谷子苗期耐低氮相关性状的QTL分析

doi:10.3864/j.issn.0578-1752.2023.20.002

Abstract

Abstract:

【Objective】The analysis of quantitative trait loci (QTL) related to low nitrogen tolerance traits of millet (Setaria italica L.) laid a foundation for fine mapping, cloning and functional research of low nitrogen tolerance genes. At the same time, it also provided technical support for revealing the genetic mechanism of low nitrogen tolerance of millet and breeding low nitrogen tolerance varieties. 【Method】The recombinant inbred line (RIL) population consisting of 120 family lines was used as experimental materials, that was constructed from parents Yugu 28, a low nitrogen tolerant variety, and Qiyehuang, a low nitrogen sensitive variety. The RIL populations were treated with low nitrogen and normal nitrogen at seedling stage, and seven traits were analyzed of hydroponic for 21 days, which inculding seedling length, maximum root length, root dry weight, seedling dry weight, plant dry weight, relative chlorophyll content and plant nitrogen content. At the same time, we used composite interval mapping (CIM) to locate and analyze QTLs for traits related to low nitrogen tolerance, and predicted the candidate genes in the confidence intervals of QTL_S. 【Result】The traits associated with low nitrogen tolerance of RIL populations exhibited continuous distribution with apparent transgressive segregation both under low nitrogen and normal nitrogen levels, which conformed to the typical genetic characteristics of quantitative traits and were suitable for QTL genetic analysis. Correlation analysis showed that seeding length was positively correlated with maximum root length, root dry weight, seeding dry weight, plant dry weight and relative chlorophyll content, and maximum root length was negatively correlated with plant nitrogen content. A total of thirty-four QTLs related to seeding length, maximum root length, root dry weight, seeding dry weight, plant dry weight, relative chlorophyll content and plant nitrogen content were located under low nitrogen and normal nitrogen levels, which distributed on chromosomes from 1 to 9. They explained individually 5.15%-52.42% phenotypic variation. Ten QTLs were simultaneously detected under both two nitrogen levels, eleven and thirteen QTLs were only identified under single low nitrogen and normal nitrogen conditions, respectively. A total of fifteen QTLs were major QTL, and five major QTLs were repeatedly detected under both two nitrogen levels, which including qRDW3, qMRL1.1, qMRL1.2, qSL5 and qSPAD1. Five QTL overlaps were detected with gathering multiple QTLs under two nitrogen levels. Six candidate genes related to nitrogen metabolism were identified from the confidence interval of the five QTL overlaps, suggesting that genes related to nitrogen assimilation, absorption and utilization probably control the expression of these genes. 【Conclusion】Thirty-four QTLs were scattered on sixteen clusters of nine chromosomes. Based on gene annotation, a total of 6 candidate genes related to nitrogen metabolism were screened in foxtail millet, indicating the different traits involved in common genetic mechanisms, and the favorable alleles for low nitrogen tolerance can be polymerized by marker-assisted selection.

Key words: foxtail millet, recombinant inbred line (RIL), low nitrogen tolerance, QTL

QIN Na, FU SenJie, ZHU CanCan, DAI ShuTao, SONG YingHui, WEI Xin, WANG ChunYi, YE ZhenYan, LI JunXia. QTL Analysis for Seeding Traits Related to Low Nitrogen Tolerance in Foxtail Millet[J].Scientia Agricultura Sinica, 2023, 56(20): 3931-3945.

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Figures/Tables 7

Fig. 1

Table 1

Fig. 2

Table 2

Table 3

QTLs mapping for traits related to nitrogen tolerance"

