Scientia Agricultura Sinica ›› 2021, Vol. 54 ›› Issue (24): 5163-5176.doi: 10.3864/j.issn.0578-1752.2021.24.001


Construction of High-Density Genetic Map and QTL Analysis of Grain Shape in Rice RIL Population

ZHANG YaDong(),LIANG WenHua,HE Lei,ZHAO ChunFang,ZHU Zhen,CHEN Tao,ZHAO QingYong,ZHAO Ling,YAO Shu,ZHOU LiHui,LU Kai,WANG CaiLin   

  1. Institute of Food Crops, Jiangsu Academy of Agricultural Sciences/Jiangsu High Quality Rice R&D Center/Nanjing Branch of China National Center for Rice Improvement, Nanjing 210014
  • Received:2021-06-07 Accepted:2021-08-03 Online:2021-12-16 Published:2021-12-28
  • Contact: CaiLin WANG


【Objective】Rice grain shape is an important agronomic trait directly related to yield, which affects the appearance quality and commercial value of rice. Research on new rice grain shape genes is of great value for revealing the genetic mechanism of rice grain shape, and it can provide some new genetic resources for molecular breeding. 【Method】In the present study, a RIL population which constructed by an extra-large grain japonica rice variety TD70 and a small-grain indica rice variety Kasalath was used as the research material. The phenotypic data of grain shape, such as grain length, grain width, grain thickness and thousand grain weight were investigated. Using the Genotyping-By-Sequencing approach to re-sequence the parents and RILs to obtain SNP information. The sliding window method (the number of SNP/InDel is 15) was used for genotype calling and recombination breakpoint determination. Based on these results, a high-density Bin map was constructed. Meanwhile, the compound interval mapping method of QTL IciMapping software was used to map the QTLs related to grain shape. 【Result】A high-density genetic map containing 12 328 Bin markers was constructed. The number of Bin markers on each chromosome is 763 to 1367, and the average physical distance between markers was 30.26 kb. The frequency distribution of each trait for RIL population was continuous, which were consistent with the characteristics of quantitative characters, so it was suitable for the detection of QTL. QTL analysis of RIL population in 2018 showed that 40 grain-shape QTL were detected, including 12 grain length QTL, 9 grain width QTL, 8 grain thickness QTL, and 11 thousand-grain weight QTL. QTL analysis was performed of RIL population in 2019, and 56 grain-related QTL were detected, including 15 grain length QTL, 11 grain width QTL, 13 grain thickness QTL, and 17 thousand-grain weight QTL. Based on the two-year mapping results, we have mapped a total of 96 grain shape QTL. We found that 11 QTL could be detected for two consecutive years; among them, 7 QTL have been cloned and 4 new QTL were distributed on 1, 3, 4 and 5 chromosomes. Among the 4 new QTL, qGL-1-2 and qGL-5-2 was related to grain length, qGT-3-2 related grain thickness and qGW-4-1 related to grain width. 【Conclusion】We constructed a molecular genetic linkage map containing 12 328 Bin markers and used the map to analyze the grain shape loci of extra-large grain rice resources. Four new QTLs related to grain shape were obtained, which can be used for subsequent fine mapping, cloning and functional studies.

Key words: rice (Oryza sativa L.), Bin genetic map, grain shape, QTL mapping

Fig. 1

Grain shape of the parent and different lines of RIL P1: TD70; P2: Kasalath; 1-14: Part lines of RIL population"

Table 1

Phenotypic variation of parents and RIL populations in two years"

亲本 Parents 重组自交系 RIL populations
TD70 Kasalath 平均值 Average 变异范围 Range 变异系数 CV (%)
GL (mm)
2018 13.40 8.04 9.72 7.77—13.00 12.72
2019 13.29 8.07 9.58 7.42—12.74 12.57
GW (mm)
2018 4.42 2.48 3.12 2.37—4.34 11.52
2019 4.29 2.48 3.11 2.31—4.42 11.88
GT (mm)
2018 2.99 1.84 2.10 1.71—2.63 7.35
2019 2.93 1.90 2.17 1.79—2.78 8.12
TGW (g)
2018 64.95 17.40 30.60 17.95—55.40 22.58
2019 66.52 17.83 27.99 14.77—49.75 22.68

Fig. 2

Distribution of grain size and thousand-grain weight in RIL population GL: Grain length; GW: Grain width; GT: Grain thickness; TGW: 1000-grain weight. T: TD70; K: Kasalath"

Table 2

Bin map information"

