谷子生育期及穗相关性状的QTL定位

doi:10.3864/j.issn.0578-1752.2022.15.002

Abstract

Abstract:

【Objective】Growth period and panicle-related traits are the main factors affecting foxtail millet yield and adaptability. To analyze the molecular genetic mechanisms of the growth period and panicle-related traits, it is necessary to map the related genes. 【Method】A recombinant inbred line (RIL) population of foxtail millet consisting of 400 lines derived from a cross between the elite varieties, Yugu18 and Jigu19, were used in this study. Phenotype surveys were carried out across four independent environments in 2018—2019 to study the days from emergence to heading and heading to maturity, the growth period, panicle length, panicle diameter, and single panicle weight. Based on a high-density genetic map that spanned 2 196 cM of the foxtail millet genome with an average of 1.68 cM/bin, composed of 1 304 bin markers, QTL for growth period and panicle-related traits were mapped using composite interval mapping (CIM), and the candidate genes of the confidence interval were predicted. 【Result】The growth period and panicle-related traits of RIL population exhibited continuous distribution with apparent transgressive segregation in the 4 environments, which accorded with the genetic characteristics of typical quantitative traits and were suitable for QTL genetic analysis. Correlation analysis showed that the days from emergence to heading were significantly positively correlated with the growth period, and negatively correlated with those heading to maturity. A positive correlation between panicle length and diameter was also observed. A total of 88 QTL for the growth period and panicle-related traits were detected on chromosomes 1, 3, 5, 6, 8 and 9, respectively. Among them, 45 QTL were significantly associated with the days from emergence to heading and explained 1.61%-27.60% of the phenotypic variance; seven QTL were significantly associated with the days from heading to maturity and explained 2.65%-12.14% of the phenotypic variance; 20 QTL were significantly associated with the growth period and explained 1.98%-16.97% of the phenotypic variance; nine QTL was significantly associated with the panicle length and explained 3.51%-11.65% of the phenotypic variance; five QTL were significantly associated with the panicle diameter and explained 3.74%-8.34% of the phenotypic variance; two QTL was significantly associated with the single panicle weight and explained 5.16%-5.20% of the phenotypic variance. A total of 12 major QTL were detected in this study, of which five major QTL, including qEHD-9-1, qEHD-9-2, qHMD-9-2, qGRP-9-2 and qPL-5-1, were repeatedly detected in at least two environments and BLUP value. The genomic regions of qEHD-9-1, qHMD-9-1, qGRP-9-1 and qPL-9-1 overlapped on chromosome 9, the genomic regions of qEHD-9-2, qHMD-9-3, qGRP-9-2 and qPL-9-3 overlapped on chromosome 9, and the genomic regions of qPL-5-1 overlapped with qPD-5-1 on chromosome 5. Five candidate genes related to the growth period and panicle-related traits were identified from the confidence interval of the 3 QTL clusters, of which two candidate genes played an important role both in the growth period and panicle-related traits.【Conclusion】A total of 88 QTL for the growth period and panicle-related traits were detected, and 12 were major QTL, of which 5 major QTL were repeatedly detected in multiple environments by clustering distribution. Five candidate genes related to the growth period and panicle-related traits were identified via gene annotation.

Key words: foxtail millet, RIL, growth period, panicle-related traits, QTL

GUO ShuQing,SONG Hui,CHAI ShaoHua,GUO Yan,SHI Xing,DU LiHong,XING Lu,XIE HuiFang,ZHANG Yang,LI Long,FENG BaiLi,LIU JinRong,YANG Pu. QTL Analysis for Growth Period and Panicle-Related Traits in Foxtail Millet[J].Scientia Agricultura Sinica, 2022, 55(15): 2883-2898.

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URL: https://www.chinaagrisci.com/EN/10.3864/j.issn.0578-1752.2022.15.002

https://www.chinaagrisci.com/EN/Y2022/V55/I15/2883

Figures/Tables 8

Table 1

Statistical analysis result of the phenotypic data of the growth period and panicle-related traits for the parents and the population"