性状 Traits	氮水平 N level	染色体 Chromosome	数量性状位点 QTL	标记区间 Marker interval	LOD值 LOD value	加性效应 Additive effect	贡献率 Proportion of the variance explained (%)
苗长 SL	低氮-N	1	qSL1	SICAAS1060—SICAAS1057	3.20	-0.88	5.15
		3	qSL3	SICAAS3043—SICAAS3048	4.70	-1.71	20.39
		5	qSL5	SICAAS5085—SICAAS5049	2.99	1.22	10.37
		6	qSL6	SICAAS6055—SICAAS6008	3.30	1.13	8.72
		7	qSL7	SICAAS7028—SICAAS7038	3.56	1.08	8.08
	正常氮+N	3	qSL3.1	SICAAS3022—SICAAS3029	3.59	1.11	7.14
		3	qSL3.2	SICAAS3029—SICAAS3034	3.37	1.15	7.51
		5	qSL5.1	SICAAS5085—SICAAS5049	3.27	1.16	7.92
		5	qSL5.2	SICAAS5055—SICAAS5083	3.49	-1.14	8.62
		5	qSL5.3	SICAAS5083—SICAAS5034	3.49	-1.02	6.82
		6	qSL6	SICAAS6019—SICAAS6005	2.99	-0.97	5.79
主根长 MRL	低氮-N	1	qMRL1.1	SICAAS1034—SICAAS1039	2.52	-2.82	27.31
		1	qMRL1.2	SICAAS1039—SICAAS1008	4.72	-2.23	13.76
		1	qMRL1.3	SICAAS1008—SICAAS1010	3.64	1.32	6.00
		3	qMRL3	SICAAS3043—SICAAS3048	3.18	1.40	6.78
		5	qMRL5.1	SICAAS5083—SICAAS5034	2.42	1.27	5.19
		5	qMRL5.2	SICAAS5034—SICAAS5035	4.31	1.88	11.07
		7	qMRL7	SICAAS7005—SICAAS7028	3.55	1.71	9.82
		9	qMRL9	SICAAS9033—SICAAS9130	3.60	-1.74	8.31
	正常氮+N	1	qMRL1.1	SICAAS1034—SICAAS1039	5.80	-2.44	28.50
		1	qMRL1.2	SICAAS1039—SICAAS1008	7.35	-2.48	26.56
		2	qMRL2	SICAAS2046—SICAAS2016	2.82	1.09	5.64
		6	qMRL6	SICAAS6005—SICAAS6058	2.28	-1.18	6.56
		8	qMRL8	SICAAS8001—SICAAS8007	3.39	-1.36	8.65
根干质量 RDW	低氮-N	3	qRDW3	SICAAS3043—SICAAS3048	7.51	-0.36	28.54
		6	qRDW6	SICAAS6055—SICAAS6008	2.23	0.19	6.86
		7	qRDW7	SICAAS7005—SICAAS7028	2.30	-0.22	9.06
	正常氮+N	1	qRDW1	SICAAS1057—SICAAS1052	2.95	-0.2	5.58
		3	qRDW3.1	SICAAS3069—SICAAS3022	3.19	-0.26	8.59
		3	qRDW3.2	SICAAS3022—SICAAS3029	4.32	-0.33	13.40
		3	qRDW3.3	SICAAS3043—SICAAS3048	4.13	-0.34	14.62
		4	qRDW4.1	SICAAS4060—SICAAS4033	2.20	0.26	9.04
		4	qRDW4.2	SICAAS4003—SICAAS4062	3.45	0.38	17.53
		9	qRDW9	SICAAS9024—SICAAS9027	2.02	0.28	7.89
苗干质量 SDW	低氮-N	1	qSDW1.1	SICAAS1060—SICAAS1057	2.76	-0.50	8.71
		1	qSDW1.2	SICAAS1057—SICAAS1052	2.25	-0.41	5.69
		3	qSDW3	SICAAS3043—SICAAS3048	5.43	0.78	23.00
	正常氮+N	5	qSDW5.1	SICAAS5083—SICAAS5034	3.17	0.78	10.71
		5	qSDW5.2	SICAAS5035—SICAAS5036	2.20	0.61	5.39
		6	qSDW6.1	SICAAS6055—SICAAS6008	2.60	-0.63	6.86
		6	qSDW6.2	SICAAS6008—SICAAS6081	2.81	-0.79	10.24
总干质量 PDW	低氮-N	1	qPDW1.1	SICAAS1060—SICAAS1057	3.64	-0.92	15.67
		1	qPDW1.2	SICAAS1057—SICAAS1052	2.28	-0.68	8.64
		2	qPDW2	SICAAS2010—SICAAS2068	2.78	0.97	17.74
		3	qPDW3	SICAAS3043—SICAAS3048	6.88	1.7	52.42
		5	qPDW5	SICAAS5036—SICAAS5037	3.64	0.90	15.01
		7	qPDW7	SICAAS7005—SICAAS7028	2.04	-0.59	6.28
	正常氮+N	3	qPDW3	SICAAS3029—SICAAS3034	2.26	1.20	10.25
叶绿素相对含量SPAD	低氮-N	1	qSPAD1	SICAAS1034—SICAAS1039	2.99	0.85	9.41
		2	qSPAD2	SICAAS2035—SICAAS2038	2.40	-0.86	9.77
		5	qSPAD5.1	SICAAS5034—SICAAS5035	3.22	1.05	13.28
		5	qSPAD5.2	SICAAS5035—SICAAS5036	2.94	0.84	8.27
		8	qSPAD8.1	SICAAS8007—SICAAS8014	2.15	0.98	12.44
		8	qSPAD8.2	SICAAS8014—SICAAS8019	2.41	1.02	12.64
	正常氮+N	1	qSPAD1	SICAAS1034—SICAAS1039	3.57	1.11	13.47
		9	qSPAD9.1	SICAAS9001—SICAAS9107	2.77	-0.73	5.67
		9	qSPAD9.2	SICAAS9006—SICAAS9013	2.42	-0.76	5.40
植株含氮量 PNC	低氮-N	1	qPNC1.1	SICAAS1060—SICAAS1057	2.87	0.12	8.77
		1	qPNC1.2	SICAAS1057—SICAAS1052	2.51	0.10	6.78
		2	qPNC2	SICAAS2046—SICAAS2016	2.90	0.09	6.04
		3	qPNC3	SICAAS3043—SICAAS3048	2.40	0.99	6.82
		5	qPNC5.1	SICAAS5083—SICAAS5034	4.44	0.17	16.92
		5	qPNC5.2	SICAAS5034—SICAAS5035	5.03	0.18	20.80
		6	qPNC6.1	SICAAS6019—SICAAS6005	2.50	-0.10	6.96
		6	qPNC6.2	SICAAS6055—SICAAS6008	2.79	-0.12	9.06
	正常氮+N	2	qPNC2	SICAAS2053—SICAAS2020	2.01	-0.16	8.84
	正常氮+N	8	qPNC8	SICAAS8014—SICAAS8019	2.82	0.18	9.62

