Welcome to Acta Agronomica Sinica,

Acta Agronomica Sinica ›› 2022, Vol. 48 ›› Issue (11): 2813-2825.doi: 10.3724/SP.J.1006.2022.12069

• CROP GENETICS & BREEDING · GERMPLASM RESOURCES · MOLECULAR GENETICS • Previous Articles     Next Articles

Mapping and analysis of QTLs for grain size in rice based on high density genetic map

SONG Bo-Wen1(), WANG Chao-Huan1, ZHAO Zhe1, CHEN Chun1, HUANG Ming1, CHEN Wei-Xiong2, LIANG Ke-Qin1,*(), XIAO Wu-Ming1,*()   

  1. 1South China Agricultural University / National Engineering Research Center of Plant Space Breeding, Guangzhou 510642, Guangdong, China
    2Guangzhou Academy of Agricultural Sciences, Guangzhou 510338, Guangdong, China
  • Received:2021-10-06 Accepted:2022-03-25 Online:2022-11-12 Published:2022-04-20
  • Contact: LIANG Ke-Qin,XIAO Wu-Ming E-mail:1476399189@qq.com;1-hlld@163.com;heredity24@126.com
  • Supported by:
    Key Research & Development program of Guangzhou Science and Technology Project(202103000083);The Guangdong Provincial Key Research and Development Program(2020B020219004)

Abstract:

In this study, QTL mapping of grain size related traits and pyramided effect analysis of different sites were carried out to lay a foundation for marker-assisted breeding and fine mapping of grain size related genes. A total of 192 recombinant inbred lines derived from indica rice cultivars Modasui and Hanghui 315 were planted in three different seasons. A high-density genetic map was constructed based on whole-genome resequencing technology, and the QTLs for grain shape traits were mapped by complete interval mapping (ICIM) and verified by composite interval mapping (CIM). QTL mapping was carried out for grain length, grain width, length-width ratio, 1000-grain weight, cross-sectional area, and grain circumference. 50 and 81 QTLs were detected by ICIM and CIM, respectively, and 43 QTLs could be detected simultaneously. They were clustered on chromosome 2, 3, 5, 7, 8, and 9. Their highest phenotypic contribution rates were 15.74%, 54.07%, and 15.89%, respectively. These loci could be the candidate loci for subsequent gene function studies. According to the genotyping of bin markers, different progenies were divided into Hanghui 315 type and Modasui type in each QTL interval. Further data processing and analysis were performed to analyze the pyramided effect of QTLs. It was found that individuals with more pyramided synergistic alleles showed higher phenotypic values in different environments. The identified lines with multiple synergistic alleles can be used as donor parents in breeding practices.

Key words: rice, recombinant inbred lines, high density genetic map, grain size, QTL mapping

Table 1

Performance of grain traits in two parents and the RILs population under three environments"

性状
Trait
季节
Season
亲本Parents 重组自交系RILs 广义遗传力
h2 (%)
R315 MDS 均值±标准差
Mean ± SD
变异系数
CV (%)
最大值
Maximum
最小值
Minimum
偏度
Skewness
峰度
Kurtosis
粒长
GL (mm)
2019L 10.93** 8.62 9.59±0.72 7.54 11.37 7.97 0.09 -0.56 96.68
2020E 10.70** 8.57 9.69±0.65 6.65 11.21 8.24 -0.05 -0.68
2020L 11.06** 8.57 9.66±0.72 7.49 11.22 8.10 -0.06 -0.78
粒宽
GW (mm)
2019L 2.11 2.27** 2.14±0.16 7.64 2.52 1.81 0.22 -0.76 94.14
2020E 1.99 2.17** 2.11±0.17 7.94 2.52 1.73 0.06 -1.02
2020L 2.06 2.22** 2.09±0.16 7.83 2.61 1.78 0.28 -0.74
长宽比
GLWR
2019L 5.20** 3.82 4.55±0.58 12.81 6.03 3.43 0.01 -1.34 97.28
2020E 5.42** 3.96 4.66±0.62 13.21 5.86 3.60 0.08 -1.41
2020L 5.40** 3.89 4.70±0.64 13.56 5.81 3.64 -0.03 -1.54
千粒重
TGW (g)
2019L 18.86** 18.47 17.85±1.91 10.69 23.20 13.10 0.22 -0.32 92.69
2020E 20.49** 19.09 19.48±1.89 9.68 24.04 14.43 -0.08 -0.23
2020L 21.60** 20.01 19.37±2.14 11.03 26.67 12.00 0.05 0.77
谷粒截面积
GAS (mm2)
2019L 17.89** 15.53 16.07±1.28 7.98 19.25 12.70 0.20 -0.26 88.80
2020E 16.66* 14.79 16.00±1.03 6.43 18.87 13.30 0.19 -0.11
2020L 17.73** 14.85 15.77±1.11 7.05 18.22 13.10 0.13 -0.44
谷粒周长
GPL (mm)
2019L 24.67** 19.92 21.78±1.48 6.79 25.54 18.39 0.10 -0.35 96.30
2020E 24.08** 19.75 21.99±1.30 5.91 25.39 18.82 -0.04 -0.48
2020L 24.94** 19.76 21.97±1.48 6.75 25.16 18.57 -0.07 -0.62