性状 Traits	环境 Environments	亲本 Parents		RIL群体 RIL population
性状 Traits	环境 Environments	豫谷18 Yugu 18	冀谷19 Jigu 19	平均值±标准差 Mean±SD	变异系数 Variable coefficient (%)	最小值 Min value	最大值 Max value	偏度 Skewness	峰度 Kurtosis
抽穗期 EHD (d)	2018AY	44.00	52.00	45.55±2.42	5.30	40.00	54.00	0.38	0.38
	2018CD	56.00	62.00	61.89±4.10	6.62	50.00	71.00	0.20	-0.55
	2019BZW	43.00	48.00	44.47±2.11	4.75	38.00	50.00	-0.02	-0.14
	2019BZZ	53.00	61.00	53.97±3.58	6.63	44.00	66.00	0.01	0.32
抽穗-成熟天数HMD (d)	2018AY	40.00	41.00	38.04±2.47	6.51	30.00	46.00	-0.04	-0.13
	2018CD	28.00	31.00	32.39±3.58	11.04	19.00	55.00	0.65	4.43
	2019BZW	40.00	34.00	39.23±2.36	6.01	33.00	48.00	0.37	0.29
	2019BZZ	39.00	33.00	36.54±2.44	6.69	29.00	43.00	-0.01	0.01
全生育期 GRP (d)	2018AY	84.00	83.00	83.59±1.52	1.82	82.00	91.00	0.99	1.62
	2018CD	94.00	93.00	94.29±2.85	3.02	88.00	105.00	0.64	-0.18
	2019BZW	83.00	82.00	83.70±3.33	3.98	77.00	91.00	0.28	-0.65
	2019BZZ	92.00	94.00	90.50±3.05	3.37	83.00	104.00	0.20	0.76
穗长 PL cm)	2018AY	19.87	22.23	20.52±1.87	9.12	14.50	25.33	0.002	-0.12
	2018CD	25.43	28.37	26.65±2.95	11.06	11.25	38.66	0.06	1.80
	2019BZW	18.00	22.00	21.00±1.93	9.19	16.33	26.33	0.02	-0.21
	2019BZZ	19.33	20.83	22.24±2.25	10.13	16.50	30.33	0.27	-0.10
穗粗 PD (cm)	2018AY	2.50	3.23	2.55±0.31	11.98	0.33	4.67	0.10	11.70
	2018CD	2.80	3.23	2.86±0.74	25.92	1.10	5.10	-0.30	-0.14
	2019BZW	2.50	2.67	2.49±0.26	10.37	2.00	3.67	0.39	0.56
	2019BZZ	2.67	2.83	2.56±0.27	10.60	2.00	4.00	1.11	3.58
单穗重 SPW (g)	2018AY	26.33	24.95	25.07±5.86	23.36	10.92	60.53	1.72	5.68
	2018CD	29.98	27.83	30.96±5.67	18.32	15.30	48.02	0.26	-0.09
	2019BZW	12.81	14.13	14.35±2.32	16.16	6.64	21.28	0.04	0.22
	2019BZZ	18.38	11.32	16.72±3.57	21.34	8.97	35.00	1.37	4.72

Table 1

Fig. 1

Fig. 2

Table 2

QTL associated with the growth period of the foxtail millet"