Table 3

Fig. 3

Table 4

Annotation of candidate genes"

性状 Traits	数量性状位点 QTL	染色体 Chromosome	候选基因 Candidate genes	KO注释 KO annotation	GO注释 GO annotation	功能注释 Functional annotation
主根长 MRL	qMRL1.1	1	Seita.1G121100	K18482	GO:0004084	D-氨基酸转氨酶 D-amino-acid transaminase
主根长 MRL	qMRL3	3	Seita.3G051900		GO:0006808	氮利用调控蛋白P-Ⅱ Nitrogen regulatory protein P-Ⅱ
主根长 MRL	qMRL5.1	5	Seita.5G068000	K14638	GO:0015333	蛋白NRT1/PTR6.1家族 Protein NRT1/PTR FAMILY 6.1
主根长 MRL	qMRL5.2	5	Seita.5G400900	K14209	GO:0003333	氨基酸透性酶第8亚家族 Amino acid permease 8
叶绿素相对含量 SPAD	qSPAD1	1	Seita.1G121100	K18482	GO:0004084	D-氨基酸转氨酶 D-amino-acid transaminase
叶绿素相对含量 SPAD	qSPAD5.1	5	Seita.5G400900	K14209	GO:0003333	氨基酸透性酶第8亚家族 Amino acid permease 8
根干质量 RDW	qRDW3	3	Seita.3G051900		GO:0006808	氮利用调控蛋白P-Ⅱ Nitrogen regulatory protein P-Ⅱ
根干质量 RDW	qRDW6	6	Seita.6G078400	K19476	GO:0016021	赖氨酸组氨酸转运子1 Lysine histidine transporter 1
根干质量 RDW	qRDW9	9	Seita.9G477200	K14638	GO:0022857	蛋白NRT1/PTR8.3家族 Protein NRT1/PTR FAMILY 8.3
苗干质量 SDW	qSDW3	3	Seita.3G051900		GO:0006808	氮利用调控蛋白P-Ⅱ Nitrogen regulatory protein P-Ⅱ
苗干质量 SDW	qSDW5.1	5	Seita.5G068000	K14638	GO:0015333	蛋白NRT1/PTR6.1家族 Protein NRT1/PTR FAMILY 6.1
苗干质量 SDW	qSDW6.1	6	Seita.6G078400	K19476	GO:0016021	赖氨酸组氨酸转运子1 Lysine histidine transporter 1
总干质量 PDW	qPDW3	3	Seita.3G051900		GO:0006808	氮利用调控蛋白P-Ⅱ Nitrogen regulatory protein P-Ⅱ
植株含氮量 PNC	qPNC3	3	Seita.3G051900		GO:0006808	氮利用调控蛋白P-Ⅱ Nitrogen regulatory protein P-Ⅱ
植株含氮量 PNC	qPNC5.1	5	Seita.5G068000	K14638	GO:0015333	蛋白NRT1/PTR6.1家族 Protein NRT1/PTR FAMILY 6.1
植株含氮量 PNC	qPNC5.2	5	Seita.5G400900	K14209	GO:0003333	氨基酸透性酶第8亚家族 Amino acid permease 8
植株含氮量 PNC	qPNC6.2	6	Seita.6G078400	K19476	GO:0016021	赖氨酸组氨酸转运子1 Lysine histidine transporter 1
苗长 SL	qPH5.3	5	Seita.5G068000	K14638	GO:0015333	蛋白NRT1/PTR6.1家族 Protein NRT1/PTR FAMILY 6.1
苗长 SL	qPH6	6	Seita.6G078400	K19476	GO:0016021	赖氨酸组氨酸转运子1 Lysine histidine transporter 1

