ABSTRACT
Diallel crosses have been used in genetic research to determine the inheritance of a trait among a set of genotypes and to identify superior parents for hybrid or cultivar development. Conventional analysis of diallel data is limited to partitioning the total variation attributable to crosses into general combining ability (GCA) of each parent and specific combining ability (SCA) of each cross. The SCA effects are just residuals not explained by the GCA effects; they are cross specific and do not provide much information on parents. The biplot approach of diallel data analysis introduced in this chapter allows a much better understanding of parents. For a given set of data, the following information can be easily visualized: 1) the GCA effect of each parent; 2) the SCA effect of each parent (not cross); 3) the best crosses; 4) the best testers; 5) the heterotic groups; and 6) genetic constitutions of parents with regard to the trait under investigation.
Diallel crosses represent matings made in all possible combinations among a set of genotypes. They have been widely used in genetic research for investigating inheritance of quantitative traits among a set of genotypes. There are four types of diallel mating schemes (Griffing, 1956): 1) method 1 — diallel cross with parents and reciprocals; 2) method 2 — diallel cross with parents but without reciprocals; 3) method 3 — diallel cross with reciprocals but without parents; and 4) method 4 — diallel cross without parents and reciprocals. Reciprocals are made for the purpose of detecting any maternal effect. We confine our discussion to method 2 diallel cross, although other types of diallel crosses can be easily accommodated. Conventionally, analysis of diallel cross data is conducted to partition total genetic variation into GCA of the parents and SCA of the crosses.
In this chapter, we will use two sets of diallel data (Tables 9.1 and 9.2) to demonstrate the biplot approach to diallel analysis. The first dataset is to demonstrate the general steps and utilities of biplot analysis for diallel data. The second dataset is used to exemplify analysis of a large dataset for which a biplot of the first principal component (PC1) vs. the second principal component (PC2) may not be adequate. A discussion of both datasets is necessary because they contain contrasting entry × tester interaction or gene action patterns.