性状 Traits	数量性状位点 QTL	染色体 Chromosome	环境 Environments	标记区间 Marker interval	遗传位置 Genetic position (cM)	物理区间 Physical interval (bp)	阈值 LOD	加性效应 Additive effect	贡献率 R² (%)
抽穗期EHD (d)	qEHD-1-1	1	BLUP	Chr.1-bin22—Chr.1-bin24	33.159—34.970	7383232—7618736	4.90	0.24	2.73
	qEHD-1-2	1	BLUP	Chr.1-bin26—Chr.1-bin27	39.370—42.472	7826425—8072796	4.52	0.24	2.59
	qEHD-1-3	1	BLUP (2019BZZ)	Chr.1-bin27—Chr.1-bin28	42.472—44.998	7857646—8213561	4.59	0.24	2.60
	qEHD-3-1	3	BLUP	Chr.3-bin55—Chr.3-bin56	72.136—73.672	8372841—8432775	4.62	-0.24	2.42
	qEHD-3-2	3	BLUP (2019BZZ)	Chr.3-bin61—Chr.3-bin62	83.766—88.344	11405077—11814796	3.72	-0.24	2.04
	qEHD-3-3	3	BLUP	Chr.3-bin64—Chr.3-bin65	91.860—93.092	11841659—12225276	4.75	-0.27	2.49
	qEHD-3-4	3	BLUP (2019BZZ)	Chr.3-bin71—Chr.3-bin72	100.512—101.845	12837291—12961507	5.87	-0.31	3.06
	qEHD-3-5	3	BLUP (2019BZZ)	Chr.3.bin75—Chr.3.bin76	105.389—106.721	13029966—13628423	6.08	-0.32	3.16
	qEHD-3-6	3	BLUP (2019BZW \2019BZZ)	Chr.3-bin77—Chr.3-bin78	107.902—109.058	13628424—13713052	6.87	-0.34	3.55
	qEHD-3-7	3	BLUP (2018CD/ 2019BZZ)	Chr.3-bin79—Chr.3-bin80	110.365—111.646	13713053—13756530	6.80	-0.34	3.52
	qEHD-3-8	3	BLUP (2019BZZ)	Chr.3-bin82—Chr.3-bin85	114.260—115.895	13793248—14081551	6.51	-0.34	3.38
	qEHD-3-9	3	BLUP (2018CD/ 2019BZZ)	Chr.3-bin86—Chr.3-bin87	120.269—121.426	14081552—14212812	6.41	-0.33	3.33
	qEHD-3-10	3	BLUP (2018CD)	Chr.3-bin90—Chr.3-bin91	124.367—125.448	14260453—14335548	5.48	-0.31	2.86
	qEHD-3-11	3	BLUP	Chr.3-bin93—Chr.3-bin94	129.172—130.127	14401525—14566486	5.22	-0.32	2.73
	qEHD-3-12	3	BLUP (2019BZZ)	Chr.3-bin98—Chr.3-bin99	135.938—137.296	14637541—14778894	3.35	-0.25	1.77
	qEHD-3-13	3	BLUP (2018AY/ 2018AY)	Chr.3-bin131—Chr.3-bin132	175.610—176.465	17715556—18022340	3.39	-0.27	1.79
	qEHD-3-14	3	BLUP	Chr.3-bin140—Chr.3-bin141	183.103—184.339	18750824—18860666	3.65	-0.28	1.93
	qEHD-3-15	3	BLUP (2019BZZ)	Chr.3-bin146—Chr.3-bin147	188.538—189.544	19257021—20089242	3.10	-0.27	1.64
	qEHD-3-16	3	BLUP (2019BZZ)	Chr.3-bin155—Chr.3-bin156	196.458—197.262	20677208—20864551	3.17	-0.28	1.68
	qEHD-3-17	3	BLUP	Chr.3-bin194—Chr.3-bin195	243.337—244.468	26706784—27289107	15.87	-0.49	9.82
	qEHD-3-18	3	BLUP (2019BZZ)	Chr.3-bin198—Chr.3-bin199	248.188—249.545	30727891—37792728	16.77	-0.51	10.33
	qEHD-3-19	3	BLUP (2019BZZ)	Chr.3-bin202—Chr.3-bin203	253.919—256.168	38165509—38853342	16.85	-0.51	10.37
	qEHD-3-20	3	BLUP (2019BZZ)	Chr.3_bin204—Chr.3_bin205	257.853—259.538	38853343—39335961	16.80	-0.51	10.34
	qEHD-3-21	3	BLUP	Chr.3-bin207—Chr.3-bin208	262.605—264.038	39570932—40234675	16.59	-0.50	10.23
	qEHD-3-22	3	BLUP (2018AY/ 2019BZW/ 2019BZZ)	Chr.3-bin245—Chr.3-bin246	319.052—320.434	44710972—44885104	4.76	-0.3	2.49
	qEHD-3-23	3	BLUP	Chr.3-bin251—Chr.3-bin252	328.392—331.223	45689213—45877762	4.72	-0.28	2.47
	qEHD-3-24	3	BLUP	Chr.3-bin255—Chr.3-bin256	335.295—339.407	46042065—46582016	3.87	-0.25	2.24
	qEHD-3-25	3	BLUP	Chr.3-bin256—Chr.3-bin257	339.407—340.815	46483582—46618927	3.81	-0.24	2.01
	qEHD-3-26	3	BLUP	Chr.3-bin260—Chr.3-bin261	344.988—346.421	46728763—46855047	3.97	-0.24	2.09
	qEHD-3-27	3	BLUP (2019BZZ)	Chr.3-bin271—Chr.3-bin272	360.630—362.213	47273755—47391186	5.2	-0.27	2.72
	qEHD-3-28	3	BLUP	Chr.3-bin273—Chr.3-bin274	363.978—365.360	47391187—47593066	4.83	-0.26	2.53
	qEHD-5-1	5	BLUP	Chr.5-bin80—Chr.5-bin81	164.573—165.654	34202091—34304938	3.18	-0.19	1.68
	qEHD-5-2	5	BLUP	Chr.5-bin85—Chr.5-bin86	171.164—172.371	34517958—34583021	3.07	-0.19	1.62
	qEHD-6-1	6	BLUP	Chr.6-bin75—Chr.6-bin76	123.248—124.732	21173085—22142445	3.13	-0.19	1.66
	qEHD-8-1	8	BLUP	Chr.8-bin142—Chr.8-bin143	148.642—149.398	12625705—12853225	5.05	0.25	2.84