Fig. 1

Distribution of grain traits in the RILs populations in three environments Abbreviations are the same as those given in Table 1."

Table 2

Correlation analysis of six grain traits in RILs"

性状
Trait
粒长
GL
粒宽
GW
长宽比
GLWR
千粒重
TGW
谷粒截面积
GAS
谷粒周长
GPL
粒长GL 1 -0.592** 0.887** -0.049 0.454** 0.997**
粒宽GW -0.592** 1 -0.895** 0.731** 0.443** -0.535**
长宽比GLWR 0.887** -0.895** 1 -0.446** -0.005 0.853**
千粒重TGW -0.049 0.731** -0.446** 1 0.755** 0.01
谷粒截面积GAS 0.454** 0.443** -0.005 0.755** 1 0.514**
谷粒周长GPL 0.997** -0.535** 0.853** 0.010 0.514** 1

Table 3

Information of grain size-related QTLs in RILs under three environments"

性状
Trait
QTL 染色体
Chr.
位置
Position
LOD值
LOD score
加性效应
Additive
贡献率
PVE (%)
环境
Environment
置信区间
Confidence interval (cM)
标记区间
Marker interval
粒长
GL
qGL2-1 2 56.2 7.36 -0.13 3.12 2020L 55.75-56.95 bin550-bin551
qGL2-2 2 70.9 8.03 -0.15 4.23 2019L 70.65-71.25 bin579-bin580
qGL2-3 2 95.1 3.83 -0.10 2.29 2020E 94.75-97.05 bin622-bin623
qGL3 3 8.9 5.96 0.13 3.06 2019L 7.75-9.45 bin700-bin701
qGL5-1 5 94.1 11.33 -0.18 6.22 2019L 93.85-94.35 bin1179-bin1180
94.1 4.27 -0.10 2.57 2020E 93.85-94.35 bin1179-bin1180
qGL5-2 5 95.6 6.69 -0.12 2.81 2020L 95.15-95.95 bin1182-bin1183
qGL7 7 93.6 54.3 -0.54 53.42 2019L 93.45-93.95 bin1444-bin1445
93.6 52.13 -0.49 59.48 2020E
93.6 66.74 -0.59 64.97 2020L
qGL8-1 8 38.2 4.91 -0.10 2.02 2020L 37.75-38.25 bin1520-bin1521
粒长
GL
qGL8-2 8 39.9 7.05 -0.14 3.67 2019L 39.05-41.85 bin1524-bin1525
40.8 8.70 -0.15 5.62 2020E
qGL9-1 9 82.0 5.17 -0.11 3.17 2020E 80.85-86.85 bin1743-bin1744
82.6 6.37 -0.14 3.39 2019L
qGL9-2 9 91.3 12.4 -0.19 6.35 2020L 88.45-93.85 bin1744-bin1745
qGL12-1 12 14.1 5.15 -0.11 2.13 2020L 13.65-14.55 bin396-bin397
qGL12-2 12 36.9 5.03 -0.12 2.56 2019L 36.85-37.25 bin424-bin426
粒宽
GW
qGW2 2 30.8 3.60 -0.02 2.09 2019L 30.15-31.75 bin523-bin524
qGW3-1 3 137.8 42.68 -0.10 20.62 2020L 137.75-138.15 bin847-bin848
qGW3-2 3 138.8 34.26 0.08 14.40 2020L 138.45-139.35 bin850-bin851
qGW4-1 4 58.1 9.56 -0.04 2.90 2020L 57.95-58.55 bin1028-bin1029
qGW4-2 4 62.9 5.35 -0.04 4.44 2020E 62.65-63.35 bin1013-bin1033
qGW4-3 4 65.2 9.36 -0.04 5.84 2019L 65.05-65.35 bin1011-bin1010
qGW5-1 5 96.5 4.25 -0.03 2.49 2019L 95.95-97.05 bin1185-bin1186
qGW5-2 5 109.1 4.17 -0.03 1.61 2020L 99.75-114.55 bin1191-bin1192
qGW7 7 93.3 45.47 0.13 61.86 2020E 93.05-93.65 bin1443-bin1445
93.5 61.42 0.14 38.26 2020L
93.6 55.35 0.13 63.57 2019L
长宽比
LWR
qLWR1-1 1 137.0 4.07 -0.07 1.07 2020L 135.85-138.95 bin170-bin171
qLWR1-2 1 166.1 3.30 -0.08 1.50 2020E 165.85-166.75 bin185-bin186
qLWR4-1 4 68.9 7.93 0.10 2.18 2020L 68.35-68.95 bin1004-bin1002
qLWR4-2 4 73.2 10.32 0.12 3.62 2019L 72.75-73.45 bin991-bin992
73.2 5.41 0.11 2.53 2020E
qLWR7 7 93.6 82.93 -0.53 81.10 2019L 93.45-93.95 bin1444-bin1445
93.6 66.96 -0.55 72.45 2020E
93.6 91.42 -0.58 84.89 2020L
qLWR11 11 52.8 3.55 0.08 1.62 2020E 52.15-52.95 bin356-bin357
