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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 #401020

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: Imputation accuracy from low to medium-density SNP chips for US crossbred dairy cattle

item DERU, VANILLE - North Carolina State University
item TIEZZI, FRANCESCO - University Of Florence
item Vanraden, Paul
item LOZADA-SOTO, EMMANUEL - North Carolina State University
item Toghiani, Sajjad
item MALTECCA, CHRISTIAN - North Carolina State University

Submitted to: Journal of Dairy Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 6/16/2023
Publication Date: N/A
Citation: N/A

Interpretive Summary: The quality of imputation accuracy in US crossbred dairy cattle was studied with a population of Holstein - Jersey. Imputation accuracy was moderate to high (85-90%) but lower than in the purebred population. Imputation accuracy increased significantly with the addition of related individuals in the reference population. These findings may provide information to assist future studies involving genomic data in crossbred US dairy cattle. Further analyses should validate the impact of imputation on the genetic values of these crossbreds and their purebreds’ parents.

Technical Abstract: The objective of this study was to evaluate the quality of imputation accuracy (IA) by marker (IA_m) and by individual (IA_i) in US crossbred dairy cattle. Holstein - Jersey crossbreds were used to evaluate imputation accuracy from a low (7K) to medium density (50K) SNP chip. Crossbred animals, as well as their sires (53), dams (77), and maternal grandsires (63), were all genotyped with a 78K chip (GeneSeek, Neogen Corporation). Eight reference populations were tested using different family relationships and supplementary scenarios with the addition of random purebred and crossbred individuals. To compare these results to those obtained in purebred animals, the same scenarios were tested with purebred Holstein and Jersey. The genotype imputation was performed with findhap (version 4) software. IA was lowest, around 70%, when there were no reference population individuals. No significant differences in IA results were observed depending on the sire was Holstein and the dam was Jersey, or the contrary. The IA increased significantly with the addition of related individuals in the reference population, from 86.70 +- 0.06 % with only sires or dams in the reference population to 90.09 +- 0.06 % with sires, dams, and maternal grandsires genomic information combined in the reference population. In all previous scenarios with related individuals, IA_m and IA_i were significantly superior in purebred Jersey and Holstein animals than in crossbreds, ranging from 90.75 +- 0.06 to 94.02 +- 0.06 %, and from 90.88 +- 0.11 to 94.04 +- 0.10 %, respectively. A scenario close to the genomic evaluations performed in crossbreds in the US, called S_PB+D_LD, was also performed. The information from the five evaluated breeds (Ayrshire, Brown Swiss, Guernsey, Holstein, and Jersey) genotyped with a 50K and genomic information from the dams genotyped with a 7K were combined in the reference population. In this scenario, IA_m and IA_i were moderate (80.87 +- 0.06 %, and 80.85 +- 0.08 %, respectively). Adding randomly nonrelated genotyped individuals in the reference population reduced IA for both purebred and crossbred cows, except in scenario S_PB+D_LD, where the addition of crossbreds in reference increased IA values. All chromosomes had similar IA. To conclude, our results show that IA is moderate to high in US crossbred dairy cattle and highlight the importance of the choice of a reference population.