Scientia Agricultura Sinica ›› 2023, Vol. 56 ›› Issue (21): 4137-4149.doi: 10.3864/j.issn.0578-1752.2023.21.001


QTL Mapping and Molecular Marker Development of Traits Related to Grain Weight in Wheat

ZHANG ZeYuan1(), LI Yue2, ZHAO WenSha1, GU JingJing3, ZHANG AoYan1, ZHANG HaiLong1, SONG PengBo1, WU JianHui1, ZHANG ChuanLiang1, SONG QuanHao4, JIAN JunTao5, SUN DaoJie1(), WANG XingRong2()   

  1. 1 College of Agronomy, Northwest A&F University, Yangling 712100, Shaanxi
    2 Institute of Crop Science, Gansu Academy of Agricultural Sciences, Lanzhou 730000
    3 Luoyang Academy of Agriculture and Forestry Sciences, Luoyang 471023, Henan
    4 Zhumadian Academy of Agricultural Sciences, Zhumadian 463000, Henan
    5 Nanyang Academy of Agricultural Sciences, Nanyang 473000, Henan
  • Received:2023-03-28 Accepted:2023-04-20 Online:2023-11-01 Published:2023-11-06
  • Contact: SUN DaoJie, WANG XingRong


【Objective】The yield of wheat, the second-highest-yielding food product in the world, has a major impact by grain weight. This research used materials from a recombinant inbred line (RIL) population derived from Heshangtou (HST) and Longchun 23 (LC23). Based on 55K SNP genotype data, QTL mapping was performed for traits related to grain weight of wheat, and co-segregation markers of major grain length QTL were developed and verified to provide reference for molecular marker assisted selection breeding.【Method】The wheat 55K SNP microarray was used to genotype parents and RIL populations, and a high density genetic linkage map was constructed, and its correlation with Chinese spring reference genome IWGSC RefSeq v1.0 was analyzed. QTL mapping of traits related to grain weight in multiple environments based on inclusive composite interval mapping method. The analysis of variance of major effect QTLs were performed to judge the additive interaction effect among different QTLs, and to analyse its effect on traits related to grain weight. At the same time, the corresponding kompetitive allele specific PCR marker was developed according to the closely linked SNP loci of major QTL for grain length, and verified in 242 wheat accessions worldwide.【Result】In this study, a high density genetic map of Heshangtou/Longchun 23 RIL population was constructed, with full length 4 543 cM, including 22 linkage groups, covering 21 chromosomes of wheat, and the average genetic distance was 1.7 cM. There was a significant correlation between genetic map and physical map, and the Pearson correlation coefficient were 0.77-0.99 (P<0.001). A total of 51 QTLs related to grain weight were detected, among them, 4 stable major QTLs were found in multi-environments (three or more environments) and distributed on 2D, 5A, 6B and 7D chromosomes. According to the physical interval and functional markers, it is inferred that stable major QTLs Qtkw.nwafu-2D.1 and Qtkw.nwafu-7D are photoperiod gene Ppd-D1 and flowering gene FT-D1, respectively. The analysis of variance shows that there is a significant interaction between them. The favorite alleles polymerization of Qtkw.nwafu-2D.1 and Qtkw.nwafu-7D can significantly increase thousand grain weight and grain width of wheat. In addition, the corresponding KASP molecular detection marker AX-111067709 was developed based on the co-segregated SNP of the major locus Qgl.nwafu-5A for grain length, which was significantly correlated with grain length and grain weight traits in a diversity panel comprising of 242 wheat accessions, and could increase grain length by 3.33% to 4.59% and grain weight 5.70% to 10.35% in different environments (P<0.001).【Conclusion】There are several genetic loci that affect traits linked to grain weight in Heshangtou (HST) and Longchun 23 (LC23), and Qtkw.nwafu-2D.1 and Qtkw.nwafu-7D dramatically increased thousand grain weight and grain width through additive interaction effects. Qgl.nwafu-5A is significantly correlated with grain weight and grain length, and its co-segregated molecular marker AX-11106770 can be used in molecular marker assisted selection breeding.

