利用抗旱选择导入系定位向日葵产量性状QTL

doi:10.3724/SP.J.1006.2018.00385

Abstract

Abstract:

Drought is one of the most important factors to decrease the yield of sunflower. The BC₃F₂ selected backcrossing introgression lines of oil sunflower population including 45 lines were developed by drought-tolerance screening for yield using Helianthus annuus K55 with excellent comprehensive characters and drought sensitivity as recurrent parent and K58 with drought-tolerance as donor parent. The genotypes of selected backcrossing introgression lines were obtained with the whole genome SSR and SNP markers. QTLs affecting five yield traits were detected under both drought stress and well watered conditions in Hohhot and Wuchuan respectively by one-way ANOVA and Chi-square test based on the Genetic Hitchhiking Effect. The QTLs detected by one-way ANOVA were grouped into three types based on their behaviors: type I was the QTLs detected in both watered conditions, including four QTLs affecting hundred-seed weight (HSW) in Wuchuan and two QTLs affecting seed yield (SY) and three QTLs affecting filled seeds per plant (FSP) in Hohhot, which were considered to be able to directly contribute to drought tolerance; type II was the QTLs detected only under drought stress, including 30 QTLs in Hohhot and 27 QTLs in Wuchuan; type III was the QTLs detected only in well watered condition, including 38 QTLs in Hohhot and 64 QTLs in Wuchuan. There 274 loci were detected by chi-square test, among them 14 loci could be detected by ANOVA and chi-square test simultaneously, which might be the key loci for drought tolerance of sunflower. The results lay a foundation of efficient drought tolerance molecular breeding and provide useful drought tolerance materials for sunflower.

Key words: sunflower, drought tolerance, selected backcrossing introgression lines, yield traits, QTL

Pin LYU, Hai-Feng YU, Jian-Hua HOU. QTL Mapping of Yield Traits Using Drought Tolerance Selected Backcrossing Introgression Lines in Sunflower[J].Acta Agronomica Sinica, 2018, 44(03): 385-396.

Figures/Tables 3

Table 1

Table 2

The χ2-test for drought-tolerance population"

