基于水稻RIL群体的加工品质性状QTL分析

doi:10.6048/j.issn.1001-4330.2023.10.010

新疆农业科学 ›› 2023, Vol. 60 ›› Issue (10): 2419-2425.DOI: 10.6048/j.issn.1001-4330.2023.10.010

• 作物遗传育种·种质资源·分子遗传学 • 上一篇下一篇

基于水稻RIL群体的加工品质性状QTL分析

陈丽¹(), 马静¹, 朱志明², 刘炜¹, 孙建昌¹()

1.宁夏农林科学院农作物研究所,银川 750002
2.宁夏农业技术推广总站, 银川 750001

收稿日期:2023-02-09 出版日期:2023-10-20 发布日期:2023-11-01
通信作者: 孙建昌(1975-),男,宁夏盐池人,研究员,博士,研究方向为水稻遗传育种,(E-mail)nxsjch@163.com
作者简介:陈丽(1985-),女,陕西商洛人,助理研究员,硕士研究生,研究方向为水稻遗传育种,(E-mail)chen1985li@163.com
基金资助:
宁夏回族自治区水稻育种专项(2018NYYZ0302);宁夏回族自治区自然科学基金项目(2022AAC03459);宁夏回族自治区农业科技自主创新资金项目(NGSB-2021-13-3-3)

QTL mapping of processing quality traits on RIL population in rice

CHEN Li¹(), MA Jing¹, ZHU Zhiming², LIU Wei¹, SUN Jianchang¹()

1. Institute of Crop Science,Ningxia Academy of Agricultural and Forestry Science,Yinchuan 750002,China
2. Agricultural technology extension station of Ningxia,Yingchuan 750001,China

Received:2023-02-09 Online:2023-10-20 Published:2023-11-01
Correspondence author: SUN Jancahng (1975-), male, native place: Yanchi, Ningxia. Professor, research field: Rice genetics and breeding research,(E-mail)nxsjch@163.com
Supported by:
Rice Breeding Program Project of Ningxia Hui Autonomous Region(2018NYYZ0302);ZProject of Natural Science Foundation of Ningxia Hui Autonomous Region(2022AAC03459);Agricultural Science and Technology Independent Innovation Fund of Ningxia Hui Autonomous Region(NGSB-2021-13-3-3)

摘要/Abstract

摘要：

【目的】 研究挖掘水稻加工品质性状相关基因位点。【方法】 以13HJZ-44/13HJZ-19构建的包括243个家系的RIL群体F₇代为材料,于2019~2021年采集稻米出糙率、精米率和整精米率表型数据,并分析加工品质相关性状QTL。【结果】 共检测到加工品质性状相关QTL 9个,其中糙米率的2个QTL分别位于4号和11号染色体上,表型贡献率为18.68%和75.32%;精米率3个QTL分别位于1号、2号和5号染色体上,贡献率为4.35%、0.75%和16.21%;整精米率4个QTL分别位于5号、6号和8号染色体上,贡献率为10.76%、5.86%、5.42%和3.66%。【结论】 qHR-6-1、qHR-6-2和qBR-11是控制糙米率和整精米率位点,其中qHR-6-1、qHR-6-2对整精米率有微效性,qBR-11是一个新的控制糙米率主效QTL。

关键词: 水稻; 重组自交系; 加工品质; 性状; QTL分析

Abstract:

【Objective】 To explore the fundamental factors related to the quality of processed rice. 【Methods】 A RIL population F₇ with 243 families constructed by 13JZ-44/13JZ-19 was used as the test material, and the phenotypic data of brown rate, milled rice rate and head milled rice rate were collected from 2019 to 2021, and QTL related to processing quality was analyzed.【Results】 The results showed that 9 QTLs related to processing quality traits were detected, among which 2 QTLs of brown rice percentage were located on chromosome 4 and 11, respectively, with phenotypic contribution rates of 18.68% and 75.32%. The three QTLs of milled rice rate were located on chromosomes 1, 2 and 5, respectively, with the contribution rates of 4.35%, 0.75% and 16.21%. The four QTLs of head rice rate were located on chromosomes 5, 6 and 8, respectively, with the contribution rates of 10.76%, 5.86%, 5.42% and 3.66%.【Conclusion】 qHR-6-1, qHR-6-2 and qBR-11 are newly discovered Loci for controlling brown rice rate and head rice rate, qHR-6-1 and qHR-6-2 have slight effect on head rice rate. And qBR-11 is a new major QTL for controlling brown rice rate.