Key words: wheat, thousand-grain weight, QTL, KASP marker, molecular marker-assisted selection breeding

Table 1

Phenotypic analysis of HST/LC23 population"

亲本Parents 平均值±标准差
Mean ± SD
P 遗传力
和尚头HST 陇春LC23
TKW (g)
E1 46.77 39.56** 43.22±6.74 23.85 59.74 0.97 0.24 0.81
E2 45.81 39.00** 41.64±5.85 24.09 54.73 0.97 0.07
E3 48.03 39.32** 43.94±5.53 26.32 57.71 0.97 0.07
E4 47.93 46.82 42.02±6.87 23.36 60.69 0.98 0.75
BLUP 46.33 41.47** 42.75±4.04 30.53 52.94 0.98 0.32
GL (mm)
E1 7.26 6.69** 6.99±0.58 4.95 8.92 0.99 0.98 0.58
E2 6.53 6.10** 6.36±0.28 5.52 7.19 0.98 0.63
E3 7.47 7.09** 8.03±0.93 5.59 10.29 0.96 0.00
E4 6.41 6.02** 6.20±0.56 4.38 7.39 0.84 0.00
BLUP 6.91 6.65 ** 6.91±0.24 6.29 7.71 0.98 0.72
GW (mm)
E1 3.31 3.07 3.15±0.31 2.10 3.99 0.98 0.63 0.56
E2 3.01 2.88 2.95±0.15 2.41 3.34 0.96 0.01
E3 3.64 3.42* 3.79±0.42 2.46 4.87 0.96 0.00
E4 3.22 3.21 3.01±0.30 1.97 3.53 0.85 0.00
BLUP 3.27 3.18 3.23±0.11 2.85 3.55 0.97 0.29
E1 2.19 2.18 2.22±0.15 1.88 2.98 0.96 0.00 0.84
E2 2.17 2.12* 2.16±0.13 1.89 2.53 0.96 0.00
E3 2.05 2.07 2.12±0.13 1.77 2.61 0.98 0.52
E4 1.99 1.88** 2.06±0.14 1.72 2.69 0.97 0.31
BLUP 2.11 2.08** 2.14±0.09 1.88 2.47 0.98 0.63

Fig. 1

Correlation analysis of traits related to grain weight The same as below"

Table 2

Statistics of marker distribution on genetic map of HST/LC23 RIL population"

Genetic length
No. of Bin
No. of marker
Bin density
Max genetic distance (cM)
1A LG1A 165.15 161 1059 1.03 9.21
1B LG1B 198.34 153 1388 1.30 14.02
1D LG1D 133.99 46 377 2.91 19.57
2A LD2A 211.24 140 937 1.51 15.13
2B LG2B 237.42 175 1295 1.36 11.76
2D LG2D 286.16 109 464 2.63 20.80
3A LG3A 259.25 174 1008 1.49 14.16
3B LG3B 249.84 165 1331 1.51 15.08
3D LG3D 251.05 94 638 2.67 18.11
4A LG4A 209.20 108 641 1.94 11.16
4B LG4B 122.92 69 256 1.78 13.47
4D LG4D 180.03 112 442 1.61 18.11
5A LG5A 247.18 174 816 1.42 22.91
5B LG5B 247.07 172 760 1.44 10.86
5D LG5D 305.92 91 293 3.36 17.21
6A LG6A 183.40 101 727 1.82 17.92
6B LG6B 160.84 138 953 1.17 17.20
6D LG6D 215.15 90 713 2.39 31.82
7A LG7A.1 217.90 148 1104 1.47 10.74
LG7A.2 15.94 19 82 0.84 3.30
7B LG7B 206.74 151 901 1.37 14.96
7D LG7D 238.28 82 344 2.91 15.93
A基因组 A genome 1509.26 1025 6374 1.47 22.91
B基因组 B genome 1423.17 1023 6884 1.39 17.20
D基因组 D genome 1610.57 624 3271 2.58 31.82
总计 Total 4543.00 2672 16529 1.70 31.82