位点 Locus	卡方值 χ²	位点 Locus	卡方值 χ²	位点 Locus	卡方值 χ²	位点 Locus	卡方值 χ²	位点 Locus	卡方值 χ²
ORS510	36.31	M53762	14.46	ORS883_1	24.15	M29997	11.78	M12324	20.64
ORS665	20.64	M63117	14.46	M26548	34.89	M29998	11.78	M26553	39.36
ORS331_3	56.38	M63118	14.46	M23014	9.38	M76524	14.46	M26549	30.70
ORS188_2	17.42	M47251	17.42	M23015	9.38	M158109	17.41	M32978	16.68
ORS1097_1	27.93	M47252	17.42	M60631	9.38	ORS749_3	50.95	M32979	16.68
M166096	14.37	M47253	17.42	M60632	9.38	M71304	70.86	M26753	27.93
M166095	9.83	M9848	17.42	M73785	9.38	M71305	70.86	M26754	31.98
M65009	9.68	M70258	18.46	M18564	14.46	M71306	54.41	M26755	31.98
M78469	9.38	M70259	18.46	M68521	14.46	ORS804	62.08	M41255	20.64
M42596	9.38	M70256	18.46	M68522	14.46	ORS710_2	24.15	M41256	20.64
M70648	9.38	M74282	21.17	M29444	20.64	ORS200_2	62.08	M44066	14.46
M13019	14.46	M92574	17.41	M29443	24.15	ORS502	45.79	M44065	14.46
M56579	11.78	M92575	23.14	M30447	24.15	M16656	11.78	M18546	17.42
M13018	11.78	M64800	27.93	M30448	24.15	M87334	15.43	M65260	14.46
M3679	11.78	M36341	18.46	M66317	24.15	M31406	20.64	M132062	10.73
M3680	11.78	M36342	19.08	M39702	31.98	M31407	20.64	M132063	8.50
M3681	11.78	M73285	17.42	M39703	31.98	M31408	20.64	M35489	11.78
M7600	11.78	M73284	11.78	M21816	31.98	M29706	9.38	M63604	14.46
M76785	9.76	M16682	9.38	M67939	31.98	ORS534_2	18.46	M64318	17.90
M62922	9.38	M16683	9.38	M67940	31.98	ORS1118	50.95	M73839	17.42
M17674	11.78	M16684	9.38	M91206	31.98	ORS761	45.79	M67864	17.90
M16151	11.78	M70270	9.76	M24719	27.93	ORS511	9.38	M43523	12.20
M82389	11.78	M70271	9.76	M20920	24.15	ORS1164	9.38	Ha494_2	31.98
M5389	11.78	M106626	8.81	M28413	24.15	M66318	17.42	M98429	14.56
M13708	14.46	M108409	8.31	M28414	24.15	M106782	14.46	M166171	19.08
M85412	11.78	ORS1143	17.42	M12140	27.93	M106783	14.46	M48356	52.54
M38509	11.78	ORS495	14.46	M12141	27.93	M17838	14.46	M48358	58.04
M45298	11.78	M55404	11.78	M1555	27.93	M17839	14.46	M48359	63.81
M19983	31.98	M17295	24.15	M72531	27.93	M17840	14.46	ORS12	27.93
M31217	31.98	Ha494_1	27.93	ORS604	56.38	M12322	20.64	ORS1143_1	14.46
M46874	31.98	Ha4057	20.64	M45299	11.78	M27515	20.64	ORS257_2	9.38
M46875	31.98	ORS1197_2	20.64	M87077	11.78	M39973	21.17	ORS1024	9.38
M66650	31.98	ORS516_3	20.64	M74063	9.38	M60579	17.42	ORS257_1	20.64
M66651	31.98	ORS769	20.64	M74064	9.38	M60580	17.42	ORS257_3	20.64
M66652	31.98	ORS1129_1	14.46	M74065	9.38	M93277	12.20	ORS525_1	9.38
M9089	31.98	ORS65_2	14.46	M31222	9.38	M10378	9.38	ORS965_4	14.46
M9090	31.98	M21636	9.38	M31223	9.38	M75855	8.01	M109397	24.15
M18321	31.98	M21637	9.38	M5387	9.38	M36906	11.78	M109396	27.93
M49169	32.61	M34808	9.38	M5388	9.38	M36907	11.78	M28142	20.64
M94707	32.61	M21634	9.38	M74062	9.38	M36908	11.78	M28141	24.15
M86618	31.98	M21635	9.38	M13016	11.78	M36909	11.78	M98430	22.41
M108153	32.61	M52731	9.38	M13017	11.78	ORS1040	11.78	M94939	28.52
M108154	32.61	M52732	9.38	M82388	9.38	M91533	13.24	M102100	17.41
M108155	32.61	M52733	9.38	M76708	29.90	ORS1242	9.38	ORS519	45.79
位点 Locus	卡方值 χ²	位点 Locus	卡方值 χ²	位点 Locus	卡方值 χ²	位点 Locus	卡方值 χ²	位点 Locus	卡方值 χ²
M115788	26.02	M52734	9.38	ORS418_2	45.79	ORS516_2	11.78	ORS575	14.46
M77329	21.75	M25595	11.78	M29707	11.78	M27654	27.93	M32713	22.41
M77330	21.75	M35009	9.38	M29708	11.78	M64799	24.15	ORS881_2	45.79
M49170	21.17	M31914	11.78	ORS1112	45.79	M67544	27.93	ORS315_4	20.64
M85256	21.17	M3202	11.78	ORS749_4	45.79	M22786	27.93	ORS486	56.38
M64415	21.17	M3203	11.78	ORS749_5	45.79	M22787	27.93	M19982	31.98
M65118	17.90	M33360	11.78	ORS1065_1	45.79	M86304	12.20	ORS881_3	45.79
M46192	17.42	M65122	11.78	M47014	9.38	M86305	12.20	ORS403_2	45.79
M46193	17.42	M70448	11.78	M47015	9.38	M82844	16.02	ORS380	27.93
M46191	17.42	M20592	9.38	M29995	11.78	M114875	17.42	ORS297	45.79
M64315	20.64	M17294	9.38	M29996	11.78	M12323	20.64

