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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 #388311

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: Multibreed genomic evaluation for dairy cattle in the US using single-step GBLUP

item CESARANI, ALBERTO - University Of Georgia
item LOURENCO, DANIELA - University Of Georgia
item TSURUTA, SHOGO - University Of Georgia
item MASUDA, YUTAKA - University Of Georgia
item LEGARRA, ANDRES - Institut National De La Recherche Agronomique (INRA)
item NICOLAZZI, EZEQUIEL - Council On Dairy Cattle Breeding
item Vanraden, Paul
item MISZTAL, IGNACY - University Of Georgia

Submitted to: Journal of Dairy Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 1/27/2022
Publication Date: N/A
Citation: N/A

Interpretive Summary: Multibreed genetic evaluations for US dairy cattle were implemented in 2007 and extended to a multistep genomic evaluation in 2009. The multistep evaluation uses single-breed SNP marker effects. Single-step evaluations allow for multibreed, multi-trait genomic evaluations in a single run. We investigated the feasibility of single-step genomic best linear unbiased prediction (ssGBLUP) for yield traits in a combined population of Holstein, Jersey, Ayrshire, Brown Swiss, and Guernsey with about 3.9 million genotyped animals. Multibreed ssGBLUP evaluations are possible and provide similar reliabilities as the single-breed evaluations; however, having evaluations for Ayrshire, Brown Swiss, and Guernsey separate from Holstein and Jerseys may reduce inflation of GEBV for the first three breeds.

Technical Abstract: Official multibreed genomic evaluations for dairy cattle in the US are based on multibreed BLUP evaluation followed by single-breed estimation of SNP effects. Single-step GBLUP (ssGBLUP) allows the straight computation of genomic (G)EBV in a multibreed context. This work aimed to develop ssGBLUP multibreed genomic predictions for US dairy cattle using the algorithm for proven and young (APY) to compute the inverse of the genomic relationship matrix. Only purebred Ayrshire (AY), Brown Swiss (BS), Guernsey (GU), Holstein (HO), and Jersey (JE) animals were considered. A three-trait model with milk (MY), fat (FY), and protein (PY) yields was applied using about 45 million phenotypes recorded from January 2000 to June 2020. The whole dataset included about 29.5 million animals, of which almost 4 million were genotyped. All the effects in the model were breed-specific, and breed was also considered as a fixed effect. Evaluations were done for: i) each single breed separately (SINGLE); ii) HO and JE together (HO_JE); iii) AY, BS and GU together (AY_BS_GU); iv) all the five breeds together (5_BREEDS). Initially, 15k core animals were used in APY for iii and iv, but larger core sets with more animals from the least represented breeds were also tested. The HO_JE evaluation had a fixed set of 20k core animals, with an equal representation of the two breeds. Validation for cows was based on correlations between adjusted phenotypes and (G)EBV, whereas for bulls on the regression of daughter yield deviations (DYD) on (G)EBV. Because breed was correctly considered in the model, BLUP results for single-breed, AY_BS_GU, and 5_BREEDS were the same. Under ssGBLUP, predictability (reliability) for AY, BS, and GU was on average 7% (2%) lower in 5_BREEDS compared to single-breed evaluations. However, these differences were positive when 35k animals were included in the core set for 5_BREEDS. Evaluations for Holsteins were more stable across scenarios because of the greatest number of genotyped animals and amount of data. Combining AY, BS, and GU into one evaluation resulted in predictions similar to the ones from single breed, especially when using about 30k core animals in APY. These preliminary results showed that single-step large-scale multibreed evaluations are computationally feasible, but fine-tuning is needed to avoid a reduction in reliability when numerically dominant breeds are combined. Having evaluations for Ayrshire, Brown Swiss, and Guernsey separate from Holstein and Jerseys may reduce inflation of GEBV for the first three breeds.