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Short communication: Genomic prediction using imputed whole-genome sequence variants in Brown Swiss Cattle

Frischknecht, M.; Meuwissen, T.H.E.; Bapst, B.; Seefried, F.R.; Flury, C.; Garrick, D.; Signer-Hasler, H.; Stricker, C.; Consortium, Intergenomics; Bieber, Anna; Fries, R.; Russ, I.; Sölkner, J.; Bagnato, A. and Gredler-Grandl, B. (2018) Short communication: Genomic prediction using imputed whole-genome sequence variants in Brown Swiss Cattle. Journal of Dairy Science, 101 (2), pp. 1292-1296.

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Online at: https://www.ncbi.nlm.nih.gov/pubmed/29153527


The accuracy of genomic prediction determines response to selection. It has been hypothesized that accuracy of genomic breeding values can be increased by a higher density of variants. We used imputed whole-genome sequence data and various single nucleotide polymorphism (SNP) selection criteria to estimate genomic breeding values in Brown Swiss cattle. The extreme scenarios were 50K SNP chip data and whole-genome sequence data with intermediate scenarios using linkage disequilibrium-pruned whole-genome sequence variants, only variants predicted to be missense, or the top 50K variants from genome-wide association studies. We estimated genomic breeding values for 3 traits (somatic cell score, nonreturn rate in heifers, and stature) and found differences in accuracy levels between traits. However, among different SNP sets, accuracy was very similar. In our analyses, sequence data led to a marginal increase in accuracy for 1 trait and was lower than 50K for the other traits. We concluded that the inclusion of imputed whole-genome sequence data does not lead to increased accuracy of genomic prediction with the methods.

EPrint Type:Journal paper
Keywords:Brown Swiss, genomic prediction, whole-genome sequence data, animal breeding, dairy cattle
Subjects: Animal husbandry > Production systems > Dairy cattle
Animal husbandry > Breeding and genetics
Research affiliation: Switzerland > FiBL - Research Institute of Organic Agriculture Switzerland > Animal Husbandry and Breeding
DOI:DOI: https://doi.org/10.3168/jds.2017-12890
Deposited By: Bieber, Anna
ID Code:35104
Deposited On:20 Mar 2019 17:31
Last Modified:20 Mar 2019 17:31
Document Language:English
Refereed:Peer-reviewed and accepted

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