Molecular Plant Breeding 2016, Vol.7, No.29, 1
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1.5 Rank distribution of genotypes within cluster
It would be highly fruitful to select one potential homozygous genotype from each cluster rather than two or more
and test these genotypes by a diallel analysis (Singh and Chudhary, 1885). Therefore, ranking of genotypes within
each cluster based on variance was performed to choose suitable homozygous parents from each cluster. Similar
method was practiced by Hasanuzzaman and Golam (2011) in chili. Table 3 showed the value of phenotypic
variability. Genotypes having lowest phenotypic variability were ranked lowest and genotypes containing highest
phenotypic variability were given highest rank. Based on the values from table 3, rank distribution of genotypes
within cluster has been generated (Table 4). Six relatively stable parents were selected from six different clusters
based on lowest ranking value within each cluster (Table 4). G1 was most stable among nine genotypes from
cluster I as it showed lowest ranking value (44). Therefore, G1 was selected from cluster I. Next, G30 was
selected from Cluster II due to its lowest ranking value. Similarly, G13, G11 and G27 were selected from cluster
III, IV and VI, respectively. Finally, G25 was the single genotype in cluster V. Therefore, these six genotypes can
be selected as parent for 6X6 diallel cross. Table 5 showed the mean performance of six selected genotypes.
Among six genotypes no one showed highest mean value for all characters.
1.6 Genotype by trait (GT) biplot
A Genotype by trait (GT) biplot was constructed by plotting PCA1 scores against PCA2 scores for six parents and
twelve traits to display the genetic variability among parents selected from six different clusters along with their
suitable characters in one frame (Figure 3). In the GT biplot, a vector is drawn from the biplot origin to each
marker of the traits to facilitate visualization of the relationships between and among the traits. The length of the
vector measures the magnitude of its effects. The correlation coefficient between any two traits is approximated
by the cosine of the angle between their vectors. Acute angles show a positive correlation, obtuse angles show a
negative correlation and right angles no correlation (Yan and Rajcan 2002). Therefore, by visualizing the GT
biplot, indirect selection of appropriate traits (vectors showing acute angles to the target trait vector) and less
important (redundant) traits (vectors showing obtuse angles or right angles to the target trait vector) can be done
easily.
In our study, the GT biplot captured 69.17% of the total variation. This relatively high percentage variation
reflects the accuracy of inter-relationships among the measured traits. From GT biplot (Figure. 3), we can
visualize genotype 1 from cluster I was the best genotype for number of primary branches only. Next, genotype 30
from cluster II was best for traits like fruit diameter (mm) and plant height (cm). Genotypes 13 from cluster III
was suitable for number of secondary branches. However, Genotype 11 from cluster IV and genotype 25 from
cluster V were suitable for fruit pedicle length (cm) and, fruit length (cm), fruit weight (gm) and yield/plant (gm),
respectively. And lastly, genotype 27 from cluster was suitable for fruit/plant only.
In case of traits like, genotype showing high performance should be considered as unsuitable for hybridization as
earliness is preferred in any breeding strategies. Therefore, if genotype 30, genotype 13 and genotype 11 are
selected as parents, days to 50% flowering, days to first flowering and days to fruit maturity would be indirectly
selected as negative traits, respectively.
In our previous report (Hasan et al., 2016), we generated a biplot by considering only fruit weight (gm),
fruits/plant, yield/plant (gm) and relative genetic score, and found that genotype 25 (surjomukhi) was the ideal
genotype G29, G27 and G19. In this study, we considered all the 12 traits and found that genotype 25 from cluster
V is the superior one for the traits like fruit length (cm), fruit weight (gm) and yield per plant (gm). Therefore, if
we target to improve the traits like yield/plant (gm), fruit weight (gm) and fruit length (cm), for instance, for
future hybridization programme, we should select genotype 25 as a suitable parent. And a crossing between
genotype 25 from cluster V and genotype 27 from cluster VI will be fruitful for indirect selection of fruit/plant
along with traits yield/plant (gm), fruit weight (gm) and fruit length (cm) as main target traits.
