基于QTL和转录组测序鉴定甘蓝型油菜耐旱候选基因

doi:10.3724/SP.J.1006.2024.34144

作物学报 ›› 2024, Vol. 50 ›› Issue (4): 820-835.doi: 10.3724/SP.J.1006.2024.34144

• 作物遗传育种·种质资源·分子遗传学 • 上一篇下一篇

基于QTL和转录组测序鉴定甘蓝型油菜耐旱候选基因

李阳阳¹^,²^,³(), 吴丹²^,³, 许军红²^,³, 陈倬永¹^,²^,³, 徐昕媛¹^,²^,³, 徐金盼¹^,²^,³, 唐钟林¹^,²^,³, 张娅茹¹^,²^,³, 朱丽¹^,²^,³, 严卓立¹^,²^,³, 周清元¹^,²^,³, 李加纳¹^,²^,³, 刘列钊¹^,²^,³, 唐章林¹^,²^,³^,^*()

¹西部(重庆)科学城种质创制大科学中心, 重庆 401329
²西南大学农学与生物科技学院, 重庆 400715
³西南大学农业科学研究院, 重庆 400715

收稿日期:2023-08-24 接受日期:2023-10-23 出版日期:2024-04-12 网络出版日期:2023-11-13
通讯作者: * 唐章林, E-mail: tangzhlin@swu.edu.cn
作者简介:E-mail: yyli1993swu@163.com
基金资助:
国家重点研发计划项目(2022YFD1201600);重庆市技术创新与应用发展专项重点项目(cstc2021jscx-cylhX0003)

Identification of candidate genes associated with drought tolerance based on QTL and transcriptome sequencing in Brassica napus L.

LI Yang-Yang¹^,²^,³(), WU Dan²^,³, XU Jun-Hong²^,³, CHEN Zhuo-Yong¹^,²^,³, XU Xin-Yuan¹^,²^,³, XU Jin-Pan¹^,²^,³, TANG Zhong-Lin¹^,²^,³, ZHANG Ya-Ru¹^,²^,³, ZHU Li¹^,²^,³, YAN Zhuo-Li¹^,²^,³, ZHOU Qing-Yuan¹^,²^,³, LI Jia-Na¹^,²^,³, LIU Lie-Zhao¹^,²^,³, TANG Zhang-Lin¹^,²^,³^,^*()

¹Integrative Science Center of Germplasm Creation in Western China (Chongqing) Science City, Chongqing 401329, China
²College of Agronomy and Biotechnology, Southwest University, Chongqing 400715, China
³Academy of Agricultural Sciences, Southwest University, Chongqing 400715, China

Received:2023-08-24 Accepted:2023-10-23 Published:2024-04-12 Published online:2023-11-13
Contact: * E-mail: tangzhlin@swu.edu.cn
Supported by:
National Key Research and Development Program of China(2022YFD1201600);Chongqing Technology Innovation and Application Development Key Project(cstc2021jscx-cylhX0003)

摘要/Abstract

摘要：

干旱胁迫严重限制了甘蓝型油菜种植面积的扩大和产量的提升。耐旱性是由多基因控制的复杂数量性状, 将QTL定位与转录组测序相结合, 是鉴定甘蓝型油菜耐旱候选基因的有效手段。本研究对甘蓝型油菜干旱敏感品系三六矮和耐旱品系科里纳-2构建的F_2:6和F_2:8重组自交系群体幼苗进行正常灌溉和干旱胁迫处理, 测定地上部鲜重、地上部干重、叶片相对含水量、丙二醛和可溶性糖含量, 利用SSR和SNP多态性分子标记构建遗传连锁图谱, 鉴定耐旱相关QTL和候选区间, 结合耐旱材料No11和干旱敏感材料No28的转录组测序, 筛选耐旱相关候选基因。研究结果表明: 干旱胁迫使甘蓝型油菜幼苗地上部鲜重、地上部干重和叶片相对含水量下降, 使叶片丙二醛和可溶性糖含量上升; 耐旱相关QTL和候选区间分布于A01、A02、A06、A08、A09、A10、C02、C03、C04、C06和C09染色体; 对耐旱材料和干旱敏感材料正常灌溉、干旱24 h、36 h和48 h进行转录组分析, 主要差异表达基因显著富集到光合作用、脂肪酸代谢、氨基酸代谢、植物激素信号转导、核糖体、昼夜节律及角质、木栓素和蜡质的生物合成等相关途径; 将QTL与转录组测序相结合, 鉴定到28个耐旱相关候选基因, 主要编码FLC、bHLH105、TGA4、TEM1、ERF003、ACO3、CHLI1、LHCB6和PORC等, 具有转录因子活性、乙烯产生和信号传导、叶绿素生物合成与结合、叶绿素氧化还原酶以及编码核糖体相关蛋白等功能。这些结果可为揭示甘蓝型油菜耐旱机理及分子标记辅助选育耐旱新品种奠定基础。

