人工合成小麦和地方品种穗发芽抗性育种利用效率

doi:10.3864/j.issn.0578-1752.2024.07.004

Abstract

Abstract:

【Objective】Pre-harvest sprouting (PHS) is a serious limiting factor for wheat (Triticum aestivum L.) grain yield and end-use quality. Synthetic hexaploid wheats (SHW) and wheat landraces (WL) are important germplasm resources for improving PHS resistance in wheat. The objective of this study is to utilize PHS-resistant loci from SHW and WL for breeding PHS-resistant elite materials, which will provide a theoretical basis for improving PHS resistance of wheat cultivars.【Method】In this study, SYN792 (a synthetic hexaploid wheat from CIMMYT) and Fulingxuxumai (a Chinese wheat landrace) were used as female parents to cross and backcross with Chuanmai 45 (a sensitive variety to PHS), respectively. Two BC₁F₇ populations including 1 796 lines were established. Seed germination index (GI) and seed germination rate of each spike (SGR) in different environments were used to evaluate PHS resistance. Two germination temperature of 25 ℃ (18GI) and 32 ℃ (19GI) were set to examine seed germinability in 2018 and 2019. 1 796 BC₁F₇ lines were evaluated preliminarily by SGR phenotype and molecular markers detection in 2017, and the introgression lines with PHS-3D and PHS-A1 resistant loci and SGR less than 35% were screened. Introgression lines with PHS-3D and PHS-A1 resistant loci were used to analyze utilization efficiency of SHW and WL in PHS-resistance breeding by identifying PHS-resistance and yield related traits in 2018 and 2019.【Result】PHS resistance of 1 796 lines was evaluated preliminarily, and 537 lines with SGR value less than 35% were screened for further molecular marker detection. A total of 332 lines with PHS-3D and PHS-A1 were selected by SSR marker, and the frequency of WL introgression lines was significantly higher than that of SHW introgression lines. 332 introgression lines were used to analyze PHS-resistance and yield related traits in 2018 and 2019. There was a significant positive correlation between different PHS indexes in different years, but there was no significant difference in the values of 18GI, 18SGR and 19SGR between SHW and WL introgression lines. The average values of 18SGR, 19SGR and 18GI in SHW and WL introgression lines were lower than 23%. As far as GI value was concerned, there was obvious difference between different germination temperatures. At the germination temperature of 32 ℃, the mean 19GI value of SHW PHS-3D introgression lines was significantly lower than that of WL PHS-A1 introgression lines. Grain color was associated with PHS resistance in SHW introgression lines, and the red-grained SHW introgression lines had lower the mean GI and SGR values than the white-grained lines. Among 73 SHW introgression lines, 11 white-grained lines showed medium or higher resistance to PHS，and the GI values of 14 red-grained lines at different germination temperatures were lower than 35%. According to the data of agronomic traits in 2018 and 2019, thousand grain weight of SHW introgression lines was significantly higher than that of WL introgression lines, but the number of grains per spike was significantly lower than that of WL introgression lines. 23 elite introgression lines including seven SHW introgression lines and 16 WL introgression lines were selected. Two SHW white-grained introgression lines had better resistance to PHS, and the GI values of two red-grained introgression lines at different germination temperatures were lower than 25%.【Conclusion】It is feasible to transfer PHS-3D and PHS-A1 resistance loci to PHS from SHW and WL for improving PHS-resistance of modern wheat cultivars. In this study, the breeding efficiency of WL for PHS-resistance was better than that of SHW. However, the stability of PHS-resistance of SHW introgression lines was better than that of WL introgression lines. 23 SHW and WL elite introgression lines could be used as parents to improve the PHS-resistance and yield traits in wheat. In particular, the white-grained SHW introgression line No.5201 and the red-grained SHW introgression lines No.5497 and No.5505 were very valuable parents for wheat breeding of PHS resistance.

Key words: wheat, synthetic hexaploid wheat (SHW), wheat landraces, pre-harvest sprouting (PHS), yield related yield

LIU ZeHou, WANG Qin, YE MeiJin, WAN HongShen, YANG Ning, YANG ManYu, YANG WuYun, LI Jun. Utilization Efficiency of Improving the Resistance for Pre-Harvest Sprouting by Synthetic Hexaploid Wheat and Chinese Wheat Landrace[J].Scientia Agricultura Sinica, 2024, 57(7): 1255-1266.

