Three-way methods for multiattribute genotype by environment data: An illustrated partial survey

FSW - Gezinsopvoeding - Ou

Relationships among analytical methods used to study genotypic variation and genotype-by-environment interaction in plant breeding multi-environment experiments

Following the recognition of the importance of dealing with the effects of genotype-by-environment (G ×E) interaction in multi-environment testing of genotypes in plant breeding programs, there has been substantial development in the area of analytical methodology to quantify and describe these interactions. Three major areas where there have been developments are the analysis of variance, indirect selection, and pattern analysis methodologies. This has resulted in a wide range of analytical methods each with their own advocates. There is little doubt that the development of these methodologies has greatly contributed to an enhanced understanding of the magnitude and form of G ×E interactions and our ability to quantify their presence in a multi-environment experiment. However, our understanding of the environmental and physiological bases of the nature of G ×E interactions in plant breeding has not improved commensurably with the availability of these methodologies. This may in part be due to concentration on the statistical aspects of the analytical methodologies rather than on the complementary resolution of the biological basis of the differences in genotypic adaptation observed in plant breeding experiments. There are clear relationships between many of the analytical methodologies used for studying genotypic variation and G ×E interaction in plant breeding experiments. However, from the numerous discussions on the relative merits of alternative ways of analysing G ×E interactions which can be found in the literature, these relationships do not appear to be widely appreciated. This paper outlines the relevant theoretical relationships between the analysis of variance, indirect selection and pattern analysis methodologies, and their practical implications for the plant breeder interested in assessing the effects of G ×E interaction on the response to selection. The variance components estimated from the combined analysis of variance can be used to judge the relative magnitude of genotypic and G ×E interaction variance. Where concern is on the effect of lack of correlation among environments, the G ×E interaction component can be partitioned into a component due to heterogeneity of genotypic variance among environments and another due to the lack of correlation among environments. In addition, the pooled genetic correlation among all environments can be estimated as the intraclass correlation from the variance components of the combined analysis of variance. Where G ×E interaction accounts for a large proportion of the variation among genotypes, the individual genetic correlations between environments could be investigated rather than the pooled genetic correlation. Indirect selection theory can be applied to the case where the same character is measured on the same genotypes in different environments. Where there are no correlations of error effects among environments, the phenotypic correlation between environments may be used to investigate indirect response to selection. Pattern analysis (classification and ordination) methods based on standardised data can be used to summarise the relationships among environments in terms of the scope to exploit indirect selection. With the availability of this range of analytical methodology, it is now possible to investigate the results of more comprehensive experiments which attempt to understand the nature of differences in genotypic adaptation. Hence a greater focus of interest on understanding the causes of the interaction can be achieved

Relationships among international testing sites of spring durum wheat

Knowledge of the relationships among international test sites is valuable information for effective targeting of germplasm exchange. Five years of grain yield data from the Elite Durum Yield Trial (EDYT), consisting of 132 trials from 32 locations in 22 countries, were studied. Pattern analysis, the combination of ordination and cluster analysis. was used to identify groupings of international test sites that represent similar selection environments and compare location association with the mega-environment designations of the International Maize and Wheat Improvement Center (CIMMYT) breeding program. Two main environmental groups and six subgroups were identified for durum wheat (Triticum turgidum L. var. durum). The major determinants of the groupings were latitude and moisture supply. Biotic and abiotic stresses influenced further delineation of the clusters. Discrepancies between mega-environment designation and pattern analysis results warrant further investigations of the underlying causes. The relationships among test sites documented in this study should provide a framework for effectively targeting germplasm and information exchange between comparable programs

Genetic disequilibrium mapping using high-throughput data from a plant breeding program

The development of affordable high-throughput marker systems for plants will allow routine genotyping in plant breeding programs and so enable the use of plant breeding populations for genome-wide association analysis. To obtain the full benefit of these analyses a reliable genetic map is essential. However as each new set of germplasm genotyped will have a different set of (polymorphic) markers, an appropriate map is rarely if ever available. We describe a procedure to produce a genetic map, with or without parental data, using the maker set reported for each study of any plant population. Using the procedures reported we were able to produce a map of 1447 polymorphic DArT makers used to genotype 644 entries in the first 25 years of a CIMMYT international Elite Spring Wheat Yield Trial (ESWYT) plant breeding population. When applied, either with or without parental information to the Synthetic x Opata mapping population, our procedure produced an almost identical map to that obtained using EasyMap software. Our procedure can be used to produce genetic disequilibrium maps using the data from any marker study. These maps, though not necessarily linkage disequilibrium maps when derived from a plant breeding population, are suitable for association analysis and QTL detection. They eliminate the need for biparental mapping populations to be created and genotyped to produce maps for the unique marker sets reported for each new study