川麦42和川农16抗穗发芽QTL定位及聚合效应分析

doi:10.3864/j.issn.0578-1752.2020.17.001

Abstract

Abstract:

【Objective】Pre-harvest sprouting (PHS) is one of the serious problems for wheat production, which significantly reduces grain yield and end-use quality, especially in rainy or high humidity regions. The objective of this study is to identify and aggregate quantitative trait loci (QTLs) for traits associated with PHS resistance, which will provide a theoretical basis for improving PHS resistance in Sichuan wheat cultivars.【Method】A recombinant inbred line (RIL) population derived from Chuanmai 42 and Chuannong 16 was used to detect QTL and assess the new germplasm resources for PHS resistance. 127 lines in RIL population were analyzed by phenotypic and genetic identification for PHS-related traits. Seed germination index (GI), seed germination rate (GR) and seed germination rate of in each spike (SGR) in two different environments were used to evaluate PHS resistance. All QTLs for PHS resistance were mapped by an available high-density single nucleotide polymorphism (SNP, 90K). The pyramiding of the resistant QTL was also analyzed according to the genotype of every line in RILs. 【Result】There were significant difference in GI, GR and SGR between two parents. PHS resistance of Chuannong 16 was superior than that of Chuanmai 42. A total of 11 QTLs for PHS were detected on chromosomes 2B, 2D, 3A, 3D, 4A, 5A, 5B and 6B. QSgr.saas-5B was significantly associated with PHS resistance in single environment and explained 29.03% phenotypic variation. QSgr.saas-2D, QSgr.saas-3A, QGi.saas-5A and QGr.saas-5A could express stably in two environments, and the alleles of enhancing PHS resistance were from Chuannong 16. The results of genotype analysis showed that the number of resistant QTL in different lines ranged from one to nine. Six excellent lines in RILs with high resistance carried seven or eight additive QTLs for PHS resistance. These additive QTLs included the minor QTLs on chromosome 4A from Chuanmai 42 and the major QTLs on chromosomes 2D and 5B from Chuannong 16. No. 104 and No. 125 in RIL population were released in China or Sichuan province because of its high yield and PHS resistance, and were named Chuanmai 104 and Chuanmai 64, respectively. Chuanmai 104 showed high yield and good resistance for stripe rust, powdery mildew and PHS in the Sichuan provincial trials and the national trails for Upper and Middle Yangtze River region in 2010 and 2012. The QTL analysis for PHS resistance revealed that Chuanmai 104 carried seven QTLs, including four QTLs on chromosomes 2B, 2D and 5B from Chuannong 16 and three QTLs on chromosomes 4A and 6B from another parent Chuanmai 42. The pyramiding of these additive QTL alleles from each parent led directly to the character of high PHS resistance in Chuanmai 104. In recent years, Chuanmai 104 was widely used to wheat improvement in Southwest China, and 18 wheat varieties (lines) have been bred. 【Conclusion】Eleven QTLs for PHS resistance, including three QTLs from Chuanmai 42 and eight QTLs from chuannong16, were detected in this study. Four to nine resistant QTLs were generally carried by the resistant lines in RIL population. Two pyramiding lines (Chuanmai 104 and Chuanmai 64) with high PHS resistance carried seven resistant QTLs.

Key words: wheat, pre-harvest sprouting (PHS), QTL mapping, QTL pyramiding, pyramiding effect

WANG Qin,LIU ZeHou,WAN HongShen,WEI HuiTing,LONG Hai,LI Tao,DENG GuangBing,LI Jun,YANG WuYun. Identification and Pyramiding of QTLs for Traits Associated with Pre-Harvest Sprouting Resistance in Two Wheat Cultivars Chuanmai 42 and Chuannong 16[J].Scientia Agricultura Sinica, 2020, 53(17): 3421-3431.