关键词: 甘蓝型油菜, 耐旱, QTL, 转录组, 候选基因

Abstract:

Drought stress severely limits planting promotion and yield increase in Brassica napus L. Drought tolerance is a complex quantitative trait controlled by multiple genes. Combining QTL and transcriptome is an effective method for identifying candidate genes associated with drought tolerance in B. napus. In this study, the seedlings of F_2:6and F_2:8 recombinant inbred lines, constructed by Sanliu’ai (drought sensitivity line) and Kelina-2 (drought tolerance line), were treated with drought stress and well watering at seedling stage. Shoot fresh weight, shoot dry weight, leaf relative water content, malondialdehyde content, and soluble sugar content were measured. The QTL and candidate intervals were identified based on genetic linkage maps, which were constructed using SSR and SNP markers with polymorphism. Subsequently, candidate genes associated with drought tolerance were screened by combining transcriptome sequencing of No11 (drought tolerance material) and No28 (drought sensitivity material). Drought stress decreased shoot fresh weight, shoot dry weight, and leaf relative water content, and increased the contents of malondialdehyde and soluble sugar. QTL and candidate intervals related to drought tolerance were distributed on chromosome A01, A02, A06, A08, A09, A10, C02, C03, C04, C06, and C09. By transcriptome analysis of drought tolerance and sensitivity materials under well water, drought stress for 24, 36, and 48 h, the major different expression genes were enriched in the pathways associated with photosynthesis, fatty acid metabolism, amino acid metabolism, plant hormone signal transduction, ribosome, circadian rhythm and biosynthesis of keratin, cork and wax. A total of 28 candidate genes related to drought tolerance were identified by combining QTL and transcriptome. They coded FLC, bHLH105, TGA4, TEM1, ERF003, ACO3, CHLI1, LHCB6, PORC, etc., which had transcription factor activity, ethylene production and signal transduction, chlorophyll biosynthesis and binding, chlorophyll oxidoreductase and encoding ribosome proteins. These results could provide a basis for revealing drought tolerance mechanism and molecular breeding of drought tolerance variety in B. napus.

Key words: Brassica napus L., drought tolerance, QTL, transcriptome, candidate genes

李阳阳, 吴丹, 许军红, 陈倬永, 徐昕媛, 徐金盼, 唐钟林, 张娅茹, 朱丽, 严卓立, 周清元, 李加纳, 刘列钊, 唐章林. 基于QTL和转录组测序鉴定甘蓝型油菜耐旱候选基因[J]. 作物学报, 2024, 50(4): 820-835.

LI Yang-Yang, WU Dan, XU Jun-Hong, CHEN Zhuo-Yong, XU Xin-Yuan, XU Jin-Pan, TANG Zhong-Lin, ZHANG Ya-Ru, ZHU Li, YAN Zhuo-Li, ZHOU Qing-Yuan, LI Jia-Na, LIU Lie-Zhao, TANG Zhang-Lin. Identification of candidate genes associated with drought tolerance based on QTL and transcriptome sequencing in Brassica napus L.[J]. Acta Agronomica Sinica, 2024, 50(4): 820-835.

图/表 11

表1

表2

图1

图2

图3

图4

表3

甘蓝型油菜耐旱相关性状QTL及候选区间"