千粒重
TGW
qTGW2-1 2 30.5 4.22 -0.38 4.47 2019L 29.45-33.25 bin523-bin526
32.6 5.26 -0.54 6.76 2020L
qTGW2-2 2 77.9 3.27 -0.36 3.45 2020E 77.35-77.95 bin594-bin596
qTGW3 3 137.2 4.10 -0.40 4.38 2020E 136.85-137.65 bin846-bin847
qTGW4-1 4 58.0 5.59 -0.58 7.08 2020L 57.95-58.65 bin1027-bin1028
qTGW4-2 4 64.8 5.36 -0.48 6.23 2019L 64.15-65.15 bin1033-bin1012
qTGW5 5 96.5 4.19 -0.40 4.46 2020E 95.95-98.65 bin1185-bin1186
97.0 6.91 -0.49 7.37 2019L
97.0 5.28 -0.54 6.67 2020L
qTGW7-1 7 67.6 3.78 0.36 3.99 2019L 67.45-67.75 bin1400-bin1399
qTGW7-2 7 93.5 16.32 0.80 19.64 2019L 93.05-93.65 bin1444-bin1445
93.5 18.70 0.93 23.88 2020E
93.5 12.35 0.86 17.12 2020L
qTGW8-1 8 40.3 3.99 -0.37 4.24 2019L 39.35-41.55 bin1524-bin1525
qTGW8-2 8 65.4 5.34 -0.46 5.75 2020E 65.35-66.15 bin1576-bin1577
千粒重
TGW
qTGW8-3 8 75.2 4.74 -0.51 5.96 2020L 74.65-75.65 bin1589-bin1590
qTGW9-1 9 9.9 3.26 0.42 4.02 2020L 9.35-10.65 bin1651-bin1652
qTGW9-2 9 89.9 8.71 -0.64 11.14 2020E 86.85-94.4 bin1744-bin1745
91.4 11.59 -0.70 14.64 2019L
93.2 6.08 -0.61 8.45 2020L
谷粒截面积
GAS
qGAS2-1 2 31.5 4.02 -0.25 5.90 2020E 30.85-33.25 bin524-bin525
31.5 5.32 -0.29 6.38 2020L
qGAS2-2 2 74.8 6.96 -0.43 10.61 2019L 74.45-75.15 bin588-bin589
qGAS4-1 4 63.1 3.30 -0.27 5.74 2020E 62.65-63.55 bin1013-bin1033
qGAS4-2 4 65.3 4.58 -0.28 5.32 2020L 65.15-65.55 bin1011-bin1010
qGAS5 5 95.7 5.54 -0.29 7.98 2020E 95.15-96.45 bin1183-bin1184
95.7 7.95 -0.35 9.59 2020L
95.9 9.97 -0.51 15.78 2019L
qGAS7 7 16.3 4.69 -0.26 5.44 2020L 15.65-16.35 bin1344-bin1345
qGAS8-1 8 43.7 4.88 -0.27 5.71 2020L 42.65-43.95 bin1530-bin1531
qGAS8-2 8 53.8 3.62 -0.30 5.30 2019L 53.35-54.15 bin1551-bin1552
53.8 8.70 -0.38 13.07 2020E
qGAS9-1 9 6.7 3.95 0.24 5.61 2020E 6.05-7.25 bin1644-bin1645
qGAS9-2 9 23.2 3.85 0.24 4.53 2020L 21.35-23.65 bin1666-bin1667
qGAS9-3 9 92.1 3.53 -0.25 5.81 2020E 86.85-94.4 bin1744-bin1745
92.1 10.78 -0.45 15.43 2020L
谷粒周长
GPL
qGPL2-1 2 30.9 4.09 -0.22 2.89 2020E 30.15-31.75 bin524-bin525
qGPL2-2 2 70.9 8.18 -0.31 4.36 2020L 70.65-71.25 bin579-bin580
qGPL2-3 2 74.8 7.54 -0.34 5.50 2019L 74.45-75.15 bin588-bin589
qGPL2-4 2 95.2 3.99 -0.22 2.85 2020E 94.75-96.95 bin623-bin624
qGPL3 3 9.4 3.44 0.22 2.38 2019L 7.75-9.45 bin700-bin701
qGPL5-1 5 94.1 10.64 -0.41 8.04 2019L 93.85-94.35 bin1179-bin1180
94.1 3.95 -0.21 2.76 2020E
qGPL5-2 5 95.6 8.07 -0.30 4.29 2020L 95.15-95.95 bin1182-bin1183
qGPL7-1 7 93.2 41.92 -1.00 47.97 2019L 92.75-93.75 bin1443-bin1445
93.5 57.95 -1.14 60.17 2020L
qGPL7-2 7 94.1 44.88 -0.95 54.04 2020E 93.95-94.75 bin1447-bin1448
qGPL8 8 39.9 5.90 -0.30 4.18 2019L 39.85-41.85 bin1524-bin1526
41.2 7.14 -0.29 5.28 2020E
41.8 4.5 -0.22 2.29 2020L
qGPL9-1 9 81.9 5.84 -0.26 4.19 2020E 80.85-86.85 bin1743-bin1744
85.1 3.55 -0.23 2.57 2019L
qGPL9-2 9 91.1 9.64 -0.36 5.98 2020L 87.95-93.95 bin1744-bin1745
qGPL12 12 34.4 4.20 -0.21 2.13 2020L 33.55-35.75 bin420-bin421

