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

作物学报 ›› 2024, Vol. 50 ›› Issue (6): 1373-1383.doi: 10.3724/SP.J.1006.2024.31051

• 作物遗传育种·种质资源·分子遗传学 • 上一篇下一篇

基于遗传解析新模式的小麦寡分蘖QTL的鉴定和验证

张智源^1**(), 周界光^1**(), 刘家君¹^,², 王素容¹, 王同著¹, 赵聪豪¹, 尤佳宁¹^,³, 丁浦洋¹^,⁴, 唐华苹¹, 刘燕林¹, 江千涛¹, 陈国跃¹, 魏育明¹, 马建¹^,^*()

¹四川农业大学小麦研究所, 四川成都 611130
²四川文理学院四川革命老区发展研究中心, 四川达州 635000
³四川省农业科学院经济作物研究所, 四川成都 610300
⁴绵阳师范学院生命科学与技术学院, 四川绵阳 621000

收稿日期:2023-09-08 接受日期:2024-01-12 出版日期:2024-06-12 网络出版日期:2024-02-19
通讯作者: * 马建, E-mail: jianma@sicau.edu.cn
作者简介:张智源, E-mail: 1048105542@qq.com;周界光, E-mail: 351062153@qq.com
^** 同等贡献
基金资助:
国家自然科学基金项目(31971937);四川省国际合作交流项目(2021YFH0083)

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

ZHANG Zhi-Yuan^1**(), ZHOU Jie-Guang^1**(), LIU Jia-Jun¹^,², WANG Su-Rong¹, WANG Tong-Zhu¹, ZHAO Cong-Hao¹, YOU Jia-Ning¹^,³, DING Pu-Yang¹^,⁴, TANG Hua-Ping¹, LIU Yan-Lin¹, JIANG Qian-Tao¹, CHEN Guo-Yue¹, WEI Yu-Ming¹, MA Jian¹^,^*()

¹Triticeae Research Institute, Sichuan Agricultural University, Chengdu 611130, Sichuan, China
²Research Center of Sichuan Old Revolutionary Areas Development, Sichuan University of Arts and Science, Dazhou 635000, Sichuan, China
³Industrial Crop Research Institute, Sichuan Academy of Agricultural Sciences, Chengdu 610300, Sichuan, China
⁴College of Life Science & Biotechnology, Mianyang Teachers’ College, Mianyang 621000, Sichuan, China

Received:2023-09-08 Accepted:2024-01-12 Published:2024-06-12 Published online:2024-02-19
Contact: * E-mail: jianma@sicau.edu.cn
About author:
^** Contributed equally to this work
Supported by:
National Natural Science Foundation of China(31971937);International Cooperation and the Exchanges Program of Sichuan Province(2021YFH0083)

摘要/Abstract

摘要：

有效分蘖数可直接影响小麦的成穗数, 与产量关系极为密切。挖掘小麦分蘖数相关数量性状位点, 解析分蘖数与其他重要农艺性状之间的相关性, 可为分子育种提供理论依据。本研究首先提出和建立了“多个环境评价-单个性状深入-综合性状兼顾-友好标记开发-不同背景验证”的遗传解析新模式。进一步利用该模式, 以寡分蘖自然变异植株msf和川农16 (CN16)构建的F₆代重组自交系群体(MC群体)为实验材料, 以多年多点的有效分蘖数作为表型数据, 借助基于16K芯片构建的遗传连锁图谱成功定位和验证了寡分蘖遗传调控位点。QTL定位结果显示, 1A、5A和6D染色体上有4个控制寡分蘖的QTL。其中Qltn.sau-MC-1A为稳定主效的寡分蘖QTL, 解释了13.39%~60.40%的表型变异, 其正效应位点来源于msf。表型分析发现, 携带Qltn.sau-MC-1A正效应位点株系的有效分蘖数显著少于携带Qltn.sau-MC-1A负效应位点株系的有效分蘖数。相关性分析表明, 有效分蘖数和株高之间存在极显著的正相关, 与千粒重、每穗粒数、每穗粒重、旗叶宽之间存在显著的负相关, 有效分蘖数与旗叶长、开花期之间无显著相关。遗传分析结果表明, Qltn.sau-MC-1A正效应位点显著增加每穗粒数、每穗粒重和千粒重, 但推迟开花期。不同遗传背景下的验证结果表明, 携带Qltn.sau-MC-1A正效应位点的株系确实能降低有效分蘖数。综上, 本研究建立了一种遗传解析新模式, 并基于此模式解析、定位和验证了一个控制寡分蘖的主效QTL Qltn.sau-MC-1A, 为进一步精细定位和了解分蘖形成机制奠定了基础。

关键词: 小麦, 遗传解析新模式, 16K SNP芯片, QTL, 寡分蘖, 产量

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

张智源, 周界光, 刘家君, 王素容, 王同著, 赵聪豪, 尤佳宁, 丁浦洋, 唐华苹, 刘燕林, 江千涛, 陈国跃, 魏育明, 马建. 基于遗传解析新模式的小麦寡分蘖QTL的鉴定和验证[J]. 作物学报, 2024, 50(6): 1373-1383.

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

基于遗传解析新模式的小麦寡分蘖QTL的鉴定和验证

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

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