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Multivariate Whole Genome Average Interval Mapping: Qtl Analysis For Multiple Traits And/Or Environments, Arunas P. Verbyla, Brian R. Cullis
Multivariate Whole Genome Average Interval Mapping: Qtl Analysis For Multiple Traits And/Or Environments, Arunas P. Verbyla, Brian R. Cullis
Professor Brian Cullis
A major aim in some plant-based studies is the determination of quantitative trait loci (QTL) for multiple traits or across multiple environments. Understanding these QTL by trait or QTL by environment interactions can be of great value to the plant breeder. A whole genome approach for the analysis of QTL is presented for such multivariate applications. The approach is an extension of whole genome average interval mapping in which all intervals on a linkage map are included in the analysis simultaneously. A random effects working model is proposed for the multivariate (trait or environment) QTL effects for each interval, with …
The Analysis Of Longitudinal Data Using Mixed Model L-Splines, S. Welham, Brian Cullis, M. Kenward, R Thompson
The Analysis Of Longitudinal Data Using Mixed Model L-Splines, S. Welham, Brian Cullis, M. Kenward, R Thompson
Professor Brian Cullis
L-splines are a large family of smoothing splines defined in terms of a linear differential operator. This article develops L-splines within the context of linear mixed models and uses the resulting mixed model L-spline to analyze longitudinal data from a grassland experiment. In the spirit of time-series analysis, a periodic mixed model L-spline is developed, which partitions data into a smooth periodic component plus smooth long-term trend.
A Comparison Of Analysis Methods For Late-Stage Variety Evaluation Trials, Sue Welham, Beverley Gogel, Alison Smith, Robin Thompson, Brian Cullis
A Comparison Of Analysis Methods For Late-Stage Variety Evaluation Trials, Sue Welham, Beverley Gogel, Alison Smith, Robin Thompson, Brian Cullis
Professor Brian Cullis
The statistical analysis of late-stage variety evaluation trials using a mixed model is described, with one- or two-stage approaches to the analysis. Two sets of trials, from Australia and the UK, were used to provide realistic scenarios for a simulation study to evaluate the different methods of analysis. This study showed that a one-stage approach gave the most accurate predictions of variety performance overall or within each environment, across a range of models, as measured by mean squared error of prediction or realized genetic gain. A weighted two-stage approach performed adequately for variety predictions both overall and within environments, but …
The Design And Analysis Of Multi-Phase Plant Breeding Experiments, A B. Smith, P Lim, Brian R. Cullis
The Design And Analysis Of Multi-Phase Plant Breeding Experiments, A B. Smith, P Lim, Brian R. Cullis
Professor Brian Cullis
Despite the importance of selection for quality characteristics in plant improvement programmes, literature on experimental design and statistical analysis for these traits is scarce. Most quality traits are obtained from multi-phase experiments in which plant varieties are first grown in a field trial then further processed in the laboratory. In the present paper a general mixed model approach for the analysis of multi-phase data is described, with particular emphasis on quality trait data that are often highly unbalanced and involve substantial sources of non-genetic variation and correlation. Also detailed is a new approach for experimental design that employs partial replication …
The Analysis Of Qtl By Simultaneous Use Of The Full Linkage Map, A. Verbyla, Brian Cullis, R Thompson
The Analysis Of Qtl By Simultaneous Use Of The Full Linkage Map, A. Verbyla, Brian Cullis, R Thompson
Professor Brian Cullis
An extension of interval mapping is presented that incorporates all intervals on the linkage map simultaneously. The approach uses a working model in which the sizes of putative QTL for all intervals across the genome are random effects. An outlier detection method is used to screen for possible QTL. Selected QTL are subsequently fitted as fixed effects. This screening and selection approach is repeated until the variance component for QTL sizes is not statistically significant. A comprehensive simulation study is conducted in which map uncertainty is included. The proposed method is shown to be superior to composite interval mapping in …
Analysis Of Yield And Oil From A Series Of Canola Breeding Trials. Part I. Fitting Factor Analytic Mixed Models With Pedigree Information, C Beeck, W Cowling, A Smith, Brian Cullis
Analysis Of Yield And Oil From A Series Of Canola Breeding Trials. Part I. Fitting Factor Analytic Mixed Models With Pedigree Information, C Beeck, W Cowling, A Smith, Brian Cullis
Professor Brian Cullis
In this paper multiplicative mixed models have been used for the analysis of multi-environment trial (MET) data for canola oil and grain yield. Information on pedigrees has been included to allow for the modelling of additive and nonadditive genetic effects. The MET data set included a total of 19 trials (synonymous with sites or environments), which were sown across southern Australia in 2007 and 2008. Each trial was designed as a p-rep design using DiGGeR with the default prespecified spatial model. Lines in their first year of testing were unreplicated, whereas there were two or three replications of advanced …
The Analysis Of Crop Cultivar Breeding And Evaluation Trials: An Overview Of Current Mixed Model Approaches, A Smith, Brian Cullis, R Thompson
The Analysis Of Crop Cultivar Breeding And Evaluation Trials: An Overview Of Current Mixed Model Approaches, A Smith, Brian Cullis, R Thompson
Professor Brian Cullis
The analysis of series of crop variety trials has a long history with the earliest approaches being based on ANOVA methods. Kempton (1984) discussed the inadequacies of this approach, summarized the alternatives available at that time and noted that all of these approaches could be classified as multiplicative models. Recently, mixed model approaches have become popular for the analysis of series of variety trials. There are numerous reasons for their use, including the ease with which incomplete data (not all varieties in all trials) can be handled and the ability to appropriately model within-trial error variation. Currently, the most common …
Enhanced Diagnostics For The Spatial Analysis Of Field Trials, Katia Stefanova, Alison Smith, Brian Cullis
Enhanced Diagnostics For The Spatial Analysis Of Field Trials, Katia Stefanova, Alison Smith, Brian Cullis
Professor Brian Cullis
We report an analysis of a series of uniformity field trials using the technique proposed by Gilmour, Cullis, and Verbyla. In particular, we clarify the role of the sample variogram and present a range of enhanced graphical diagnostics to aid the spatial modeling process.We highlight the implications of the presence of extraneous variation related to commonly used agronomic practices, such as serpentine harvesting.