RESUMO
Crop breeding programs using conventional approaches, as well as new biotechnological tools, rely heavily on data resulting from the evaluation of genotypes in different environmental conditions (agronomic practices, locations, and years). Statistical methods used for designing field and laboratory trials and for analyzing the data originating from those trials need to be accurate and efficient. The statistical analysis of multi-environment trails (MET) is useful for assessing genotype × environment interaction (GEI), mapping quantitative trait loci (QTLs), and studying QTL × environment interaction (QEI). Large populations are required for scientific study of QEI, and for determining the association between molecular markers and quantitative trait variability. Therefore, appropriate control of local variability through efficient experimental design is of key importance. In this chapter we present and explain several classes of augmented designs useful for achieving control of variability and assessing genotype effects in a practical and efficient manner. A popular procedure for unreplicated designs is the one known as "systematically spaced checks." Augmented designs contain "c" check or standard treatments replicated "r" times, and "n" new treatments or genotypes included once (usually) in the experiment.
RESUMO
The need for a new analytical approach was encountered in the course of characterizing newly developed tomato lines resistant to late blight. Late blight resistant tomato lines were created in independent breeding programs using the accession Solanum pimpinellifolium L. (formerly Lycopersicon pimpinellifolium (L.) Miller) L3708 as the source of the resistance. However, initial field observation suggested that the late blight resistance in the lines produced by two independent breeding programs differed. Possible causes included a partial transfer of the late blight resistance derived from S. pimpinellifolium L3708 or the possibility of race specificity of this resistance. A crucial issue was determining the most appropriate and robust analytical method to use with data from laboratory analyses of the responses of nine tomato lines against five P. infestans isolates. Prior analysis by standard ANOVA revealed significant differences across tomato lines but could not determine whether the disease responses in the CLN-R lines were different from those of the heterozygous F(1) hybrids, created by crossing susceptible tomatoes with the fixed CU-R lines. A different analytical method was needed. Therefore, sporangia numbers/leaflet and diseased area data were analyzed using a half-normal probability plot and regression analysis. The results of this analysis show its utility for genetic or pathology studies. Considering only populations of the uniform tomato lines, this method confirms the results obtained by using a standard ANOVA, but provides a clearer demonstration of the distributions of the individuals within the populations and how this distribution impacts variance and the difference among the populations. This method also allows a joint analysis of the uniform lines with an additional population that is less uniform, because it is segregating. Such an analysis would be invalid using a standard ANOVA. The results of this joint analysis determined that the additional population was divergent from the fixed CU-R lines, and, against some isolates, against the CLN-R lines as well. Half-normal probability plot analysis method would be applicable more broadly beyond analysis of disease resistance data. It could be useful for data from populations that are not normally distributed, for traits which are affected by epistatic gene action, and could be useful for selection of extremes.