Table 4

Stable QTLs associated with grain size"

QTL簇
QTL cluster
染色体
Chr.
表型贡献率
PVE (%)
标记区间
Marker range
遗传区间
Genetic interval
相关性状
Related traits
已报道基因
Reported genes
Loci2 2 2.09-6.76 bin523-bin526 29.45-33.25 GW, TGW, GAS, GPL
Loci3 3 2.38-3.06 Bin700-bin701 8.90-9.40 GL, GPL
Loci5 5 2.49-15.78 bin1179-bin1186 93.85-98.65 GL, GPL, GW, TGW, GAS OsPUP7[13]
Loci7 7 17.12-84.89 bin1443-bin1448 92.75-94.75 GL, GW, LWR, TGW, GPL GL7/GW7[11-12]
Loci8 8 2.29-5.62 bin1524-bin1525 39.05-41.85 GL, TGW, GPL
Loci9 9 2.57-15.43 bin1743-bin1745 80.85-94.40 GL, TGW, GAS, GPL APX9[14]

Fig. 2

Location of six stably expressed QTLs clusters on a high-density genetic map (cM)"

Fig. 3

Mapping of GL7/GW7 controlling grain size by CIM method in RIL population. a: mapping curve of QTLs controlling grain size on 12 chromosomes; b: the box inside is the zoom-in image of grain size traits at its peak on chromosome 7 and red dots represent QTLs at 88.5, 93.6, and 99.1 cM, respectively. The arrow shows the genetic location of the GL7/GW7 gene; c: the functional markers of GL7/GW7 are used to genotype MDS, R315, NPB (Nipponbare, without GL7/GW7), and TFA (Taifeng A, with GL7/GW7). M: M5 DL2000 plus DNA marker (Mei5bio)."

