基于16K SNP芯片的小麦株高QTL鉴定及其遗传分析

doi:10.3864/j.issn.0578-1752.2023.12.001

中国农业科学 ›› 2023, Vol. 56 ›› Issue (12): 2237-2248.doi: 10.3864/j.issn.0578-1752.2023.12.001

• 作物遗传育种·种质资源·分子遗传学 • 上一篇下一篇

基于16K SNP芯片的小麦株高QTL鉴定及其遗传分析

姚琦馥¹(), 陈黄鑫²(), 周界光², 马瑞莹³, 邓亮³, 谭陈芯雨³, 宋靖涵⁴, 吕季娟⁵(), 马建²^,⁽⁾

¹ 铜仁学院农林工程与规划学院/贵州省梵净山地区生物多样性保护与利用重点实验室，贵州铜仁 554300
² 四川农业大学小麦研究所，成都 611130
³ 四川农业大学农学院，成都 611130
⁴ 北京外国语大学，北京 100089
⁵ 四川省种子站，成都 610041

收稿日期:2023-02-14 接受日期:2023-03-17 出版日期:2023-06-16 发布日期:2023-06-27
通信作者: 吕季娟，E-mail：sccdwhljj@163.com。马建，E-mail：jianma@sicau.edu.cn。
联系方式: 姚琦馥，E-mail：yaoqifu@126.com。陈黄鑫，E-mail：1252153393@qq.com。姚琦馥和陈黄鑫为同等贡献作者。
基金资助:
贵州省科技计划基础研究项目（黔科合基础-ZK[2021]一般131(黔科合基础-ZK[2021]一般131)

QTL Identification and Genetic Analysis of Plant Height in Wheat Based on 16K SNP Array

YAO QiFu¹(), CHEN HuangXin²(), ZHOU JieGuang², MA RuiYing³, DENG Liang³, TAN ChenXinYu³, SONG JingHan⁴, LÜ JiJuan⁵(), MA Jian²^,⁽⁾

¹ College of Agroforestry Engineering and Planning, Tongren University/Guizhou Key Laboratory of Biodiversity Conservation and Utilization in the Fanjing Mountain Region, Tongren 554300, Guizhou
² Triticeae Research Institute, Sichuan Agricultural University, Chengdu 611130
³ College of Agronomy, Sichuan Agricultural University, Chengdu 611130
⁴ Beijing Foreign Studies University, Beijing 100089
⁵ Sichuan Provincial Seed Station, Chengdu 610041

Received:2023-02-14 Accepted:2023-03-17 Published:2023-06-16 Online:2023-06-27

摘要/Abstract

摘要：

【目的】株高与产量之间关系密切。进一步挖掘具有育种利用价值的小麦株高数量性状位点（quantitative trait loci，QTL），并解析株高QTL对产量相关性状的遗传效应，为分子育种提供理论依据。【方法】以田间自然变异株msf为母本、小麦品种川农16为父本杂交衍生的F₆代重组自交系群体（MC群体）为试验材料，于2020—2022年在四川省温江区、崇州市和雅安市试验基地进行2年5个生态环境点的种植和株高表型鉴定。使用16K SNP芯片所构建的高质量、高密度遗传连锁图谱定位株高性状。同时，利用株高主效QTL侧翼标记的基因型分析其正效应位点对于产量相关性状的遗传效应，评估主效QTL对产量提升的潜力。【结果】定位结果显示，分别在1A、3D、4D、5A和7B染色体共鉴定到8个控制株高的QTL。其中，定位到2个稳定的主效QTL：QPh.sau-MC-1A和QPh.sau-MC-5A，分别解释9.09%—25.56%和3.91%—13.09%的表型变异率，其正效应位点均来源于川农16。加性效应分析发现同时携带QPh.sau-MC-1A和QPh.sau-MC-5A正效应位点株系的株高显著高于仅携带单一正效应位点或没有携带任何正效应位点的株系。相关性分析发现，株高与有效分蘖数之间存在极显著的正相关性，与旗叶宽之间存在显著的负相关性，而与每穗粒数、每穗粒重、千粒重、旗叶长和开花期之间无显著相关性。遗传效应分析发现，QPh.sau-MC-1A正效应位点极显著增加有效分蘖数（56.51%），显著减少每穗粒数（-11.26%）、每穗粒重（-13.04%）、千粒重（-5.47%）和旗叶宽（-2.85%），促进开花期提前（-0.61%）。QPh.sau-MC-5A正效应位点显著增加有效分蘖数（10.57%）、每穗粒重（4.32%）和千粒重（2.92%），延迟开花期（1.07%）。【结论】在5A染色体定位到1个株高主效QTL—QPh.sau-MC-5A，其正效应位点显著提高有效分蘖数、每穗粒重及千粒重，对产量提高可能有积极效应。