Bin number
distance (cM)
Mean distance
between markers (cM)
1 1367 2173.32 1.589846379
2 991 1290.30 1.302018163
3 948 1158.52 1.222067511
4 1177 2400.93 2.039872557
5 954 1522.71 1.596132075
6 1103 1849.13 1.676455122
7 1032 2198.84 2.130658915
8 1045 2055.74 1.967215311
9 788 1453.83 1.844961929
10 763 1006.01 1.318492792
11 1098 1938.07 1.765091075
12 1062 2248.04 2.116798493

Fig. 3

Bin genetic linkage map"

Fig. 4

Distribution of the QTLs for grain traits The black locus was the grain traits QTL located in 2018, the green locus was the grain traits QTL located in 2019, and the red locus was the grain traits QTL located in 2018 and 2019"

Table 3

QTL analysis of grain traits"

Trait and loc
Marker interval
Cloned gene
LOD值 LOD score 贡献率PVE (%) 加性效应 Additive
2018 2019 2018 2019 2018 2019
粒长 GL
qGL-1-1 1 RBN0626—RBN0627 3.32 1.41 -0.14
qGL-1-2* 1 RBN0752—RBN0746 3.60 3.15 -0.29
qGL-2-2 2 RBN2285—RBN2286 TGW2 7.47 8.27 4.26 3.69 -0.22 -0.23
qGL-2-1 2 RBN1612—RBN1613 4.47 2.00 -0.16
qGL-2-3 2 RBN2346—RBN2347 3.31 1.50 0.62
qGL-3-1 3 RBN2661—RBN2665 10.97 6.66 0.87
qGL-3-2 3 RBN2808—RBN2809 GS3 27.88 27.05 20.94 15.44 -0.48 -0.46
qGL-3-3 3 RBN2901—RBN2900 11.36 6.77 -0.29
qGL-3-4 3 RBN2915—RBN2916 12.62 5.79 -0.29
qGL-3-5 3 RBN3000—RBN2997 qGL3/GL3.1 27.50 22.49 21.26 12.55 -0.52 -0.44
qGL-4 4 RBN4252—RBN4253 XIAO 10.85 8.37 6.66 3.86 -0.27 -0.22
qGL-5-1 5 RBN4645—RBN4646 7.43 3.36 0.21
qGL-5-2** 5 RBN5163—RBN5167 5.81 4.31 5.13 4.55 -0.29 -0.24
Trait and loc
Marker interval
Cloned gene
LOD值 LOD score 贡献率PVE (%) 加性效应 Additive
2018 2019 2018 2019 2018 2019
qGL-7-1 7 RBN6899—RBN6900 3.22 1.48 0.56
qGL-7-2 7 RBN7397—RBN7393 15.55 7.44 0.31
qGL-7-3 7 RBN7400—RBN7402 GL7/GW7 10.13 33.76 5.99 21.17 0.26 0.53
qGL-8 8 RBN8430—RBN8434 GW8 7.30 4.13 -0.25
qGL-9 9 RBN8628—RBN8629 2.58 1.02 0.39
qGL-11 11 RBN11026—RBN11027 3.99 2.21 -0.16
qGL-12-1 12 RBN11481—RBN11482 LARGE2 5.36 3.00 0.18
qGL-12-2 12 RBN11605—RBN11608 4.99 2.14 0.17
粒宽 GW
qGW-2-1 2 RBN1385—RBN1387 7.16 4.55 -0.08
qGW-2-2 2 RBN1564—RBN1566 GW2 35.15 32.03 28.82 30.07 -0.20 -0.21
qGW-2-3 2 RBN2138—RBN2136 8.47 5.61 -0.09
qGW-2-4 2 RBN2203—RBN2204 GS2 3.05 1.61 -0.05
qGW-4-1** 4 RBN3403—RBN3404 5.17 3.22 3.05 3.65 0.13 -0.38
qGW-4-2 4 RBN4252—RBN4253 XIAO 4.40 2.50 0.06
qGW-4-3 4 RBN4420—RBN4421 12.66 7.73 -0.10
qGW-4-4 4 RBN4427—RBN4428 5.45 3.48 -0.07
qGW-5 5 RBN4641—RBN4642 GW5 32.44 22.82 25.59 18.31 -0.18 -0.16
qGW-7-1 7 RBN7188—RBN7189 GLW7 7.20 4.11 -0.07
qGW-7-2 7 RBN7481—RBN7482 5.03 2.78 -0.09
qGW-8-1 8 RBN7840—RBN7842 3.51 2.71 -0.41
qGW-8-2 8 RBN8134—RBN8135 3.98 2.54 -0.06