Table 5

Pyramiding effect analysis of the three stable QTLs"

株系类型
Type of lines
QTL 株系数
No. of line
粒长
GL (mm)
粒宽
GW (mm)
长宽比
LWR
谷粒截面积
GAS (mm2)
谷粒周长
GPL (mm)
千粒重
TGW (g)
Loci5 Loci7 Loci9
MDS MDS MDS MDS 1 8.62±0.04 2.24±0.02 3.86±0.03 15.26±0.30 19.89±0.10 19.19±0.63
Line.1 MDS MDS MDS 14 8.73±0.33 2.18±0.07 4.03±0.21 14.86±0.72 20.01±0.70 18.19±1.34
Line.2 MDS MDS R315 20 9.01±0.30 2.24±0.07 4.06±0.18 15.76±0.77 20.63±0.64 19.40±1.37
Line.3 MDS R315 MDS 14 9.80±0.24 1.96±0.08 5.04±0.24 15.10±0.64 22.12±0.51 16.74±1.42
Line.4 MDS R315 R315 25 10.23±0.33 1.97±0.09 5.24±0.31 15.87±0.78 23.03±0.68 18.33±1.38
Line.5 R315 MDS MDS 13 8.92±0.33 2.27±0.09 3.96±0.19 15.76±0.94 20.50±0.78 19.67±1.60
Line.6 R315 MDS R315 15 9.50±0.19 2.29±0.08 4.17±0.16 17.09±0.71 21.75±0.42 21.22±1.19
Line.7 R315 R315 MDS 17 10.17±0.28 2.00±0.05 5.12±0.14 16.04±0.75 22.93±0.62 17.57±1.37
Line.8 R315 R315 R315 22 10.42±0.43 2.01±0.08 5.23±0.17 16.47±1.16 23.46±1.00 19.17±1.30
R315 R315 R315 R315 1 10.97±0.07 2.07±0.03 5.33±0.09 17.67±0.21 24.70±0.19 20.32±1.13
增效位点来源a
Source of
favorable alleles a
R315 R315 — R315 R315 R315
R315 MDS R315 — R315 MDS
R315 — — R315 R315 R315
[1] Xing Y Z, Zhang Q F. Genetic and molecular bases of rice yield. Annu Rev Plant Biol, 2010, 61: 421-442.
doi: 10.1146/annurev-arplant-042809-112209
[2] McCouch S R, Kochert G, Yu Z H, Wang Z Y, Khush G S, Coffman W R, Tanksley S D. Molecular mapping of rice chromosomes. Theor Appl Genet, 1988, 76: 815-829.
doi: 10.1007/BF00273666 pmid: 24232389
[3] Shabir G, Aslam K, Khan A R, Shahid M, Manzoor H, Noreen S, Khan M A, Baber M, Sabar M, Shah S M, Arif M. Rice molecular markers and genetic mapping: current status and prospects. J Integr Agric, 2017, 16: 1879-1891.
doi: 10.1016/S2095-3119(16)61591-5
[4] Kaur S, Panesar P S, Bera M B, Kaur V. Simple sequence repeat markers in genetic divergence and marker-assisted selection of rice cultivars: a review. Crit Rev Food Sci, 2015, 55: 41-49.
doi: 10.1080/10408398.2011.646363
[5] Kumar V, Singh A, Mithra S V A, Krishnamurthy S L, Parida S K, Jain S, Tiwari K K, Kumar P, Rao A R, Sharma S K, Khurana J P, Singh N K, Mohapatra T. Genome-wide association mapping of salinity tolerance in rice (Oryza sativa). DNA Res, 2015, 22: 133-145.
doi: 10.1093/dnares/dsu046
[6] 孙佳丽, 彭锐, 彭既明. 水稻数量性状(QTL)定位主要作图群体及统计方法概述. 湖南农业科学, 2016, (7): 120-123.
Sun J L, Peng R, Peng J M. Over view of rice quantitative traits (QTL) mapping main construction population and statistical methods. Hunan Agric Sci, 2016, (7): 120-123. (in Chinese with English abstract)
[7] Scott M F, Ladejobi O, Amer S, Bentley A R, Biernaskie J, Boden S A, Clark M, Dell’Acqua M, Dixon L E, Filippi C V, Fradgley N, Gardner K A, Mackay I J, O’Sullivan D, Percival-Alwyn L, Roorkiwal M, Singh R K, Thudi M, Varshney R K, Venturini L, Whan A, Cockram J, Mott R. Multi-parent populations in crops: a toolbox integrating genomics and genetic mapping with breeding. Heredity, 2020, 125: 396-416.
doi: 10.1038/s41437-020-0336-6
[8] Yang M, Lu K, Zhao F, Xie W, Ramakrishna P, Wang G, Du Q, Liang L, Sun C, Zhao H, Zhang Z, Liu Z, Tian J, Huang X, Wang W, Dong H, Hu J, Ming L, Xing Y, Wang G, Xiao J, Salt D E, Lian X. Genome-wide association studies reveal the genetic basis of ionomic variation in rice. Plant Cell, 2018, 30: 2720-2740.
