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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 #314693

Title: Genetic interactions for heat stress and production level: predicting foreign from domestic data

item Wright, Janice
item Vanraden, Paul

Submitted to: Journal of Dairy Science
Publication Type: Abstract Only
Publication Acceptance Date: 3/19/2015
Publication Date: 7/12/2015
Citation: Wright, J.R., Van Raden, P.M. 2015. Genetic interactions for heat stress and production level: predicting foreign from domestic data. Journal of Dairy Science. 98(Suppl. 2)/Journal of Animal Science 93(Suppl. 3):350(abstr. T103).

Interpretive Summary:

Technical Abstract: Genetic by environmental interactions were estimated from U.S. national data by separately adding random regressions for heat stress (HS) and herd production level (HL) to the all-breed animal model to improve predictions of future records and rankings in other climate and production situations. Yield data included 79 million lactation records of 40 million cows; somatic cell score, productive life, and daughter pregnancy rate were also tested but had somewhat fewer records. Coefficients for HS were the state’s July average temperature-humidity index and coefficients for HL were management level weighted means for energy corrected milk (ECM) divided by breed-year mean ECM; coefficients were then standardized to mean 0 and variance 1. Predictions of current (August 2014) from historical records (August 2011) were tested with a model including herd management group (absorbed), sire estimated breeding value (EBV), dam EBV, and interaction term (HS or HL) from the truncated data; records were weighted by lactation length for records in progress and herd heritability using the same weights as in national evaluations. Estimated regression coefficients for sire EBV and dam EBV were always near their expected values of 0.5 and did not change when HS or HL interactions were added to the model. Estimated regressions for interaction terms, expected to be near 1, were 0.80 to 0.93 for HS and 0.61 to 0.72 for HL in yield traits. Squared correlations increased by <.0003 for both HS and HL; increases for non-yield traits were even smaller. An additional test used multitrait across-country evaluations (MACE) to predict rankings of the same bulls in the US and 14 other countries with somewhat different environments. Bulls were restricted to those with at least 100 daughters in both countries. The HS coefficient was significant (P<.05) in 9 of the 14 countries for milk and protein, and 10 for fat; the HL term was significant in 8 countries for milk, 5 for protein and just 1 for fat. Squared correlations after adding interaction increased by <.004 for HL and <.01 for HS. The small changes in rank and gains in correlation when HS and HL interactions were included in national evaluations, indicate current genetic predictions perform very well in a variety of environments.