Scientia Agricultura Sinica ›› 2023, Vol. 56 ›› Issue (15): 2995-3005.doi: 10.3864/j.issn.0578-1752.2023.15.013

• HORTICULTURE • Previous Articles     Next Articles

Background Selection and Comparison of Marker Superiority and Inferiority of Aphid-Resistant Seedlings in an Interspecific Cross Peach Population

LIU SuNing1(), BIE HangLing1, WANG JunXiu1, CHEN XueJia1, WANG XinWei1,2, WANG LiRong1,2, CAO Ke1()   

  1. 1 Zhengzhou Fruit Research Institute, Chinese Academy of Agricultural Sciences, Zhengzhou 450009
    2 Western Agricultural Research Center, Chinese Academy of Agricultural Sciences, Changji 831100, Xinjiang
  • Received:2022-11-03 Accepted:2023-02-28 Online:2023-08-01 Published:2023-08-05

Abstract:

【Objective】 To establish a background selection system in peach, the seedlings contained aphid-resistance locus and high female parent recovery rate were screened from an F2 population crossed by Xiang Pi You Tao peach (big fruit and susceptible to aphid) and Zhou Xing Shan Tao peach (small fruit and resistant to aphid). 【Method】 Firstly, three methods were used to select background markers, including the high polymorphic single nucleotide polymorphism (SNP) obtained from the previous study (Pre-work SNP), SNP randomly selected in the whole genome (Random SNP), and functional SNP affecting the start and stop codon (Functional SNP). The number of final SNP selected by the above methods were 775. Then, using these SNPs, the parents recovery rate for all 121 individuals of the F2 population were calculated, respectively. The repeatability of the selection methods was evaluated by comparing whether the top 10 seedlings with different selection markers were coincident or not. After completing the evaluation of aphid resistance, single fruit weight, and soluble solids content of F2 population, 10 seedlings with extreme phenotypes for the single fruit weight and soluble solids content were selected, respectively. And the superiority and inferiority of different selection methods were estimated by comparing the significance of the differences in Xiang Pi You Tao recovery rates between the two types of phenotypes. Finally, the SNPs in the aphid-resistant location area were used as the foreground markers to screen the elite seedlings with high maternal genetic background and aphid resistance. 【Result】 The background recovery rates of the F2 seedlings which calculated by the three methods were 36.34%-71.99%, 31.75%-74.92%, and 4.51%-66.53%, respectively. Among the top 10 seedlings with high Xiang Pi You Tao recovery rates screened by the three background markers, Pre-work SNP and Random SNP had two duplicate single plants, and so do Pre-work SNP and Functional SNP, and there were 6 repetitive single plants in Random SNP and Functional SNP. This result indicated that the repeatability between the Random SNP and Functional SNP was the highest among all comparisons. When single fruit weight was selected as the breeding target, among the extreme phenotypic monocots, the three background markers, such as Pre-work SNP, Random SNP, and Functional SNP, had a significant Xiang Pi You Tao background recovery rate of 0.069, 0.26, and 0.092, respectively, which meant high relativity was found between the background recovery rate calculated by Pre-work SNP and their fruit weight, followed by Functional SNP, and Random SNP difference was not significant. When soluble solids content was selected as the target, the Xiang Pi You Tao background recovery rates among extreme phenotypic monocots were significant at 0.77, 0.65 and 0.31, respectively, and the differences among the three background markers were not significant. Finally, two individuals with high recovery rate of Xiang Pi You Tao peach were screened, including N20 and N36. Among them, N20 comprised the aphid-resistant markers, and this individual showed aphid resistance with an average fruit weight of 34.42 g and soluble solids content of 16.1%, which was considered to be the superior single strain of this population. 【Conclusion】 In this study population, Pre-work SNP showed a stronger correlation between single fruit weight and Xiang Pi You Tao background recovery rates than Functional SNP and Random SNP, confirming the superiority of this background marker selection method, and the superior performance of N20 plants, which selected with this background marker in the target traits also supported this result. This study provided an idea of background selection and a method to judge the superiority and inferiority of different background markers in the study population, which could effectively improve the efficiency of resistance breeding in fruit crops.

