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On the design of field experiments with correlated treatment effects

Journal Article


Abstract


  • Large-scale field evaluation of genetic material forms an important part of the selection

    process in the early stages of plant breeding programs. These experiments are typically

    designed ignoring information on genetic relatedness, often available in the form of

    crossing history, or plant pedigree records. This paper considers the design of plant

    breeding experiments where the residuals may be correlated with an assumed autoregressive

    process, and there is a known genetic covariance structure among genotype

    effects. This structure is frequently more complex than simple nested family models,

    arising more generally from the pedigree, or possibly identity in state measures. It is

    widely accepted that the analysis of these data is improved using information on related

    individuals. The design of these experiments exploiting known genetic relatedness is

    considered using three case studies from industry that differ in selection goals, genetic

    complexity and scale.

Publication Date


  • 2014

Citation


  • Butler, D. G., Smith, A. B. & Cullis, B. R. (2014). On the design of field experiments with correlated treatment effects. Journal of Agricultural, Biological, and Environmental Statistics, 19 (4), 541-557.

Scopus Eid


  • 2-s2.0-84925507669

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/3189

Has Global Citation Frequency


Number Of Pages


  • 16

Start Page


  • 541

End Page


  • 557

Volume


  • 19

Issue


  • 4

Place Of Publication


  • United States

Abstract


  • Large-scale field evaluation of genetic material forms an important part of the selection

    process in the early stages of plant breeding programs. These experiments are typically

    designed ignoring information on genetic relatedness, often available in the form of

    crossing history, or plant pedigree records. This paper considers the design of plant

    breeding experiments where the residuals may be correlated with an assumed autoregressive

    process, and there is a known genetic covariance structure among genotype

    effects. This structure is frequently more complex than simple nested family models,

    arising more generally from the pedigree, or possibly identity in state measures. It is

    widely accepted that the analysis of these data is improved using information on related

    individuals. The design of these experiments exploiting known genetic relatedness is

    considered using three case studies from industry that differ in selection goals, genetic

    complexity and scale.

Publication Date


  • 2014

Citation


  • Butler, D. G., Smith, A. B. & Cullis, B. R. (2014). On the design of field experiments with correlated treatment effects. Journal of Agricultural, Biological, and Environmental Statistics, 19 (4), 541-557.

Scopus Eid


  • 2-s2.0-84925507669

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/3189

Has Global Citation Frequency


Number Of Pages


  • 16

Start Page


  • 541

End Page


  • 557

Volume


  • 19

Issue


  • 4

Place Of Publication


  • United States