Fig. 2

Collinearity analysis of genetic map and physical map The red scatter point indicates collinearity, and the black histogram indicates the recombination rate of Bin markers on the reference genome. **: P<0.01, ***: P<0.001"

Table 3

Partial QTLs of traits related to grain weight"

QTL 环境
Left marker
Right marker
Physical interval (Mb)
LOD 贡献率
PVE (%)
千粒重TKW Qtkw.nwafu-2A E1\E2 AX-169336957 AX-109601471 94.16—136.56 3.47—7.93 5.22—7.42 1.45—2.12
Qtkw.nwafu-2D.1 E1\E2\E4\BLUP AX-111696354 AX-109755068 28.09—47.89 4.33—11.89 7.19—12.92 -2.44—-1.42
Qtkw.nwafu-2D.2 E4\BLUP AX-110373068 AX-109710757 148.29—197.50 3.58—4.16 5.69—5.80 -1.75—-1.01
Qtkw.nwafu-7D E1\E2\E3\E4\BLUP AX-110826147 AX-111618969 65.50—75.23 4.63—10.71 7.53—13.26 1.55—2.46
Qgl.nwafu-2A.1 E2\BLUP AX-109402024 AX-169336957 86.97—94.16 3.88—6.74 3.74—8.05 0.06—0.09
Qgl.nwafu-4B E3\BLUP AX-110076607 AX-109852046 11.43—12.82 4.03—5.6 6.14—8.06 0.08—0.28
Qgl.nwafu-5A E1\E2\E3\BLUP AX-111067709 AX-110670888 0.65—0.75 3.93—59.43 4.56—17.86 0.09—0.81
Qgl.nwafu-6B E2\BLUP AX-110927266 AX-110464369 135.98—173.59 5.06—7.10 5.10—8.78 -0.10—-0.07
Qgw.nwafu-2D.1 E2\E3\BLUP AX-111096297 AX-109755068 32.97—47.89 4.41—10.14 7.28—12.36 -0.12—-0.04
Qgw.nwafu-4B E1\BLUP AX-110076607 AX-94699353 11.43—19.76 3.93—4.10 5.50—7.91 0.03—0.10
Qgw.nwafu-5A.1 E3\BLUP AX-111067709 AX-110670888 0.65—0.75 4.06—5.17 5.97—6.57 0.03—0.12
Qgw.nwafu-7D.2 E2\E4\BLUP AX-111843581 AX-111618969 67.45—75.23 4.76—16.00 7.85—20.13 0.05—0.10
Qlwr.nwafu-2A E3\E4\BLUP AX-109287352 AX-111258161 613.88—626.08 4.54—7.01 5.41—11.90 0.02—0.05
Qlwr.nwafu-2D E1\E2\BLUP AX-109422526 AX-109755068 35.02—47.89 8.32—12.93 7.68—14.51 0.03—0.06
Qlwr.nwafu-4A E1\E2\BLUP AX-110629864 AX-111561234 72.00—129.02 5.53—9.57 5.75—8.11 -0.04—-0.03
Qlwr.nwafu-4D E3\BLUP AX-110015970 AX-109181699 33.04—35.23 3.30—4.90 2.54—8.16 -0.04—-0.02
Qlwr.nwafu-6B.2 E1\E2\E3\BLUP AX-110603992 AX-95659567 455.92—559.34 4.40—9.13 4.87—10.48 -0.04—-0.03
Qlwr.nwafu-7D E1\E2\BLUP AX-109866327 AX-111618969 61.80—75.23 4.12—12.57 4.67—12.16 -0.05—-0.03

Fig. 3

Distribution of QTLs related to grain weight on chromosomes"

Table 4

Analysis of variance of Qtkw.nwafu-2D.1 and Qtkw.nwafu-7D under different environments"