Key words: rice; RIL population; processing quality; traits; QTL analysis

中图分类号:

S511

陈丽, 马静, 朱志明, 刘炜, 孙建昌. 基于水稻RIL群体的加工品质性状QTL分析[J]. 新疆农业科学, 2023, 60(10): 2419-2425.

CHEN Li, MA Jing, ZHU Zhiming, LIU Wei, SUN Jianchang. QTL mapping of processing quality traits on RIL population in rice[J]. Xinjiang Agricultural Sciences, 2023, 60(10): 2419-2425.

图/表 5

参考文献 30

[1]	王惠贞, 吴瑞芬, 李丹. 稻米品质形成和调控机理概述[J]. 中国稻米, 2016, 22(1):10-13.
	WANG Huizhen, WU Ruifen, LI Dan. Review on Rice Quality Formation and its Regulation Mechanism[J]. China Rice, 2016, 22(1):10-13. DOI
[2]	胡培松, 翟虎渠, 唐绍清, 等. 利用RVA 快速鉴定稻米蒸煮及食味品质的研究[J]. 作物学报, 2004, 30(6):519-524.
	HU Peisong, ZHAI Huqu, TANG Shaoqing, et al. Rapid Evaluation identification of Rice Cooking and Palatability Quality by RVA profile[J]. Acta Agronomica Sinica, 2004, 30(6):519-524.
[3]	莫惠栋. 我国稻米品质的改良[J]. 中国农业科学, 1993, 26(4): 8-14.
	MO Huidong. Improvement of rice quality in China[J]. Scientia Agricultura Sinica, 1993, 26(4): 8-14.
[4]	王炎钦. mRNA 水平水稻品质相关基因的大规模鉴定及应用初探[D]. 杭州: 浙江大学, 2006.
	WANG Yanqin. Primary Investigation in Rice Quality-related Genes Identification and Their Application in mRNA Level[D]. Hangzhou: Zhejiang University, 2006.
[5]	YAN Wengui, BAO Jinsong. Rice-Germplasm, Genetics and Improvement[M]. InTech Open Access Publisher. 2014: 239-278.
[6]	Tan Y F, Xing Y Z, Li J X, et al. Genetic Bases of Appearance Quality of Rice Grains in Shanyou63, an Elite rice Hybrid[J]. Theoretical and Applied Genetics, 2000, 101:823-829. DOI URL
[7]	Tan Y F, Sun M, Xing Y Z, et al. Mapping Quantitative Trait Loci for Milling Quality, Protein content and Color Characteristics of Rice using a Recombinant Inbred Line Population Derived from an Elite Rice Hybrid[J]. Theoretical and Applied Genetics, 2001, 103:1037-1045. DOI URL
[8]	Kepiro J L, McClung A M, Chen M H, et al. Mapping QTLs for Milling Yield and Grain Characteristics in a Tropical Japonica Long Grain Cross[J]. Cereal Science, 2008, 48(2): 477-485. DOI URL
[9]	穆平, 郭咏梅, 刘家富, 等. 稻米外观和碾磨品质 QTL 定位及其与土壤水分环境互作分析[J]. 农业生物技术学报, 2007, 15(4): 654-660.
	MU Ping, GUO Yongmei, LIU Jiafu, et al. QTL Mapping and QTL×Environments Interactions of Grain Milling and Appearance Quality Traits in Rice under Upland and Lowland Environments[J]. Journal of Agricultural Biotechnology, 2007, 15(4): 654-660.
[10]	Dong Y J, Tsuzuki E, Lin D Z, et al. Molecular Genetic Mapping of Quantitative Trait Loci for Milling Quality in Rice (Oryza sativa L.)[J]. Cereal Science, 2004, 40(2): 109-114. DOI URL
[11]	翁建峰, 万向元, 郭涛, 等. 利用CSSL 群体研究稻米加工品质相关 QTL 表达的稳定性[J]. 中国农业科学, 2007, 40(10): 2128-2135.
	WONG Jianfeng, WANG Xiangyuan, GUO Tao, et al. Stability Analysis of QTLs for Milling Quality of Rice using CSSL population[J]. Scientia Agricultura Sinica, 2007, 40(10): 2128-2135.
[12]	Wan X Y, Wan J M, Weng J F, et al. Stability of QTLs for Rice Grain Dimension and Endosperm Chalkiness Characteristics Across Eight Environments[J]. Theor Appl Genet, 2005, 110(7):1334-1346. DOI PMID
[13]	Jiang G H, Hong X Y, Xu C G, et al. Identification of Quantitative Trait Loci for Grain Appearance and Milling Quality using a Doubled-haploid Rice Population[J]. Integr Plant Biol, 2005, 47(11): 1391-1403. DOI URL
[14]	Li Z F, Wan J M, Xia J F, et al. Identification of Quantitative Trait Loci underlying Milling Quality of Rice (Oryza sativa L.) grains[J]. Plant Breed, 2004, 123(3): 229-234. DOI URL