Table 4

References 45

[1]	LU H Y, ZHANG J P, LIU K B, WU N Q, LI Y M, ZHOU K S, YE M L, ZHANG T Y, ZHANG H J, YANG X Y, SHEN L C, XU D K, LI Q.Earliest domestication of common millet (Panicum miliaceum) in East Asia extended to 10,000 years ago. Proceedings of the National Academy of Sciences of the United States of America, 2009, 106(18): 7367-7372.
[2]	石全红, 王宏, 陈阜, 褚庆全. 中国中低产田时空分布特征及增产潜力分析. 中国农学通报, 2010, 26(19): 369-373.
	SHI Q H, WANG H, CHEN F, CHU Q Q. The spatial-temporal distribution characteristics and yield potential of medium-low yielded farmland in China. Chinese Agricultural Science Bulletin, 2010, 26(19): 369-373. (in Chinese) doi: 10.11924/j.issn.1000-6850.2010-1435
[3]	张敏. 谷子苗期响应低氮胁迫生理及转录组特征分析[D]. 太谷: 山西农业大学, 2021.
	ZHANG M. Physiological and transcriptome characteristics of millet in response to low nitrogen stress at seedling stage[D]. Taigu: Shanxi Agricultural University, 2021. (in Chinese)
[4]	刁现民. 禾谷类杂粮作物耐逆和栽培技术研究新进展. 中国农业科学, 2019, 52(22): 3943-3949. doi: 10.3864/j.issn.0578-1752.2019.22.001
	DIAO X M. Progresses in stress tolerance and field cultivation studies of orphan cereals in China. Scientia Agricultura Sinica, 2019, 52(22): 3943-3949. (in Chinese) doi: 10.3864/j.issn.0578-1752.2019.22.001
[5]	BORRELL A K, GARSIDE A L, FUKAI S, REID D J. Season, nitrogen rate, and plant type affect nitrogen uptake and nitrogen use efficiency in rice. Australian Journal of Agricultural Research, 1998, 49(5): 829-843. doi: 10.1071/A97057
[6]	BORRELL A, HAMMER G, OOSTEROM E. Stay-green: A consequence of the balance between supply and demand for nitrogen during grain filling? Annals of Applied Biology, 2001, 138(1): 91-95. doi: 10.1111/aab.2001.138.issue-1
[7]	LIAN X M, XING Y Z, YAN H, XU C G, LI X H, ZHANG Q F. QTLs for low nitrogen tolerance at seedling stage identified using a recombinant inbred line population derived from an elite rice hybrid. Theoretical and Applied Genetics, 2005, 112(1): 85-96. pmid: 16189659
[8]	MATSON P, LOHSE K A, HALL S J. The globalization of nitrogen deposition: Consequences for terrestrial ecosystems. Ambio, 2002, 31(2): 113-119. pmid: 12077999
[9]	ROBERTSON G P, VITOUSEK P M. Nitrogen in agriculture: balancing the cost of an essential resource. Annual Review of Environment and Resources, 2009, 34: 97-125. doi: 10.1146/energy.2009.34.issue-1
[10]	GUO J H, LIU X J, ZHANG Y, SHEN J L, HAN W X, ZHANG W F, CHRISTIE P, GOULDING K W T, VITOUSEK P M, ZHANG F S. Significant acidification in major Chinese croplands. Science, 2010, 327(5968): 1008-1010. doi: 10.1126/science.1182570 pmid: 20150447
[11]	贾冠清, 刁现民. 谷子(Setaria italica (L.) P. Beauv.)作为功能基因组研究模式植物的发展现状及趋势. 生命科学, 2017, 29(3): 292-301.
	JIA G Q, DIAO X M. Current status and perspectives of researches on foxtail millet (Setaria italica (L.) P. Beauv.): A potential model of plant functional genomics studies. Chinese Bulletin of Life Sciences, 2017, 29(3): 292-301. (in Chinese)
[12]	贾冠清, 刁现民. 中国谷子种业创新现状与未来展望. 中国农业科学, 2022, 55(4): 653-665. doi: 10.3864/j.issn.0578-1752.2022.04.003
	JIA G Q, DIAO X M. Current status and perspectives of innovation studies related to foxtail millet seed industry in China. Scientia Agricultura Sinica, 2022, 55(4): 653-665. (in Chinese) doi: 10.3864/j.issn.0578-1752.2022.04.003
