Use of GGEbiplot methodology and Griffing's diallel method for genetic analysis of partial resistance to phoma black stem disease in sunflower

Abstract

The objectives of the present study were to estimate the general combining ability (GCA) and specific combining ability (SCA) for partial resistance to phoma black stem and to identify the most promising combination for the selection of improved breeding lines. The response of five parental genotypes and their F1 hybrids to a phoma black stem isolate (MA6) were evaluated in a diallel programme under controlled growth chamber conditions. Significant GCA and SCA indicate that both additive and non additive gene effects contributed in the inheritance of partial resistance to phoma black stem, however, the Baker ratio showed that the additive genetic effects were more important than nonadditive ones. It is recommended that the GGEbiplot methodology could be an excellent tool for visualizing entry by tester (diallel) data. By using this technique to analyse black stem severity data, interaction among the sunflower genotypes in providing partial resistance to phoma black stem was clearly identified. Based on GGEbiplot presentation and Griffing's diallel analysis, the mutant line 'M6 54 1' showed the largest GCA, indicating contribution towards partial resistance, and the genotype B454/ 03 presented the smallest GCA, indicating contribution towards susceptibility. Our results show that the F1 hybrids 'SDR18)B454/03' and 'M6 54 1 )B454/03' showing heterosis for partial resistance to phoma black stem come from the crosses between a susceptible genotype 'B454/03' and two partially resistant genotypes (SDR18 and M6 54 1), originated from different breeding programmes. We conclude therefore that these genotypes possess at least some different resistance genes, which were expressed in the hybrids and led to the observed effects.

Identifying superior spring wheat genotypes through diallel approaches

“…The agreement between these methods with respect to the GCA can be evaluated based on the magnitude of their associations (Griffing x PC1 = 0.81; Griffing x REML/BLUP = 0.99 and PC1 x REML/BLUP = 0.89) ( Table 3). A positive correlation between Griffing and REML/BLUP (Valério et al, 2009;Nascimento et al, 2010;Baldissera et al, 2012) and between Griffing and GGE biplot (Darvishzadeh et al, 2009;Bocánski et al, 2011) was also observed by other authors. The combination CD 150 x Fundacep Cristalino presented the highest observed (5521 kg ha -1 ) and predicted (5437 kg ha -1 ) GY averages, and positive values for SCA according to the Griffing, multivariate (PC1, PC2 and PC3) and REML/BLUP analysis (Table 2).…”

“…The results reported here, indicate similarities between the Griffing, multivariate (PC1), REML/BLUP and GGE biplot methods with respect to the GCA. Darvishzadeh et al (2009), Malla et al (2010 and Boćanski et al (2011) also reported such similarities between the Griffing and GGE biplot analyses. The highest SCA estimates were obtained in the parents CD 113, CD 150 and Fundacep Cristalino ( Fig.…”

“…This methodology was developed originally to analyze multi-environment trial data (Yan, 2001). It allows extracting visual information to assess the performance of the GCA and SCA of each genotype, classify groups of parents with a similar genetic background (Yan and Hunt, 2002;Bertoia et al, 2006;Darvishzadeh et al, 2009). GGE biplot has the advantage of graphically displays the effects of genotype (G) and genotype by environment (GE) interaction, and enhancing the ability to understand the patterns of the data in comparison with conventional methods of diallel analysis (Badu-Apraku et al, 2011;Dehghani et al, 2012).…”

Use of biplot approach for genetic analysis of yield and related traits in cotton (Gossypiumbarbadense)

“…These methods are less interpretative, difficult to understand without the aid of some graphical display (Dehghani et al, 2012), Yan and Hunt (2002) have developed a quick evaluation method called GGE biplot model for analyzing the diallel data, this technique enhances the capability of interpreting the phenotypic variation to obtain combining ability and interrelationships of parents based on graphical presentation using PC1 and PC2 which are derived through PC analysis of environment-centered yield data. GGE biplot is recent method and has been widely used to determine combining ability and heterotic responses (Shang et al, 2006;Darvishzadeh et al, 2009 Tester code Origin Categories Current position G86 g86 Giza 86 Long staple Commercial variety G93 g93 Giza 93 Extra long staple Commercial variety G92 g92 Giza 92 Extra long staple Commercial variety G75xS g75xS Giza 75 x Sea Long staple New promising hybrid 10xG86 10xg86 10229 x Giza 86 Long staple New promising hybrid G85 g85 Giza 85 Long staple Commercial variety G88 g88 Giza 88 Long staple Commercial variety environments are testers. Yan's GGE biplot is also preferred to the conventional diallel approach because it gives jointly GCA and SCA effects of the population and the preferences of the crosses as well as grouping pattern of similar genotypes (Bertoia et al, 2006).…”

Analysis of Resistance to Cercospora Leaf Spot and Bolting in Sugar Beet as Winter Crop Using Griffing’s Diallel Method and GGE Biplot

“…Although the GGE biplot methodology was developed for multi-environment variety trials (MET) data analysis, it is applicable to all types of two way data that assume an entry-by-tester data structure (Yan and Hunt, 2002). In a diallel data set, each genotype is both an entry and a tester (Darvishzadeh et al, 2009). Applying GGE biplot allows breeders to visualize those varieties that show the GCA and SCA effects of each parent; the best testers; the heterotic groups; and genetic constitutions of parents with regard to the trait under investigation (Yan and Hunt, 2002).…”