Alison B. Smith

The analysis of crop cultivar breeding and evaluation trials: An overview of current mixed model approaches

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 mixed model approaches for series of variety trials are mixed model versions of the methods summarized by Kempton (1984). In the present paper a general formulation that encompasses all of these methods is described, then individual methods are considered in detail.

On the Design of Early Generation Variety Trials With Correlated Data

This article considers the design of early generation variety trials with a prespecified spatial correlation structure and introduces a new class of partially replicated designs called p-rep designs in which the plots of standard varieties are replaced by additional plots of test lines. We show how efficient p-rep designs can be readily generated using the modified Reactive TABU search algorithm. The expected and realized genetic gain of p-rep and grid plot designs is compared in a simulation study.

THE ANALYSIS OF CROP VARIETY EVALUATION DATA IN AUSTRALIA

The major aim of crop variety evaluation is to predict the future performance of varieties. This paper presents the routine statistical analysis of data from late-stage testing of crop varieties in Australia. It uses a two-stage approach for analysis. The data from individual trials from the current year are analysed using spatial techniques. The resultant table of variety-by-trial means is combined with tables from previous years to form the data for an overall mixed model analysis. Weights allow for the data being estimates with varying accuracy. In view of the predictive aim of the analysis, variety effects and interactions are regarded as random effects. Appropriate inferential tools have been developed to assist with interpretation of the results. Analyses must be conducted in a timely manner so that variety predictions can be published and disseminated to growers immediately after harvest each year. Factors which facilitate this include easy access to historic data and the use of specialist mixed model software.

Quantitative trait loci for thermal time to flowering and photoperiod responsiveness discovered in summer annual-type Brassica napus L.

Time of flowering is a key adaptive trait in plants and is conditioned by the interaction of genes and environmental cues including length of photoperiod, ambient temperature and vernalisation. Here we investigated the photoperiod responsiveness of summer annual-types of Brassica napus (rapeseed, canola). A population of 131 doubled haploid lines derived from a cross between European and Australian parents was evaluated for days to flowering, thermal time to flowering (measured in degree-days) and the number of leaf nodes at flowering in a compact and efficient glasshouse-based experiment with replicated short and long day treatments. All three traits were under strong genetic control with heritability estimates ranging from 0.85–0.93. There was a very strong photoperiod effect with flowering in the population accelerated by 765 degree-days in the long day versus short day treatments. However, there was a strong genetic correlation of line effects (0.91) between the long and short day treatments and relatively low genotype x treatment interaction indicating that photoperiod had a similar effect across the population. Bivariate analysis of thermal time to flowering in short and long days revealed three main effect quantitative trait loci (QTLs) that accounted for 57.7% of the variation in the population and no significant interaction QTLs. These results provided insight into the contrasting adaptations of Australian and European varieties. Both parents responded to photoperiod and their alleles shifted the population to earlier flowering under long days. In addition, segregation of QTLs in the population caused wide transgressive segregation in thermal time to flowering. Potential candidate flowering time homologues located near QTLs were identified with the aid of the Brassica rapa reference genome sequence. We discuss how these results will help to guide the breeding of summer annual types of B. napus adapted to new and changing environments.

The design and analysis of multi-phase plant breeding experiments

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 in all phases. The motivation for this was the high cost of obtaining quality trait data, thus the need to limit the total number of samples tested, but still allow use of the mixed model analysis. A simulation study is used to show that the combined use of the new designs and mixed model analysis has substantial benefits in terms of the genetic gain from selection.

Genetic and in silico comparative mapping of the polyphenol oxidase gene in bread wheat (Triticum aestivum L.)

Polyphenol oxidases (PPOs) are involved in the time-dependent darkening and discolouration of Asian noodles and other wheat end products. In this study, a doubled haploid (DH) population derived from Chara (moderately high PPO activity)/WW2449 (low PPO activity) was screened for PPO activity based on l-DOPA and l-tyrosine assays using whole seeds. Both these assays were significantly genetically correlated (r=0.91) in measuring the PPO activity in this DH population. Quantitative trait loci (QTLs) analysis utilising a skeleton map enabled us to identify a major QTL controlling PPO activity based on l-DOPA and l-tyrosine on the long arm of chromosome 2A. The simple sequence repeat (SSR) marker GWM294b explained over 82% of the line mean phenotypic variation from samples collected in both 2000 and 2003. Four SSR markers were validated for PPO linkage in genetically diverse backgrounds and proven to correctly predict the PPO activity in more than 92% of wheat lines. Physical mapping using deletion lines of Chinese Spring has confirmed the location of the GWM294b, GWM312 and WMC170 on chromosome 2AL, between deletion breakpoints 2AL-C to 0.85. In order to identify functional gene markers, data searches for alignments between rice BAC/PAC clones assembled on chromosome 1 and 4, chromosome 7, and (1) the wheat expressed sequence tags mapped in deletion bin (2AL-C to 0.85) and (2) the coding sequence of a previously cloned wheat PPO gene were made and found significant sequence similarities with the PPO gene or common central domain of tyrosinase. Available PPO gene sequences in the National Centre for Biotechnology Information (NCBI) database have revealed that there is a significant molecular diversity at the nucleotide and amino acid level in the wheat PPO genes.

Analysis of yield and oil from a series of canola breeding trials. Part II. Exploring variety by environment interaction using factor analysis.

