The selection index--then, now, and for the future

Area of Science:

• Animal breeding and genetics
• Quantitative genetics

Background:

• Selecting for multiple traits with varying characteristics (variability, heritability, economic importance, correlations) presents challenges.
• Traditional selection methods like independent culling and sequential selection may not be optimal for complex breeding goals.

Purpose of the Study:

• To compare the effectiveness of simultaneous multiple-trait index selection against independent culling and sequential selection.
• To define the methodology for establishing aggregate breeding values and economic weights for effective multi-trait selection.

Main Methods:

• Defined aggregate breeding value based on individual trait breeding values and their economic importance.
• Proposed economic weights approximating the partial regression of enterprise output value on trait breeding value.
• Utilized Best Linear Unbiased Prediction (BLUP) for regressed breeding value predictions, accounting for varying information and unknown means.

Main Results:

• Simultaneous multiple-trait index selection demonstrated greater effectiveness than independent culling or sequential selection for improving multiple traits.
• The proposed method for defining economic weights and aggregate breeding values allows for optimized selection across different age classes.
• BLUP predictions facilitate accurate application of economic weights and selection proportions to maximize genetic gain.

Conclusions:

• Simultaneous multiple-trait index selection is superior for achieving genetic improvement across multiple traits with diverse characteristics.
• Accurate definition of economic weights and utilization of BLUP-predicted breeding values are crucial for maximizing aggregate genetic gain.
• This approach enhances the efficiency of selection programs, particularly in crossbreeding systems and for maintaining competitive genetic merit.