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References 49

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性状 Trait	环境 Environment	亲本Parents		RIL群体 RIL population
性状 Trait	环境 Environment	川麦42 Chuanmai 42	川农16 Chuannong 16	均值 Mean	标准差 SD	最小值 Min	最大值 Max	峰度 Kurtosis	偏度 Skewness	遗传力Heritability
GI	2016GH	0.74	0.11**	0.57	0.10	0.06	0.98	-0.02	-0.27	0.57
GI	2018CD	0.76	0.17**	0.59	0.20	0.05	0.87	-0.54	-0.66	0.49
GR	2016GH	0.83	0.24**	0.82	0.09	0.07	1.00	-0.09	-0.57	0.82
GR	2018CD	0.86	0.21**	0.79	0.23	0.06	1.00	0.68	-1.21	0.69
SGR	2017CD	0.62	0.15**	0.52	0.17	0.02	0.90	-0.35	-0.28	0.61
SGR	2018CD	0.51	0.09**	0.42	0.21	0.03	0.99	-0.70	0.12	0.51

性状Trait	GI-2018CD	GR-2016GH	GR-2018CD	SGR-2017CD	SGR-2018CD
GI-2016GH	0.091	0.892**	0.037	0.177*	0.266**
GI-2018CD		0.169	0.966**	0.239**	0.289**
GR-2016GH			0.114	0.247**	0.258**
GR-2018CD				0.201*	0.224*
SGR-2017CD					0.440**

性状 Trait	QTL	环境 Environment	标记区间 Marker interval	LOD	贡献率 PVE (%)	加性效应 Additive effect^a
GI	QGi.saas-2B	2016GH	IWB217—IWB12070	2.64	9.47	0.05
	QGi.saas-4A	2016GH	IWB32870—IWB43187	2.50	4.28	-0.03
	QGi.saas-5A	2016GH	IWB22536—IWB43493	2.51	5.88	0.09
	QGi.saas-5A	2018CD	IWB6853—IWB25919	3.98	14.72	0.13
GR	QGr.saas-2B	2016GH	IWB217—IWB12070	2.59	5.02	0.03
	QGr.saas-4A	2016GH	IWB32870—IWB43187	2.51	4.79	-0.04
	QGr.saas-5A	2016GH	IWB22536—IWB43493	2.65	4.87	0.06
	QGr.saas-5A	2018CD	IWB6853—IWB25919	5.10	18.80	0.15
	QGr.saas-6B	2016GR	IWB8673—IWB22066	11.02	14.26	-0.05
SGR	QSgr.saas-2D	2017CD	IWB62848—IWB56618	3.29	8.06	0.05
	QSgr.saas-2D	2018CD	IWB9680—IWB62848	2.54	5.73	0.05
	QSgr.saas-3A	2017CD	IWB10578—IWB30094	2.57	4.72	0.04
	QSgr.saas-3A	2018CD	IWB10578—IWB30094	3.60	10.86	0.07
	QSgr.saas-3D	2017CD	IWB30686—IWB38826	4.69	11.96	0.06
	QSgr.saas-5B	2018CD	IWB60217—IWB52109	12.36	29.03	0.16

株系 Lines	GI (%)		GR (%)		SGR (%)		QTL
株系 Lines	2016GH	2018CD	2016GH	2018CD	2017CD	2018CD	QTL
12	0.14	0.12	0.12	0.15	0.07	0.05	QGi.saas-2B, QGr.saas-2B, QSgr.saas-2D, QSgr.saas-3A, QSgr.saas-3D, QGi.saas-4A, QGr.saas-4A, QSgr.saas-5B
74	0.13	0.10	0.13	0.15	0.02	0.03	QGi.saas-2B, QGr.saas-2B, QSgr.saas-2D, QSgr.saas-3D, QGi.saas-4A, QGr.saas-4A, QSgr.saas-5B, QGr.saas-6B
82	0.09	0.05	0.12	0.13	0.14	0.07	QGi.saas-2B, QGr.saas-2B, QSgr.saas-2D, QSgr.saas-3A, QGi.saas-4A, QGr.saas-4A, QSgr.saas-5B
104	0.07	0.12	0.10	0.14	0.13	0.11	QGi.saas-2B, QGr.saas-2B, QSgr.saas-2D, QGi.saas-4A, QGr.saas-4A, QSgr.saas-5B, QGr.saas-6B
112	0.06	0.09	0.13	0.11	0.14	0.15	QSgr.saas-2D, QSgr.saas-3D, QGi.saas-4A, QGr.saas-4A, QSgr.saas-5B, QGi.saas-5A, QGr.saas-5A
125	0.08	0.10	0.07	0.11	0.09	0.07	QSgr.saas-2D, QSgr.saas-3A, QSgr.saas-3D, QGi.saas-4A, QGr.saas-4A, QSgr.saas-5B, QGr.saas-6B