源Source 自由度df 千粒重TKW 粒宽GW 粒长GL 籽粒长宽比LWR
Qtkw.nwafu-2D.1 (2D) 1 76.83*** 39.88*** 3.80 61.42***
Qtkw.nwafu-7D (7D) 1 71.32*** 44.50*** 1.62 96.21***
环境Environment (E) 4 2.27 195.79*** 205.2*** 35.32***
2D×7D 1 26.75*** 11.51** 0.33 26.98***
2D×E 4 1.74 4.59** 4.21** 1.41
7D×E 4 0.73 0.74 0.06 1.52
2D×7D×E 4 0.817 1.81 1.10 0.86
误差Error 813 30.21 0.08 0.35 0.01

Fig. 4

Polymerization additive effect of Qtkw.nwafu-2D.1 and Qtkw.nwafu-7D +: Allele of the corresponding flanking marker derived from the lines of HST; -: Allele of the corresponding flanking marker derived from the lines of LC23. Different lowercase letters indicate significant differences"

Fig. 5

Significance test of grain length of 242 wheat materials (AX-111067709) a: Yangling; b: Nanyang; c: Luoyang"

SHEWRY P R, HEY S J. The contribution of wheat to human diet and health. Food and Energy Security, 2015, 4(3): 178-202.

doi: 10.1002/fes3.64 pmid: 27610232
LI S D, WANG L, MENG Y N, HAO Y F, XU H X, HAO M, LAN S Q, ZHANG Y J, LV L J, ZHANG K, PENG X H, LAN C X, LI X P, ZHANG Y L. Dissection of genetic basis underpinning kernel weight-related traits in common wheat. Plants (Basel, Switzerland), 2021, 10(4): 713.
SHIFERAW B, SMALE M, BRAUN H J, DUVEILLER E, REYNOLDS M, MURICHO G. Crops that feed the world 10. Past successes and future challenges to the role played by wheat in global food security. Food Security, 2013, 5(3): 291-317.

doi: 10.1007/s12571-013-0263-y
HU J M, WANG X Q, ZHANG G X, JIANG P, CHEN W Y, HAO Y C, MA X, XU S S, JIA J Z, KONG L R, WANG H W. QTL mapping for yield-related traits in wheat based on four RIL populations. Theoretical and Applied Genetics, 2020, 133(3): 917-933.

doi: 10.1007/s00122-019-03515-w pmid: 31897512
ZHANG J J, SHE M Y, YANG R C, JIANG Y J, QIN Y B, ZHAI S N, BALOTF S, ZHAO Y, ANWAR M, ALHABBAR Z, JUHÁSZ A, CHEN J S, LIU H, LIU Q E, ZHENG T, YANG F, RONG J K, CHEN K F, LU M Q, ISLAM S, MA W Y. Yield-related QTL clusters and the potential candidate genes in two wheat DH populations. International Journal of Molecular Sciences, 2021, 22(21): 11934.

doi: 10.3390/ijms222111934
KUMAR A, MANTOVANI E E, SEETAN R, SOLTANI A, ECHEVERRY-SOLARTE M, JAIN S, SIMSEK S, DOEHLERT D, ALAMRI M S, ELIAS E M, KIANIAN S F, MERGOUM M. Dissection of genetic factors underlying wheat kernel shape and size in an Elite×Nonadapted cross using a high density SNP linkage map. Plant Genome, 2016, 9(1): 55.
CAO J J, SHANG Y Y, XU D M, XU K L, CHENG X R, PAN X, LIU X, LIU M L, GAO C, YAN S N, YAO H, GAO W, LU J, ZHANG H P, CHANG C, XIA X C, XIAO S H, MA C X. Identification and validation of new stable QTLs for grain weight and size by multiple mapping models in common wheat. Frontiers in Genetics, 2020, 11: 584859.

doi: 10.3389/fgene.2020.584859
LIU H, ZHANG X T, XU Y F, MA F F, ZHANG J P, CAO Y W, LI L H, AN D G. Identification and validation of quantitative trait loci for kernel traits in common wheat (Triticum aestivum L.). BMC Plant Biology, 2020, 20(1): 529.