[15]	Li Z F, Wan J M, Xia J F, et al. Mapping Quantitative Traits Loci underlying Appearance Quality of Rice Grains (Oryza sativa L.)[J]. Acta Genet Sin, 2003, 30(3): 251-259.
[16]	Lou J, Chen L, Y ue G H, et al. QTL Mapping of Grain Quality Traits in Rice[J]. Cereal Science, 2009, 50(2): 145-151. DOI URL
[17]	Zheng T Q, Xu J L, Li Z K, et al. Genomic Regions Associated with Milling Quality and Grain Shape Identified in a Set of Random Introgression Lines of Rice (Oryza sativa L.)[J]. Plant Breed, 2007, 126(2): 158-163. DOI URL
[18]	梅捍卫, 罗利军, 郭龙彪, 等. 水稻加工品质数量性状基因座(QTLs)分子定位研究[J]. 遗传学报, 2002, 29(9): 791-797.
	MEI Hanwei, LUO Lijun, GUO Longbiao, et al. Molecular Mapping of QTLs for Rice Milling Yield Traits[J]. Journal of Genetics, 2002, 29(9): 791-797.
[19]	胡霞, 石瑜敏, 贾倩, 等. 影响水稻穗部性状及籽粒碾磨品质的QTL 及其环境互作分析[J]. 作物学报, 2011, 37(7): 1175-1185.
	HU Xia, SHI Yumin, JIA Qian, et al. Analysis of QTLs for Rice Panicle and Milling Quality Traits and Their Interaction with Environment[J]. Acta Agronomica Sinica, 2011, 37(7): 1175-1185.
[20]	Aluko G, Martinez C, Tohme J, et al. QTL Mapping of Grain Quality Traits from the In-terspecific Cross Oryza sativa × O. glaberrima[J]. Theor Appl Genet, 2004, 109(3): 630-639. DOI PMID
[21]	Li J M, Xiao J H, Grandillo S, et al. QTL Detection for Rice Grain Quality Traits using an Inter Specific Backcross Population Derived from Cultivated Asian (O. sativa L.) and African (O. glaberrima S.) rice[J]. Genome, 2004, 47(4): 697-704. DOI URL
[22]	Septiningsih E M, Trijatmiko K R, Moeljopawiro S, et al. Identification of Quantitative Trait Loci for Grain Quality in an Advanced Backcross Population Derived from the Oryza Sativa Variety IR64 and the Wild Relative O. Rufipogon[J]. Tag Theoretical & Applied Genetics, 2003, 107(8): 1433-1441.
[23]	刘家富, 奎丽梅, 朱作峰, 等. 普通野生稻稻米加工品质和外观品质性状 QTL 定位[J]. 农业生物技术学报, 2007, 15(1): 90-96.
	LIU Jiafu, KUI Limei, ZHU Zuofeng, et al. Identification of QTLs Associated with Processing Quality and Appearance Quality of Common Wild Rice (Oryza rufipogon Griff.)[J]. Journal of Agricultural Biotechnology, 2007, 15(1): 90-96.
[24]	代明笠, 邵丽明, 胡慧, 等. 水稻加工品质及其遗传基础研究进展[J]. 长江大学学报, 2015, 12(9):5-8.
	DAI Mingli, SHAO Liming, HU Hui, et al. Research progress of rice processing quality and its genetic basis[J]. Journal of Changjiang University, 2015, 12(9):5-8.
[25]	Li Y B, Fan C C, Xing Y Z, et al. Chalk5 Encodes a Vacuolar H+ Translocating Pyrosphatase Influencing Grain Chalkiness in Rice[J]. Nat Genet, 2014, 46:398-404. DOI
[26]	MaCouch S R, Cho Y G, Yang M, et al. Report on QTL Nomenclature[J]. Rice Gene Newslett, 1997, 14:11-13.
[27]	SUN Xiaowen, LIU Dongyuan, ZHANG Xiaofeng, et al. SLAF-seq:an Efficient Method of Large-scale Denovo SNP Discovery and Genotyping using High-throughput Sequencing[J]. Plos One, 2013, 8(3):e58700. DOI URL
[28]	Liu D, Ma C, Hong W, et al. Construction and Analysis of High-density Linkage Map using High-throughput Sequencing Data[J]. Plos One, 2014, 9(6):e98855. DOI URL
[29]	梅德勇, 朱玉君, 樊叶杨. 籼稻稻米碾磨与外观品质性状的 QTL 定位[J]. 遗传, 2012, 34(12):1591-1598.
	MEI Deiyong, ZHU Yujun, FAN Yeyang. QTL Mapping for milling and appearance quality traits in Indica Rice[J]. Heredity, 2012, 34(12):1591-1598.
[30]	Wang X, Pang Y, Wang C, et al. New Candidate Genes Affecting Rice Grain Appearance and Milling Quality Detected by Genome-wide and Gene-based Association Analyses[J]. Frontiers in Plant Science, 2017, 7:1998.