群体 Population	QTL	置信区间 Confidence interval (cM)	邻近标记 Marker	物理位置 Physical position (bp)	阈值 LOD	染色体 Chr.	候选区间 Candidate range (bp)
F_2:6	qSFWCK-A01	78.85-86.82	AX-95683221	4227188	3.04	A01	4047188-4407188
			AX-179306831	4399498	2.89	A01	4219498-4579498
F_2:8	qRWCDRI-A02	17.64-31.87	AX-177910459	262823	3.07	A02	82823-442823
F_2:8	qRWCDS-A02	16.64-33.30	AX-177910459	262823	2.99	A02	82823-442823
F_2:8	qSFWCK-A02	43.41-47.22	AX-177829332	4692035	2.53	A02	4512035-4872035
F_2:8	qSUGCK-A02	0.00-8.00	AX-177834286	20489037	3.20	A02	20309037-20669037
F_2:6	qSDWCK-A02	0.00-11.74	SWUA02_198	893419-893684	3.14	A02	713419-1073684
			A02_2	893423-893813	3.47	A02	713423-1073813
F_2:8	qSFWDRI-A06	136.38-140.39	AX-95506320	20609909	2.50	A06	20429909-20789909
F_2:8	qMDACK-A08	28.40-30.85	AX-95638560	11831251	2.58	A08	11331251-12331251
F_2:8	qSFWDRI-A08	89.56-109.10	AX-95637797	16969405	3.04	A08	16469405-17469405
F_2:6	qSDWCK-A09	37.27-44.43	AX-179306992	2292222	2.59	A09	1912222-2672222
F_2:6	qSFWCK-A09	37.27-44.43	AX-179306992	2292222	2.70	A09	1912222-2672222
F_2:8	qMDADRI-A09	163.36-165.01	AX-177830392	26135201	4.09	A09	25755201-26515201
F_2:6	qMDACK-A09	0.00-2.00	nia_m046	21769576-21769946	2.91	A09	21389576-22149946
F_2:6	qRWCDS-A09	0.00-2.00	nia_m046	21769576-21769946	2.87	A09	21389576-22149946
F_2:8	qRWCCK-A10	12.22-14.56	AX-182144456	885133	3.06	A10	675133-1095133
F_2:6	qSFWDRI-A10	134.63-148.54	AX-177911667	16025485	3.04	A10	15815485-16235485
			AX-95637504	16164216	3.08	A10	15954216-16374216
			AX-182169619	16167478	3.04	A10	15957478-16377478
F_2:8	qSUGCK-C02	7.00-45.13	AX-182158861	16526368	2.58	C02	15326368-17726368
			AX-95636960	16803922	3.01	C02	15603922-18003922
			AX-182136799	24120437	2.99	C02	22920437-25320437
			AX-182139036	28872169	3.49	C02	27672169-30072169
			AX-182125192	33288173	2.98	C02	32088173-34488173
			AX-105306912	34988363	2.58	C02	33788363-36188363
			AX-95505280	41712868	2.96	C02	40512868-42912868
F_2:6	qRWCDRI-C02	29.49-31.89	SWUC316	33799496-33799694	3.36	C02	32599496-34999694
F_2:6	qRWCDS-C02	29.49-30.89	SWUC316	33799496-33799694	2.77	C02	32599496-34999694
F_2:6	qMDADS-C02	29.89-37.57	SWUC316	33799496-33799694	3.35	C02	32599496-34999694
			SWUC344	40102848-40103191	2.53	C02	38902848-41303191
			SWUC284	43919221-43919418	2.54	C02	42719221-45119418
			SWUC293	36909045-36909237 19357230-19357429	3.00	C02 A02	35709045-38109237 19177230-19537429
F_2:8	qSFWDRI-C03	153.23-157.21	AX-95636893	9150013	2.71	C03	8750013-9550013
F_2:6	qSUGDRI-C04	103.69-108.02	AX-95506064	7272757	7.10	C04	6682757-7862757
F_2:8	qSUGDS-C06	74.57-101.32	AX-105336545	29172287	3.04	C06	28772287-29572287
			AX-95665032	29186383	2.66	C06	28786383-29586383
			AX-86230901	29308911	3.04	C06	28908911-29708911
			AX-95665273	29401522	3.04	C06	29001522-29801522
			AX-95664947	29433854	3.04	C06	29033854-29833854
			AX-105310203	29489446	3.04	C06	29089446-29889446
			AX-105308711	29627549	3.04	C06	29227549-30027549
			AX-86230806	29710301	3.04	C06	29310301-30110301
			AX-182145054	29797943	3.04	C06	29397943-30197943
			AX-86215830	30202409	3.04	C06	29802409-30602409
			AX-86230835	30204371	2.92	C06	29804371-30604371
			AX-105308457	30304436	3.04	C06	29904436-30704436
			AX-95665820	30314052	3.04	C06	29914052-30714052
			AX-105310125	30791904	3.04	C06	30391904-31191904
			AX-182087795	31059928	3.04	C06	30659928-31459928
			AX-95635741	31183285	3.04	C06	30783285-31583285
			AX-86230832	31264725	3.11	C06	30864725-31664725
			AX-177912717	31340640	2.95	C06	30940640-31740640
F_2:8	qSDWDRI-C09	19.55-37.10	AX-95509234	35782323	2.70	C09	35072323-36492323
			AX-86236963	37494562	2.68	C09	36784562-38204562
			AX-182158558	46574561	2.68	C09	45864561-47284561