doi: 10.1186/s12870-020-02661-4
PANG Y L, LIU C X, WANG D F, ST AMAND P, BERNARDO A, LI W H, HE F, LI L Z, WANG L M, YUAN X F, DONG L, SU Y, ZHANG H R, ZHAO M, LIANG Y L, JIA H Z, SHEN X T, LU Y, JIANG H M, WU Y Y, LIU S B. High-resolution genome-wide association study identifies genomic regions and candidate genes for important agronomic traits in wheat. Molecular Plant, 2020, 13(9): 1311-1327.

doi: S1674-2052(20)30221-5 pmid: 32702458
XIN F, ZHU T, WEI S, HAN Y, ZHAO Y, ZHANG D, MA L, DING Q. QTL mapping of kernel traits and validation of a major QTL for kernel length-width ratio using SNP and bulked segregant analysis in wheat. Scientific Reports, 2020, 10(1): 25.

doi: 10.1038/s41598-019-56979-7 pmid: 31913328
MA J, ZHANG H, LI S Q, ZOU Y Y, LI T, LIU J J, DING P Y, MU Y, TANG H P, DENG M, LIU Y X, JIANG Q T, CHEN G Y, KANG H Y, LI W, PU Z E, WEI Y M, ZHENG Y L, LAN X J. Identification of quantitative trait loci for kernel traits in a wheat cultivar Chuannong16. BMC Genetics, 2019, 20(1): 77.

doi: 10.1186/s12863-019-0782-4 pmid: 31619163
QU X R, LI C, LIU H, LIU J J, LUO W, XU Q, TANG H P, MU Y, DENG M, PU Z E, MA J, JIANG Q T, CHEN G Y, QI P F, JIANG Y F, WEI Y M, ZHENG Y L, LAN X J, MA J. Quick mapping and characterization of a co-located kernel length and thousand-kernel weight-related QTL in wheat. Theoretical and Applied Genetics, 2022, 135(8): 2849-2860.

doi: 10.1007/s00122-022-04154-4 pmid: 35804167
YANG Y, AMO A, WEI D, CHAI Y M, ZHENG J, QIAO P F, CUI C G, LU S, CHEN L, HU Y G. Large-scale integration of meta-QTL and genome-wide association study discovers the genomic regions and candidate genes for yield and yield-related traits in bread wheat. Theoretical and Applied Genetics, 2021, 134(9): 3083-3109.

doi: 10.1007/s00122-021-03881-4 pmid: 34142166
张香宇. 小麦RHL32籽粒发育相关基因克隆及其与TaRPP13L1多效性功能初析[D]. 杨凌: 西北农林科技大学, 2022.
ZHANG X Y. Cloning of gene related to grain development in wheat RHL32 and pleiotropic function dissection of the candiated genes and TaRPP13L1[D]. Yangling: Northwestern Agriculture and Foresty University, 2022. (in Chinese)
YANG F P, LIU G Y, WU Z Y, ZHANG D X, ZHANG Y F, YOU M S, LI B Y, ZHANG X H, LIANG R Q. Cloning and functional analysis of TaWRI1Ls, the key genes for grain fatty acid synthesis in bread wheat. International Journal of Molecular Sciences, 2022, 23(10): 5293.

doi: 10.3390/ijms23105293
朱雪成. 春化和光周期基因在江苏小麦品种中的分布及其对农艺性状的效应分析[D]. 扬州: 扬州大学, 2019.
ZHU X C. Distribution of vernalization and photoperiod genes in Jiangsu wheat cultivars and their effects on agronomic traits[D]. Yangzhou: Yangzhou University, 2019. (in Chinese)
BEALES J, TURNER A, GRIFFITHS S, SNAPE J W, LAURIE D A. A Pseudo-Response Regulator is misexpressed in the photoperiod insensitive Ppd-D1a mutant of wheat (Triticum aestivum L.). Theoretical and Applied Genetics, 2007, 115(5): 721-733.

doi: 10.1007/s00122-007-0603-4
NISHIDA H, YOSHIDA T, KAWAKAMI K, FUJITA M, LONG B, AKASHI Y, LAURIE D A, KATO K. Structural variation in the 5′ upstream region of photoperiod-insensitive alleles Ppd-A1a and Ppd-B1a identified in hexaploid wheat (Triticum aestivum L.), and their effect on heading time. Molecular Breeding, 2013, 31(1): 27-37.

