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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 #416269
Research Project: Increasing Accuracy of Genomic Prediction, Developing Algorithms, Selecting Markers, and Evaluating New Traits to Improve Dairy Cattle

Location: Animal Genomics and Improvement Laboratory

Title: All-breed single-step GBLUP evaluations for fertility traits in U.S. dairy cattle

Author
item TABET, JOE-MENWER - University Of Georgia
item LOURENCO, DANIELA - University Of Georgia
item BUSSIMAN, FERNANDO - University Of Georgia
item BERMANN, MATIAS - University Of Georgia
item MISZTAL, IGNACY - University Of Georgia
item Vanraden, Paul
item VITEZICA, Z - University Of Toulouse
item LEGARRA, ANDRES - Council On Dairy Cattle Breeding

Submitted to: Journal of Dairy Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 9/24/2024
Publication Date: N/A
Citation: N/A

Interpretive Summary: Single-step GBLUP (ssGBLUP) includes pedigree, records and genotypes simultaneously for unbiased, accurate genomic prediction, becoming the preferred method for genetic evaluations in dairy cattle. Addressing missing pedigree and breed origins can be achieved through unknown parent groups (UPG) or metafounders (MF), which consider relatedness across and within groups of missing parents. This study used U.S. dairy cattle fertility records, a low heritable trait with changing genetic trends, to evaluate accuracy, bias, and dispersion in all-breed ssGBLUP. Comparing either UPG or MF, MF showed lower bias and highest accuracy providing a benchmark for future ssGBLUP use in official genomic predictions.

Technical Abstract: The U.S. dairy cattle genetic evaluation is now a multistep process, including multibreed traditional BLUP estimations followed by single-breed SNP effects estimation. Single-step GBLUP (ssGBLUP) combines pedigree and genomic data for all breeds in one analysis. Unknown parent groups (UPG) or metafounders (MF) can be used to address missing pedigree information. Fertility traits are notably difficult to evaluate due to low heritabilities, changing management, and higher recent emphasis on selection for improved fertility. We assessed bias, dispersion, and accuracy of fertility traits in all-breed U.S. dairy cattle using ssGBLUP and pedigree-based BLUP (PBLUP) models with UPG or MF; the latter with 5% (MF5) or 10% (MF10) residual polygenic effect. Validation methods included Linear Regression (LR) and Improved Genomic Validation (IGV) for Holstein (HO) and Jersey (JE) breeds. In the comparison of MF or UPG in PBLUP, we observed similar results in terms of bias, dispersion and correlations between early and recent predictions. When genomics was used, ssGBLUPMF10 consistently outperformed other models in bias, dispersion, and correlations. Comparing with multistep results, ssGBLUPMF10 showed improved bias and correlations but slightly overdispersed estimates. Overall, genomic prediction of fertility traits using ssGBLUP was accurate and unbiased, more so with MF than with UPG.