表3

图5

图6

图7

表4

参考文献 48

[1]	Batool M, El-Badri A M, Wang Z K, Mohamed I A A, Yang H Y, Ai X Y, Salah A, Hassan M U, Sami R, Kuai J, Wang B, Zhou G S. Rapeseed morpho-physio-biochemical responses to drought stress induced by PEG-6000. Agronomy, 2022, 12: 579. doi: 10.3390/agronomy12030579
[2]	周广生, 王晶, 蒯婕, 汪波. 专辑导读: 加强大田经济作物栽培措施与环境/资源配置的互作研究、推动产业高效优质发展. 作物学报, 2021, 47: 1633-1638. doi: 10.3724/SP.J.1006.2021.04633
	Zhou G S, Wang J, Kuai J, Wang B. Editorial: strengthening the research on the interaction between cultivated measures and environment/resource allocation of field economic crops to promote the development of industry with high efficiency and high quality. Acta Agron Sin, 2021, 47: 1633-1638. (in Chinese with English abstract)
[3]	宁宁, 莫娇, 胡冰, 李大双, 娄洪祥, 王春云, 白晨阳, 蒯婕, 汪波, 王晶, 徐正华, 李晓华, 贾才华, 周广生. 长江流域不同生态区油菜籽关键品质比较研究. 作物学报, 2023, 49: 3315-3327. doi: 10.3724/SP.J.1006.2023.34017
	Ning N, Mo J, Hu B, Li D S, Lou H X, Wang C Y, Xiang C Y, Kuai J, Wang B, Wang J, Xu Z H, Li X H, Jia C H, Zhou G S. Comparative study on the processing quality of winter rape in different ecological zones of the Yangtze River valley. Acta Agron Sin, 2023, 49: 3315-3327. (in Chinese with English abstract) doi: 10.3724/SP.J.1006.2023.34017
[4]	张佳运, 马淑梅, 余常兵, 王淑彬, 魏亚凤, 杨文钰, 王小春. 长江流域旱地多熟模式水分供需平衡特征与水分生产效益. 作物学报, 2022, 48: 2891-2907. doi: 10.3724/SP.J.1006.2022.14206
	Zhang J Y, Ma S M, Yu C B, Wang S B, Wei Y F, Yang W Y, Wang X C. Characteristics of water supply-demand equilibrium and water production benefits of the dryland multiple cropping patterns in the Yangtze Rive basin. Acta Agron Sin, 2022, 48: 2891-2907. (in Chinese with English abstract)
[5]	万林, 李张开, 李素, 刘丽欣, 马霓, 张春雷. 外源独脚金内酯对油菜苗期干旱胁迫的缓解效应. 中国油料作物学报, 2020, 42: 461-471.
	Wan L, Li Z K, Li S, Liu L X, Ma N, Zhang C L. Alleviation effects of exogenous strigolactone on growth of Brassica napus L. seedling under drought stress. Chin J Oil Crop Sci, 2020, 42: 461-471. (in Chinese with English abstract)
[6]	蒙姜宇, 梁光伟, 贺亚军, 钱伟. 甘蓝型油菜耐盐和耐旱相关性状的QTL分析. 作物学报, 2021, 47: 462-471. doi: 10.3724/SP.J.1006.2021.04034
	Meng J Y, Liang G W, He Y J, Qian W. QTL mapping of salt and drought tolerance related traits in Brassica napus L. Acta Agron Sin, 2021, 47: 462-471. (in Chinese with English abstract) doi: 10.3724/SP.J.1006.2021.04034
[7]	李真. 甘蓝型油菜苗期耐湿性和抗旱性相关QTL分析. 华中农业大学硕士学位论文, 湖北武汉, 2008. pp 46-48.
	Li Z. Study on QTL Associated with Waterlogging Tolerance and Drought Resistance during Seedling Stage in Brassica napus L. MS Thesis of Huazhong Agricultural University, Wuhan, Hubei, China, 2008. pp 46-48. (in Chinese with English abstract)
[8]	王丹丹. 甘蓝型油菜遗传图谱构建及苗期耐旱相关性状的QTL定位. 西南大学硕士学位论文, 重庆, 2013. pp 32-33.
	Wang D D. Mapping and QTL Analysis of Genes to Drought Tolerance in Brassica napus L. MS Thesis of Southwest University, Chongqing, China, 2013. pp 32-33. (in Chinese with English abstract)
[9]	Mahmoud G, Chao H B, Li H X, Zhao W G, Lu G Y, Li M T. QTL mapping for seed germination response to drought stress in Brassica napus. Front Plant Sci, 2021, 11: 629970. doi: 10.3389/fpls.2020.629970
[10]	Shahzad A, Qian M C, Sun B Y, Mahmood U, Li S T, Fan Y H, Chang W, Dai L S, Zhu H, Li J N, Qu C M, Lu K. Genome-wide association study identifies novel loci and candidate genes for drought stress tolerance in rapeseed. Oil Crop Sci, 2021, 6: 12-22. doi: 10.1016/j.ocsci.2021.01.002
[11]	Khanzada H, Wassan G M, He H H, Mason A S, Keerio A A, Khanzada S, Faheem M, Solangi A M, Zhou Q H, Fu D H, Huang Y J, Rasheed A. Differentially evolved drought stress indices determine the genetic variation of Brassica napus at seedling traits by genome-wide association mapping. J Adv Res, 2020, 24: 447-461. doi: 10.1016/j.jare.2020.05.019 pmid: 32577311
[12]	Tan M, Liao F, Hou L T, Wang J, Wei L J, Jian H J, Xu X F, Li J N, Liu L Z. Genome-wide association analysis of seed germination percentage and germination index in Brassica napus L. under salt and drought stresses. Euphytica, 2017, 213: 40. doi: 10.1007/s10681-016-1832-x
[13]	王浩. 基于GWAS和转录组测序鉴定谷子硒响应相关候选基因. 山西农业大学硕士学位论文, 山西太原, 2022. p 9.
	Wang H. Identification of Selenium-Responsive Candidate Genes in Foxtail Millet Based on GWAS and Transcriptome Sequencing. MS Thesis of Shanxi Agricultural University, Taiyuan, Shanxi, China, 2022. p 9. (in Chinese with English abstract)
[14]	Liu C Q, Zhang X K, Zhang K, An H, Hu K N, Wen J, Shen J X, Ma C Z, Yi B, Tu J X, Fu T D. Comparative analysis of the Brassica napus root and leaf transcript profiling in response to drought stress. Int J Mol Sci, 2015, 16: 18752-18777. doi: 10.3390/ijms160818752