Exploring and exploiting variety by environment (V × E) interaction is one of the major challenges facing plant breeders. In paper I of this series, we presented an approach to modelling V × E interaction in the analysis of complex multi-environment trials using factor analytic models. In this paper, we develop a range of statistical tools which explore V × E interaction in this context. These tools include graphical displays such as heat-maps of genetic correlation matrices as well as so-called E-scaled uniplots that are a more informative alternative to the classical biplot for large plant breeding multi-environment trials. We also present a new approach to prediction for multi-environment trials that include pedigree information. This approach allows meaningful selection indices to be formed either for potential new varieties or potential parents.

Nitrogen fixation by subterranean clover (Trifolium subterraneum L.) growing in pure culture and in mixtures with varying densities of lucerne (Medicago sativa L.) or phalaris (Phalaris aquatica L.)

The proportions of biologically fixed (Pfix) plant nitrogen (N) and the total amounts of N2 fixed by subterranean clover (Trifolium subterraneum L.) growing in pure culture and in mixtures with different densities (5, 10, 20, or 40plants/m2) of newly sown phalaris (Phalaris aquatica L.) or lucerne (Medicago sativa L.) were followed over 3 years in a field study using the 15N natural abundance technique. The amount of fixed N in subterranean clover was linearly related to shoot biomass. Over the 3-year period, subterranean clover fixed 23–34 kg N/t shoot biomass compared with 17–29 kg N/t shoot biomass in lucerne. Based on above-ground biomass, pure subterranean clover fixed 314 kg N/ha over the 3 years compared with 420–510 kg N/ha by lucerne–clover mixtures and 143–177 kg N/ha by phalaris–clover mixtures. The superior N2 fixation by the lucerneŒsubterranean clover mixtures was due to the N fixed by the lucerne and the presence of a higher subterranean clover biomass relative to that occurring in the adjacent phalaris plots. In the first year, 92% of subterranean clover shoot N was derived from fixation compared with only 59% of lucerne. The reliance of clover upon fixed N2 remained high (73–95%) throughout the 3 years in all swards, except in pure subterranean clover and lucerne in August 1996 (56 and 64%, respectively). Subterranean clover usually fixed a higher proportion of its N when grown in mixtures with phalaris than with lucerne. The calculated Pfix values for lucerne (47–61% in 1995 and 39–52% in 1996) were consistently lower than in subterranean clover and tended to increase with lucerne density. Although lucerne derived a lower proportion of its N from fixation than subterranean clover, its tissue N concentration was consistently higher, indicating it was effective at scavenging soil mineral N. It was concluded that including lucerne in wheat-belt pastures will increase inputs of fixed N. Although lucerne decreased subterranean clover biomass, it maintained or raised Pfix values compared with pure subterranean clover swards. The presence of phalaris maintained a high dependence on N2 fixation by subterranean clover, but overall these swards fixed less N due to the lower clover herbage yields. Perennial and annual legumes appear compatible if sown in a mix and can contribute more N2 to the system than where the annual is sown alone or with a perennial grass. These findings suggest that increases in the amount of N2 fixed can be achieved through different legume combinations without interfering greatly with the N fixation process. Different combinations may also result in more efficient use of fixed N2 through reduced leaching. Further work looking at combinations of annuals possibly with different maturity times, different annual and perennial legume combinations, and pure combinations of perennial (e.g. lucerne) could be investigated with the aim of maximising N2 fixation and use. Grazing management to encourage clover production in mixtures with phalaris will be necessary before the potential of subterranean clover to contribute fixed N2 in these swards is fully realised.

Analysis of yield and oil from a series of canola breeding trials. Part I. Fitting factor analytic mixed models with pedigree information

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 lines or varieties. Pedigree information on a total of 578 entries was available, and there were 69 entries that had unknown pedigrees. The degree of inbreeding varied from 0 (55 entries) to nearly fully inbred (337 entries). Subsamples of 2 g harvested grain were taken from each plot for determination of seed oil percentage by near infrared reflectance spectroscopy. The MET analysis for both yield and oil modelled genetic effects in different trials using factor analytic models and the residual plot effects for each trial were modelled using spatial techniques. Models in which pedigree information was included provided significantly better fits to both yield and oil data.

Factor analytic and reduced animal models for the investigation of additive genotype-by-environment interaction in outcrossing plant species with application to a Pinus radiata breeding programme

Key messageModelling additive genotype-by-environment interaction is best achieved with the use of factor analytic models. With numerous environments and for outcrossing plant species, computation is facilitated using reduced animal models.AbstractThe development of efficient plant breeding strategies requires a knowledge of the magnitude and structure of genotype-by-environment interaction. This information can be obtained from appropriate linear mixed model analyses of phenotypic data from multi-environment trials. The use of factor analytic models for genotype-by-environment effects is known to provide a reliable, parsimonious and holistic approach for obtaining estimates of genetic correlations between all pairs of trials. When breeding for outcrossing species the focus is on estimating additive genetic correlations and effects which is achieved by including pedigree information in the analysis. The use of factor analytic models in this setting may be computationally prohibitive when the number of environments is moderate to large. In this paper, we present an approach that uses an approximate reduced animal model to overcome the computational issues associated with factor analytic models for additive genotype-by-environment effects. The approach is illustrated using a Pinus radiata breeding dataset involving 77 trials, located in environments across New Zealand and south eastern Australia, and with pedigree information on 315,581 trees. Using this approach we demonstrate the existence of substantial additive genotype-by-environment interaction for the trait of stem diameter measured at breast height. This finding has potentially significant implications for both breeding and deployment strategies. Although our approach has been developed for forest tree breeding programmes, it is directly applicable for other outcrossing plant species, including sugarcane, maize and numerous horticultural crops.