Key words: peach, aphid-resistant, SNP, foreground selection, background selection

Fig. 1

Distribution of 775 SNP markers on chromosomes"

Table 1

Proportion of F2 plants on eight chromosomes to the bi-parents"

背景标记类型
Background marker type
染色体
Chr
橡皮油桃 Xiang Pi You Tao 帚形山桃 Zhou Xing Shan Tao
平均回复率
Average response rate (%)
变异系数
CV
(%)
变异幅度
Variation amplitude (%)
超中亲率
HM
(%)
平均回复率
Average response rate (%)
变异系数
CV
(%)
变异幅度
Variation amplitude (%)
超中亲率
HM
(%)
多态性SNP
Pre-work SNP
1 57.19 24.84 15.49-78.87 81.82 34.34 36.40 14.08-71.83 14.88
2 63.66 27.50 3.39-83.05 79.34 28.79 60.34 10.17-86.44 15.70
3 50.41 42.20 3.45-79.31 71.90 39.18 53.71 15.52-91.38 34.71
4 55.15 45.24 2.08-81.25 67.77 38.41 70.36 14.58-97.92 29.75
5 62.24 33.83 2.63-89.47 78.51 26.56 86.93 0.00-84.21 22.31
6 72.41 13.46 36.99-87.67 94.21 21.69 47.83 6.85-61.64 4.96
7 38.87 40.02 2.94-67.65 38.84 53.72 34.37 20.59-94.12 59.50
8 45.05 34.07 3.03-63.64 66.12 45.60 35.94 27.27-87.88 37.19
随机SNP
Random SNP
1 49.50 34.93 17.01-87.76 70.25 21.11 47.53 7.48-60.54 9.92
2 52.12 34.71 21.18-88.24 81.82 19.42 61.99 3.53-62.35 12.40
3 41.69 47.67 12.50-82.95 55.37 27.17 69.89 5.68-71.59 28.10
4 38.66 50.94 6.85-80.82 47.93 31.50 58.93 4.11-76.71 31.40
5 40.93 63.28 7.27-98.18 56.20 23.73 93.49 0.00-78.18 25.62
6 60.06 41.10 17.89-95.79 78.51 13.47 78.92 1.05-52.6 4.96
7 27.63 46.68 8.70-92.75 60.33 40.03 62.40 2.90-71.01 28.93
8 32.21 43.62 19.18-89.04 58.68 29.91 63.16 2.74-63.01 23.97
功能SNP
Functional SNP
1 44.63 52.50 2.61-96.08 67.77 21.95 57.48 1.96-54.90 21.49
2 38.20 58.04 3.57-85.71 52.07 29.47 53.94 1.79-77.68 35.54
3 37.26 72.27 0.00-96.00 46.28 32.01 63.16 1.11-77.00 38.02
4 35.26 76.38 1.04-93.75 42.98 35.12 56.58 2.08-75.00 46.28
5 31.51 92.74 0.00-97.44 39.67 29.40 71.29 0.00-76.92 36.36
6 40.31 37.85 0.00-77.78 58.68 28.37 47.14 11.11-77.78 16.53
7 34.14 72.87 2.30-89.66 44.63 31.45 62.23 2.30-79.31 38.84
8 39.58 62.46 3.41-90.91 57.85 29.09 67.21 2.27-79.55 39.67

Fig. 2

Frequency distribution of background recovery rate of F2 plants"

Fig. 3

Repeatability of single plants with high biparental recovery compared among different background markers a: Xiang Pi You Tao peach; b: Zhou Xing Shan Tao peach"

Fig. 4

Difference of background recovery of Xiang Pi You Tao peach with extreme differential phenotype"

Fig. 5

Distribution of N20 Pre-work SNP background markers on chromosomes"

