基于高密度遗传图谱对水稻粒形QTL定位及分析

doi:10.3724/SP.J.1006.2022.12069

作物学报 ›› 2022, Vol. 48 ›› Issue (11): 2813-2825.doi: 10.3724/SP.J.1006.2022.12069

• 作物遗传育种·种质资源·分子遗传学 • 上一篇下一篇

基于高密度遗传图谱对水稻粒形QTL定位及分析

宋博文¹(), 王朝欢¹, 赵哲¹, 陈淳¹, 黄明¹, 陈伟雄², 梁克勤¹^,^*(), 肖武名¹^,^*()

¹华南农业大学 / 国家植物航天育种工程技术研究中心, 广东广州 510642
²广州市农业科学研究院, 广东广州 510338

收稿日期:2021-10-06 接受日期:2022-03-25 出版日期:2022-11-12 网络出版日期:2022-04-20
通讯作者: 梁克勤,肖武名
作者简介:第一作者联系方式: E-mail: 1476399189@qq.com
基金资助:
本研究由广州市科技计划项目重点研发计划(202103000083);广东省重点领域研发计划基金项目(2020B020219004)

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^,^*()

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 Published:2022-11-12 Published online:2022-04-20
Contact: LIANG Ke-Qin,XIAO Wu-Ming
Supported by:
Key Research & Development program of Guangzhou Science and Technology Project(202103000083);The Guangdong Provincial Key Research and Development Program(2020B020219004)

摘要/Abstract

摘要：

通过对水稻粒形相关性状进行QTL定位和不同位点的聚合效应分析, 为后续辅助育种、粒形相关基因的精细定位奠定基础。在3个不同的季节种植籼稻品种魔大穗与航恢315的192个重组自交系群体, 根据全基因组重测序技术构建的高密度遗传图谱, 利用完备区间作图法(inclusive composite interval mapping, ICIM)定位水稻粒形性状QTL, 并用复合区间作图法(CIM)对结果进行验证。对粒长、粒宽、长宽比、千粒重、谷粒截面积、谷粒周长进行QTL定位, ICIM法和CIM法分别检测到65个和81个QTL, 其中有40个QTL能同时被检测到, 成簇分布于2号、3号、5号、7号、8号、9号染色体上。将其归类为6个QTL簇, 其中Loci5、Loci7、Loci9能被重复定位到, 且控制至少4个性状, 表型贡献率最高分别为15.74%、54.07%、15.89%, 这些位点是后续基因功能研究的候选位点。根据bin标记分型结果将不同子代在各个QTL区间分为航恢315型和魔大穗型, 在重组自交系后代进行聚合效应分析。经处理及数据分析发现聚合增效等位基因数量越多的个体, 其在不同环境对应的表型值也越高。鉴定到携带多个增效等位基因的株系, 可作为育种实践中增效等位基因的供体亲本。

关键词: 水稻, 重组自交系, 高密度遗传图谱, 粒形, QTL定位

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

宋博文, 王朝欢, 赵哲, 陈淳, 黄明, 陈伟雄, 梁克勤, 肖武名. 基于高密度遗传图谱对水稻粒形QTL定位及分析[J]. 作物学报, 2022, 48(11): 2813-2825.

SONG Bo-Wen, WANG Chao-Huan, ZHAO Zhe, CHEN Chun, HUANG Ming, CHEN Wei-Xiong, LIANG Ke-Qin, XIAO Wu-Ming. Mapping and analysis of QTLs for grain size in rice based on high density genetic map[J]. Acta Agronomica Sinica, 2022, 48(11): 2813-2825.

导出引用管理器 EndNote|Reference Manager|ProCite|BibTeX|RefWorks

链接本文: https://zwxb.chinacrops.org/CN/10.3724/SP.J.1006.2022.12069

https://zwxb.chinacrops.org/CN/Y2022/V48/I11/2813

图/表 8

表1

图1

表2

表3

3个环境下检测到的水稻粒形QTL定位结果"

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
	qGL5-1	5	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
	qGL8-2	8	40.8	8.70	-0.15	5.62	2020E	39.05-41.85	bin1524-bin1525
	qGL9-1	9	82.0	5.17	-0.11	3.17	2020E	80.85-86.85	bin1743-bin1744
	qGL9-1	9	82.6	6.37	-0.14	3.39	2019L	80.85-86.85	bin1743-bin1744
	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
	qLWR4-2	4	73.2	5.41	0.11	2.53	2020E	72.75-73.45	bin991-bin992
	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
	qTGW2-1	2	32.6	5.26	-0.54	6.76	2020L	29.45-33.25	bin523-bin526
	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
	qGAS2-1	2	31.5	5.32	-0.29	6.38	2020L	30.85-33.25	bin524-bin525
	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
	qGAS8-2	8	53.8	8.70	-0.38	13.07	2020E	53.35-54.15	bin1551-bin1552
	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
	qGAS9-3	9	92.1	10.78	-0.45	15.43	2020L	86.85-94.4	bin1744-bin1745
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
	qGPL5-1	5	94.1	3.95	-0.21	2.76	2020E	93.85-94.35	bin1179-bin1180
	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
	qGPL7-1	7	93.5	57.95	-1.14	60.17	2020L	92.75-93.75	bin1443-bin1445
	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
	qGPL9-1	9	85.1	3.55	-0.23	2.57	2019L	80.85-86.85	bin1743-bin1744
	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