doi: 10.1105/tpc.18.00375
[9] Huang R, Jiang L, Zheng J, Wang T, Wang H, Huang Y, Hong Z. Genetic bases of rice grain shape: so many genes, so little known. Trends Plant Sci, 2013, 18: 218-226.
doi: 10.1016/j.tplants.2012.11.001
[10] Peng H, Wang K, Chen Z, Cao Y H, Gao Q, Li Y, Li X X, Lu H W, Du H L, Lu M, Yang X, Liang C Z. MBKbase for rice: an integrated omics knowledgebase for molecular breeding in rice. Nucleic Acids Res, 2020, 48: D1085-D1092.
[11] Wang S K, Li S, Liu Q, Wu K, Zhang J Q, Wang S S, Wang Y, Chen X B, Zhang Y, Gao C X, Wang F, Huang H X, Fu X D. The OsSPL16-GW7 regulatory module determines grain shape and simultaneously improves rice yield and grain quality. Nat Genet, 2015, 47: 949-954.
doi: 10.1038/ng.3352
[12] Wang Y X, Xiong G S, Hu J, Jiang L, Yu H, Xu J, Fang Y X, Zeng L J, Xu E B, Xu J, Ye W J, Meng X B, Liu R F, Chen H Q, Jing Y H, Wang Y H, Zhu X O, Li J Y, Qian Q. Copy number variation at the GL7 locus contributes to grain size diversity in rice. Nat Genet, 2015, 47: 944-948.
doi: 10.1038/ng.3346
[13] Qi Z Y, Xiong L Z. Characterization of a Purine Permease Family Gene OsPUP7 Involved in Growth and Development Control in Rice. J Integr Plant Biol, 2013, 55: 1119-1135.
doi: 10.1111/jipb.12101
[14] Jeon Y, Lee H, Kim S, Shim K, Kang J, Kim H, Tai T H, Ahn S. Natural variation in rice ascorbate peroxidase gene APX9 is associated with a yield-enhancing QTL cluster. J Exp Bot, 2021, 72: 4254-4268.
doi: 10.1093/jxb/erab155
[15] Che R H, Tong H N, Shi B H, Liu Y Q, Fang S R, Liu D P, Xiao Y H, Hu B, Liu L C, Wang H R, Zhao M F, Chu C C. Control of grain size and rice yield by GL2-mediated brassinosteroid responses. Nat Plants, 2015, 2: 15195.
doi: 10.1038/nplants.2015.195
[16] Duan P G, Ni S, Wang J M, Zhang B L, Xu R, Wang Y X, Chen H Q, Zhu X D, Li Y H. Regulation of OsGRF4 by OsmiR396 controls grain size and yield in rice. Nat Plants, 2015, 2: 15203.
doi: 10.1038/nplants.2015.203
[17] Fan C H, Xing Y Z, Mao H L, Lu T T, Han B, Xu C G, Li X H, Zhang Q F. GS3, a major QTL for grain length and weight and minor QTL for grain width and thickness in rice, encodes a putative transmembrane protein. Theor Appl Genet, 2006, 112: 1164-1171.
doi: 10.1007/s00122-006-0218-1
[18] Liu J F, Chen J, Zheng X M, Wu F Q, Lin Q B, Heng Y Q, Tian P, Cheng Z J, Yu X W, Zhou K N, Zhang X, Guo X P, Wang J L, Wang H Y, Wan J M. GW5 acts in the brassinosteroid signalling pathway to regulate grain width and weight in rice. Nat Plants, 2017, 3: 17043.
doi: 10.1038/nplants.2017.43
[19] Ishimaru K, Hirotsu N, Madoka Y, Murakami N, Hara N, Onodera H, Kashiwagi T, Ujiie K, Shimizu B, Onishi A, Miyagawa H, Katoh E. Loss of function of the IAA-glucose hydrolase gene TGW6 enhances rice grain weight and increases yield. Nat Genet, 2013, 45: 707-711.
doi: 10.1038/ng.2612 pmid: 23583977
[20] 王朝欢, 宋博文, 余思佳, 肖武名, 黄明. 基于全基因组测序构建水稻RIL群体遗传图谱. 华南农业大学学报, 2021, 42(2): 44-50.
Wang C H, Song B W, Yu S J, Xiao W M, Huang M. Construction of a genetic map of rice RILs based on whole genome sequencing. J South China Agric Univ, 2021, 42(2): 44-50. (in Chinese with English abstract)