Identification and Pyramiding of QTLs for Traits Associated with Pre-Harvest Sprouting Resistance in Two Wheat Cultivars Chuanmai 42 and Chuannong 16

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[2]	YAN YanGe, ZHANG ShuiQin, LI YanTing, ZHAO BingQiang, YUAN Liang. Effects of Dextran Modified Urea on Winter Wheat Yield and Fate of Nitrogen Fertilizer [J]. Scientia Agricultura Sinica, 2023, 56(2): 287-299.
[3]	XU JiuKai, YUAN Liang, WEN YanChen, ZHANG ShuiQin, LI YanTing, LI HaiYan, ZHAO BingQiang. Nitrogen Fertilizer Replacement Value of Livestock Manure in the Winter Wheat Growing Season [J]. Scientia Agricultura Sinica, 2023, 56(2): 300-313.
[4]	ZHAO HaiXia,XIAO Xin,DONG QiXin,WU HuaLa,LI ChengLei,WU Qi. Optimization of Callus Genetic Transformation System and Its Application in FtCHS1 Overexpression in Tartary Buckwheat [J]. Scientia Agricultura Sinica, 2022, 55(9): 1723-1734.
[5]	WANG HaoLin,MA Yue,LI YongHua,LI Chao,ZHAO MingQin,YUAN AiJing,QIU WeiHong,HE Gang,SHI Mei,WANG ZhaoHui. Optimal Management of Phosphorus Fertilization Based on the Yield and Grain Manganese Concentration of Wheat [J]. Scientia Agricultura Sinica, 2022, 55(9): 1800-1810.
[6]	TANG HuaPing,CHEN HuangXin,LI Cong,GOU LuLu,TAN Cui,MU Yang,TANG LiWei,LAN XiuJin,WEI YuMing,MA Jian. Unconditional and Conditional QTL Analysis of Wheat Spike Length in Common Wheat Based on 55K SNP Array [J]. Scientia Agricultura Sinica, 2022, 55(8): 1492-1502.
[7]	MA XiaoYan,YANG Yu,HUANG DongLin,WANG ZhaoHui,GAO YaJun,LI YongGang,LÜ Hui. Annual Nutrients Balance and Economic Return Analysis of Wheat with Fertilizers Reduction and Different Rotations [J]. Scientia Agricultura Sinica, 2022, 55(8): 1589-1603.
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[9]	WANG YangYang,LIU WanDai,HE Li,REN DeChao,DUAN JianZhao,HU Xin,GUO TianCai,WANG YongHua,FENG Wei. Evaluation of Low Temperature Freezing Injury in Winter Wheat and Difference Analysis of Water Effect Based on Multivariate Statistical Analysis [J]. Scientia Agricultura Sinica, 2022, 55(7): 1301-1318.
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[11]	ZHI Lei,ZHE Li,SUN NanNan,YANG Yang,Dauren Serikbay,JIA HanZhong,HU YinGang,CHEN Liang. Genome-Wide Association Analysis of Lead Tolerance in Wheat at Seedling Stage [J]. Scientia Agricultura Sinica, 2022, 55(6): 1064-1081.
[12]	QIN YuQing,CHENG HongBo,CHAI YuWei,MA JianTao,LI Rui,LI YaWei,CHANG Lei,CHAI ShouXi. Increasing Effects of Wheat Yield Under Mulching Cultivation in Northern of China: A Meta-Analysis [J]. Scientia Agricultura Sinica, 2022, 55(6): 1095-1109.
[13]	CAI WeiDi,ZHANG Yu,LIU HaiYan,ZHENG HengBiao,CHENG Tao,TIAN YongChao,ZHU Yan,CAO WeiXing,YAO Xia. Early Detection on Wheat Canopy Powdery Mildew with Hyperspectral Imaging [J]. Scientia Agricultura Sinica, 2022, 55(6): 1110-1126.
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[15]	MA HongXiang, WANG YongGang, GAO YuJiao, HE Yi, JIANG Peng, WU Lei, ZHANG Xu. Review and Prospect on the Breeding for the Resistance to Fusarium Head Blight in Wheat [J]. Scientia Agricultura Sinica, 2022, 55(5): 837-855.