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Molecular Plant Breeding 2010, Vol.1 No.5
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1.4 Phenotypic data
The phenotypic data used in this study was based on
the mean values across three years in three
environments (HD, SY and FY). PH, SPS and GPS
were evaluated in HD, SY and FY. SL and SD were
evaluated in HD and FY, whereas TKW was
evaluated in HD and SY (Table 2). PH, SL, SPS, GPS
and SD were the largest in FY among three locations,
probably because of more favorable weather
conditions in FY. The results of the analysis of
variance of six traits among three locations indicated
that environment had a large effect on PH, SPS, TKW
and SD, whereas it had a small effect on SL and GPS.
Pearson correlation coefficients of the agronomic
traits in the 103 accessions were also evaluated (Table
3). In general, TKW was positively correlated to SL,
SPS, SD and GPS, but negatively correlated to PH. In
addition, positive correlations were also detected
among SL, SPS and GPS. PH was negatively
correlated to SL, SPS and GPS, negatively correlated
to HD and TKW, which showed that PH can have on
many other agronomic characteristics.
Table 2 Descriptive statistics for the six agronomic traits
Traits
Location
Average
Minimum
Maximum
Standard deviation
PH (cm)
HD
78.81
61.67
105.00
7.11
SY
68.05
48.00
88.00
9.27
FY
89.16
70.50
124.50
8.77
SL (cm)
HD
9.81
6.73
14.03
1.32
SY
–
–
–
–
FY
9.84
6.47
14.01
1.41
SPS (No./spike)
HD
20.38
17.83
24.00
1.30
SY
19.89
16.50
23.95
1.40
FY
22.29
18.80
27.60
1.61
SD (No./cm)
HD
2.11
1.61
2.90
0.24
SY
–
–
–
–
FY
2.31
1.79
3.22
0.28
GPS (No./spike)
HD
43.28
30.33
61.56
6.19
SY
45.01
34.05
63.50
6.40
FY
44.70
25.00
63.45
7.05
TKW (g)
HD
33.92
23.17
46.1
4.69
SY
36.86
23.50
57.00
5.76
FY
–
–
–
–
Note: “–” indicates that the data was missed; Traits: PH: Plant height; SL: Spike length; SPS: Spikelets per spike: SD Spikelets
density; GPS: Grains per spike; TKW: Thousand kernel weight; Three locations: HD: Haidian); SY: Shunyi; FY: Fuyanga
1.5 Association mapping
In the present study, we used the association mapping
approach to searching QTLs for the six agronomic
traits in different locations (Table
4). A total of 10
SSR markers were identified to be associated with the
six traits at the 0.01 probability level, and each QTL
explained 4.85% to 20.59% of phenotypic variation.
There were one, two, four and six SSR markers
showed significant correlation with PH and SPS,
TKW, SL , SD, and GPS, respectively. Wmc491 was
associated with PH and GPS both in HD and SY.
Wmc262, wmc707, wmc283 and wmc446 was
significantly correlated to SL and SD simultaneously
in HD and FY, and explained similar phenotypic
variation. Wmc707 was only associated with SPS in
one location (HD). Wmc48, barc78 and wmc491
showed significant correlation with GPS in two
locations, respectively.