	qEHD-8-2	8	BLUP	Chr.8-bin156—Chr.8-bin157	158.546—159.175	13626368—13846690	5.42	0.26	3.04
	qEHD-8-3	8	BLUP (2018AY)	Chr.8-bin162—Chr.8-bin173	160.909—161.336	14415972—22135047	5.41	0.26	3.03
	qEHD-8-4	8	BLUP (2019BZW)	Chr.8-bin169—Chr.8-bin171	162.467—163.020	21496234—21959783	5.26	0.25	2.95
	qEHD-8-5	8	BLUP (2019BZW)	Chr.8-bin168—Chr.8-bin170	163.598—164.075	21456415—21848090	5.53	0.26	3.09
	qEHD-8-6	8	BLUP (2019BZW)	Chr.8-bin186—Chr.8-bin167	164.603—164.954	18319627—23218474	5.83	0.27	3.26
	qEHD-8-7	8	BLUP	Chr.8-bin166—Chr.8-bin164	166.236—166.713	14609487—18319626	5.46	0.26	3.06
	qEHD-8-8	8	BLUP	Chr.8-bin184—Chr.8-bin175	167.743—168.296	22298819—23143912	5.51	0.26	3.09
	qEHD-9-1	9	BLUP (2018AY/ 2018CD/2019BAW /2019BZZ)	Chr.9-bin3—Chr.9-bin4	12.761—13.968	1020636—1081249	38.44	0.78	27.60
	qEHD-9-2	9	BLUP (2018AY/ 2018CD)	Chr.9-bin7—Chr.9-bin8	17.693—19.002	1165237—1323365	33.79	0.73	24.85
	qEHD-9-3	9	BLUP	Chr.9-bin51—Chr.9-bin52	109.941—111.025	17224206—17818411	3.05	0.19	1.61
抽穗-成熟天数HMD (d)	qHMD-1-1	1	BLUP (2019BZW)	Chr.1-bin11—Chr.1-bin12	11.748—13.937	5989491—6043795	5.79	-0.13	5.08
	qHMD-1-2	1	BLUP (2018AY)	Chr.1-bin13—Chr.1-bin14	15.863—17.275	6043796—6136316	6.06	-0.14	5.27
	qHMD-3-1	3	BLUP	Chr.3-bin269—Chr.3-bin270	356.978—358.944	47109073—47273754	3.12	0.10	2.65
	qHMD-3-2	3	BLUP	Chr.3-bin275—Chr.3-bin276	366.949—392.747	47593067—50652576	3.64	0.12	4.42
	qHMD-9-1	9	BLUP (2018CD)	Chr.9-bin3—Chr.9-bin4	12.761—13.968	1020636—1081249	13.24	-0.20	12.14
	qHMD-9-2	9	BLUP (2018AY\ 2019BZZ)	Chr.9-bin5—Chr.9-bin6	15.300—16.813	1081250—1165236	13.12	-0.21	12.04
	qHMD-9-3	9	BLUP	Chr.9-bin8—Chr.9-bin9	19.002—20.591	1296358—1344786	11.97	-0.19	11.05
全生育期GRP (d)	qGRP-3-1	3	BLUP	Chr.3-bin82—Chr.3-bin85	114.260—115.895	13793248—14081551	5.97	-0.21	3.8
	qGRP-3-2	3	BLUP	Chr.3-bin83—Chr.3-bin86	118.660—120.269	13892365—14106973	5.20	-0.20	3.33
	qGRP-3-3	3	BLUP	Chr.3-bin87—Chr.3-bin88	121.426—122.382	14106974—14234898	5.75	-0.21	3.66
	qGRP-3-4	3	BLUP（2019BZZ）	Chr.3-bin91—Chr.3-bin92	125.448—127.505	14303367—14401524	5.88	-0.21	3.75
	qGRP-3-5	3	BLUP	Chr.3-bin95—Chr.3-bin96	131.258—132.817	14566487—14610137	5.35	-0.2	3.41
	qGRP-3-6	3	BLUP	Chr.3-bin236—Chr.3-bin237	306.882—308.216	43369331—43507343	8.38	-0.25	5.65
	qGRP-3-7	3	BLUP (2018AY/ 2019BZW/ 2019BZZ)	Chr.3-bin244—Chr.3-bin245	316.945—319.052	44280815—44754403	11.37	-0.28	7.55
	qGRP-3-8	3	BLUP (2018AY/ 2019BZW/ 2019BZZ)	Chr.3-bin248—Chr.3-bin249	322.822—325.795	45168414—45466528	11.17	-0.28	7.43
	qGRP-8-1	8	BLUP (2019BZW)	Chr.8-bin91—Chr.8-bin92	112.889—113.844	6404083—6582741	6.05	0.18	3.88
	qGRP-8-2	8	BLUP	Chr.8-bin94—Chr.8-bin95	116.158—116.962	6658786—6888868	6.20	0.19	3.98
	qGRP-8-3	8	BLUP (2019BZW)	Chr.8-bin99—Chr.8-bin100	119.928—120.732	7369110—7779259	6.23	0.19	4.00
	qGRP-8-4	8	BLUP	Chr.8-bin103—Chr.8-bin104	123.052—123.630	8148192—8250376	6.99	0.2	4.46
	qGRP-8-5	8	BLUP	Chr.8-bin105—Chr.8-bin106	124.007—124.334	8250377—8415850	6.71	0.19	4.29
	qGRP-8-6	8	BLUP (2019BZW)	Chr.8-bin111—Chr.8-bin112	127.806—128.763	8570149—8953918	6.55	0.19	4.19
	qGRP-8-7	8	BLUP	Chr.8-bin118—Chr.8-bin119	132.885—133.463	9374426—9577974	6.16	0.19	3.95
	qGRP-9-1	9	BLUP (2018CD)	Chr.9-bin4—Chr.9-bin5	13.968—15.300	1051492—1134471	23.38	0.38	16.97
	qGRP-9-2	9	BLUP (2018CD\ 2019BZW)	Chr.9-bin7—Chr.9-bin8	17.693—19.002	1165237—1323365	20.46	0.36	15.10
	qGRP-9-3	9	BLUP	Chr.9-bin41—Chr.9-bin42	98.942—100.225	16720473—16794977	3.39	0.13	2.12
	qGRP-9-4	9	BLUP	Chr.9-bin50—Chr.9-bin51	109.137—109.941	17185562—17265906	3.90	0.14	2.43
	qGRP-9-5	9	BLUP	Chr.9-bin55—Chr.9-bin56	113.588—114.543	17993917—18066318	3.17	0.13	1.98

