Use of random regression model as an alternative for multibreed relationship matrix

Area of Science:

• Animal breeding and genetics
• Quantitative genetics
• Statistical genetics

Background:

• Multibreed variance models are crucial for genetic evaluations in diverse populations.
• Accurate estimation of breeding values requires accounting for complex genetic structures.
• Integrating information across breeds is essential for maximizing genetic gain.

Purpose of the Study:

• To present a random regression model as an approximation for multibreed variance models.
• To demonstrate the derivation of this approximation using a splitted multibreed model.
• To show the utility of the random regression model for incorporating genomic data.

Main Methods:

• Developed a random regression model as an approximation for multibreed variance models.
• Utilized a splitted multibreed model, separating single breeding values into breed-specific and segregation terms.
• Illustrated the approach with a simple, practical example.

Main Results:

• The proposed random regression model effectively approximates multibreed variance models.
• The model facilitates the extension of multibreed information to genomic data models.
• The derivation provides a clear pathway for practical implementation.

Conclusions:

• Random regression models offer a flexible and powerful tool for multibreed genetic evaluations.
• This approximation simplifies the integration of complex breed structures into genomic selection.
• The presented approach enables more accurate and efficient genetic improvement in multibreed populations.