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Related Experiment Videos

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

L N Hazel1, G E Dickerson, A E Freeman

  • 1Agricultural Research Service, University of Nebraska, Lincoln 68583-0908.

Journal of Dairy Science
|October 1, 1994
PubMed
Summary

Simultaneous multiple-trait index selection is more effective for improving multiple traits than other methods. This approach defines aggregate breeding value using trait breeding values and economic importance for optimized genetic gains.

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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.

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  • 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.