Molecular Plant Breeding 2015, Vol.6, No.17, 1
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22
12
Continuing Appendix 1
Locus/Marker
Bin
position
Repeat
length
Allele size
range (bp)
No. of alleles
for inbred
lines (n=115)
No. of alleles
for hybrids
(n=83)
No. of alleles
for OPVs and
landraces (n=84)
Total number
of alleles
phi96100
2.00
4
266-298
5
7
8
9
umc1061
10.06
3
100-112
2
4
3
5
umc1136
3.10
3
130-157
6
7
5
7
umc1143
6.00
5
73-85
3
3
3
3
umc1161
8.06
6
136-149
3
2
3
3
umc1196
10.07
6
133-158
4
4
4
5
umc1266
3.06
3
120-144
3
0
1
3
umc1304
8.02
4
124-133
2
1
5
3
umc1332
5.04
3
116-143
5
5
1
6
umc1367
10.03
3
146-159
0
0
3
3
umc1447
5.03
3
113-124
2
3
2
4
umc1545
7.00
4
69-85
3
2
3
3
umc1917
1.04
3
132-153
2
4
3
6
umc2047
1.09
4
100-134
7
7
7
8
umc2250
2.04
3
153-153
0
0
0
1
molecular analysis provides a wider genome sampling
than the phenotypic analysis, therefore it is able to
give a clear picture of genetic distance. The variation
detected by the molecular markers is non-adaptive,
hence not affected by natural or artificial selection.
Most desirable phenotypic traits in plant breeding are
a result of interaction among expressed genes, but
agronomic studies are still essential in germplasm
description and determination of molecular genetic
distance is a complement (Donini et al., 2000). Clear
estimates of the genetic distances would be closer
when there is association between the loci controlling
the phenotypic trait of interest (QTL) and the markers
used and when a larger number of the traits of interest
in relation to a particular situation are evaluated (Roy
et al., 2004; Lefebvre et al., 2001). Earlier studies
have reported that it is necessary to consider the
molecular and phenotypic data separately in genotype
divergence studies (Warburton et al., 2002). The use
of phenotypic traits is therefore, relatively less
efficient in discrimination of closely related genotypes
and analysis of their genetic relationships compared to
the use of molecular markers. Nevertheless, the use of
phenotypic traits serves as a general approach in
germplasm classification within a collection in relation
to a particular trait.
The multivariate analyses revealed high concordance
among the PCA, model-based population partitioning,
clustering based on the genetic distance and
discriminant analyses in terms of the number of
groups and members in each group. Earlier studies
have shown that principal component analysis as well
as population structure are good predictors of
grouping patterns and they can be used to complement
the clustering method analysis, since different
combinations of genetic distance matrices and clustering
algorithms can give rise to somewhat different groups
(Reif et al., 2005; Semagn et al., 2012).
The FST values form the analysis of molecular
variance indicates a moderate genetic differentiation
among groups and or populations. This is in agreement
with the results of genetic diversity studies from
previous research on maize populations (Semagn et al.,
2012; Wen et al., 2012). In addition it has been
reported that most variation in maize populations is
partitioned within, rather than between populations,
because maize is an out-crossing species a factor that
lead to reduced population differentiation (Hamrick
and Godt 1997).
Genetic divergence for resistance to stem borer and
postharvest insect pests exists in tropical maize germplasm.