[15]	Wang P, Yang C L, Chen H, Song C P, Zhang X, Wang D J. Transcriptomic basis for drought-resistance in Brassica napus L. Sci Rep, 2017, 7: 40532. doi: 10.1038/srep40532 pmid: 28091614
[16]	Zhou H W, Xiao X J, Asjad A, Han D P, Zheng W, Xiao G B, Huang Y J, Zhou Q H. Integration of GWAS and transcriptome analyses to identify SNPs and candidate genes for aluminum tolerance in rapeseed (Brassica napus L.). BMC Plant Biol, 2022, 22: 130. doi: 10.1186/s12870-022-03508-w
[17]	Jian H J, Zhang A X, Ma J Q, Wang T Y, Yang B, Shuang L S, Liu M, Li J N, Xu X F, Paterson A H, Liu L Z. Joint QTL mapping and transcriptome sequencing analysis reveal candidate flowering time genes in Brassica napus L. BMC Genomics, 2019, 20: 21. doi: 10.1186/s12864-018-5356-8
[18]	Guo J, Li C H, Zhang X Q, Li Y X, Zhang D F, Shi Y S, Song Y C, Li Y, Yang D G, Wang T Y. Transcriptome and GWAS analyses reveal candidate gene for seminal root length of maize seedlings under drought stress. Plant Sci, 2020, 292: 110380. doi: 10.1016/j.plantsci.2019.110380
[19]	Sevanthi A M, Sinha S K, Sureshkumar V, Rani M, Saini M R, Kumari S, Kaushik M, Prakash C, Venkatesh K, Singh G P, Mohapatra T, Mandal P K. Integration of dual stress transcriptomes and major QTL from a pair of genotypes contrasting for drought and chronic nitrogen starvation identifies key stress responsive genes in rice. Rice, 2021, 14: 49. doi: 10.1186/s12284-021-00487-8 pmid: 34089405
[20]	荆蓉蓉. 甘蓝型油菜苗期耐湿相关性状的全基因组关联分析. 西南大学硕士学位论文, 重庆, 2017. p 16.
	Jing R R. Genome-wide Assocaiton Mapping of Water Logging Traits in Brassica napus L. MS Thesis of Southwest University, Chongqing, China, 2017. p 16. (in Chinese with English abstract)
[21]	Van Ooijen J W. JoinMap 4: Software for the Calculation of Genetic Linkage Maps in Experimental Populations, 2006. Wageningen: Kyazma B V. pp 1-63.
[22]	Van Ooijen J W. MapQTL 6: Software for the Mapping of Quantitative Trait Loci in Experimental Populations of Diploid Species, 2009. Wageningen: Kyazma B V. pp 1-64.
[23]	Voorrips R E, MapChart: software for the graphical presentation of linkage maps and QTL. J Hered, 2002, 93: 77-78. doi: 10.1093/jhered/93.1.77 pmid: 12011185
[24]	李星, 杨会, 骆璐, 李华东, 张昆, 张秀荣, 李玉颖, 于海洋, 王天宇, 刘佳琪, 王瑶, 刘风珍, 万勇善. 栽培种花生单仁重QTL定位分析. 作物学报, 2023, 49: 2160-2170. doi: 10.3724/SP.J.1006.2023.24190
	Li X, Yang H, Luo L, Li H D, Zhang K, Zhang X R, Li Y Y, Yu H Y, Wang T Y, Liu J Q, Wang Y, Liu F Z, Wan Y S. QTL mapping for single-seed weight of cultivated peanut. Acta Agron Sin, 2023, 49: 2160-2170. (in Chinese with English abstract)
[25]	李阳阳, 荆蓉蓉, 吕蓉蓉, 石鹏程, 李欣, 王芹, 吴丹, 周清元, 李加纳, 唐章林. 甘蓝型油菜湿害胁迫响应性状的全基因组关联分析及候选基因预测. 作物学报, 2019, 45: 1806-1821. doi: 10.3724/SP.J.1006.2019.94027
	Li Y Y, Jing R R, Lyu R R, Shi P C, Li X, Wang Q, Wu D, Zhou Q Y, Li J N, Tang Z L. Genome-wide association analysis and candidate genes prediction of waterlogging-responding traits in Brassica napus. Acta Agron Sin, 2019, 45: 1806-1821. (in Chinese with English abstract)
[26]	王瑞霞, 杜海平, 田宏先. 干旱胁迫对不同生育期芥菜型春油菜幼苗生长的影响. 南方农业, 2019, 13(26): 140-143.
	Wang R X, Du H P, Tian H X. Effects of drought stress on the growth of seedlings at different growth stages in spring Brassica juncea. South China Agric, 2019, 13(26): 140-143. (in Chinese)
[27]	石鹏程. 甘蓝型油菜苗期干旱及旱后复水相关性状的全基因组关联分析. 西南大学硕士学位论文, 重庆, 2019. p 15.
	Shi P C. Genome-wide Association Mapping for Drought and Rewatering Related Traits at Seedling Stage in Brassica napus L. MS Thesis of Southwest University, Chongqing, China, 2019. p 15. (in Chinese with English abstract)
[28]	谢小玉, 张霞, 张兵. 油菜苗期抗旱性评价及抗旱相关指标变化分析. 中国农业科学, 2013, 46: 476-485. doi: 10.3864/j.issn.0578-1752.2013.03.004
	Xie X Y, Zhang X, Zhang B. Evalution of drought resistance and analysis of variation of relevant parameters at seedling stage of rapeseed (Brassica napus L.). Sci Agric Sin, 2013, 46: 476-485. (in Chinese with English abstract)
[29]	洪双. 全基因组关联分析挖掘甘蓝型油菜耐旱候选基因. 中国农业科学院硕士学位论文, 北京, 2018. pp 31-32.
	Hong S. Genome-wide Association Study Identifies Candidate Genes for Drought Tolerance in Brassica napus. MS Thesis of Chinese Academy of Agricultural Sciences, Beijing, China, 2018. pp 31-32. (in Chinese with English abstract)
[30]	吴金锋. 甘蓝型油菜SNP与SSR分析及耐旱性状的全基因组关联分析. 中国农业科学院博士学位论文, 北京, 2014. pp 27-28.
	Wu J F. SNP and SSR Analysis and Genome-wide Association Mapping of Drought Tolerance Trait in Brassica napus. PhD Dissertation of Chinese Academy of Agricultural Sciences, Beijing, China, 2014. pp 27-28. (in Chinese with English abstract)
[31]	Zhu B, Xu H X, Guo X, Lu J X, Liu X Y, Zhang T. Comparative analysis of drought responsive transcriptome in Brassica napus genotypes with contrasting drought tolerance under different potassium levels. Euphytica, 2023, 219: 25. doi: 10.1007/s10681-023-03156-7