qGW-9-1 9 RBN8981—RBN8988 4.87 3.07 0.06
qGW-10-1 10 RBN9497—RBN9501 2.53 1.60 -0.23
qGW-11-1 11 RBN10406—RBN10407 SRS5/TID1/OsTubA2 3.84 2.80 -0.19
qGW-11-2 11 RBN10496—RBN10497 3.40 2.21 0.06
粒厚 GT
qGT-1 1 RBN0651—RBN0652 8.59 6.82 -0.05
qGT-2-1 2 RBN1564—RBN1566 GW2 31.44 33.33 -0.09
qGT-2-2 2 RBN1569—RBN1573 11.80 9.67 -0.05
qGT-2-3 2 RBN1609—RBN1610 17.82 16.27 -0.06
qGT-3-1 3 RBN2565—RBN2566 5.28 4.06 -0.03
qGT-3-2* 3 RBN2710—RBN2711 6.32 5.01 -0.04
qGT-3-3 3 RBN2782—RBN2783 4.43 3.36 0.03
qGT-3-4 3 RBN2901—RBN2900 5.78 4.33 -0.04
qGT-3-5 3 RBN2987—RBN2986 10.68 8.76 -0.05
qGT-4-1 4 RBN3413—RBN3414 5.28 4.14 0.03
qGT-4-2 4 RBN3548—RBN3549 3.56 2.67 0.03
qGT-5-1 5 RBN4661—RBN4663 9.81 7.77 -0.04
Trait and loc
Marker interval
Cloned gene
LOD值 LOD score 贡献率PVE (%) 加性效应 Additive
2018 2019 2018 2019 2018 2019
qGT-5-2 5 RBN4668—RBN4669 8.16 6.66 -0.04
qGT-7 7 RBN7494—RBN7495 7.86 6.04 0.04
qGT-8 8 RBN7851—RBN7852 3.21 2.51 -0.02
qGT-10-1 10 RBN10072—RBN10074 5.68 4.49 -0.03
qGT-10-2 10 RBN10129—RBN10126 2.88 2.11 -0.02
qGT-11 11 RBN10949—RBN10950 3.43 2.73 0.03
qGT-12-1 12 RBN11790—RBN11791 6.85 5.44 -0.05
qGT-12-2 12 RBN11823—RBN11824 4.22 3.18 0.05
qGT-12-3 12 RBN11866—RBN11879 4.67 3.60 0.03
千粒重 TGW
qTGW-1-1 1 RBN0261—RBN0262 3.99 2.48 -1.03
qTGW-1-2* 1 RBN0752—RBN0746 22.24 11.34 -2.38
qTGW-1-3 1 RBN0767—RBN0766 10.25 4.31 1.46
qTGW-2-1 2 RBN1564—RBN1566 GW2 19.35 14.68 -2.55
qTGW-2-2 2 RBN1630—RBN1631 27.16 14.59 -2.56
qTGW-2-3 2 RBN1917—RBN1918 7.12 2.85 1.21
qTGW-2-4 2 RBN2043—RBN2044 7.52 3.31 -1.32
qTGW-2-5 2 RBN2121—RBN2120 4.43 3.42 -1.28
qTGW-3-1 3 RBN2539—RBN2536 8.19 3.31 -1.55
qTGW-3-2* 3 RBN2710—RBN2711 8.56 6.07 -1.69
qTGW-3-3 3 RBN2827—RBN2828 9.97 4.24 -1.42
qTGW-3-4 3 RBN2901—RBN2900 33.78 31.19 -3.91
qTGW-3-5 3 RBN3000—RBN2997 qGL3/GL3.1 23.36 12.38 -2.50
qTGW-4-1 4 RBN3699—RBN3697 3.30 2.03 0.96
qTGW-4-2 4 RBN4427—RBN4428 5.81 2.31 -1.00
qTGW-5-1 5 RBN4536—RBN4537 OsMKP1/GSN1 3.64 2.26 0.98
qTGW-5-2 5 RBN4618—RBN4620 GS5 9.63 6.55 -1.65
qTGW-5-3 5 RBN4702—RBN4701 GSK2 7.36 2.97 -1.13
qTGW-6-1 6 RBN5529—RBN5530 8.91 4.03 -1.37
qTGW-6-2 6 RBN5538—RBN5539 TGW6 11.92 5.15 1.47
qTGW-7-1 7 RBN7400—RBN7402 GL7/GW7 6.37 4.13 1.32
qTGW-7-2 7 RBN7516—RBN7517 9.62 4.03 -1.30
qTGW-10 10 RBN10129—RBN10126 3.41 2.12 -0.97
qTGW-11-1 11 RBN10406—RBN10407 SRS5/TID1/OsTubA2 2.71 1.17 -1.70
qTGW-11-2 11 RBN10523—RBN10524 5.27 3.73 -1.67
qTGW-11-3 11 RBN11260—RBN11261 4.38 2.14 -1.01
qTGW-12-1 12 RBN11790—RBN11791 10.85 4.63 -1.91
qTGW-12-2 12 RBN11948—RBN11949 5.54 2.19 1.10
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