., Legarra, A., Misztal, I. 2019. Modeling missing pedigree in single-step genomic BLUP. Journal of Dairy Science. 102(3):2336-2346.

Interpretive Summary: Livestock populations have incomplete pedigree information that requires careful modeling. We evaluated several approaches to account for these missing pedigrees and to account for genetic changes over time. Older approaches were suboptimal with the use of genomic information. The best approach involved calculating relationships among pseudo-individuals that were placeholders for missing parents. Including these relationships improved the accuracy of genetic predictions, especially for a lowly heritable trait.

Technical Abstract: The objective was to compare methods of modeling missing pedigree in single-step genomic BLUP. Options for modeling missing pedigree included ignoring the missing pedigree, unknown parent groups (UPG) based on A or H, and metafounders. The assumptions for the distribution of EBV changed with the different models. We simulated data with 0.3 and 0.1 heritabilities for dairy cattle populations that had more missing pedigrees for lesser genetic merit animals. Predictions for the youngest generation and UPG solutions were compared to the true values for validation. For both traits, ssGBLUP with metafounders provided accurate and unbiased predictions for young animals while also appropriately accounting for genetic trend. Accuracy was least and bias was greatest for ssGBLUP with UPG for H indicating this method was not optimal. For the 0.1 heritability, the UPG accuracy (SE) was -0.48 (0.03) suggesting the poor predictions were caused by poor UPG estimates. Problems with UPG estimates were likely caused by the lesser amount of information available for the lesser heritability trait. Hence, UPG needed to be defined differently based on the trait and amount of information. The G and A_22 matrices were more similar for metafounders indicating better scaling of the 2 relationship matrices to be more similar and on the same base. More research was needed to investigate accounting for UPG in A_22 to better account for missing pedigrees for genotyped animals.