Table 2

Table 3

QTL associated with panicle-related traits of the foxtail millet"

性状 Traits	数量性状位点 QTL	染色体 Chromosome	环境 Environment	标记区间 Marker interval	遗传位置 Genetic position (cM)	物理区间 Physical interval (bp)	阈值 LOD	加性效应 Additive effect	贡献率 R² (%)
穗长 PL (cm)	qPL-3-1	3	BLUP (2018AY/ 2018CD)	Chr.3-bin63—Chr.3-bin64	90.527—91.860	11814797—11910395	4.20	0.15	3.51
	qPL-3-2	3	BLUP (2018AY/ 2018CD)	Chr.3-bin67—Chr.3-bin68	95.658—96.815	12330455—12432361	5.36	0.17	4.45
	qPL-5-1	5	BLUP (2018AY/ 2019BZW)	Chr.5-bin126—Chr.5-bin127	250.396—257.529	41261506—43386429	12.64	-0.27	11.65
	qPL-6-1	6	BLUP (2018AY/ 2019BZW)	Chr.6-bin59—Chr.6-bin60	102.475—103.505	7444554—7513668	6.31	-0.18	5.21
	qPL-6-2	6	BLUP (2018CD/ 2019BZW)	Chr.6-bin120—Chr.6-bin121	193.650—201.019	29947335—31729605	10.62	-0.24	9.60
	qPL-6-3	6	BLUP (2018CD/ 2019BZW)	Chr.6-bin122—Chr.6-bin123	202.981—207.158	31729606—32246096	9.09	-0.21	7.39
	qPL-9-1	9	BLUP (2018CD/ 2019BZW)	Chr.9-bin3—Chr.9-bin4	12.761—13.968	1020636—1081249	6.05	0.18	5.44
	qPL-9-2	9	BLUP (2018AY/ 2019BZW)	Chr.9-bin90—Chr.9-bin91	156.760—157.966	22194797—22455064	8.10	0.21	7.36
	qPL-9-3	9	BLUP	Chr.9-bin7—Chr.9-bin8	17.693—19.002	1165237—1323365	5.03	0.17	4.55
穗粗 PD (cm)	qPD-5-1	5	BLUP	Chr.5-bin127—Chr.5-bin128	257.529—259.771	43359466—43412659	3.80	0.01	3.74
	qPD-5-2	5	BLUP	Chr.5-bin129—Chr.5-bin130	261.682—268.328	43412660—45336734	3.69	0.01	4.16
	qPD-9-1	9	BLUP	Chr.9-bin128—Chr.9-bin129	212.208—213.994	36259049—36379621	5.97	0.01	6.19
	qPD-9-2	9	BLUP	Chr.9-bin133—Chr.9-bin134	221.828—223.286	37257348—38464278	8.16	0.02	8.34
	qPD-9-3	9	BLUP	Chr.9-bin139—Chr.9-bin140	230.680—232.566	40138779—40323908	7.80	0.01	7.99
单穗重SPW (g)	qSPW-9-1	9	BLUP	Chr.9-bin150—Chr.9-bin151	252.257—254.167	42275808—42540717	5.07	0.01	5.20
单穗重SPW (g)	qSPW-9-2	9	BLUP	Chr.9-bin155—Chr.9-bin156	260.934—268.406	42685851—43436931	4.48	0.01	5.16

Table 3

Fig. 3

Fig. 4

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	拟南芥同源基因 Homologous genes in Arabidopsis	功能注释 Functional annotation
抽穗期 EHD (d)	qEHD-9-1	Chr.9	Seita.9G020100.1		GO:0005515	AT5G15850	CONSTANS-like 1
		Chr.9	Seita.9G019800.1		GO:0003676	AT2G27040	AGO家族蛋白 Argonaute family protein
		Chr.9	Seita.9G019800.3		GO:0003676	AT2G27040	AGO家族蛋白 Argonaute family protein
		Chr.9	Seita.9G019800.2		GO:0003676	AT2G27040	AGO家族蛋白 Argonaute family protein
	qEHD-9-2	Chr.9	Seita.9G023100.1	K09534		AT5G49580	分子伴侣DnaJ结构域超家族蛋白 Chaperone DnaJ-domain superfamily protein
	qEHD-9-2	Chr.9	Seita.9G023600.1			AT3G06610	DNA结合增强子相关蛋白 DNA-binding enhancer protein-related
抽穗-成熟天数 HMD (d)	qEHD-9-1	Chr.9	Seita.9G019800.1		GO:0003676	AT2G27040	AGO家族蛋白 Argonaute family protein
		Chr.9	Seita.9G019800.3		GO:0003676	AT2G27040	AGO家族蛋白 Argonaute family protein
		Chr.9	Seita.9G019800.2		GO:0003676	AT2G27040	AGO家族蛋白 Argonaute family protein
		Chr.9	Seita.9G020100.1		GO:0005515	AT5G15850	CONSTANS-like 1
全生育期 GRP (d)	qGRP-9-1	Chr.9	Seita.9G020100.1		GO:0005515	AT5G15850	CONSTANS-like 1
		Chr.9	Seita.9G020800.1		GO:0016787, GO:0008152	AT3G02875	肽酶M20/M25/M40家族蛋白 Peptidase M20/M25/M40 family protein
		Chr.9	Seita.9G020900.1	K14664	GO:0016787, GO:0008152	AT3G02875	肽酶M20/M25/M40家族蛋白 Peptidase M20/M25/M40 family protein
	qGRP-9-2	Chr.9	Seita.9G023600.1			AT3G06610	DNA结合增强子相关蛋白 DNA-binding enhancer protein-related
	qGRP-9-2	Chr.9	Seita.9G023100.1	K09534		AT5G49580	伴侣DNAJ结构域超家族蛋白 Chaperone DnaJ-domain superfamily protein
穗长 PL (cm)	qPL-9-1	Chr.9	Seita.9G020100.1		GO:0005515	AT5G15850	CONSTANS-like 1
		Chr.9	Seita.9G019800.1		GO:0003676	AT2G27040	AGO家族蛋白 Argonaute family protein
		Chr.9	Seita.9G019800.3		GO:0003676	AT2G27040	AGO家族蛋白 Argonaute family protein
		Chr.9	Seita.9G019800.2		GO:0003676	AT2G27040	AGO家族蛋白 Argonaute family protein
	qPL-9-3	Chr.9	Seita.9G023600.1			AT3G06610	DNA结合增强子相关蛋白 DNA-binding enhancer protein-related
	qPL-9-3	Chr.9	Seita.9G023100.1	K09534		AT5G49580	伴侣DNAJ结构域超家族蛋白 Chaperone DnaJ-domain superfamily protein