[13]	ZHANG J Y, LIU Y X, ZHANG N, HU B, JIN T, XU H R, QIN Y, YAN P X, ZHANG X N, GUO X X, HUI J, CAO S Y, WANG X, WANG C, WANG H, QU B Y, FAN G Y, YUAN L X, GARRIDO-OTER R, CHU C C, BAI Y. NRT1.1B is associated with root microbiota composition and nitrogen use in field-grown rice. Nature Biotechnology, 2019, 37(6): 676-684. doi: 10.1038/s41587-019-0104-4
[14]	HU B, JIANG Z M, WANG W, QIU Y H, ZHANG Z H, LIU Y Q, LI A F, GAO X K, LIU L C, QIAN Y W, HUANG X H, YU F F, KANG S, WANG Y Q, XIE J P, CAO S Y, ZHANG L H, WANG Y C, XIE Q, KOPRIVA S, CHU C C. Nitrate-NRT1.1B-SPX4 cascade integrates nitrogen and phosphorus signalling networks in plants. Nature Plants, 2019, 5(4): 401-413. doi: 10.1038/s41477-019-0384-1
[15]	LIU Y Q, WANG H R, JIANG Z M, WANG W, XU R N, WANG Q H, ZHANG Z H, LI A F, LIANG Y, OU S J, LIU X J, CAO S Y, TONG H N, WANG Y H, ZHOU F, LIAO H, HU B, CHU C C. Genomic basis of geographical adaptation to soil nitrogen in rice. Nature, 2021, 590(7847): 600-605. doi: 10.1038/s41586-020-03091-w
[16]	SINGH K, BATRA R, SHARMA S, SARIPALLI G, GAUTAM T, SINGH R, PAL S, MALIK P, KUMAR M, JAN I, SINGH S, KUMAR D, PUNDIR S, CHATURVEDI D, VERMA A, RANI A, KUMAR A, SHARMA H, CHAUDHARY J, KUMAR K, KUMAR S, SINGH V K, SINGH V P, KUMAR S, KUMAR R, GAURAV S S, SHARMA S, SHARMA P K, BALYAN H S, GUPTA P K.WheatQTLdb: A QTL database for wheat. Molecular Genetics and Genomics, 2021, 296(5): 1051-1056.
[17]	OSPINA NIETO C A, LAMMERTS VAN BUEREN E T, ALLEFS S, VOS P G, VAN DER LINDEN G, MALIEPAARD C A, STRUIK P C. Association mapping of physiological and morphological traits related to crop development under contrasting nitrogen inputs in a diverse set of potato cultivars. Plants, 2021, 10(8): 1727-1754. doi: 10.3390/plants10081727
[18]	HE X Y, SKINNES H, OLIVER R E, JACKSON E W, BJØRNSTAD Å. Linkage mapping and identification of QTL affecting deoxynivalenol (DON) content (Fusarium resistance) in oats (Avena sativa L.). Theoretical and Applied Genetics, 2013, 126(10): 2655-2670. doi: 10.1007/s00122-013-2163-0
[19]	HIREL B, BERTIN P, QUILLERÉ I, BOURDONCLE W, ATTAGNANT C, DELLAY C, GOUY A, CADIOU S, RETAILLIAU C, FALQUE M, GALLAIS A. Towards a better understanding of the genetic and physiological basis for nitrogen use efficiency in maize. Plant Physiology, 2001, 125(3): 1258-1270. doi: 10.1104/pp.125.3.1258 pmid: 11244107
[20]	吴婷, 李霞, 黄得润, 黄凤林, 肖宇龙, 胡标林. 应用东乡野生稻回交重组自交系分析水稻耐低氮产量相关性状QTL. 中国水稻科学, 2020, 34(6): 499-511. doi: 10.16819/j.1001-7216.2020.0408
	WU T, LI X, HUANG D R, HUANG F L, XIAO Y L, HU B L. QTL analysis for yield traits related to low nitrogen tolerance using backcrossing recombinant inbred lines derived from Dongxiang wild rice (Oryza rufipogon griff.). Chinese Journal of Rice Science, 2020, 34(6): 499-511. (in Chinese)
[21]	LIMAMI A M, ROUILLON C, GLEVAREC G, GALLAIS A, HIREL B. Genetic and physiological analysis of germination efficiency in maize in relation to nitrogen metabolism reveals the importance of cytosolic glutamine synthetase. Plant Physiology, 2002, 130(4): 1860-1870. doi: 10.1104/pp.009647 pmid: 12481069
[22]	COQUE M, GALLAIS A. Genomic regions involved in response to grain yield selection at high and low nitrogen fertilization in maize. Theoretical and Applied Genetics, 2006, 112(7): 1205-1220. pmid: 16552555
[23]	GALLAIS A, HIREL B. An approach to the genetics of nitrogen use efficiency in maize. Journal of Experimental Botany, 2004, 55(396): 295-306. doi: 10.1093/jxb/erh006 pmid: 14739258