doi: 10.1007/s11032-012-9765-0
PUGSLEY A T. A genetic analysis of the spring-winter habit of growth in wheat. Australian Journal of Agricultural Research, 1971, 22(1): 21.

doi: 10.1071/AR9710021
YAN L, FU D, LI C, BLECHL A, TRANQUILLI G, BONAFEDE M, SANCHEZ A, VALARIK M, YASUDA S, DUBCOVSKY J. The wheat and barley vernalization gene VRN3 is an orthologue of FT. Proceedings of the National Academy of Sciences of the United States of America, 2006, 103(51): 19581-19586.
SHIMADA S, OGAWA T, KITAGAWA S, SUZUKI T, IKARI C, SHITSUKAWA N, ABE T, KAWAHIGASHI H, KIKUCHI R, HANDA H, MURAI K. A genetic network of flowering-time genes in wheat leaves, in which an APETALA1/FRUITFULL-like gene, VRN1, is upstream of FLOWERING LOCUS T. The Plant Journal, 2009, 58(4): 668-681.

doi: 10.1111/j.1365-313X.2009.03806.x pmid: 19175767
MORENO-SEVILLA B, BAENZIGER P S, PETERSON C J, GRAYBOSCH R A, MCVEY D V. The 1BL/1RS translocation: Agronomic performance of F3-derived lines from a winter wheat cross. Crop Science, 1995, 35(4): 1051-1055.

doi: 10.2135/cropsci1995.0011183X003500040022x
王兴荣, 张彦军, 苟作旺, 李玥, 陈伟英, 祁旭升. 甘肃“和尚头”小麦调查报告. 甘肃农业科技, 2015(12): 49-52.
WANG X R, ZHANG Y J, GOU Z W, LI Y, CHEN W Y, QI X S. Investigation report of wheat Gansu “Heshangtou”. Gansu Agricultural Science and Technology, 2015(12): 49-52. (in Chinese)
袁俊秀, 杨文雄. 丰产广适优质春小麦新品种——陇春23号. 麦类作物学报, 2009, 29(4): 740.
YUAN J X, YANG W X. Longchun 23, a new spring wheat variety with high yield, wide adaptability and high quality. Journal of Triticeae Crops, 2009, 29(4): 740. (in Chinese)
王建康, 盖钧镒. 利用杂种F2世代鉴定数量性状主基因-多基因混合遗传模型并估计其遗传效应. 遗传学报, 1997, 24(5): 432-440.
WANG J K, GAI J Y. Identification of major gene and polygene mixed inheritance model and estimation of genetic parameters of a quantitative trait from F2 progeny. Acta Genetica Sinica, 1997, 24(5): 432-440. (in Chinese)
ALLEN P, BENNETT K. SPSS statistics version 22: A practical guide. 2014.
MENG L, LI H H, ZHANG Y L, WANG J K. QTL IciMapping: Integrated software for genetic linkage map construction and quantitative trait locus mapping in biparental populations. The Crop Journal, 2015, 3(3): 269-283.

doi: 10.1016/j.cj.2015.01.001
OOIJEN J, VAN'T VERLAAT J W, TOL J, DALÉN J, BUREN J, MEER J M, KRIEKEN J V, KESSEL J, VAN O, VOORRIPS R, HEUVEL L. JoinMap®4, Software for the calculation of genetic linkage maps in experimental populations. Biology, 2006.
VOORRIPS R E. MapChart: Software for the graphical presentation of linkage maps and QTLs. Journal of Heredity, 2002, 93(1): 77-78.

doi: 10.1093/jhered/93.1.77 pmid: 12011185
ZHAO G Y, ZOU C, LI K, WANG K, LI T B, GAO L F, ZHANG X X, WANG H J, YANG Z J, LIU X, JIANG W K, MAO L, KONG X Y, JIAO Y N, JIA J Z. The Aegilops tauschii genome reveals multiple impacts of transposons. Nature Plants, 2017, 3(12): 946-955.

