468. A practical approach to predicting expected response to selection with a new index
X. Zhang, P.R. Amer, Cheryl D. Quinton
Abstract
X. Zhang, P.R. Amer, Cheryl D. Quinton
Abstract
Selection indexes need to be periodically evaluated and re-designed to adapt to new market trends and new included traits with economic weightings. Response to selection is one of the statistics used to predict the genetic trend, and to compare old and new indexes. The most popular method for response to selection requires knowledge of the selection intensity and generation intervals, which are hard to obtain. However, the ratio of these often unknow parameters can be assumed to remain constant and can be estimated by observation in an established breeding program. Therefore, we proposed a method to estimate the response to selection using a baseline selection index. The results show how new selection indexes can be characterised for estimated response to selection in trait units and economic units while avoiding making assumptions about breeding scheme design and structure. The new method is practical and easy to calculate, making index comparison straight-forward.
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Selection indexes need to be periodically evaluated and re-designed to adapt to new market trends and new included traits with economic weightings. Response to selection is one of the statistics used to predict the genetic trend, and to compare old and new indexes. The most popular method for response to selection requires knowledge of the selection intensity and generation intervals, which are hard to obtain. However, the ratio of these often unknow parameters can be assumed to remain constant and can be estimated by observation in an established breeding program. Therefore, we proposed a method to estimate the response to selection using a baseline selection index. The results show how new selection indexes can be characterised for estimated response to selection in trait units and economic units while avoiding making assumptions about breeding scheme design and structure. The new method is practical and easy to calculate, making index comparison straight-forward.
Key concepts: Selection (genetic algorithm), Index selection, Index (typography), Computer science, Trait, Genetic algorithm, Statistics, Truncation selection