[24]	LOUDET O, CHAILLOU S, MERIGOUT P, TALBOTEC J, DANIEL-VEDELE F. Quantitative trait loci analysis of nitrogen use efficiency in Arabdopsis. Plant Physiology, 2003, 131(1): 345-358. doi: 10.1104/pp.102.010785
[25]	AN D G, SU J Y, LIU Q Y, ZHU Y G, TONG Y P, LI J M, JING R L, LI B, LI Z S. Mapping QTLs for nitrogen uptake in relation to the early growth of wheat (Triticum aestivum L.). Plant Soil, 2006, 284(1): 73-84. doi: 10.1007/s11104-006-0030-3
[26]	李微微. 谷子自噬相关基因SiATG8a调控植物低氮胁迫响应的功能分析[D]. 哈尔滨: 哈尔滨师范大学, 2017.
	LI W W. Functional analysis of foxtail millet autophagy associated gene SiATG8a in regulated plant response to low nitrogen stress[D]. Harbin: Harbin Normal University, 2017. (in Chinese)
[27]	YOSHIMOTO K, TAKANO Y, SAKAI Y. Autophagy in plants and phytopathogens. FEBS Letters, 2010, 584(7): 1350-1358. doi: 10.1016/j.febslet.2010.01.007 pmid: 20079356
[28]	TIAN J G, WANG C L, XIA J L, WU L S, XU G H, WU W H, LI D, QIN W C, HAN X, CHEN Q Y, JIN W W, TIAN F. Teosinte ligule allele narrows plant architecture and enhances high-density maize yields. Science, 2019, 365(6454): 658-664. doi: 10.1126/science.aax5482 pmid: 31416957
[29]	SAKAMOTO T, MORINAKA Y, OHNISHI T, SUNOHARA H, FUJIOKA S, UEGUCHI-TANAKA M, MIZUTANI M, SAKATA K, TAKATSUTO S, YOSHIDA S, TANAKA H, KITANO H, MATSUOKA M. Erect leaves caused by brassinosteroid deficiency increase biomass production and grain yield in rice. Nature Biotechnology, 2006, 24(1): 105-109. doi: 10.1038/nbt1173 pmid: 16369540
[30]	JIA G Q, HUANG X H, ZHI H, ZHAO Y, ZHAO Q, LI W J, CHAI Y, YANG L F, LIU K Y, LU H Y, ZHU C R, LU Y Q, ZHOU C C, FAN D L, WENG Q J, GUO Y L, HUANG T, ZHANG L, LU T T, FENG Q, HAO H F, LIU H K, LU P, ZHANG N, LI Y H, GUO E H, WANG S J, WANG S Y, LIU J R, ZHANG W F, CHEN G Q, ZHANG B J, LI W, WANG Y F, LI H Q, ZHAO B H, LI J Y, DIAO X M, HAN B. A haplotype map of genomic variations and genome-wide association studies of agronomic traits in foxtail millet (Setaria italica). Nature Genetics, 2013, 45(8): 957-961. doi: 10.1038/ng.2673
[31]	ZHANG G Y, LIU X, QUAN Z W, CHENG S F, XU X, PAN S K, XIE M, ZENG P, YUE Z, WANG W L, TAO Y, BIAN C, HAN C L, XIA Q J, PENG X H, CAO R, YANG X H, ZHAN D L, HU J C, ZHANG Y X, LI H N, LI H, LI N, WANG J Y, WANG C C, WANG R Y, GUO T, CAI Y J, LIU C Z, XIANG H T, SHI Q X, HUANG P, CHEN Q C, LI Y R, WANG J, ZHAO Z H, WANG J. Genome sequence of foxtail millet (Setaria italica) provides insights into grass evolution and biofuel potential. Nature Biotechnology, 2012, 30(6): 549-554. doi: 10.1038/nbt.2195 pmid: 22580950
[32]	中华人民共和国农业部. 谷类、豆类粗蛋白质含量的测定杜马斯燃烧法:NY/T 2007- 2011. 北京: 中国农业出版社, 2011.
	Ministry of Agriculture of the People's Republic of China. Determination of the crude protein content in cereals and pules seeds by combustion according to the Dumas principle:NY/T 2007- 2011. Beijing: China Agriculture Press, 2011. (in Chinese)
[33]	MCCOUCH S R, YONG G C, YANO M, KINOSHITA T CHO Y G, YANO M.Report on QTL nomenclature. Rice Genetics Newsletter, 1997, 14(1):11-13.
[34]	秦娜, 朱灿灿, 代书桃, 宋迎辉, 王春义, 李君霞, 平西栓. 施氮时期对谷子产量、品质和氮素利用率的影响. 中国农业大学学报, 2023, 28(1): 67-78.
	QIN N, ZHU C C, DAI S T, SONG Y H, WANG C Y, LI J X, PING X S. Effects of nitrogen fertilizer application stage on the grain yield and quality and nitrogen use efficiency of foxtail millet. Journal of China Agricultural University, 2023, 28(1): 67-78. (in Chinese)
[35]	秦娜, 马春业, 朱灿灿, 代书桃, 宋迎辉, 王春义, 芮战许, 李君霞. 谷子氮高效基因型筛选及相关特性分析. 河南农业科学, 2019, 48(5): 22-29.