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References 44

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反应型 Infection type	人工合成小麦群体Synthetic hexaploid wheat population		地方品种群体Wheat landrace population
反应型 Infection type	株系数No. of lines	占比Proportion (%)	株系数No. of lines	占比Proportion (%)
高抗HR	110	10.29	267	36.73
抗R	26	2.43	53	7.29
中抗MR	29	2.72	52	7.15
中感MS	38	3.55	64	8.80
感S	96	8.98	78	10.73
高感HS	770	72.03	213	29.30
合计Total	1069	100.00	727	100.00

性状Traits	18SGR	19SGR	18GI
19SGR	0.476**
18GI	0.327**	0.244**
19GI	0.342**	0.257**	0.467**

性状 Traits	PHS-3D导入系PHS-3D introgression lines			PHS-A1导入系PHS-A1 introgression lines
性状 Traits	均值±标准差 Mean (%)±SD	最小值 Min (%)	最大值 Max (%)	均值±标准差 Mean (%)±SD	最小值 Min (%)	最大值 Max (%)
18SGR	20.41±0.29	0.00	98.78	22.82±0.21	0.00	98.97
19SGR	20.65±0.28	0.00	96.04	18.28±0.22	0.00	99.60
18GI	18.88±0.15	1.70	76.32	15.51±0.12	0.33	56.10
19GI	44.67**±0.26	3.84	93.44	60.83**±0.24	1.74	96.32

性状 Traits	粒色 Grain colors	PHS-3D导入系PHS-3D introgression lines
性状 Traits	粒色 Grain colors	均值Mean (%)	标准差SD	标准误SE
18SGR	W	27.50*	0.3526	0.0633
18SGR	R	14.80*	0.2356	0.0373
19SGR	W	39.46**	0.3231	0.0580
19SGR	R	5.56**	0.1002	0.0158
18GI	W	12.20	0.1860	0.0334
18GI	R	8.67	0.1082	0.0171
19GI	W	52.38*	0.2803	0.0503
19GI	R	37.72*	0.2288	0.0362

反应型 Infection type	红粒系Red-grains lines		白粒系White-grains lines
反应型 Infection type	株系数No. of lines	占比Proportion (%)	株系数No. of lines	占比Proportion (%)
高抗HR	11	26.83	0	0.00
抗R	8	19.51	5	15.63
中抗MR	10	24.39	6	18.75
中感MS	5	12.20	8	25.00
感S	7	17.07	6	18.75
高感HS	0	0.00	7	21.87
合计Total	41	100.00	32	100.00

Utilization Efficiency of Improving the Resistance for Pre-Harvest Sprouting by Synthetic Hexaploid Wheat and Chinese Wheat Landrace

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性状 Traits	PHS-3D导入系PHS-3D introgression lines			PHS-A1导入系PHS-A1 introgression lines
性状 Traits	均值±标准差 Mean±SD	最小值 Min	最大值 Max	均值±标准差 Mean±SD	最小值 Min	最大值 Max
18PH	113.7±15.47	88.00	151.50	111.02±14.06	54.00	145.50
19PH	110.22±14.43	85.67	149.00	110.44±12.11	59.67	143.67
18TN	4.42±0.96	3.00	7.00	4.28±0.97	2.00	8.00
19TN	2.74±0.69	1.00	4.00	2.82±0.75	1.30	6.80
18GN	31.04±9.85**	13.00	62.33	37.17±9.34**	14.88	66.86
19GN	32.60±8.24**	14.90	57.10	39.67±8.05**	19.80	61.40
18TKW	45.97±10.49**	16.50	66.30	38.92±7.06**	16.50	56.70
19TKW	59.16±7.66**	38.32	76.80	47.66±8.48**	22.30	68.20