Table 4

References 37

[1]	李顺国, 刘斐, 刘猛, 程汝宏, 夏恩君, 刁现民. 中国谷子产业和种业发展现状与未来展望. 中国农业科学, 2021, 54(3): 459-470.
	LI S G, LIU F, LIU M, CHENG R H, XIA E J, DIAO X M. Current status and future prospective of foxtail millet production and seed industry in China. Scientia Agricultura Sinica, 2021, 54(3): 459-470. (in Chinese)
[2]	GUPTA N, SRIVASTAVA A K, PANDEY V N. Biodiversity and nutraceutical quality of some Indian millets. Proceedings of the National Academy of Sciences, India Section B: Biological Sciences, 2012, 82(2): 265-273.
[3]	韩飞, 诸葛玉平, 娄燕宏, 王会, 张乃丹, 何伟, 晁赢. 63份谷子种质的耐盐综合评价及耐盐品种筛选. 植物遗传资源学报, 2018, 19(4): 685-693.
	HAN F, ZHUGE Y P, LOU Y H, WANG H, ZHANG N D, HE W, CHAO Y. Evaluation of salt tolerance and screening for salt tolerant accessions of 63 foxtail millet germplasm. Journal of Plant Genetic Resources, 2018, 19(4): 685-693. (in Chinese)
[4]	DOUST A N, DEVOS K M, GADBERRY M D, GALE M D, KELLOGG E A. The genetic basis for inflorescence variation between foxtail and green millet (poaceae). Genetics, 2005, 169: 1659-1672. doi: 10.1534/genetics.104.035543
[5]	TIAN B H, ZHANG L X, LIU Y L, WU P P, WANG W, ZHANG Y, LI H J. Identification of QTL for resistance to leaf blast in foxtail millet by genome re-sequencing analysis. Theoretical and Applied Genetics, 2021, 134(Suppl): 1-12. doi: 10.1007/s00122-020-03709-7
[6]	CHARU L, MANOJ P. Association of an allele-specific marker with dehydration stress tolerance in foxtail millet suggests SiDREB2 to be an important QTL. Journal of Plant Biochemistry and Biotechnology, 2014, 23(1): 119-122. doi: 10.1007/s13562-013-0193-y
[7]	贾小平, 张博, 董志平, 全建章, 王永芳, 张小梅, 袁玺垒, 李剑峰, 戴凌峰. 海南短日照条件下谷子穗部性状的全基因组关联分析. 河南农业科学, 2018, 47(9): 33-40.
	JIA X P, ZHANG B, DONG Z P, QUAN J Z, WANG Y F, ZHANG X M, YUAN X L, LI J F, DAI L F. Genome-wide association analysis of panicle traits of foxtail millet under Hainan short-day condition. Journal of Henan Agricultural Sciences, 2018, 47(9): 33-40. (in Chinese)
[8]	张艾英, 郭二虎, 刁现民, 范惠萍, 李瑜辉, 王丽霞, 郭红亮, 程丽萍, 吴引生. 2005-2015年西北春谷中晚熟区谷子育成品种评. 中国农业科学, 2017, 50(23): 4486-4505.
	ZHANG A Y, GUO E H, DIAO X M, FAN H P, LI Y H, WANG L X, GUO H L, CHENG L P, WU Y S. Evaluation of foxtail millet cultivars developed in the middle and late-maturing spring-sowing region in Northwest China in 2005-2015. Scientia Agricultura Sinica, 2017, 50(23): 4486-4505. (in Chinese)
[9]	WANG J, WANG Z L, DU X F, YANG H Q, HAN F, HAN Y H, YUAN F, ZHANG L Y, PENG S Z, GUO E H. A high-density genetic map and QTL analysis of agronomic traits in foxtail millet [Setaria italica (L.) P. Beauv.] using RAD-seq. PLoS ONE, 2017, 12(6): 0179717.
[10]	ZHI H, HE Q, TANG S, YANG J J, ZHANG W, LIU H F, JIA Y C, JIA G Q, ZHANG A Y, LI Y H, GUO E H, GAO M, LI S J, LI J X, QIN N, ZHU C C, MA C Y, ZHANG H J, CHEN G Q, ZHANG W F, WANG H G, QIAO Z J, LI S G, CHENG R H, XING L, WANG S Y, LIU J R, LIU J, DIAO X M. Genetic control and phenotypic characterization of panicle architecture and grain yield-related traits in foxtail millet (Setaria italica). Theoretical and Applied Genetics, 2021, 134(9): 3023-3036. doi: 10.1007/s00122-021-03875-2
[11]	张艾英, 刁现民, 郭二虎, 范惠萍, 王丽霞, 李瑜辉, 程丽萍, 吴引生, 张莉. 西北春谷早熟区谷子品种十五年变化趋势及主要性状分析. 中国农业科学, 2017, 50(23): 4496-4511.
	ZHANG A Y, DIAO X M, GUO E H, FAN H P, WANG L X, LI Y H, CHENG L P, WU Y S, ZHANG L. Research progress and major traits of foxtail millet cultivars developed and and in the early-mature spring-sowing region in the past 15years. Scientia Agricultura Sinica, 2017, 50(23): 4496-4511. (in Chinese)
[12]	相吉山, 张恒儒, 于佳东, 索良喜, 郭普丞, 李岩, 韩奕, 杜鹤恬, 马静泽, 陈佳宁. 495个不同产区谷子品种(系)在赤峰地区的农艺性状分析. 河南农业科学, 2020, 49(08):16-30.