Table 2

Table3

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性状 Trait	环境 Env.	干旱胁迫 Drought stress			正常供水 Well watered
性状 Trait	环境 Env.	P₁	P₂	BC₃F₂	P₁	P₂	BC₃F₂
单株产量 Seed yield (g plant^-1)	E1	19.94	26.82	21.59±11.06	28.55	33.12	31.29±14.38
单株产量 Seed yield (g plant^-1)	E2	18.99	25.05	19.69±6.16	28.60	32.07	38.38±15.08
盘径 Disk stem (cm)	E1	13.67	13.17	12.74±2.27	15.77	15.04	16.91±2.41
盘径 Disk stem (cm)	E2	10.03	9.19	11.04±2.54	15.07	14.71	14.33±2.35
结实率 Seed-setting rate (%)	E1	47.70	55.86	52.40±7.05	72.94	79.03	79.10±5.87
结实率 Seed-setting rate (%)	E2	52.00	61.95	62.00±9.28	74.00	80.23	84.11±6.11
百粒重 Hundred-seed weight (g)	E1	4.05	3.76	3.62±0.84	5.00	4.56	4.87±1.16
百粒重 Hundred-seed weight (g)	E2	3.80	3.51	3.53±0.60	4.40	4.31	5.40±0.88
单株实粒数 Filled-seeds per plant	E1	473.00	684.00	558.70±184.25	589.00	762.33	689.82±207.42
单株实粒数 Filled-seeds per plant	E2	469.00	695.00	531.80±148.51	571.00	733.20	780.99±183.28

QTL Mapping of Yield Traits Using Drought Tolerance Selected Backcrossing Introgression Lines in Sunflower