[21] Huang X H, Feng Q, Qian Q, Zhao Q, Wang L, Wang A H, Guan J P, Fan D L, Weng Q J, Huang T, Dong G J, Sang T, Han B. High-throughput genotyping by whole-genome resequencing. Genome Res, 2009, 19: 1068-1076.
doi: 10.1101/gr.089516.108
[22] Li H H, Ye G Y, Wang J K. A modified algorithm for the improvement of composite interval mapping. Genetics, 2007, 175: 361-374.
doi: 10.1534/genetics.106.066811
[23] McCouch S R, Rice G C. Gene nomenclature system for rice. Rice, 2008, 1: 72-84.
doi: 10.1007/s12284-008-9004-9
[24] Hina A M, Cao Y C, Song S Y, Li S G, Sharmin R A, Elattar M A, Bhat J A, Zhao T J. High-resolution mapping in two RIL populations refines major “QTL hotspot” regions for seed size and shape in soybean (Glycine max L.). Int J Mol Sci, 2020, 21: 1040-1073.
doi: 10.3390/ijms21031040
[25] 李慧慧, 张鲁燕, 王建康. 数量性状基因定位研究中若干常见问题的分析与解答. 作物学报, 2010, 36: 918-931.
doi: 10.3724/SP.J.1006.2010.00918
Li H H, Zhang L Y, Wang J K. Analysis and answers to frequently asked questions in quantitative trait locus mapping. Acta Agron Sin, 2010, 36: 918-931. (in Chinese with English abstract)
doi: 10.3724/SP.J.1006.2010.00918
[26] Zhao Q A, Huang X H, Lin Z X, Han B. SEG-Map: a novel software for genotype calling and genetic map construction from next-generation sequencing. Rice, 2010, 3: 98-102.
doi: 10.1007/s12284-010-9051-x
[27] 苏成付, 赵团结, 盖钧镒. 不同统计遗传模型QTL定位方法应用效果的模拟比较. 作物学报, 2010, 36: 1100-1107.
doi: 10.3724/SP.J.1006.2010.01100
Su C F, Zhao T J, Gai Y J. Simulation comparisons of effectiveness among QTL mapping procedures of different statistical genetic models. Acta Agron Sin, 2010, 36: 1100-1107. (in Chinese with English abstract)
doi: 10.3724/SP.J.1006.2010.01100
[28] 李珊珊. QTL与环境互作的完备区间作图方法研究. 中国农业科学院硕士学位论文, 北京, 2015.
Li S S. Inclusive Composite Interval Mapping of Quantitative Trait Loci by Environment Interactions. MS Thesis of Chinese Academy of Agricultural Sciences, Beijing, China, 2015. (in Chinese with English abstract)
[29] Xie X B, Jin F X, Song M H, Suh J P, Hwang H G, Kim Y G, McCouch S R, Ahn S N. Fine mapping of a yield-enhancing QTL cluster associated with transgressive variation in an Oryza sativa × O. rufipogon cross. Theor Appl Genet, 2008, 116: 613-622.
doi: 10.1007/s00122-007-0695-x
[1] Zheng Yu-Zhen, Qi Fei-Yan, Sun Zi-Qi, Liu Hua, Qin Li, Shi Lei, Wang Juan, Wang Meng-Meng, Han Suo-Yi, Xu Jing, Miao Li-Juan, Huang Bing-Yan, Dong Wen-Zhao, Zheng Zheng, Zhang Xin-You. QTL mapping of total very long-chain fatty acids and seven fatty acid components in peanut seeds [J]. Acta Agronomica Sinica, 2026, 52(6): 1646-1657.
[2] Hu Zhao, Qian Run, Xie Feng-Pu, Ying Su-Ping. Genome-wide identification and expression analysis of the SPX gene family in rice under phosphorus treatment [J]. Acta Agronomica Sinica, 2026, 52(6): 1902-1912.
[3] Zou Yi-Mei, Xu Min, Wang Hai-Yang, Yao Hui, Wang Jia-Feng, Liu Hao, Ren Dai-Sheng. Analysis of transcription factor regulatory networks in two-line male sterile rice seedling roots in response to salt stress [J]. Acta Agronomica Sinica, 2026, 52(6): 1728-1742.
[4] Yan An, Jiang Kun-Wei, Wang Rong-Yuan, Tian Lin, Zhang Lu, Wang Yun, Xu Jian-Long. Identification and cloning of SVN7 controlling small vascular bundle number in the rice flag leaf [J]. Acta Agronomica Sinica, 2026, 52(5): 1364-1372.