[32]	Fang S, Zhao P M, Tan Z D, Peng Y, Xu L T, Jin Y T, Wei F, Guo L, Yao X. Combining physio-biochemical characterization and transcriptome analysis reveal the responses to varying degrees of drought stress in Brassica napus L. Int J Mol Sci, 2022, 23: 8555. doi: 10.3390/ijms23158555
[33]	Xiong J L, Dai L L, Ma N, Zhang C L. Transcriptome and physiological analyses reveal that AM1 as an ABA-mimicking ligand improves drought resistance in Brassica napus. Plant Growth Regul, 2018, 85: 73-90. doi: 10.1007/s10725-018-0374-8
[34]	Chen Q, Zheng Y, Luo L D, Yang Y P, Hu X Y, Kong X X. Functional FRIGIDA allele enhances drought tolerance by regulating the P5CS1 pathway in Arabidopsis thaliana. Biochem Biophys Res Commun, 2018, 495: 1102-1107. doi: 10.1016/j.bbrc.2017.11.149
[35]	Sun K L, Wang H Y, Xia Z L. The maize bHLH transcription factor bHLH105 confers manganese tolerance in transgenic tobacco. Plant Sci, 2019, 280: 97-109. doi: S0168-9452(18)31005-7 pmid: 30824033
[36]	Zhong L, Chen D D, Min D H, Li W W, Xu Z S, Zhou Y B, Li L C, Chen M, Ma Y Z. AtTGA4, a bZIP transcription factor, confers drought resistance by enhancing nitrate transport and assimilation in Arabidopsis thaliana. Biochem Biophys Res Commun, 2015, 457: 433-439. doi: 10.1016/j.bbrc.2015.01.009
[37]	Luo G Y, Liu A L, Zhou X Y, Zhang X W, Peng Y, Chen X B. Arabidopsis TEMPRANILLO1 transcription factor AtTEM1 negatively regulates drought tolerance. Plant Growth Regul, 2017, 83: 119-127. doi: 10.1007/s10725-017-0288-x
[38]	Naing A H, Campol J R, Kang H, Xu J P, Chung M Y, Kim C K. Role of ethylene biosynthesis genes in the regulation of salt stress and drought stress tolerance in Petunia. Front Plant Sci, 2022, 13: 844449. doi: 10.3389/fpls.2022.844449
[39]	Liu W J, Wang Y C, Gao C Q. The ethylene response factor (ERF) genes from Tamarix hispida respond to salt, drought and ABA treatment. Trees, 2013, 28: 317-327. doi: 10.1007/s00468-013-0950-5
[40]	Rong W, Qi L, Wang A Y, Ye X G, Du L P, Liang H X, Xin Z Y, Zhang Z Y. The ERF transcription factor TaERF3 promotes tolerance to salt and drought stresses in wheat. Plant Biotechnol J, 2014, 12: 468-479. doi: 10.1111/pbi.12153 pmid: 24393105
[41]	Xu Y H, Liu R, Yan L, Liu Z Q, Jiang S C, Shen Y Y, Wang X F, Zhang D P. Light-harvesting chlorophyll a/b-binding proteins are required for stomatal response to abscisic acid in Arabidopsis. J Exp Bot, 2012, 63: 1095-1106. doi: 10.1093/jxb/err315 pmid: 22143917
[42]	Pattanayak G K, Tripathy B C. Overexpression of protochlorophyllide oxidoreductase C regulates oxidative stress in Arabidopsis. PLoS One, 2011, 6: e26532. doi: 10.1371/journal.pone.0026532
[43]	Tomiyama M, Inoue S I, Tsuzuki T, Soda M, Morimoto S, Okigaki Y, Ohishi T, Mochizuki N, Takahashi K, Kinoshita T. Mg-chelatase I subunit 1 and Mg-protoporphyrin IX methyltransferase affect the stomatal aperture in Arabidopsis thaliana. J Plant Res, 2014, 127: 553-563. doi: 10.1007/s10265-014-0636-0
[44]	Condori-Apfata J A, Batista-Silva W, Medeiros D B, Vargas J R, Valente L M L, Pérez-Díaz J L, Fernie A R, Araújo W L, Nunes- Nesi A. Downregulation of the E2 subunit of 2-Oxoglutarate dehydrogenase modulates plant growth by impacting carbon- nitrogen metabolism in Arabidopsis thaliana. Plant Cell Physiol, 2021, 62: 798-814. doi: 10.1093/pcp/pcab036 pmid: 33693904
[45]	Niu C D, Jiang L J, Cao F G, Liu C, Guo J X, Zhang Z T, Yue Q Y, Hou N, Liu Z Y, Li X W, Tahir M M, He J Q, Li Z X, Li C, Ma F W, Guan Q M. Methylation of a MITE insertion in the MdRFNR1-1 promoter is positively associated with its allelic expression in apple in response to drought stress. Plant Cell, 2022, 34: 3983-4006. doi: 10.1093/plcell/koac220
[46]	Seraj R G M, Tohidfar M, Irani M A, Esmaeilzadeh-Salestani K, Moradian T, Ahmadikhah A, Behnamian M. Metabolomics analysis of milk thistle lipids to identify drought-tolerant genes. Sci Rep, 2022, 12: 12827. doi: 10.1038/s41598-022-16887-9 pmid: 35896570
[47]	Pandian B A, Sathishraj R, Djanaguiraman M, Prasad P V V, Jugulam M. Role of cytochrome P450 enzymes in plant stress response. Antioxidants, 2020, 9: 454. doi: 10.3390/antiox9050454
[48]	Pal G, Bakade R, Deshpande S, Sureshkumar V, Patil S S, Dawane A, Agarwal S, Niranjan V, PrasannaKumar M K, Vemanna R S. Transcriptomic responses under combined bacterial blight and drought stress in rice reveal potential genes to improve multi-stress tolerance. BMC Plant Biol, 2022, 22: 349. doi: 10.1186/s12870-022-03725-3 pmid: 35850621

