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Full-Text Articles in Physical Sciences and Mathematics
Joint Modeling Of Additive And Non-Additive Genetic Line Effects In Single Field Trials, H Oakey, A Verbyla, Brian Cullis, W. Pitchford, H. Kuchel
Joint Modeling Of Additive And Non-Additive Genetic Line Effects In Single Field Trials, H Oakey, A Verbyla, Brian Cullis, W. Pitchford, H. Kuchel
Professor Brian Cullis
A statistical approach is presented for selection of best performing lines for commercial release and best parents for future breeding programs from standard agronomic trials. The method involves the partitioning of the genetic effect of a line into additive and non-additive effects using pedigree based inter-line relationships, in a similar manner to that used in animal breeding. A difference is the ability to estimate non-additive effects. Line performance can be assessed by an overall genetic line effect with greater accuracy than when ignoring pedigree information and the additive effects are predicted breeding values. A generalized definition of heritability is developed …
Joint Modeling Of Additive And Non-Additive (Genetic Line) Effects In Multi-Environment Trials, H Oakey, A Verbyla, Brian Cullis, X. Wei, W. Pitchford
Joint Modeling Of Additive And Non-Additive (Genetic Line) Effects In Multi-Environment Trials, H Oakey, A Verbyla, Brian Cullis, X. Wei, W. Pitchford
Professor Brian Cullis
A statistical approach for the analysis of multienvironment trials (METs) is presented, in which selection of best performing lines, best parents, and best combination of parents can be determined. The genetic effect of a line is partitioned into additive, dominance and residual nonadditive effects. The dominance effects are estimated through the incorporation of the dominance relationship matrix, which is presented under varying levels of inbreeding. A computationally efficient way of fitting dominance effects is presented which partitions dominance effects into between family dominance and within family dominance line effects. The overall approach is applicable to inbred lines, hybrid lines and …
Application Of Multi-Phase Experiments In Plant Pathology To Identify Genetic Resistance To Diaporthe Toxica In Lupinus Albus, R. B. Cowley, G. J. Ash, J. D. I. Harper, Alison Smith, Brian Cullis, D. J. Luckett
Application Of Multi-Phase Experiments In Plant Pathology To Identify Genetic Resistance To Diaporthe Toxica In Lupinus Albus, R. B. Cowley, G. J. Ash, J. D. I. Harper, Alison Smith, Brian Cullis, D. J. Luckett
Professor Brian Cullis
Phenotyping assays in plant pathology using detached plant parts are multi-phase experimental processes. Such assays involve growing plants in field or controlled-environment trials (Phase 1) and then subjecting a sample removed from each plant to disease assessment, usually under laboratory conditions (Phase 2). Each phase may be subject to nongenetic sources of variation. To be able to separate these sources of variation in both phases from genetic sources of variation requires a multi-phase experiment with an appropriate experimental design and statistical analysis. To achieve this, a separate randomization is required for each phase, with additional replication in Phase 2. In …
Estimation In A Multiplicative Mixed Model Involving A Genetic Relationship Matrix, Alison M. Kelly, Brian R. Cullis, Arthur R. Gilmour, John A. Eccleston, Robin Thompson
Estimation In A Multiplicative Mixed Model Involving A Genetic Relationship Matrix, Alison M. Kelly, Brian R. Cullis, Arthur R. Gilmour, John A. Eccleston, Robin Thompson
Professor Brian Cullis
Genetic models partitioning additive and non-additive genetic effects for populations tested in replicated multi-environment trials (METs) in a plant breeding program have recently been presented in the literature. For these data, the variance model involves the direct product of a large numerator relationship matrix A, and a complex structure for the genotype by environment interaction effects, generally of a factor analytic (FA) form. With MET data, we expect a high correlation in genotype rankings between environments, leading to non-positive definite covariance matrices. Estimation methods for reduced rank models have been derived for the FA formulation with independent genotypes, and we …