Genetic characterization of introgression lines with an Indica-type rice (Oryza sativa L.) variety IR64 genetic background
P091
M N Uddin1, M Obara2, S Yanagihara2, N Kobayashi3, T Ishimaru4, Y Fukuta5 Correspond person:
[email protected] 1 University of Tsukuba, Japan, 2 BRPHD, JIRCAS, Japan, 3 RRD, NICS, Japan 4 PBGB, IRRI, the Philippines, 5 TARF, JIRCAS, Japan (Upland)
Background & Rationale The introgression lines with chromosome segments from different 10 Japonica-type varieties, such as New Plant types (NTPs), were developed to improve the yield potential of IR64 (Fujita et al. 2009). However, characterization of each lines are yet to be done. The objective of this study were to characterize these introgression lines (INLs) under different cultivated conditions and identify the genetic factors responsible for the improvement Materials & Methods » A total of 333 introgression lines (INLs, BC3F9) with IR64 genetic background (Fujita et al. 2009) were used » INLs were grown in irrigated lowland and upland conditions for two consecutive year 2011 and 2012 at Tsukuba, Japan » Ten agronomic traits related to yield components (YP) and dry matter production (DMP) were measured and 10 individuals per line were investigated » Association analysis was carried out to detect the QTLs for improvement using phenotype and genotype data of (＞200) SSR markers by WinQTLcart Ver. 2.5
A (n=189)
(n=111)
300
↧⇣ ▿▾
↧▿
200
I (n=131)
100
II (n=76)
0 10 20 30 40 50 60 70 80 Harvest index (%)
210 140
▿
↧
▾
⇣
▿▾
↧ ⇣
0 0 65 95 125 155 185 215 245 No. of total spikelets
Triangle (▿,▾)indicate the average value of IR64; average value of INLs (n=333)
20 40 60 80 100 Culm weight (g)
⇣▾
0 5 10 15 20 25 30 35 40 No. of panicles
▿▾ 0 30 60 90 120 150 180 210 No. of fertile seeds
Conclusion & Perspectives » These INLs will be used as materials for rice breeding of Indica-type variety, and genetic studies to identify genetic factor(s) for yield improvement.
B
-
Sum C Sum Total
0(0) -
58 (18) 10 (YP5) 8 (YP6)
0(0) 21 (YP6) 21 (YP7) 29 (YP8) 16 (YP9) 6 (YP10) 93 (28) 21 (YP10) 21 (6) 114 (34)
18 (5) -
131 (39)
AI
DH; Days to heading, CL; Culm length, PL; Panicle length, CW; Culm weight, PW; Panicle weight, P/T; Harvest index, PN; Panicle number, FS; Fertile seed, TS; Total spikelet, FS/TS; Fertility rate
Arrows (↧,⇣) indicate the
IV (n=12)
No. of lines in each cluster groups (%) Irrigated lowland II III IV 12 (YP11) 46 (YP5) -
↧ ⇣
Fig. 1 Distribution of Introgression lines (INLs) for agronomic traits in 2011 in irrigated lowland (Black bar) and upland (White bar) conditions.
» INLs showed wide variations, and agronomic traits were improved than IR64 (*Fig 1), and those under upland were superior than irrigated lowland. » These INLs were classified into 3 (A to C) and 4 (I to IV) cluster groups (Fig 2) in upland and irrigated lowland, respectively. » Characterization results showed that the AI, AII and BII were the high performance groups (high biomass, harvest index, higher number of spikelet with lower panicle number) compare with IR64 and average INLs (Fig 3). The BIII, CIII and CIV were the low performance (low biomass, harvest index and higher panicle number) (*Fig 3). » A total of 265 QTLs were detected by association analysis in 10 INLs groups. And the most No. of QTLs, 60 QTLs 9 traits were detected in YP11-INLs on chromosome 1, 4, 8, 9 and 11 (Table 2) and most cases few markers were linked to several traits. » Among them, 54 QTLs on chromosome 4 and 9 in YP11-INLs were new.
(*Indicates the representative figure used here)
0
▿
▾▿
▿▾
70
↧
↧⇣
⇣
15 20 25 30 35 Panicle length (cm)
Upland
No. of lines
↧
0
Sum
I 35 (YP1) 23 (Y P3) 45 (YP4) 28 (YP11) 131 (39)
A
0 0 30 40 50 60 70 80 90 Culm length (cm)
III (n=114)
Table 1. Relationship between irrigated lowland and upland
▿▾ 0 100 110 120 130 140 150 160 Days to heading
(n=33)
Fig. 2 Classification of 333 INLs. Cluster analysis was carried out with 13 agronomic traits following Ward`s hierarchical analysis (Ward 1963) using JMP7.0
↧ ⇣
▿↧
▾⇣
⇣▾
C
(Irrigated lowland)
Cluster group
Results and Discussion
B
76 (23)
AII
CIII
Total
189 (57)
0(0)
-
111 (33)
0(0) 12 (YP10) 12 (4) 12 (4)
33 (10) 333 (100)
BIII
BII
Fig. 3 Radar chart showing the variations among the cluster groups (AI to CIV) for agronomic traits in upland (2011). IR64 was used as control to show the improvements or reductions of performances
CIV
Table 2. QTL detected by association analysis using sib-INLs derived form the corss between YP5 & YP11, and IR64 as a recurrent parent Effects of QTL detected
Trait Chr.
