Predicting Breeding Values with Applications in Forest Tree Improvement

1. Front Matter

   Pages i-xi

   PDF

2. Matrix Algebra

   • Timothy L. White, Gary R. Hodge

   Pages 2-19

3. Statistics

   • Timothy L. White, Gary R. Hodge

   Pages 20-47

4. Concepts of Progeny Test Analysis

   • Timothy L. White, Gary R. Hodge

   Pages 48-61

5. Theory of Best Linear Prediction

   • Timothy L. White, Gary R. Hodge

   Pages 62-85

6. Best Linear Prediction with Half-sib Progeny Test Data

   • Timothy L. White, Gary R. Hodge

   Pages 86-110

7. BLP with Full-sib and Multiple Sources of Data

   • Timothy L. White, Gary R. Hodge

   Pages 112-135

8. Best Linear PredictiOn: Further Topics

   • Timothy L. White, Gary R. Hodge

   Pages 136-171

9. Best Linear Prediction: An Operational Example

   • Timothy L. White, Gary R. Hodge

   Pages 172-206

10. Selection Index Theory

    • Timothy L. White, Gary R. Hodge

    Pages 208-230

11. Selection Index Applications

    • Timothy L. White, Gary R. Hodge

    Pages 232-275

12. Best Linear Unbiased Prediction: Introduction

    • Timothy L. White, Gary R. Hodge

    Pages 276-298

13. Best Linear Unbiased Prediction: Applications

    • Timothy L. White, Gary R. Hodge

    Pages 300-327

14. Back Matter

    Pages 328-369

    PDF

In most breeding programs of plant and animal species, genetic data (such as data from field progeny tests) are used to rank parents and help choose candidates for selection. In general, all selection processes first rank the candidates using some function of the observed data and then choose as the selected portion those candidates with the largest (or smallest) values of that function. To make maximum progress from selection, it is necessary to use a function of the data that results in the candidates being ranked as closely as possible to the true (but always unknown) ranking. Very often the observed data on various candidates are messy and unbalanced and this complicates the process of developing precise and accurate rankings. For example, for any given candidate, there may be data on that candidate and its siblings growing in several field tests of different ages. Also, there may be performance data on siblings, ancestors or other relatives from greenhouse, laboratory or other field tests. In addition, data on different candidates may differ drastically in terms of quality and quantity available and may come from varied relatives. Genetic improvement programs which make most effective use of these varied, messy, unbalanced and ancestral data will maximize progress from all stages of selection. In this regard, there are two analytical techniques, best linear prediction (BLP) and best linear unbiased prediction (BLUP), which are quite well-suited to predicting genetic values from a wide variety of sources, ages, qualities and quantities of data.

• forest
• plant
• tree
• Tree Biology

• Department of Forestry, University of Florida, Gainesville, USA

  Timothy L. White, Gary R. Hodge

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