	QIN N, MA C Y, ZHU C C, DAI S T, SONG Y H, WANG C Y, RUI Z X, LI J X. Screening of foxtail millet genotype with high nitrogen use efficiency and analysis of related characters. Journal of Henan Agricultural Sciences, 2019, 48(5): 22-29. (in Chinese)
[36]	AHMAD N, IBRAHIM S, TIAN Z, KUANG L Q, WANG X F, WANG H Z, DUN X L. Quantitative trait loci mapping reveals important genomic regions controlling root architecture and shoot biomass under nitrogen, phosphorus, and potassium stress in rapeseed (Brassica napus L.). Frontiers in Plant Science, 2022, 13: 994666. doi: 10.3389/fpls.2022.994666
[37]	赵春芳, 赵凌, 张亚东, 陈涛, 赵庆勇, 朱镇, 周丽慧, 姚姝, 王才林. 水稻苗期耐低氮相关性状的QTL定位. 华北农学报, 2015, 30(6): 1-7. doi: 10.7668/hbnxb.2015.06.001
	ZHAO C F, ZHAO L, ZHANG Y D, CHEN T, ZHAO Q Y, ZHU Z, ZHOU L H, YAO S, WANG C L. QTL mapping for seedling traits related to low nitrogen tolerance in rice. Acta Agriculturae Boreali-Sinica, 2015, 30(6): 1-7. (in Chinese)
[38]	贾佩陇, 李彪, 黎明辉, 刘剑镔, 李容柏, 罗继景. 基于水稻染色体片段代换系的苗期耐低氮QTL分析. 华南农业大学学报, 2019, 40(4): 16-24.
	JIA P L, LI B, LI M H, LIU J B, LI R B, LUO J J. QTL analysis of low nitrogen tolerance in rice seedlings based on chromosome segment substitution lines. Journal of South China Agricultural University, 2019, 40(4): 16-24. (in Chinese)
[39]	郭淑青, 宋慧, 柴少华, 郭岩, 石兴, 杜丽红, 邢璐, 解慧芳, 张扬, 李龙, 冯佰利, 刘金荣, 杨璞. 谷子生育期及穗相关性状的QTL定位. 中国农业科学, 2022, 55(15): 2883-2898. doi: 10.3864/j.issn.0578-1752.2022.15.002
	GUO S Q, SONG H, CHAI S H, GUO Y, SHI X, DU L H, XING L, XIE H F, ZHANG Y, LI L, FENG B L, LIU J R, YANG P. QTL Analysis for growth period and panicle-related traits in foxtail millet. Scientia Agricultura Sinica, 2022, 55(15): 2883-2898. (in Chinese) doi: 10.3864/j.issn.0578-1752.2022.15.002
[40]	DE ZAMAROCZY M, DELORME F, ELMERICH C. Characterization of three different nitrogen-regulated promoter regions for the expression of glnB and glnA in Azospirillum brasilense. Molecular and General Genetics, 1990, 224(3): 421-430. doi: 10.1007/BF00262437
[41]	张瑞娟, 屈聪玲, 贺榆婷, 杨致荣, 王兴春. 谷子硝酸盐转运蛋白NRT1家族的鉴定及表达分析. 山西农业大学学报(自然科学版), 2018, 38(4): 37-43, 76.
	ZHANG R J, QU C L, HE Y T, YANG Z R, WANG X C.Identification and gene expression analysis of the nitrate transporter NRT1 gene family in foxtail millet. Journal of Shanxi Agricultural University (Natural Science Edition), 2018, 38(4): 37-43, 76. (in Chinese)
[42]	LIU K H, HUANG C Y, TSAY Y F. CHL1 is a dual-affinity nitrate transporter of Arabidopsis involved in multiple phases of nitrate uptake. The Plant Cell, 1999, 11(5): 865-874. doi: 10.1105/tpc.11.5.865
[43]	YANG X H, NONG B X, CHEN C, WANG J R, XIA X Z, ZHANG Z Q, WEI Y, ZENG Y, FENG R, WU Y Y, GUO H, YAN H F, LIANG Y T, LIANG S H, YAN Y, LI D T, DENG G F. OsNPF3.1, a member of the NRT1/PTR family, increases nitrogen use efficiency and biomass production in rice. The Crop Journal, 2023, 11(1): 108-118. doi: 10.1016/j.cj.2022.07.001
[44]	SHI C L, DONG N Q, GUO T, YE W W, SHAN J X, LIN H X. A quantitative trait locus GW6 controls rice grain size and yield through the gibberellin pathway. The Plant Journal, 2020, 103(3): 1174-1188. doi: 10.1111/tpj.v103.3
[45]	LEE Y H, TEGEDER M. Selective expression of a novel high-affinity transport system for acidic and neutral amino acids in the tapetum cells of Arabidopsis flowers. The Plant Journal, 2004, 40(1): 60-74. doi: 10.1111/tpj.2004.40.issue-1