关键词: 小麦, 16K SNP芯片, QTL, 株高, 产量

Abstract:

【Objective】There is a close relationship between plant height (PH) and yield. The aim of this study is to further explore quantitative trait loci (QTL) of PH with breeding value in wheat and analyze the genetic effects of major QTL for PH on other yield related traits toward to providing a theoretical basis for molecular breeding. 【Method】A recombinant inbred line population (MC) derived from a cross between the natural mutant msf and Chuannong 16 (CN16) was used for QTL analysis. During 2020 to 2022, planting and PH phenotype identification were conducted at five environments in Wenjiang, Chongzhou, and Ya’an of Sichuan Province. The high-quality genetic linkage map constructed using the 16K SNP array was used for QTL mapping of PH. Genotypes of flanking markers of major QTL for PH were used to analyze the genetic effects of positive alleles on yield related traits and evaluate the potentiality of QTL for yield improvement. 【Result】Eight QTL controlling PH were identified on chromosomes 1A, 3D, 4D, 5A, and 7B, respectively. Among them, two stable and major QTL, QPh.sau-MC-1A and QPh.sau-MC-5A, were located, which explained 9.09% to 25.56% and 3.91% to 13.09% of the phenotypic variation rate, respectively. Their positive alleles were all from CN16. The additive effect analysis showed that PH of the lines carrying positive alleles from QPh.sau-MC-1A and QPh.sau-MC-5A was significantly higher than that of the lines carrying only a single positive allele or none. Correlation analysis showed that PH has a significantly positive correlation with effective tiller number (ETN), a significantly negative correlation with flag leaf width (FLW), and no significant correlation with kernel number per spike (KNPS), kernel weight per spike (KWPS), thousand kernel weight (TKW), flag leaf length (FLL) and anthesis date (AD). Genetic effects analysis showed that positive allele of QPh.sau-MC-1A had a significant effect on improving ETN (56.51%), a significant effect on decreasing KNPS (-11.26%), KWPS (-13.04%), TKW (-5.47%), and FLW (-2.85%), and a significant effect on advancing AD (-0.61%). Positive allele of QPh.sau-MC-5A had a significant effect on improving ETN (10.57%), KNPS (4.32%), and TKW (2.92%), and a significant effect on delaying AD (1.07%). 【Conclusion】A major QTL QPh.sau-MC-5A for PH was mapped on chromosome 5A, and its positive allele significantly increased ETN, KNPS, and TKW, indicating that it may have a positive impact on yield.

Key words: wheat, 16K SNP array, QTL, plant height, yield

姚琦馥, 陈黄鑫, 周界光, 马瑞莹, 邓亮, 谭陈芯雨, 宋靖涵, 吕季娟, 马建. 基于16K SNP芯片的小麦株高QTL鉴定及其遗传分析[J]. 中国农业科学, 2023, 56(12): 2237-2248.

YAO QiFu, CHEN HuangXin, ZHOU JieGuang, MA RuiYing, DENG Liang, TAN ChenXinYu, SONG JingHan, LÜ JiJuan, MA Jian. QTL Identification and Genetic Analysis of Plant Height in Wheat Based on 16K SNP Array[J]. Scientia Agricultura Sinica, 2023, 56(12): 2237-2248.