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群体 Population	变异来源 Source of variation	RWC		SFW		SDW		SUG		MDA
群体 Population	变异来源 Source of variation	DF	F-value	DF	F-value	DF	F-value	DF	F-value	DF	F-value
F_2:6	水分间Water	1	11.18^**	1	850.03^**	1	214.32^**	1	30.86^**	1	91.75^**
	家系间Line	103	4.64^**	104	14.41^**	104	14.96^**	104	15.29^**	104	552.39^**
	水分×家系 Water×Line	103	3.10^**	104	10.55^**	104	5.98^**	104	9.96^**	104	392.10^**
	误差Error	208		210		210		210		210
F_2:8	水分间Water	1	3175.43^**	1	2237.27^**	1	389.89^**	1	1490.35^**	1	1139.48^**
	家系间Line	125	2.36^**	125	6.25^**	125	6.21^**	125	15.72^**	125	21.62^**
	水分×家系 Water×Line	125	2.30^**	124	3.65^**	124	1.76^**	124	13.32^**	123	11.86^**
	误差Error	446		426		414		462		471

性状 Trait	群体 Population	处理 Treatment	亲本Parent		群体Population
性状 Trait	群体 Population	处理 Treatment	三六矮 Sanliu’ai	科里纳-2 Kelina-2	平均值 Average	标准误差 SE	变幅 Range	峰度 Kurtosis	偏度 Skewness
RWC (%)	F_2:6	CK	91.72	93.35	86.51	0.54	68.96-96.55	0.95	-0.76
		DS	83.98	86.24	85.16	0.58	61.78-96.87	2.27	-1.19
	F_2:8	CK	88.00	86.84	89.74	0.28	72.10-95.14	7.26	-1.64
		DS	65.46	76.11	71.27	0.42	60.83-82.91	-0.55	-0.06
SFW (g)	F_2:6	CK	20.18	10.78	16.55	0.46	4.79-30.27	0.01	0.21
		DS	15.67	9.00	11.71	0.36	4.79-23.90	0.15	0.71
	F_2:8	CK	44.78	27.06	36.79	1.02	11.84-75.42	1.57	1.04
		DS	19.22	22.70	14.36	0.38	6.58-25.85	-0.03	0.62
SDW (g)	F_2:6	CK	1.27	0.72	1.18	0.04	0.41-2.34	0.06	0.64
		DS	0.95	0.61	0.95	0.03	0.37-1.92	0.47	0.87
	F_2:8	CK	3.80	2.65	2.72	0.08	0.89-5.90	0.85	0.74
		DS	2.30	2.10	1.81	0.05	0.59-3.48	0.90	0.59
SUG (μg g^-1)	F_2:6	CK	1496.20	542.03	2744.17	226.85	186.82-16219.47	12.23	2.83
		DS	6485.20	1081.01	3271.71	249.66	222.49-12447.88	3.50	1.69
	F_2:8	CK	3888.00	4581.34	5349.75	131.73	2144.61-10444.09	0.82	0.75
		DS	6265.26	7643.01	7278.97	131.53	3370.63-10742.93	0.17	0.15
MDA (μmol g^-1)	F_2:6	CK	7.49	13.97	9.80	0.44	3.43-34.67	8.58	2.26
		DS	12.77	17.70	10.04	0.34	5.28-23.09	1.81	1.31
	F_2:8	CK	41.18	20.43	29.26	0.97	9.34-64.77	0.54	0.83
		DS	50.15	50.63	41.03	1.04	23.76-71.73	-0.63	0.51

