Literature DB >> 12926722

Planning incomplete block experiments when treatments are genetically related.

Júlio S de S Bueno Filho1, Steven G Gilmour.   

Abstract

Selection trials in plant and animal breeding, in incomplete blocks, are described by linear models with random effect parameters associated with treatments with known genetic covariance structure. It is now well known that the information on relatives can improve the analysis and many extensions of this model have been proposed, but no studies have been done on the consequences of this genetical relatedness among treatments for the optimality of block designs. Using a suitable optimality criterion, we show that the knowledge on relatedness may imply that the optimal design is not in the class of designs which are optimal for unrelated treatments. Implications for practical applications are discussed.

Mesh:

Year:  2003        PMID: 12926722     DOI: 10.1111/1541-0420.00044

Source DB:  PubMed          Journal:  Biometrics        ISSN: 0006-341X            Impact factor:   2.571


  8 in total

1.  Graph-based data selection for the construction of genomic prediction models.

Authors:  Steven Maenhout; Bernard De Baets; Geert Haesaert
Journal:  Genetics       Date:  2010-05-17       Impact factor: 4.562

Review 2.  Design of microarray experiments for genetical genomics studies.

Authors:  Júlio S S Bueno Filho; Steven G Gilmour; Guilherme J M Rosa
Journal:  Genetics       Date:  2006-08-03       Impact factor: 4.562

3.  A comparison of experimental designs for selection in breeding trials with nested treatment structure.

Authors:  H P Piepho; E R Williams
Journal:  Theor Appl Genet       Date:  2006-10-07       Impact factor: 5.699

4.  REML approach for adjusting the Fusarium head blight rating to a phenological date in inoculated selection experiments of wheat.

Authors:  K Emrich; F Wilde; T Miedaner; H P Piepho
Journal:  Theor Appl Genet       Date:  2008-04-05       Impact factor: 5.699

5.  Prediction of maize single cross hybrids using the total effects of associated markers approach assessed by cross-validation and regional trials.

Authors:  Wagner Mateus Costa Melo; Renzo Garcia Von Pinho; Marcio Balestre
Journal:  ScientificWorldJournal       Date:  2014-07-03

6.  Use of Contemporary Groups in the Construction of Multi-Environment Trial Datasets for Selection in Plant Breeding Programs.

Authors:  Alison Smith; Aanandini Ganesalingam; Christopher Lisle; Gururaj Kadkol; Kristy Hobson; Brian Cullis
Journal:  Front Plant Sci       Date:  2021-02-02       Impact factor: 5.753

7.  Assessment of genomic prediction reliability and optimization of experimental designs in multi-environment trials.

Authors:  Simon Rio; Deniz Akdemir; Tiago Carvalho; Julio Isidro Y Sánchez
Journal:  Theor Appl Genet       Date:  2021-11-22       Impact factor: 5.699

8.  Information Based Diagnostic for Genetic Variance Parameter Estimation in Multi-Environment Trials.

Authors:  Chris Lisle; Alison Smith; Carole L Birrell; Brian Cullis
Journal:  Front Plant Sci       Date:  2021-12-07       Impact factor: 5.753

  8 in total

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