[5] Chen Wei, Wei Wan-Juan, Zhao Qi-Bing, Chang Dong-Wei, Yu Ling-Bo, Zhai Peng-Fei, Feng Zhi-Ming, Chen Zong-Xiang, Ren Yang-Tao, Yang Peng, Liu Hai-Lang, Li Zhen-Fu, Yang Yong-Le, Jin Yan-Gang, Zuo Shi-Min. Developing new germplasm of high-quality and early-maturing rice by editing Hd6 via CRISPR/Cas9 [J]. Acta Agronomica Sinica, 2026, 52(4): 1046-1056.
[6] Shi Shao-Jie, Liu Kai, Chen Zi-Yi, Wang Hui-Ying, Li San-He, Zhou Lei, You Ai-Qing. Cloning and functional analysis of the dwarf and multi-tiller gene DMT1 in rice [J]. Acta Agronomica Sinica, 2026, 52(4): 1022-1034.
[7] Qin Yi-Yan, Fu Yao, Su Chang, Li Na, Xu Jing-Ru, Cheng Xiao-Ran, Zhang Qi, Zhao Ming-Hui. Functional analysis of OsST41 regulating salt tolerance in rice seedlings [J]. Acta Agronomica Sinica, 2026, 52(3): 802-812.
[8] Liu Chang-You, Wang Shen, Shi Hui-Ying, Shen Ying-Chao, Sun Lei, Wang Yan, Zhang Zhi-Xiao, Su Qiu-Zhu, Tian Jing, Fan Bao-Jie. QTL mapping for bruchid resistance in an adzuki bean distant hybridization population using rice bean genetic resources [J]. Acta Agronomica Sinica, 2026, 52(3): 936-944.
[9] Ye Fan, Li Shuai, Li Si-Yu, Chen Yun, Dou Chao-Yin, Liu Li-Jun. Effects of water-saving irrigation on rice yield and population quality in Northeast China [J]. Acta Agronomica Sinica, 2026, 52(3): 895-907.
[10] Liu Ning, Fan Ping, Wang Cheng, Chen Qi-Qi, Cheng Qing-Yue, Tie Xia-Na, Tang Jing-Sha, Liu Bin-Bin, Xie Hong-Kun, Wang Jia-Yue, Shi Yuan-Qing, Ma Jun. Effects of reduced nitrogen application combined with organic fertilizer on yield formation and nitrogen utilization in mechanically transplanted rice [J]. Acta Agronomica Sinica, 2026, 52(3): 866-880.
[11] Zhu Jin-Juan, Wang Hui-Ping, Yang Guo-Dong, Wang Yu-Cheng, Yang Chen, Wang Bin, Agustiani Nurwulan, Tu Jun-Ming, Bi Jun-Guo, Cui Ke-Hui, Huang Jian-Liang, Peng Shao-Bing, Yuan Shen. Effects of water management and variety type on grain yield and quality in ratoon rice [J]. Acta Agronomica Sinica, 2026, 52(1): 295-315.
[12] WANG Chan, WU Ying-Ying, LI Wen-Qi, LI Xia, WANG Fang-Quan, ZHOU Tong, YANG Jie. Development of functional markers of rice stripe disease resistance gene STV11 based on HRM technique [J]. Acta Agronomica Sinica, 2025, 51(9): 2547-2556.
[13] GUO Bao-Wei, WANG Wang, WANG Kai, WANG Yan, ZENG Xin, JING Xiu, WANG Jing, NI Xin-Hua, XU Ke, ZHANG Hong-Cheng. Population dynamic characteristics and formation mechanisms of super high-yielding of two types of glutinous rice in the middle and lower reaches of the Yangtze Rive [J]. Acta Agronomica Sinica, 2025, 51(9): 2433-2453.
[14] CHEN Hui-Ying, HE Jia-Xin, ZHU Bin, HUANG Shi-Xuan, ZHOU Xing-You, WU Jun-Quan, YANG Mei-Yan. Whole genome analysis and biological characterization of phage vB_XaS_ HDB2 infected with Xanthomonas oryzae pv. oryzae [J]. Acta Agronomica Sinica, 2025, 51(8): 2087-2099.
[15] HU Meng, SHA Dan, ZHANG Sheng-Rui, GU Yong-Zhe, ZHANG Shi-Bi, LI Jing, SUN Jun-Ming, QIU Li-Juan, LI Bin. QTL mapping and candidate gene screening for branch number in soybean [J]. Acta Agronomica Sinica, 2025, 51(7): 1747-1756.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!