SSR Position Season and Markers (CMc) condition
DH DH PL PL PN PN PN DH DH DH DH CL
8 8 4 4 4 1 1 9 9 9 9 4
RM7049 RM331 RM1867 RM5503 RM1867 RM581 RM1032 RM7048 RM6543 RM242 RM5535 RM6089
76.7 54.3 5.4 100.7 5.4 46.3 49.3 62.4 63 72.1 74.7 97.7
CL
4
RM1113
123.8
CL CL CL PL
4 4 9 9
RM7187 RM3836 RM3164 RM3164
85.5 108.2 72.1 72.1
PL PW PW PW PW P/T P/T P/T P/T P/T P/T P/T P/T P/T
RM242 4 RM6909 4 RM3836 4 RM348 4 RM1113 4 RM6089 4 RM303 4 RM6909 4 RM3836 4 RM348 4 RM1113 11 RM5349 11 RM021 11 RM5961
72.1 106 108.2 111.3 123.8 97.7 116.9 106 108.2 111.3 123.8 10 10 80
2012 irrigated 2012 upland 2011 upland 2011 upland 2011 irrigated 2011 upland 2011 upland 2012 irrigated 2012 irrigated 2012 irrigated 2012 irrigated 2011 irrigated 2012 irrigated 2012 irrigated 2011 irrigated 2012 irrigated 2012 upland 2012 irrigated 2012 irrigated 2012 irrigated 2011 irrigated 2012 upland 2011 irrigated 2012 irrigated 2012 irrigated 2012 irrigated 2012 irrigated 2011 irrigated 2011 irrigated 2012 irrigated 2012 irrigated 2012 irrigated 2012 irrigated 2011 upland 2011 upland 2011 upland
F
R2
13.5 13.0 17.0 11.7 13.7 13.1 15.2 18.0 16.9 34.3 34.3 13.0 15.8 17.4 15.8 35.2 18.8 23.0 15.0 19.2 19.8 18.0 16.9 25.1 24.3 19.8 20.6 15.3 17.6 25.5 23.2 17.5 14.1 19.8 19.8 19.8
0.26 0.26 0.30 0.25 0.26 0.23 0.29 0.33 0.33 0.46 0.48 0.27 0.30 0.28 0.30 0.49 0.34 0.38 0.29 0.34 0.35 0.33 0.30 0.41 0.40 0.31 0.36 0.29 0.28 0.44 0.39 0.30 0.28 0.35 0.36 0.35
Genotype of Additive increasing trait's value 1.30 2.63 1.13 -1.08 -1.28 2.70 2.94 -1.20 -1.16 -1.44 -1.44 -2.26 -2.39 -2.48 -2.33 -2.92 -3.57 -2.86 -2.26 -2.48 -1.10 -3.57 -1.05 -3.56 -3.47 -3.31 -3.30 -0.04 -0.04 -0.04 -0.04 -0.03 -0.03 0.05 0.05 0.05
IR64 IR64 IR64 YP5 YP5 IR64 IR64 YP11 YP11 YP11 YP11 YP11 YP11 YP11 YP11 YP11 YP11 YP11 YP11 YP11 YP11 YP11 YP11 YP11 YP11 YP11 YP11 YP11 YP11 YP11 YP11 YP11 YP11 IR64 IR64 IR64
PN PN PN PN FS FS TS
4 4 4 4 9 9 4
RM6089 RM6909 RM3836 RM348 RM242 RM5535 RM7187
74.7 97.7 106 108.2 72.1 74.7 85.5
TS
4
RM6089
97.7
TS
4
RM6909
106
TS
4
RM3836
108.2
TS
4
RM348
111.3
TS
4
RM1113
123.8
FS/TS FS/TS
4 4
RM6909 RM3836
106 108.2
2012 irrigated 2012 irrigated 2012 irrigated 2012 irrigated 2012 irrigated 2012 irrigated 2011 irrigated 2012irrigated 2011 upland 2012 upland 2011 irrigated 20112irrigated 2011 upland 2012 upland 2011 irrigated 20112irrigated 2011 upland 2012 upland 2011 irrigated 20112irrigated 2011 upland 2012 upland 2011 irrigated 20112irrigated 2011 upland 2012 upland 2011 irrigated 20112irrigated 2012 upland 20112irrigated 20112irrigated
20.3 15.4 16.7 17.5 22.2 22.2 19.0 33.1 28.1 19.4 45.3 58.2 41.5 33.3 28.3 45.4 28.9 24.9 28.6 52.9 33.1 28.3 25.8 50.1 32.5 26.3 18.5 32.5 25.9 15.8 14.0
0.36 0.30 0.31 0.35 0.35 0.38 0.35 0.47 0.43 0.35 0.56 0.61 0.54 0.48 0.43 0.55 0.44 0.40 0.44 0.59 0.47 0.43 0.3821 0.5255 0.4311 0.3873 0.3389 0.4741 0.4185 0.3055 0.2798
0.95 0.84 0.85 0.88 -11.67 -11.67 -18.51 -24.06 -27.86 -27.36 -22.21 -25.79 -29.06 -30.25 -19.22 -23.87 -25.81 -27.17 -19.00 -24.27 -26.38 -27.75 -18.75 -24.40 -26.67 -27.64 -16.60 -21.64 -27.04 -0.06 -0.06
IR64 IR64 IR64 IR64 YP11 YP11 YP11 YP11 YP11 YP11 YP11 YP11 YP11 YP11 YP11 YP11 YP11 YP11 YP11 YP11 YP11 YP11 YP11 YP11 YP11 YP11 YP11 YP11 YP11 YP11 YP11
References Fujita D, Santos RE, Ebron LA, Telebanco-Yanoria MJ,Kato H, Kobayashi S, Uga Y, Araki E, Takai T, Tsunematsu H, Imbe T, Khush GS, Brar DS, Fukuta Y and Kobayashi N (2009). Field Crops Research, Vol. 114:244-254 Kobayashi N, Fukuta Y and Ito O (2010). JIRCAS Working Report No.66: 13
