Dissection of Koch's residual feed intake: implications for selection

Area of Science:

• Animal Science
• Genetics
• Quantitative Biology

Background:

• Residual feed intake (RFI) is a proposed biological measure of feed utilization efficiency, but its components remain poorly understood.
• Traditional fixed effect models for RFI lack biological insight into its underlying mechanisms.
• Feed conversion ratio (FCR) is less biologically informative than RFI.

Purpose of the Study:

• To dissect residual feed intake (RFI) into its components: maintenance (RFIM) and growth (RFIG).
• To compare the predictive power of a novel random effect model against the traditional fixed effect model for RFI.
• To ascertain the heritability and genetic correlations of RFIM and RFIG.

Main Methods:

• A random effect model was developed to estimate RFIM and RFIG separately.
• The random effect model's performance was evaluated against a traditional fixed effect model using residual variance, R(2), and deviance information criterion.
• Heritability (h(2)) and genetic correlations were estimated for RFIM and RFIG.

Main Results:

• The random effect model significantly outperformed the fixed effect model in predicting RFI.
• Heritability for RFIM and RFIG was moderate (~0.50), with a highly negative genetic correlation (-0.95) between them.
• RFIM demonstrated a clear biological basis for selection, unlike RFIG.

Conclusions:

• The random effect model provides a superior, biologically relevant dissection of feed utilization efficiency.
• Selecting for RFIM can lead to smaller, more efficient animals by reducing maintenance requirements.
• RFIG is not a direct efficiency parameter and should not be used as the sole selection criterion; however, RFIM and RFIG offer flexibility for geneticists.