doi: 10.1038/s41477-017-0067-8
YU M, MAO S L, HOU D B, CHEN G Y, PU Z E, LI W, LAN X J, JIANG Q T, LIU Y X, DENG M, WEI Y M. Analysis of contributors to grain yield in wheat at the individual quantitative trait locus level. Plant Breeding, 2018, 137(1): 35-49.

doi: 10.1111/pbr.2018.137.issue-1
RÖDER M S, HUANG X Q, BÖRNER A. Fine mapping of the region on wheat chromosome 7D controlling grain weight. Functional & Integrative Genomics, 2008, 8(1): 79-86.
HUANG X Q, CÖSTER H, GANAL M W, RÖDER M S. Advanced backcross QTL analysis for the identification of quantitative trait loci alleles from wild relatives of wheat (Triticum aestivum L.). Theoretical and Applied Genetics, 2003, 106(8): 1379-1389.

doi: 10.1007/s00122-002-1179-7
MIR R R, KUMAR N, JAISWAL V, GIRDHARWAL N, PRASAD M, BALYAN H S, GUPTA P K. Genetic dissection of grain weight in bread wheat through quantitative trait locus interval and association mapping. Molecular Breeding, 2012, 29(4): 963-972.

doi: 10.1007/s11032-011-9693-4
CHEN Z Y, CHENG X J, CHAI L L, WANG Z H, BIAN R L, LI J, ZHAO A J, XIN M M, GUO W L, HU Z R, PENG H R, YAO Y Y, SUN Q X, NI Z F. Dissection of genetic factors underlying grain size and fine mapping of QTgw.cau-7D in common wheat (Triticum aestivum L.). Theoretical and Applied Genetics, 2020, 133(1): 149-162.

doi: 10.1007/s00122-019-03447-5
LIU H Y, SONG S, XING Y Z. Beyond heading time: FT-like genes and spike development in cereals. Journal of Experimental Botany, 2019, 70(1): 1-3.

doi: 10.1093/jxb/ery408 pmid: 30590673
韩领锋, 亢玲, 张博, 王宪国, 王中华, 张晓科, 陈东升. 小麦光周期基因在我国不同麦区中的组成分布. 麦类作物学报, 2016, 36(12): 1617-1622.
HAN L F, KANG L, ZHANG B, WANG X G, WANG Z H, ZHANG X K, CHEN D S. Composition and distribution of wheat photoperiod genes in different wheat regions in China. Journal of Triticeae Crops, 2016, 36(12): 1617-1622. (in Chinese)
MOCKLER T, YANG H Y, YU X H, PARIKH D, CHENG Y C, DOLAN S, LIN C. Regulation of photoperiodic flowering by Arabidopsis photoreceptors. Proceedings of the National Academy of Sciences of the United States of America, 2003, 100(4): 2140-2145.
WEI J, FANG Y, JIANG H, WU X T, ZUO J H, XIA X C, LI J Q, STICH B, CAO H, LIU Y X. Combining QTL mapping and gene co-expression network analysis for prediction of candidate genes and molecular network related to yield in wheat. BMC Plant Biology, 2022, 22(1): 288.

doi: 10.1186/s12870-022-03677-8 pmid: 35698038
吕波. 植物开花基因FT的遗传转化及其参与开花调控的研究[D]. 泰安: 山东农业大学, 2014.
B. Genetic transformation of plant flowering gene FT and its involvement in flowering control[D]. Taian: Shandong Agricultural University, 2014. (in Chinese)
LI T, DENG G B, SU Y, YANG Z, TANG Y Y, WANG J H, ZHANG J Y, QIU X, PU X, YANG W Y, LI J, LIU Z H, ZHANG H L, LIANG J J, YU M Q, WEI Y M, LONG H. Genetic dissection of quantitative trait loci for grain size and weight by high-resolution genetic mapping in bread wheat (Triticum aestivum L.). Theoretical and Applied Genetics, 2022, 135(1): 257-271.

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