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Interpretive Summary: Esfandyari and Jensen Bayesian genetic selection beef cattle

By: Caitlin Vonderohe

Beef producers have always been interested in genetically selecting for heavier animals that grow quickly but still have excellent meat quality. However, recent trends in the market focus on selecting production-based traits such as feed efficiency and feed intake, leading for greater selection for fast-growing animals with lower residual feed intake, the amount of feed consumed that deviates from the expected feed intake for a given animal’s expected growth trajectory. Residual feed intake is a preferred trait to use in genetic selection because it is linear and easier to select upon. 

Esfandyari and Jensen conducted a Bayesian analysis of growth performance records of Danish beef herds to calculate residual feed intake and demonstrated its statistical utility in genetic selection of beef bulls over a period of 392 days. As expected, the estimated genetic correlation for weight was high at all ages of bull, but daily feed intake fluctuated too much to be of any predictive value.  When daily feed intake was regressed on weight, the authors were able to calculate maintenance requirements per unit of body weight. Other derived traits, such as average weight, total feed intake, average daily gain and total residual feed intakes were calculated, and the heritability estimated. The heritability of average daily gain was 0.4 and total residual feed intake started at .5 at the beginning of the test period but increased over time.

Growth related traits are easy to assess and provide a basis for calculating other traits, all of which have relatively high genetic correlation values and heritability. However, feed efficiency estimates were less consistent in their correlation and heritability assessments. Residual feed intake removes the efficiency-based inconsistency from the calculation, resulting in higher heritability and genetic correlation, making it a promising trait for use in genetically selecting more efficient cattle.