株系<BOLD>L</BOLD>ines	编号No. of lines	GN	TKW	PH	TN	GI (%)	SGR (%)
PHS-3D导入系 PHS-3D introgression lines	5201	44.5	47.28	103.33	4.3	22.08	8.21
	5203	47.1	47.43	100.33	4.0	30.59	19.46
	5207	40.0	52.48	107.67	2.5	23.34	13.27
	5212	48.9	53.31	101.67	2.0	28.21	10.33
	5213	38.3	54.31	95.67	2.3	26.01	9.67
	5497	38.7	52.84	105.33	2.9	10.73	2.80
	5505	34.0	62.38	96.67	3.7	15.84	11.08
PHS-A1导入系PHS-A1 introgression lines	5224	37.3	44.93	97.33	3.0	27.99	17.35
	5231	43.8	48.45	100.67	3.7	30.95	4.76
	5234	44.2	44.41	95.33	3.0	21.69	8.90
	5236	51.9	47.69	92.33	3.2	32.99	6.68
	5237	41.7	52.57	75.67	3.5	34.16	1.86
	5249	44.0	47.39	104.00	3.5	26.85	16.83
	5259	49.0	43.04	88.00	2.9	33.66	5.51
	5263	41.2	44.83	84.00	2.8	20.05	4.61
	5269	51.5	41.40	105.33	3.3	19.69	1.77
	5327	42.6	50.96	103.83	3.0	34.02	20.45
	5354	47.5	47.41	101.00	3.8	33.95	18.97
	5392	45.5	40.42	59.67	3.2	35.01	18.73
	5395	40.4	46.29	93.00	3.1	32.32	12.04
	5411	38.9	54.06	78.00	3.3	23.75	19.32
	5425	48.0	52.10	103.67	2.7	34.74	14.23
	5435	49.5	53.09	98.00	2.6	33.53	13.32