	XIANG J S, ZHANG H R, YU J D, SUO L X, GUO P C, LI Y, HAN Y, DU H T, MA J Z, CHEN J N. Agronomic traits of 495 foxtail millet [Setaria italica(L.)P. Beauv.] varieties (lines) from different regions planted in Chifeng area. Journal of Henan Agricultural Sciences, 2020, 49(8): 16-30. (in Chinese)
[13]	李志江, 刁现民. 谷子分子标记与功能基因组研究进展. 中国农业科技导报, 2009, 11(4): 16-22.
	LI Z J, DIAO X M. Research progress on molecular marker and functional genomic of foxtail millet, Setaria italica Beauv. Journal of Agricultural Science and Technology, 2009, 11(4): 16-22. (in Chinese)
[14]	NI X M, XIA Q J, ZHANG H B, CHENG S, LI H, FAN G Y, GUO T, HUANG P, XIANG H T, CHEN Q C, LI N, ZOU H F, CAI X M, LEI X J, WANG X M, ZHOU C S, ZHAO Z H, ZHANG G Y, DU G H, CAI W, QUAN Z W. Updated foxtail millet genome assembly and gene mapping of nine key agronomic traits by resequencing a RIL population. Gigascience, 2017, 6(2): 1-8.
[15]	WANG J, WANG Z L, DU X F, YANG H Q, HAN F, HAN Y H, YUAN F, ZHANG L Y, PENG S Z, GUO E H. A high-density genetic map and QTL analysis of agronomic traits in foxtail millet [Setaria italica (L.) P. Beauv.] using RAD-seq. PLoS ONE, 2017, 12(6): e0179717.
[16]	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
[17]	谢丽莉. 谷子光周期敏感相关性状的QTL定位与分析[D]. 郑州: 河南农业大学, 2012.
	XIE L L. QTL map and analysis for the related traits of photoperiod sensitivity in Setaria italica[D]. Zhengzhou: Henan Agricultural University, 2012. (in Chinese)
[18]	JAISWAL V, GUPTA S, GAHLAUT V, MUTHAMILARASAN M, BANDYOPADHYAY T, RAMCHIARY N, PRASAD M. Genome- wide association study of major agronomic traits in foxtail millet (Setaria italica L.) using ddRAD sequencing. Scientific Reports, 2019, 9: 5020. doi: 10.1038/s41598-019-41602-6
[19]	占小登, 于萍, 林泽川, 陈代波, 沈希宏, 张迎信, 付君林, 程式华, 曹立勇. 利用大粒籼/小粒粳重组自交系定位水稻生育期及产量相关性状QTL. 中国水稻科学, 2014, 28(6): 570-580.
	ZHAN X D, YU P, LIN Z C, CHEN D B, SHEN X H, ZHANG Y X, FU J L, CHENG S H, CAO L Y. QTL mapping of heading data yield-related traits in rice using a recombination inbred lines (RILs) population derived from BJ1×XLJ. Chinese Journal of Rice Science, 2014, 28(6): 570-580. (in Chinese)
[20]	王晓宇, 刁现民, 王节之, 王春芳, 王根全, 郝晓芬, 梁增浩, 王雪梅, 赵芳芳. 谷子SSR分子图谱构建及主要农艺性状QTL定位. 植物遗传资源学报, 2013, 14(5):871-878.
	WANG X Y, DIAO X M, WANG J Z, WANG C F, WANG G Q, HAO X F, LIANG Z H, WANG X M, ZHAO F F, Construction of genetic map and QTL analysis of some main agronomic traits in millet. Journal of Plant Genetic Resources, 2013, 14(5):871-878. (in Chinese)
[21]	李海权, 耿玲玲, 李振侠. 谷子重要农艺性状QTL初步定位//2015年学术年会论文摘要集, 北京: 中国作物学会, 2015.
	LI H Q, GENG L L, LI Z X. The preliminary location study on the main agronomic characters in foxtail millet//Abstracts of Papers of Annual Meeting in 2015, BeiJing: The Crop Science Society of China, 2015. (in Chinese)
[22]	FANG X M, DONG K J, WANG X Q, LIU T P, HE J H, REN R Y, ZHANG L, LIU R, LIU X Y, LI M, HUANG M Z, ZHANG Z S, YANG T Y. A high density genetic map and QTL for agronomic and yield traits in Foxtail millet [Setaria italica (L.) P. Beauv]. BMC Genomics, 2016, 17(1): 336. doi: 10.1186/s12864-016-2628-z
[23]	ZHANG K, FAN G, ZHANG X. Identification of QTLs for 14 agronomically important traits in Setaria italica based on SNPs generated from high-throughput sequencing. Genes Genomes Genetics, 2017, 7(5): 1587-1594.
[24]	LIU T P, HE J H, DONG K J, WANNG X W, WANG W W, YANG P, REN R Y, ZHANG L, ZHANG Z S, YANG T Y. QTL mapping of yield component traits on bin map generated from resequencing a RIL population of foxtail millet (Setaria italica). BMC Genomics, 2020, 21(1): 1-13. doi: 10.1186/s12864-019-6419-1
