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Theoretical and Applied Genetics

, Volume 129, Issue 10, pp 1933–1949 | Cite as

Walking through the statistical black boxes of plant breeding

  • Alencar Xavier
  • William M. Muir
  • Bruce Craig
  • Katy Martin RaineyEmail author
Review

Abstract

Key message

The main statistical procedures in plant breeding are based on Gaussian process and can be computed through mixed linear models.

Abstract

Intelligent decision making relies on our ability to extract useful information from data to help us achieve our goals more efficiently. Many plant breeders and geneticists perform statistical analyses without understanding the underlying assumptions of the methods or their strengths and pitfalls. In other words, they treat these statistical methods (software and programs) like black boxes. Black boxes represent complex pieces of machinery with contents that are not fully understood by the user. The user sees the inputs and outputs without knowing how the outputs are generated. By providing a general background on statistical methodologies, this review aims (1) to introduce basic concepts of machine learning and its applications to plant breeding; (2) to link classical selection theory to current statistical approaches; (3) to show how to solve mixed models and extend their application to pedigree-based and genomic-based prediction; and (4) to clarify how the algorithms of genome-wide association studies work, including their assumptions and limitations.

Keywords

Quantitative Trait Locus Variance Component Hide Markov Model Ridge Regression Kernel Matrix 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Compliance with ethical standards

Conflict of interest

Authors declare no conflict of interest.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Alencar Xavier
    • 1
  • William M. Muir
    • 2
  • Bruce Craig
    • 3
  • Katy Martin Rainey
    • 1
    Email author
  1. 1.Department of AgronomyPurdue UniversityWest LafayetteUSA
  2. 2.Department of Animal SciencePurdue UniversityWest LafayetteUSA
  3. 3.Department of StatisticsPurdue UniversityWest LafayetteUSA

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