[1]
潘磊, 牛良, 鲁振华, 曾文芳, 崔国朝, 王志强. 桃树蚜虫的危害及其药剂防控. 果农之友, 2021(3): 37.
PAN L, NIU L, LU Z H, ZENG W F, CUI G C, WANG Z Q. Harm of peach aphid and its chemical control. Fruit Growers' Friend, 2021(3): 37. (in Chinese)
[2]
柳强, 刘翠美, 王春燕. 果树农药污染的危害与解决措施. 农业工程技术, 2019, 39(20): 36.
LIU Q, LIU C M, WANG C Y. Harm of pesticide pollution in fruit trees and its solutions. Applied Engineering Technology, 2019, 39(20): 36. (in Chinese)
[3]
EL-GENDY I, EL-BANOBI M I, VILLANUEVA-JIMÉNEZ J A. Bio-pesticides alternative diazinon to control peach fruit fly, Bactrocera zonata (Saunders) (Diptera: Tephritidae). Egyptian Journal of Biological Pest Control, 2021, 31: 1-8.

doi: 10.1186/s41938-020-00345-7
[4]
CRISAN L, BOROTA A, SUZUKI T, FUNAR-TIMOFEI S. An approach to identify new insecticides against Myzus persicae. in silico study based on linear and non-linear regression techniques. Molecular Informatics, 2019, 38(8/9): 1800119.

doi: 10.1002/minf.v38.8-9
[5]
牛良. 寿星桃抗蚜性鉴定及分子机制解析[D]. 武汉: 华中农业大学, 2019.
NIU L. Identification and molecular mechanism analysis of aphid resistance of Shouxing peach[D]. Wuhan: Huazhong Agricultural University, 2019. (in Chinese)
[6]
周洪昌. 玉米丝黑穗病分子标记辅助选择育种研究[D]. 长春: 吉林农业大学, 2011.
ZHOU H C. Study on molecular marker-assisted selection breeding of maize head smut[D]. Changchun: Jilin Agricultural University, 2011. (in Chinese)
[7]
冯艳霞. 果树育种中的新技术应用. 河北农业, 2022(7): 49-50.
FENG Y X. Application of new techniques in fruit tree breeding. Hebei Agriculture, 2022(7): 49-50. (in Chinese)
[8]
邓世峰, 王先如, 张安存, 陈次娥, 吴明. 分子标记辅助选择在我国水稻抗病育种中的研究进展. 江西农业, 2019(22): 40, 46.
DENG S F, WANG X R, ZHANG A C, CHEN C E, WU M. Research progress of molecular marker-assisted selection in rice disease resistance breeding in China. Jiangxi Nongye, 2019(22): 40, 46. (in Chinese)
[9]
杨大兵. 全基因背景分子选择改良水稻光温敏核不育系丰39S的病虫抗性[D]. 武汉: 华中农业大学, 2021.
YANG D B. Improvement of disease and pest resistance of photo- thermo sensitive genic male sterile line Feng 39S by whole gene background molecular selection[D]. Wuhan: Huazhong Agricultural University, 2021. (in Chinese)
[10]
RAI A, MAHENDRU-SINGH A, RAGHUNANDAN K, KUMAR T P J, SHARMA P, AHLAWAT A K, SINGH S K, GANJEWALA D, SHUKLA R B, SIVASAMY M. Marker-assisted transfer of PinaD1a gene to develop soft grain wheat cultivars. 3 Biotech, 2019, 9(5): 183-190.

doi: 10.1007/s13205-019-1717-5
[11]
赵雅楠. 无核抗寒葡萄胚挽救育种与分子标记辅助选择应用[D]. 杨凌: 西北农林科技大学, 2018.
ZHAO Y N. Embryo rescue breeding and molecular marker-assisted selection of seedless and cold-resistant grapes[D]. Yangling: Northwest A & F University, 2018. (in Chinese)
[12]
GUAN L P, XU Q, CAO K, LI Y, ZHU G R, FANG W C, WANG X W, CHEN C W, GUO J, WANG Q, ZHAO Y L, WANG L R. Development of a 775 SNP array for peach based on whole-genome resequencing data, and assessment of the potential of its application. Scientia Horticulturae, 2021, 276: 109760.