性状 Trait	亲本Parent			重组自交系群体RIL population
性状 Trait	13HJZ -44	13HJZ -19	差值 Difference	均值 Mean	标准差 SD	最大值 Max	最小值 Min	变异系数 Coefficient of variation(%)	峰度 Kurtosis	偏度 Skewness
出糙率 Brown rice rate (%)	83.68	84.15	0.47	84.07	1.72	90.00	70.00	48.91	48.96	-6.122
精米率 Milled rice rate (%)	70.25	73.25	2.99	72.57	2.81	86.00	57.00	25.83	8.239	-1.275
整精米率 Head rice rate (%)	41.27	61.02	19.75	54.10	5.86	66.00	37.00	9.23	-0.389	-0.48

性状 Trait	亲本Parent			重组自交系群体RIL population
性状 Trait	13HJZ -44	13HJZ -19	差值 Difference	均值 Mean	标准差 SD	最大值 Max	最小值 Min	变异系数 Coefficient of variation(%)	峰度 Kurtosis	偏度 Skewness
出糙率 Brown rice rate (%)	83.68	84.15	0.47	84.07	1.72	90.00	70.00	48.91	48.96	-6.122
精米率 Milled rice rate (%)	70.25	73.25	2.99	72.57	2.81	86.00	57.00	25.83	8.239	-1.275
整精米率 Head rice rate (%)	41.27	61.02	19.75	54.10	5.86	66.00	37.00	9.23	-0.389	-0.48

性状 Trait	QTL	标记区间 Marker interval	区间距离 Interval distance/cM	LOD值			贡献率PVE(%)			加性效应Add(%)
性状 Trait	QTL	标记区间 Marker interval	区间距离 Interval distance/cM	2019	2020	2021	2019	2020	2021	2019	2020	2021
BR	qBR-11	Marker1690523-Marker1651369	0.623	6.06			75.32			-1.21
BR	qBR-4	Marker2308870-Marker2433873	0.21		2.53			18.68			0.47
MR	qMR-5	Marker49932-Marker141199	0.415	10.62			16.21			2.24
	qMR-1	Marker405598-Marker617137	0.415		3.7			4.35			0.35
	qMR-2	Marker2776334-Marker2942331	0			2.6			0.75			-0.5
HR	qHR-5	Marker160033-Marker137238	0.419	6.95			10.76			2.7
	qHR-6-1	Marker3365801-Marker3142370	0.42		4.07			5.86			1.25
	qHR-6-2	Marker3340147-Marker3099891	0.208			3.41			5.42			-2.47
	qHR-8	Marker2591821-Marker2551026	0			3.32			3.66			2.03

性状 Trait	QTL	标记区间 Marker interval	区间距离 Interval distance/cM	LOD值			贡献率PVE(%)			加性效应Add(%)
性状 Trait	QTL	标记区间 Marker interval	区间距离 Interval distance/cM	2019	2020	2021	2019	2020	2021	2019	2020	2021
BR	qBR-11	Marker1690523-Marker1651369	0.623	6.06			75.32			-1.21
BR	qBR-4	Marker2308870-Marker2433873	0.21		2.53			18.68			0.47
MR	qMR-5	Marker49932-Marker141199	0.415	10.62			16.21			2.24
	qMR-1	Marker405598-Marker617137	0.415		3.7			4.35			0.35
	qMR-2	Marker2776334-Marker2942331	0			2.6			0.75			-0.5
HR	qHR-5	Marker160033-Marker137238	0.419	6.95			10.76			2.7
	qHR-6-1	Marker3365801-Marker3142370	0.42		4.07			5.86			1.25
	qHR-6-2	Marker3340147-Marker3099891	0.208			3.41			5.42			-2.47
	qHR-8	Marker2591821-Marker2551026	0			3.32			3.66			2.03

基于水稻RIL群体的加工品质性状QTL分析

QTL mapping of processing quality traits on RIL population in rice

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