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性状 Trait	氮水平 N level	亲本Parents		t测验 t test	RIL群体RIL population
性状 Trait	氮水平 N level	豫谷28 Yugu 28	七叶黄Qiyehuang	t测验 t test	均值Mean	变异范围Range
苗长 SL (cm)	+N	29.10±1.60	24.40±0.90	*	27.90±1.10	15.20—39.50
苗长 SL (cm)	-N	21.10±0.90	17.50±0.60	*	20.40±1.01	8.00—31.00
差异Difference (%)		-27.50*	-28.30*		-26.88*
主根长 MRL (cm)	+N	16.10±1.10	11.20±0.70	ns	17.20±0.40	6.50—35.50
主根长 MRL (cm)	-N	18.10±0.30	14.20±0.40	*	22.50±1.10	8.20—40.50
差异Difference (%)		12.50*	17.86*		30.81**
根干质量 RDW (g)	+N	0.040±0.0010	0.030±0.0010	*	0.030±0.010	0.010—0.050
根干质量 RDW (g)	-N	0.030±0.0020	0.020±0.0010	ns	0.020±0.0020	0.0010—0.040
差异Difference (%)		-25.00*	-33.30 ns		-33.33**
苗干质量 SDW (g)	+N	0.10 ±0.020	0.060±0.0010	*	0.080±0.0020	0.040—0.160
苗干质量 SDW (g)	-N	0.060±0.0020	0.010±0.0010	*	0.040±0.0010	0.010—0.10
差异Difference (%)		-40.00**	-83.30**		-50.00**
总干质量 PDW (g)	+N	0.15±0.030	0.080±0.0020	*	0.11±0.010	0.040—0.22
总干质量 PDW (g)	-N	0.080 ±0.0020	0.040±0.0010	*	0.060±0.0020	0.010—0.12
差异Difference (%)		-46.67*	-50.00*		-45.45**
叶绿素相对含量 SPAD	+N	31.70±1.90	25.20±2.01	*	31.30±2.20	23.05—36.57
叶绿素相对含量 SPAD	-N	28.50±2.10	21.20±0.80	*	22.50±1.90	16.75—28.13
差异Difference (%)		-10.10 ns	-15.87*		-28.11*
植株氮含量 PNC (mg·100 mg^-1)	+N	2.60±0.10	2.20±0.090	ns	2.70±0.10	1.89—4.82
植株氮含量 PNC (mg·100 mg^-1)	-N	2.20±0.080	1.60±0.10	*	1.90±0.30	1.21—2.97
差异Difference (%)		-15.38 ns	-27.27*		-29.63*

氮水平 N level	性状 Trait	苗长 SL	主根长 RL	根干质量 RDW	苗干质量 SDW	总干质量 PDW	叶绿素相对含量 SPAD	植株含氮量 PNC
低氮-N	苗长SL		0.653**	0.584*	0.814**	0.798**	0.692**	0.142
	主根长MRL			0.701**	0.568*	0.684**	0.583*	-0.223*
	根干质量RDW				0.646**	0.708**	0.561*	0.285*
	苗干质量SDW					0.825**	0.675**	0.305*
	总干质量PDW						0.794**	0.313*
	叶绿素相对含量SPAD							0.724**
正常氮+N	苗长SL	0.795**	0.817**	0.792**	0.884**	0.830**	0.751**	0.256*
	主根长MRL		0.806**	0.726**	0.806**	0.784**	0.696**	-0.348**
	根干质量RDW			0.652**	0.734**	0.805**	0.265*	0.217*
	苗干质量SDW				0.633**	0.794**	0.852**	0.324*
	总干质量PDW					0.749**	0.631**	0.352*
	叶绿素相对含量SPAD						0.760**	0.554*
	植株含氮量PNC							0.670**

QTL Analysis for Seeding Traits Related to Low Nitrogen Tolerance in Foxtail Millet

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