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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 #306119

Research Project: Improving Genetic Predictions in Dairy Animals Using Phenotypic and Genomic Information

Location: Animal Genomics and Improvement Laboratory

Title: Comparison of single-trait to multi-trait national evaluations for yield, health, and fertility

Author
item Vanraden, Paul
item Tooker, Melvin
item Wright, Janice
item Sun, Chuanyu - National Association Of Animal Breeders
item Hutchison, Jana - Edwards

Submitted to: Journal of Dairy Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 8/31/2014
Publication Date: 12/1/2014
Citation: Van Raden, P.M., Tooker, M.E., Wright, J.R., Sun, C., Hutchison, J.L. 2014. Comparison of single-trait to multi-trait national evaluations for yield, health, and fertility. Journal of Dairy Science. 97(12):7952-7962.

Interpretive Summary: Accurate predictions of genetic merit allow breeders to make faster progress. Multi-trait predictions that combined information from several correlated traits of dairy cattle were compared to single-trait predictions that analyzed each trait separately. Benefits were largest for fertility traits where many records were missing. New software was developed and implemented for official genetic evaluations of 63 million dairy cattle, and methods to include data from foreign bulls were tested.

Technical Abstract: Flexible software was designed to replace the current animal model programs used for national genetic evaluations. Model improvements included 1) multi-trait processing, 2) multiple fixed class and regression variables, 3) differing models for different traits, 4) random regressions, and 5) foreign data included using pseudo-records. Computational improvements included 6) parallel processing, 7) renumbering class variables to equation numbers within the program so that estimated effects are output with original IDs, and 8) reliability computed within the same program. When applied to 3 fertility traits of 27,971,895 cows and heifers, the new model added information from crossbreds and used daughter pregnancy rate as a correlated trait to improve heifer and cow conception rate evaluations for older animals and in herd-years where records are missing. When applied to 7 traits and 76,846,327 lactation records of 30,064,300 cows, gains in accuracy were small for yield and somatic cell score, moderate for daughter pregnancy rate, and larger for productive life as compared to single-trait evaluations. Multi-trait productive life was computed with exact rather than approximate methods; however, correlated information from conformation was excluded, reducing advantages of the new model over the previous software. Estimates of breed differences, inbreeding depression, and heterosis were similar to previous estimates; new estimates were obtained for conception rates. Predictions were compared by truncating 4 years of data, and genetic trend validation was applied to all breed-trait combinations. The estimates of trend account for increases in inbreeding across time. Incorporation of foreign data gave correlations above 0.98 for new with previous evaluations of foreign Holstein bulls, but lower for other breeds. The 7-trait model required 35 Gbytes of memory and 3 days to converge using 7 processors. The new software was implemented for fertility traits in 2013 and is scheduled for implementation with yield, somatic cell score, and productive life in 2014. Further revision of the models and software may be needed in the near future to account for genomic pre-selection.