基于遗传解析新模式的小麦寡分蘖QTL的鉴定和验证

doi:10.3724/SP.J.1006.2024.31051

Abstract

Abstract:

Effective tiller number (ETN) directly affects panicle number and is closely related to wheat grain yield. Mining quantitative trait loci (QTLs) associated with wheat tiller number and analyzing the correlation between tiller number and other important agronomic traits can provide the theoretical basis for molecular breeding. In this study, we first proposed and established a novel genetic analysis framework of “multiple environmental assessments-depth analysis of individual traits-comprehensive evaluation of various traits-friendly marker development-verification in different backgrounds”. Using this approach, low-tillering QTLs were identified and validated base on an F₆ recombinant inbred line population (MC population) derived from the low-tillering plant msf and Chuannong 16 (CN16), phenotypic data of effective tillers from multiple environments, and a 16K chip-based constructed genetic linkage map. QTL mapping results showed that there were four QTLs controlling tillering on chromosomes 1A, 5A, and 6D, respectively. Qltn.sau-MC-1A was a stable and major low-tillering QTL explaining 13.39%-60.40% of the phenotypic variation rate, and its positive allele was from msf. Phenotypic analysis showed that ETN of the lines carrying the positive allele of Qltn.sau-MC-1A was significantly less than those with the negative alleles. Correlation analysis showed that ETN had a significantly positive correlation with plant height (PH), and a significantly negative correlation with thousand kernel weight (TKW), kernel number per spike (KNPS), kernel weight per spike (KWPS), and flag leaf width (FLW), but no significantly correlation with flag leaf length (FLL) and anthesis date (AD). Genetic analysis showed that positive allele of Qltn.sau-MC-1A had a significant effect on increasing KNPS, KWPS, and TKW, but delaying AD. Validation results in different backgrounds suggested that the ETN of lines carrying positive alleles from Qltn.sau-MC-1A could be significantly decreased. Collectively, we established a new genetic mapping approach and further used it to identify and validate a major QTL controlling low-tiller number, Qltn.sau-MC-1A, which laid a foundation for further fine mapping and understanding the mechanism of tiller formation.

Key words: wheat, new genetic analysis model, 16K SNP array, QTL, low-tiller number, yield

ZHANG Zhi-Yuan, ZHOU Jie-Guang, LIU Jia-Jun, WANG Su-Rong, WANG Tong-Zhu, ZHAO Cong-Hao, YOU Jia-Ning, DING Pu-Yang, TANG Hua-Ping, LIU Yan-Lin, JIANG Qian-Tao, CHEN Guo-Yue, WEI Yu-Ming, MA Jian. Identification and verification of low-tillering QTL based on a new model of genetic analysis in wheat[J].Acta Agronomica Sinica, 2024, 50(6): 1373-1383.

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生态环境 Environment	亲本 Parents		重组自交系 RILs				遗传力H²
生态环境 Environment	msf	CN16	平均值 Average	最小值 Min.	最大值 Max.	标准差 SD	遗传力H²
2021WJ	3.90	9.00^**	7.46	1.00	18.67	3.66	0.63
2021CZ	3.67	7.89^**	4.75	1.00	10.75	2.06
2021YA	1.50	7.00^N	3.36	1.00	13.00	2.29
2022WJ	2.38	8.00^**	3.60	1.00	9.38	2.01
2022CZ	4.13	7.38^*	4.31	1.00	13.50	2.85
BLUP	3.52	7.04	4.69	2.08	8.02	1.37

生态环境 Environment	2021WJ	2021CZ	2021YA	2022WJ	2022CZ
2021WJ	1
2021CZ	0.50^**	1
2021YA	0.31^**	0.10	1
2022WJ	0.38^**	0.42^**	0.35^**	1
2022 CZ	0.32^**	0.27^**	0.42^**	0.75^**	1

数量性状位点 QTL	生态环境 Environment	遗传位置 Position	标记范围 Marker interval	阈值 LOD	表型变异 PVE (%)
Qltn.sau-MC-1A	2021WJ	1	1A_3911208-1A_10060497	8.74	17.96
	2021CZ	1	1A_3911208-1A_10060497	7.97	17.05
	2021YA	1	1A_3911208-1A_10060497	4.78	13.39
	2022WJ	1	1A_3911208-1A_10060497	42.10	60.40
	2022CZ	1	1A_3911208-1A_10060497	26.70	45.86
	BLUP	1	1A_3911208-1A_10060497	39.86	54.36
Qltn.sau-MC-5A.1	2022WJ	97	5A_556727472-5A_558969437	4.37	3.81
Qltn.sau-MC-5A.2	BLUP	108	5A_572980290-5A_575428307	5.92	5.07
Qltn.sau-MC-6D	2022CZ	60	6D_455134794-6D_463743339	3.92	5.02

遗传位置 Position	标记范围 Marker interval	阈值 LOD	LOD (A)	LOD (AbyE)	表型变异 PVE	PVE (A)	PVE (AbyE)
1	1A_3911208-1A_10060497	90.21	58.65	31.56	35.01	31.23	3.78
30	1A_23131668-1A_39616563	3.10	1.67	1.43	1.91	0.67	1.24
100	2A_717144670-2A_720056407	3.52	3.26	0.27	2.29	1.30	1.00
177	3A_730807251-3A_732757911	3.22	1.59	1.63	1.91	0.65	1.27
18	4A_618529916-4A_623834197	3.30	0.75	2.55	1.90	0.30	1.60
97	5A_556727472-5A_558969437	8.54	5.30	3.24	2.28	2.17	0.11
108	5A_572980290-5A_575428307	5.65	5.24	0.41	2.85	2.13	0.72
0	1B_489269981-1B_510813813	3.23	1.47	1.76	2.80	0.61	2.19
36	1B_619666190-1B_624217667	3.04	1.11	1.94	1.22	0.45	0.77
27	2B_18638491-2B_26062934	3.48	0.90	2.58	1.85	0.33	1.52
103	4B_648178224-4B_649491388	3.12	1.52	1.60	1.17	0.58	0.58
56	6B_687067128-6B_692736644	4.31	0.93	3.38	1.94	0.38	1.55
0	1D_304194-1D_12332065	3.32	0.01	3.31	0.93	0.00	0.93
84	1D_408180035-1D_431188242	3.07	2.14	0.93	2.13	0.81	1.33
57	3D_16429915-3D_39118733	3.75	0.15	3.60	0.73	0.06	0.67
0	3D_588334228-3D_594062466	3.71	3.29	0.42	1.71	1.35	0.36
53	4D_481390193-4D_484488681	3.07	1.66	1.42	1.31	0.65	0.66
88	5D_394108741-5D_403477419	3.23	0.97	2.25	1.56	0.40	1.16
60	6D_455134794-6D_463743339	4.95	2.28	2.67	1.74	0.93	0.82

Identification and verification of low-tillering QTL based on a new model of genetic analysis in wheat

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