Agriculture Reference
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narrow range of variation for these characters and provides very least scope for se-
lection. This also described that genetic factors were predominantly responsible for
expression of these attributes and selection could be made effectively on the basis
of phenotypic performance (Roychowdhury et al.
2011c
).
The heritability estimates indicate the relative amount of estimates have been
found to be satisfactory tools for selection based on phenotypic performance. The
high estimates of heritability suggested that selection based on phenotypic perfor-
mance would be more effective. However, heritability values alone may not provide
clear predictability of the breeding value. Heritability in conjugation with genetic
advance over mean (GAM) and/or genetic gain is more effective and reliable in
predicting the resultant effect of selection (Roychowdhury and Tah
2011b
; Roy-
chowdhury et al.
2011c
,
2012
). High heritability combined with high genetic gain
indicates less influence of environment in expression of these characters; and preva-
lence of additive gene action in their inheritance (Panse
1957
). Hence, these metri-
cal traits require simple selection in breeding programmes. High heritability with
moderate genetic gain indicates that the characters were governed by additive gene
interaction. High heritability coupled with low genetic gain indicating non-additive
gene action; hence heterosis breeding would be recommended for that trait.
Mutational Analysis of Plant Structure and Function
As a prerequisite for functional genomics, mutational analysis of the most important
characters that determine the plant productivity should be considered for the most
important crops. Germplasm collection and maintenance is necessary for the re-
covery of various crop mutants. Rice, maize, barley, mung bean, carnation, tomato
are the only positive examples of crop mutant germplasm conservation. In all these
collections, the number of mutants with described and characterized mutations of
genes responsible for plant productivity or for other agronomically important and
desirable characters for breeding is exceptionally low.
According to Brown and Peters (
1996
), during investigation on mouse genom-
ics for dissection of basic pathophysiological mechanisms, the first defined term
'
Phenotype gap
' depicts that many mouse mutations are extremely valuable for the
investigation of human diseases and for identification of the critical genes involved
in human pathologies. This 'phenotype gap' concept can easily be extended to ge-
netic investigation of plant species as a basic component of the mutational analysis
of any crop plants. The phenotype gap will reflect the gulf between available mutant
resources and the full range of phenotypes of an investigated plant species.
In
Arabidopsis thaliana
, having a very low amount of DNA per haploid genome,
it seems that the phenotype gap is also very wide. Probably only 1.8 % of visible
markers have been described in which 167 genes are expected per megabase (Mb)
and an average number of identified visible markers of 3 per Mb have been identi-
fied (Vizir et al.
1994
). The genome of rice (
Oryza sativa
), barley (
Hordeum vul-
gare
) and wheat (
Triticum aestivum
) contain about 4, 37 and 115 times more DNA
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