Comparing SNP Panels and Statistical Methods for Estimating Genomic Breed Composition of Individual Animals in Ten Cattle Breeds

Authors

Jun He, Hunan Agricultural University
Yage Guo, University of Nebraska–LincolnFollow
Jiaqi Xu, University of Nebraska-Lincoln
Hao Li, University of Wisconsin-Madison
Anna Fuller, Neogen GeneSeek Operations
Richard G. Tait Jr., Neogen GeneSeek Operations
Xiao-Lin Wu, University of WisconsinFollow
Stewart Bauck, Neogen GeneSeek Operations

ORCID IDs

https://orcid.org/0000-0002-5604-9220

Document Type

Article

Date of this Version

2018

Citation

BMC Genetics (2018) 19: 56. https://doi.org/10.1186/s12863-018-0654-3. Also available at https://bmcgenet.biomedcentral.com/track/pdf/10.1186/s12863-018-0654-3.

Comments

Copyright 2018, the authors. Open Access. Creative Commons Attribution 4.0 International License.

Abstract

Background: SNPs are informative to estimate genomic breed composition (GBC) of individual animals, but selected SNPs for this purpose were not made available in the commercial bovine SNP chips prior to the present study. The primary objective of the present study was to select five common SNP panels for estimating GBC of individual animals initially involving 10 cattle breeds (two dairy breeds and eight beef breeds). The performance of the five common SNP panels was evaluated based on admixture model and linear regression model, respectively. Finally, the downstream implication of GBC on genomic prediction accuracies was investigated and discussed in a Santa Gertrudis cattle population.

Results: There were 15,708 common SNPs across five currently-available commercial bovine SNP chips. From this set, four subsets (1,000, 3,000, 5,000, and 10,000 SNPs) were selected by maximizing average Euclidean distance (AED) of SNP allelic frequencies among the ten cattle breeds. For 198 animals presented as Akaushi, estimated GBC of the Akaushi breed (GBCA) based on the admixture model agreed very well among the five SNP panels, identifying 166 animals with GBCA = 1. Using the same SNP panels, the linear regression approach reported fewer animals with GBCA = 1. Nevertheless, estimated GBCA using both models were highly correlated (r = 0.953 to 0.992). In the genomic prediction of a Santa Gertrudis population (and crosses), the results showed that the predictability of molecular breeding values using SNP effects obtained from 1,225 animals with no less than 0.90 GBC of Santa Gertrudis (GBCSG) decreased on crossbred animals with lower GBCSG.

Conclusions: Of the two statistical models used to compute GBC, the admixture model gave more consistent results among the five selected SNP panels than the linear regression model. The availability of these common SNP panels facilitates identification and estimation of breed compositions using currently-available bovine SNP chips. In view of utility, the 1 K panel is the most cost effective and it is convenient to be included as add-on content in future development of bovine SNP chips, whereas the 10 K and 16 K SNP panels can be more resourceful if used independently for imputation to intermediate or high-density genotypes.