[25]	WANG Z L, WANG J, PENG J X, DU X F, JIANG M S, LI Y F, HAN F, DU G H, YANG H Q, LIAN S C, YONG J P, CAI W, CUI J D, HAN K N, YUAN F, CHANG F, YUAN G B, ZHANG W N, ZHANG L Y, PENG S Z, ZOU H F, GUO E H. QTL mapping for 11 agronomic traits based on a genome-wide Bin-map in a large F2 population of foxtail millet (Setaria italica (L.) P. Beauv). Molecular Breeding, 2019, 39(2): 1-13.
[26]	WANG J, YANG H Q, DU G H, WANG Z L, ZOU H F, DU X F, LI Y F, PENG J X, GUO E H, YONG J P, HAN F, CAI W, XIA Q J, YUAN G B, YUAN F, NI X M, ZHANG L X, PENG S Z. Mapping of Sihc1, which controls hull color, using a high-density genetic map based on restriction site-associated DNA sequencing in foxtail millet [Setaria italica (L.) P.Beauv.]. Molecular Breeding, 2017, 37(10): 1-10. doi: 10.1007/s11032-016-0586-4
[27]	闫宏山, 刘金荣, 王素英, 路志国, 刘海平, 蒋自可, 宋中强, 王淑君. 谷子新品种豫谷18的选育. 作物杂志, 2012(3): 147-148.
	YAN H S, LIU J R, WANG S Y, LU Z G, LIU H P, JIANG Z K, SONG Z Q, WANG S J. Breeding of new foxtail millet cultivar Yugu 18. Journal of Crops, 2012(3): 147-148. (in Chinese)
[28]	程汝宏, 刘正理, 师志刚, 夏雪岩, 杨万桥. 水分高效利用型谷子新品种“冀谷19”选育研究. 河北农业科学, 2006(1): 80-81.
	CHENG R H, LIU Z L, SHI Z G, XIA X Y, YANG W Q, Breeding of foxtail millet cultivar Jigu19. Hebei Agricultural Sciences, 2006(1): 80-81. (in Chinese)
[29]	陆平. 谷子种质资源描述规范和数据标准. 北京: 中国农业出版社, 2006.
	LU P. Descriptors and Data Standard for Foxtail Millet Germplasm Resources. Beijing: China Agriculture Press, 2006. (in Chinese)
[30]	XIE H F, HOU J L, FU N, WEI M H, LI Y F, YU K, SONG H, LI S M, LIU J R. Identification of QTL related to anther color and hull color by RAD sequencing in a RIL population of Setaria italica. BMC Genomics, 2021, 22(1): 1-13. doi: 10.1186/s12864-020-07350-y
[31]	MAURO-HERRERA M, WANG X W, BARBIER H, BRUTNELL T P, DEVOS K M, DOUST A N. Genetic control and comparative genomic analysis of flowering time in Setaria (Poaceae). Genes Genomes Genetics, 2013, 3(2): 283-295.
[32]	王海岗. 山西谷子种质资源遗传多样性与主要农艺性状的QTL关联分析[D]. 晋中: 山西农业大学, 2019.
	WANG H G. Genetic diversity and association analysis of major agronomic traits in foxtail millet [Setaria italica (L.) P.Beauv.] of Shanxi province[D]. Jinzhong: Shanxi Agricultural University, 2019. (in Chinese)
[33]	刘丹青, 金玉环, 郭力, 李永光, 黄先忠. 四种十字花科植物CONSTANS-like基因家族的鉴定和进化分析. 植物生理学报, 2021, 57(6):1241-1260.
	LIU D Q, JIN Y H, GUO L, LI Y G, HUANG X Z. Identification and evolutionary analysis of CONSTANS-like gene family in four cruciferous plants. Plant Physiology Journal, 2021, 57(6): 1241-1260. (in Chinese)
[34]	翟立红, 孙伟, 李知洪, 腾峰. 植物Argonaute基因研究进展. 生命科学, 2018, 30(11): 1210-1220.
	ZHAI L H, SUN W, LI Z H, TENG F. Argonaute family in plants. Chinese Bulletin of Life Sciences, 2018, 30(11): 1210-1220. (in Chinese)
[35]	PACHAMUTHU K, SWETHA C, BASU D, DAS S, SINGH I, SUNDAR V H, SUIJTH T N, SHIVAPRASAD P. Rice-specific Argonaute 17 controls reproductive growth and yield-associated phenotypes. Plant Molecular Biology, 2020, 105(1): 99-104. doi: 10.1007/s11103-020-01071-2
[36]	薛红丽, 杨军军, 汤沙, 智慧, 王蕊, 贾冠清, 乔治军, 刁现民. 谷子穗顶端败育突变体sipaa1的表型分析和基因定位. 中国农业科学, 2018, 51(9): 1627-1640.
	XUE H L, YANG J J, TANG S, ZHI H, WANG R, JIA G Q, QIAO Z J, DIAO X M. Morphological characterization and gene mapping of panicle apical abortion mutant (sipaa1) in foxtail millet. Scientia Agricultura Sinica, 2018, 51(9): 1627-1640. (in Chinese)
[37]	LEE H S, CHOI I, JEON Y, AHN H K, CHO H, KIM J, KIM J H, LEE J M, LEE S, BUNTING J, SEO D H, LEE T, LEE D H, LEE I, OH M H, KIM T W, BELKHADIR, PAI H S. Chaperone-like protein DAY plays critical roles in photomorphogenesis. Nature Communications, 2021, 12(1): 1-13. doi: 10.1038/s41467-020-20314-w

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