表3

表4

图2

图3

表5

参考文献 29

[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

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed

性状 Trait	季节 Season	亲本Parents		重组自交系RILs						广义遗传力 h²(%)
性状 Trait	季节 Season	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	广义遗传力 h²(%)
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

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

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]

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)
株系类型 Type of lines	Loci5	Loci7	Loci9	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)
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^aSource of favorable alleles ^a					R315	R315	—	R315	R315	R315
					R315	MDS	R315	—	R315	MDS
					R315	—	—	R315	R315	R315

基于高密度遗传图谱对水稻粒形QTL定位及分析

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

RichHTML

PDF

摘要/Abstract

引用本文

使用本文

图/表 8

参考文献 29

相关文章 15

Metrics

本文评价

推荐阅读 0

关于本刊

在线期刊

作者中心

相关单位

联系我们

[1]	郑玉珍, 齐飞艳, 孙子淇, 刘华, 秦利, 石磊, 王娟, 汪蒙蒙, 韩锁义, 徐静, 苗利娟, 黄冰艳, 董文召, 郑峥, 张新友. 花生籽仁总超长链脂肪酸和7种脂肪酸组分的QTL定位[J]. 作物学报, 2026, 52(6): 1646-1657.
[2]	胡赵, 钱润, 谢丰璞, 应素平. 水稻SPX基因家族鉴定及响应磷处理的表达分析[J]. 作物学报, 2026, 52(6): 1902-1912.
[3]	邹仪妹, 徐敏, 汪海洋, 姚辉, 王加峰, 刘浩, 任代胜. 两系不育系水稻幼苗根系响应盐胁迫的转录因子调控网络鉴定[J]. 作物学报, 2026, 52(6): 1728-1742.
[4]	闫安, 蒋昆炜, 王蓉圆, 田林, 张璐, 王韵, 徐建龙. 水稻剑叶小维管束数基因SVN7的鉴定与克隆[J]. 作物学报, 2026, 52(5): 1364-1372.
[5]	陈伟, 卫万娟, 赵其兵, 常东伟, 余凌波, 翟鹏飞, 冯志明, 陈宗祥, 任仰涛, 杨鹏, 刘海浪, 李珍富, 杨永乐, 金彦刚, 左示敏. 利用CRISPR/Cas9编辑Hd6基因创制优质早熟水稻新种质[J]. 作物学报, 2026, 52(4): 1046-1056.
[6]	石少阶, 刘凯, 陈姿夷, 王卉颖, 李三和, 周雷, 游艾青. 水稻矮化多分蘖基因DMT1的克隆与功能分析[J]. 作物学报, 2026, 52(4): 1022-1034.
[7]	覃奕琰, 付瑶, 苏畅, 李娜, 徐静茹, 程笑然, 张琪, 赵明辉. OsST41调控水稻苗期耐盐性的功能分析[J]. 作物学报, 2026, 52(3): 802-812.
[8]	刘长友, 王珅, 时会影, 沈颖超, 孙蕾, 王彦, 张志肖, 苏秋竹, 田静, 范保杰. 基于饭豆基因资源的小豆远缘杂交群体抗豆象QTL定位[J]. 作物学报, 2026, 52(3): 936-944.
[9]	叶凡, 李帅, 李思宇, 陈云, 窦超银, 刘立军. 不同节水灌溉方式对东北稻区水稻产量和群体质量的影响[J]. 作物学报, 2026, 52(3): 895-907.
[10]	王婵, 吴莹莹, 李文奇, 李霞, 王芳权, 周彤, 杨杰. 基于HRM技术开发水稻抗条纹叶枯病基因STV11功能标记[J]. 作物学报, 2025, 51(9): 2547-2556.
[11]	陈惠莹, 何嘉欣, 朱斌, 黄士轩, 周星佑, 伍君权, 杨美艳. 水稻黄单胞菌噬菌体vB_XaS_HDB2的全基因组分析和生物学特性研究[J]. 作物学报, 2025, 51(8): 2087-2099.
[12]	胡蒙, 沙丹, 张晟瑞, 谷勇哲, 张世碧, 李静, 孙君明, 邱丽娟, 李斌. 大豆分枝数QTL定位及候选基因筛选[J]. 作物学报, 2025, 51(7): 1747-1756.
[13]	邵顺伟, 陈卓, 兰振东, 蔡兴奎, 邹华芬, 李晨曦, 唐景华, 朱熙, 张彧, 董建科, 金辉, 宋波涛. 基于BSA-seq技术的块茎芽眼深度QTL定位分析[J]. 作物学报, 2025, 51(7): 1725-1735.
[14]	杨海洋, 吴林宣, 李博纹, 石翰峰, 袁禧龙, 刘金朝, 蔡海荣, 陈诗怡, 郭涛, 王慧. 基于QTL定位发现的OsWRI3调控水稻种子的落粒性[J]. 作物学报, 2025, 51(7): 1712-1724.
[15]	雷松翰, 范骏扬, 车艳奕, 代永东, 郑雨萌, 田维江, 桑贤春, 王晓雯. 水稻内卷叶突变体acl3的鉴定及调控基因的功能分析[J]. 作物学报, 2025, 51(6): 1467-1479.