[1]	ZHANG YuZhou, WANG YiZhao, GAO RuXi, LIU YiFan. Research Progress on Root System Architecture and Drought Resistance in Wheat [J]. Scientia Agricultura Sinica, 2024, 57(9): 1633-1645.
[2]	ZHOU Quan, LU QiuMei, ZHAO ZhangChen, WU ChenRan, FU XiaoGe, ZHAO YuJiao, HAN Yong, LIN HuaiLong, CHEN WeiLin, MOU LiMing, LI XingMao, WANG ChangHai, HU YinGang, CHEN Liang. Identification of Drought Resistance of 244 Spring Wheat Varieties at Seedling Stage [J]. Scientia Agricultura Sinica, 2024, 57(9): 1646-1657.
[3]	ZHANG Ying, SHI TingRui, CAO Rui, PAN WenQiu, SONG WeiNing, WANG Li, NIE XiaoJun. Genome-Wide Association Study of Drought Tolerance at Seedling Stage in ICARDA-Introduced Wheat [J]. Scientia Agricultura Sinica, 2024, 57(9): 1658-1673.
[4]	YAN Wen, JIN XiuLiang, LI Long, XU ZiHan, SU Yue, ZHANG YueQiang, JING RuiLian, MAO XinGuo, SUN DaiZhen. Drought Resistance Evaluation of Synthetic Wheat at Grain Filling Using UAV-Based Multi-Source Imagery Data [J]. Scientia Agricultura Sinica, 2024, 57(9): 1674-1686.
[5]	ZANG ShaoLong, LIU LinRu, GAO YueZhi, WU Ke, HE Li, DUAN JianZhao, SONG Xiao, FENG Wei. Classification and Identification of Nitrogen Efficiency of Wheat Varieties Based on UAV Multi-Temporal Images [J]. Scientia Agricultura Sinica, 2024, 57(9): 1687-1708.
[6]	HAN XiaoJie, REN ZhiJie, LI ShuangJing, TIAN PeiPei, LU SuHao, MA Geng, WANG LiFang, MA DongYun, ZHAO YaNan, WANG ChenYang. Effects of Different Nitrogen Application Rates on Carbon and Nitrogen Content of Soil Aggregates and Wheat Yield [J]. Scientia Agricultura Sinica, 2024, 57(9): 1766-1778.
[7]	ZHAO BoHui, ZHANG YingQuan, JING DongLin, LIU BaoHua, CHENG YuanYuan, SU YuHuan, TANG Na, ZHANG Bo, GUO BoLi, WEI YiMin. A Study on the Quality Stability of Wheat Grains at Designated Locations Across Multiple Years [J]. Scientia Agricultura Sinica, 2024, 57(9): 1833-1844.
[8]	LI YongFei, LI ZhanKui, ZHANG ZhanSheng, CHEN YongWei, KANG JianHong, WU HongLiang. Effects of Postponing Nitrogen Fertilizer Application on Flag Leaf Physiological Characteristics and Yield of Spring Wheat Under High Temperature Stress [J]. Scientia Agricultura Sinica, 2024, 57(8): 1455-1468.
[9]	DONG HuiXue, CHEN Qian, GUO XiaoJiang, WANG JiRui. Research on the Mechanisms of Pre-Harvest Sprouting and Resistant Breeding in Wheat [J]. Scientia Agricultura Sinica, 2024, 57(7): 1237-1254.
[10]	LIANG WangZhuang, TANG YaNan, LIU JiaHui, GUO XiaoJiang, DONG HuiXue, QI PengFei, WANG JiRui. Effect of Flour and Cooking/Baking Qualities by Sprouted Wheat [J]. Scientia Agricultura Sinica, 2024, 57(7): 1267-1280.
[11]	GAO ChenKai, LIU ShuiMiao, LI YuMing, ZHAO ZhiHeng, SHAO Jing, YU HaoLin, WU PengNian, WANG YanLi, GUAN XiaoKang, WANG TongChao, WEN PengFei. The Related Driving Factors of Water Use Efficiency and Its Prediction Model Construction in Winter Wheat [J]. Scientia Agricultura Sinica, 2024, 57(7): 1281-1294.
[12]	DANG JianYou, JIANG WenChao, SUN Rui, SHANG BaoHua, PEI XueXia. Response of Wheat Grain Yield and Water Use Efficiency to Ploughing Time and Precipitation and Its Distribution in Dryland [J]. Scientia Agricultura Sinica, 2024, 57(6): 1049-1065.
[13]	ZHAO KaiNan, DING Hao, LIU AKang, JIANG ZongHao, CHEN GuangZhou, FENG Bo, WANG ZongShuai, LI HuaWei, SI JiSheng, ZHANG Bin, BI XiangJun, LI Yong, LI ShengDong, WANG FaHong. Nitrogen Fertilizer Reduction and Postponing for Improving Plant Photosynthetic Physiological Characteristics to Increase Wheat- Maize and Annual Yield and Economic Return [J]. Scientia Agricultura Sinica, 2024, 57(5): 868-884.
[14]	GAO ShangJie, LIU XingRen, LI YingChun, LIU XiaoWan. Effects of Biochar and Straw Return on Greenhouse Gas Emissions and Global Warming Potential in the Farmland [J]. Scientia Agricultura Sinica, 2024, 57(5): 935-949.
[15]	ZHU RuiMing, ZHAO RongQin, JIAO ShiXing, LI XiaoJian, XIAO LianGang, XIE ZhiXiang, YANG QingLin, WANG Shuai, ZHANG HuiFang. Spatial Distribution and Driving Factors of Winter Wheat Irrigation Carbon Emission Intensity at Township Level in Henan Province [J]. Scientia Agricultura Sinica, 2024, 57(5): 950-964.

性状 Traits	18SGR	19SGR	18GI	18GN	19GN	18TKW	19TKW	18PH	19PH	18TN
19SGR	0.476**
18GI	0.327**	0.244**
19GI	0.342**	0.257**	0.467**
18GN	0.253**	0.194**	-0.118*
19GN	0.122*	0.191**	-0.038	0.434**
18TKW	0.061	0.077	0.126*	-0.265**	-0.312**
19TKW	-0.142	-0.075	0.111	-0.302**	-0.202**	0.570**
18PH	-0.187**	-0.276**	-0.151	-0.135*	-0.210**	0.173**	0.093
19PH	-0.171**	-0.183**	-0.167**	-0.152**	-0.149**	0.132*	0.101	0.722**
18TN	-0.095	-0.065	-0.038	-0.189**	-0.185**	0.004	-0.041	0.220**	0.23**
19TN	-0.09	-0.031	-0.084	-0.213**	-0.049	0.088	0.039	0.024	0.161**	0.081