基于QTL和转录组测序鉴定甘蓝型油菜耐旱候选基因

Identification of candidate genes associated with drought tolerance based on QTL and transcriptome sequencing in Brassica napus L.

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QTL	候选基因Candidate gene	QTL	候选基因Candidate gene
qSFWCK-A01	BnaA01g08750D (MYC4) BnaA01g09270D (CHLI1)	qMDADRI-A09	BnaA09g36380D (RR9)
qRWCDRI-A02	BnaA02g00310D (TGA4)	qMDACK-A09	BnaA09g28670D (TEM1) BnaA09g29410D (PRE6)
qRWCDS-A02	BnaA02g00310D (TGA4)	qRWCDS-A09	BnaA09g28670D (TEM1) BnaA09g29410D (PRE6)
qSFWCK-A02	BnaA02g09540D (RPL24)	qRWCCK-A10	BnaA10g01420D (EL22Z) BnaA10g02050D (PORC)
qSUGCK-A02	BnaA02g27860D (CYP71B22) BnaA02g27940D (bHLH105)	qSFWDRI-A10	BnaA10g25310D (SUVH1)
qSDWCK-A02	BnaA02g01690D (RPL10)	qSUGCK-C02	BnaC02g20870D (HPA1)
qMDACK-A08	BnaA08g13440D (RPL28C) BnaA08g14220D (ACO3)	qMDADS-C02	BnaC02g40790D (KNAT3) BnaC02g40810D (ERF003)
qSFWDRI-A08	BnaA08g23760D (LHCB6) BnaA08g25090D (E2-OGDH) BnaA08g25130D (ADT1)	qSFWDRI-C03	BnaC03g17470D (PRK1) BnaC03g17780D (AIP1) BnaC03g18600D (PXG3)
qSDWCK-A09	BnaA09g04650D (ERF003) BnaA09g05170D (RUP2)	qSDWDRI-C09	BnaC09g45770D (RPL37B) BnaC09g46500D (FLC)
qSFWCK-A09	BnaA09g04650D (ERF003) BnaA09g05170D (RUP2)

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