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Acta Agronomica Sinica ›› 2024, Vol. 50 ›› Issue (6): 1373-1383.doi: 10.3724/SP.J.1006.2024.31051

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• CROP GENETICS & BREEDING·GERMPLASM RESOURCES·MOLECULAR GENETICS • Previous Articles     Next Articles

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

ZHANG Zhi-Yuan1**(), ZHOU Jie-Guang1**(), 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,*()   

  1. 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 Online:2024-06-12 Published:2024-02-19
  • Contact: * E-mail: jianma@sicau.edu.cn E-mail:1048105542@qq.com;351062153@qq.com;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)

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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

Fig. 1

Phenotype of parents and selected lines in the MC population"

Table 1

Phenotypic variation of effective tiller number for parents and RIL in MC population"

生态环境
Environment		 亲本 Parents 重组自交系 RILs 遗传力H2
msf CN16 平均值 Average 最小值 Min. 最大值 Max. 标准差 SD
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 7.00N 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

Fig. 2

Quantile-quantile (Q-Q) plots of effective tiller number for MC population in different environments mu: average; sigma: standard deviation. Abbreviations are the same as those given in Table 1."

Table 2

Correlation analysis of effective tiller number in different environments in MC population"

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

Table 3

QTL related to low-tiller number in MC population"

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

Table 4

Multi-environment QTL related to the effective tiller number in MC population"

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

Fig. 3

Genetic map of Qltn.sau-MC-1A and genetic effect of Qltn.sau-MC-1A in MC population A: genetic map; B: LOD values; C: genetic effect of Qltn.sau-MC-1A in MC population. Markers’ genetic positions are shown on the left of the map, and their names are shown on the right (cM). + and -: the number of lines carrying and not carrying the positive allele of corresponding QTL; n: the number of lines. **, P<0.01."

Fig. 4

Effect of the major QTL for the effective tiller number on yield related traits **, P<0.01."

Fig. 5

Validation of Qltn.sau-MC-1A in different genetic backgrounds **, P<0.01."

Fig. 6

Genetic analysis model of a major QTL"

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