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[2]	ZHOU Wen-Qi, QIANG Xiao-Xia, WANG Sen, JIANG Jing-Wen, WEI Wan-Rong. Mechanism of drought and salt tolerance of OsLPL2/PIR gene in rice [J]. Acta Agronomica Sinica, 2022, 48(6): 1401-1415.
[3]	YU Chun-Miao, ZHANG Yong, WANG Hao-Rang, YANG Xing-Yong, DONG Quan-Zhong, XUE Hong, ZHANG Ming-Ming, LI Wei-Wei, WANG Lei, HU Kai-Feng, GU Yong-Zhe, QIU Li-Juan. Construction of a high density genetic map between cultivated and semi-wild soybeans and identification of QTLs for plant height [J]. Acta Agronomica Sinica, 2022, 48(5): 1091-1102.
[4]	HUANG Li, CHEN Yu-Ning, LUO Huai-Yong, ZHOU Xiao-Jing, LIU Nian, CHEN Wei-Gang, LEI Yong, LIAO Bo-Shou, JIANG Hui-Fang. Advances of QTL mapping for seed size related traits in peanut [J]. Acta Agronomica Sinica, 2022, 48(2): 280-291.
[5]	ZHANG Yan-Bo, WANG Yuan, FENG Gan-Yu, DUAN Hui-Rong, LIU Hai-Ying. QTLs analysis of oil and three main fatty acid contents in cottonseeds [J]. Acta Agronomica Sinica, 2022, 48(2): 380-395.
[6]	ZHANG Bo, PEI Rui-Qing, YANG Wei-Feng, ZHU Hai-Tao, LIU Gui-Fu, ZHANG Gui-Quan, WANG Shao-Kui. Mapping and identification QTLs controlling grain size in rice (Oryza sativa L.) by using single segment substitution lines derived from IAPAR9 [J]. Acta Agronomica Sinica, 2021, 47(8): 1472-1480.
[7]	LUO Lan, LEI Li-Xia, LIU Jin, ZHANG Rui-Hua, JIN Gui-Xiu, CUI Di, LI Mao-Mao, MA Xiao-Ding, ZHAO Zheng-Wu, HAN Long-Zhi. Mapping QTLs for yield-related traits using chromosome segment substitution lines of Dongxiang common wild rice (Oryza rufipogon Griff.) and Nipponbare (Oryza sativa L.) [J]. Acta Agronomica Sinica, 2021, 47(7): 1391-1401.
[8]	HAN Yu-Zhou, ZHANG Yong, YANG Yang, GU Zheng-Zhong, WU Ke, XIE Quan, KONG Zhong-Xin, JIA Hai-Yan, MA Zheng-Qiang. Effect evaluation of QTL Qph.nau-5B controlling plant height in wheat [J]. Acta Agronomica Sinica, 2021, 47(6): 1188-1196.
[9]	WANG Wu-Bin, TONG Fei, KHAN Mueen-Alam, ZHANG Ya-Xuan, HE Jian-Bo, HAO Xiao-Shuai, XING Guang-Nan, ZHAO Tuan-Jie, GAI Jun-Yi. Detecting QTL system of root hydraulic stress tolerance index at seedling stage in soybean [J]. Acta Agronomica Sinica, 2021, 47(5): 847-859.
[10]	ZHOU Xin-Tong, GUO Qing-Qing, CHEN Xue, LI Jia-Na, WANG Rui. Construction of a high-density genetic map using genotyping by sequencing (GBS) for quantitative trait loci (QTL) analysis of pink petal trait in Brassica napus L. [J]. Acta Agronomica Sinica, 2021, 47(4): 587-598.
[11]	LI Shu-Yu, HUANG Yang, XIONG Jie, DING Ge, CHEN Lun-Lin, SONG Lai-Qiang. QTL mapping and candidate genes screening of earliness traits in Brassica napus L. [J]. Acta Agronomica Sinica, 2021, 47(4): 626-637.
[12]	SHEN Wen-Qiang, ZHAO Bing-Bing, YU Guo-Ling, LI Feng-Fei, ZHU Xiao-Yan, MA Fu-Ying, LI Yun-Feng, HE Guang-Hua, ZHAO Fang-Ming. Identification of an excellent rice chromosome segment substitution line Z746 and QTL mapping and verification of important agronomic traits [J]. Acta Agronomica Sinica, 2021, 47(3): 451-461.
[13]	MENG Jiang-Yu, LIANG Guang-Wei, HE Ya-Jun, QIAN Wei. QTL mapping of salt and drought tolerance related traits in Brassica napus L. [J]. Acta Agronomica Sinica, 2021, 47(3): 462-471.
[14]	WANG Rui-Li, WANG Liu-Yan, LEI Wei, WU Jia-Yi, SHI Hong-Song, LI Chen-Yang, TANG Zhang-Lin, LI Jia-Na, ZHOU Qing-Yuan, CUI Cui. Screening candidate genes related to aluminum toxicity stress at germination stage via RNA-seq and QTL mapping in Brassica napus L. [J]. Acta Agronomica Sinica, 2021, 47(12): 2407-2422.
[15]	LYU Guo-Feng, BIE Tong-De, WANG Hui, ZHAO Ren-Hui, FAN Jin-Ping, ZHANG Bo-Qiao, WU Su-Lan, WANG Ling, WANG Zun-Jie, GAO De-Rong. Evaluation and molecular detection of three major diseases resistance of new bred wheat varieties (lines) from the lower reaches of the Yangtze River [J]. Acta Agronomica Sinica, 2021, 47(12): 2335-2347.