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生态环境 Environment	亲本Parents		重组自交系RIL
生态环境 Environment	msf	CN16	范围 Range	均值 Mean	标准差 SD
2021WJ	109.4±2.4**	77.8±2.7	44.0—120.0	93.6	11.8
2021CZ	101.1±4.4**	83.6±9.9	67.4—106.2	86.4	7.8
2021YA	80.0±8.0	67.0^N	60.0—102.0	78.1	8.8
2022WJ	92.6±3.8**	69.0±3.7	44.1—105.8	75.8	10.8
2022CZ	93.3±3.1**	65.1±1.9	40.0—98.0	71.2	10.4
BLUP	91.9	74.5	68.1—94.7	81.0	5.5

差异来源 Source	基因型 Genotype	环境 Environment	基因型×环境互作 G×E interaction	区组/环境 Block/E	误差 Error
自由度Degrees of freedom	197	4	739	5	929
均方Mean square	501.25	31662.90	131.60	19.45	15.39
F	32.57	2057.66	8.55	1.26
P值P value	<0.001	<0.001	<0.001	0.28

性状 Trait	株高 Plant height
有效分蘖数Effective tiller number	0.54**
每穗粒数Kernel number per spike	-0.13
每穗粒重Kernel weight per spike	-0.10
千粒重Thousand kernel weight	-0.12
旗叶长Flag leaf length	0.01
旗叶宽Flag leaf width	-0.16*
开花期Anthesis date	0.10

QTL	生态环境 Environment	标记区间 Marker interval	遗传位置 Genetic position (cM)	阈值 LOD	表型变异率 PVE (%)	加性效应 Add
QPh.sau-MC-1A	2021WJ	1A_1208254—1A_3911208	0	6.23	13.83	-4.22
	2021YA	1A_3911208—1A_10060497	1	3.31	9.09	-2.62
	2022WJ	1A_3911208—1A_10060497	1	16.28	25.56	-5.31
	2022CZ	1A_3911208—1A_10060497	1	13.71	25.03	-5.17
	BLUP	1A_3911208—1A_10060497	1	14.26	23.67	-2.65
QPh.sau-MC-3D.1	2022WJ	3D_68039763—3D_70735603	55	2.59	3.33	1.92
QPh.sau-MC-3D.2	BLUP	3D_190976341—3D_147552757	61	3.28	4.61	1.17
QPh.sau-MC-4D	BLUP	4D_456676970—4D_467767587	42	2.69	3.82	-1.07
	2022WJ	4D_467767587—4D_474629240	43	3.88	5.10	-2.38
QPh.sau-MC-5A	2022WJ	5A_563833704—5A_568561734	101	9.05	13.09	-3.81
	BLUP	5A_569543892—5A_572006166	105	3.75	5.54	-1.29
	2022CZ	5A_572006166—5A_572980290	106	2.55	3.91	-2.06
QPh.sau-MC-7B.1	2022CZ	7B_43849—7B_3090779	0	2.68	7.02	2.31
QPh.sau-MC-7B.2	2021YA	7B_11708841—7B_19685092	31	2.82	3.66	2.01
QPh.sau-MC-7B.3	2022WJ	7B_31556403—7B_33812777	38	2.98	4.59	2.21

QTL	标记区间 Marker interval	阈值 LOD	LOD (A)	LOD (AbyE)	表型变异率 PVE (%)	PVE (A)	PVE (AbyE)	加性效应 Add
QPh.sau-MC-1A	1A_3911208—1A_10060497	43.35	33.92	9.43	25.91	19.12	6.79	-3.42
-	3D_122396589—3D_138793245	6.91	5.52	1.39	4.17	2.89	1.28	1.33
QPh.sau-MC-4D	4D_456676970—4D_467767587	7.19	5.15	2.04	3.33	2.69	0.64	-1.29
QPh.sau-MC-5A	5A_563833704—5A_568561734	11.01	5.27	5.74	5.23	2.69	2.53	-1.28
QPh.sau-MC-7B.1	7B_43849—7B_3090779	5.18	3.48	1.70	2.62	1.81	0.80	1.05

基于16K SNP芯片的小麦株高QTL鉴定及其遗传分析

QTL Identification and Genetic Analysis of Plant Height in Wheat Based on 16K SNP Array

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