doi: 10.1016/j.scienta.2020.109760
[13]
王力荣, 王君秀, 李勇, 王新卫, 朱更瑞, 曹珂. 一组用于鉴定山桃杂交群体抗/感桃蚜性状的InDel标记及其应用. CN113186339A, 2021.
WANG L R, WANG J X, LI Y, WANG X W, ZHU G R, CAO K. A set of InDel markers for identification of peach aphid resistance/ susceptibility traits in a peach hybrid population and their applications, CN113186339A, 2021. (in Chinese)
[14]
王力荣, 朱更瑞, 方伟超, 左覃元, 韩立新. 桃种质资源对桃蚜的抗性评价. 果树学报, 2001, 18(3): 145-147.
WANG L R, ZHU G R, FANG W C, ZUO Q Y, HAN L X. Study on the resistance to peach aphid (Myzus persicae Sulzer) of peach germplasm. Journal of Fruit Science, 2001, 18(3): 145-147. (in Chinese)
[15]
王力荣, 朱更瑞. 桃种质资源描述规范和数据标准. 北京: 中国农业出版社, 2005.
WANG L R, ZHU G R. Descriptors and Data Standard for Peach (Prunus persica L.). Beijing: China Agriculture Press, 2005. (in Chinese)
[16]
LI H, DURBIN R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics, 2009, 25(14): 1754-1760.

doi: 10.1093/bioinformatics/btp324 pmid: 19451168
[17]
陈凤珍, 李玲, 操利超, 严志祥. 四种常用的生物序列比对软件比较. 生物信息学, 2016, 14(1): 56-60.
CHEN F Z, LI L, CAO L C, YAN Z X. Comparison of four common biological sequence alignment tools. Chinese Journal of Bioinformatics, 2016, 14(1): 56-60. (in Chinese)
[18]
VERDE I, JENKINS J, DONDINI L, MICALI S, PAGLIARANI G, VENDRAMIN E, PARIS R, ARAMINI V, GAZZA L, ROSSINI L, BASSI D, TROGGIO M, SHU S Q, GRIMWOOD J, TARTARINI S, DETTORI M T, SCHMUTZ J. The Peach v2.0 release: High- resolution linkage mapping and deep resequencing improve chromosome- scale assembly and contiguity. BMC Genomics, 2017, 18: 225.

doi: 10.1186/s12864-017-3606-9
[19]
LI H. A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data. Bioinformatics, 2011, 27(21): 2987-2993.

doi: 10.1093/bioinformatics/btr509 pmid: 21903627
[20]
MCKENNA A, HANNA M, BANKS E, SIVACHENKO A, CIBULSKIS K, KERNYTSKY A, GARIMELLA K, ALTSHULER D, GABRIEL S, DALY M, DEPRISTO M A. The genome analysis toolkit: A MapReduce framework for analyzing next-generation DNA sequencing data. Genome Research, 2010, 20(9): 1297-1303.

doi: 10.1101/gr.107524.110 pmid: 20644199
[21]
WANG K, LI M Y, HAKONARSON H. ANNOVAR: Functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Research, 2010, 38(16): e164.

doi: 10.1093/nar/gkq603
[22]
DANECEK P, AUTON A, ABECASIS G, ALBERS C A, BANKS E, DEPRISTO M A, HANDSAKER B, LUNTER G, MARTH G, SHERRY S, MCVEAN G, DURBIN R. The variant call format and VCFtools. Bioinformatics, 2011, 27(15): 2156-2158.

doi: 10.1093/bioinformatics/btr330 pmid: 21653522
[23]
王力荣, 朱更瑞, 方伟超. 中国桃遗传资源. 北京: 中国农业出版社, 2012.
WANG L R, ZHU G R, FANG W C. Peach Genetic Resource in China. Beijing: China Agriculture Press, 2012. (in Chinese)
[24]
曾梅, 韩立新, 高九思. 桃品种(系)抗桃蚜研究初报//华中三省(河南、湖北、湖南)昆虫学会2006年学术年会论文集, 2006: 116-118.
ZENG M, HAN L X, GAO J S. Preliminary report on peach aphid resistance in peach varieties (lines)//2006 Annual Academic Conference of the Entomological Society of Three Provinces in Central China, 2006: 116-118. (in Chinese)
[25]
张南南, 鲁振华, 崔国朝, 潘磊, 曾文芳, 牛良, 王志强. 基于SNP标记桃抗蚜性状的基因定位. 中国农业科学, 2017, 50(23): 4613-4621. doi: 10.3864/j.issn.0578-1752.2017.23.014.

doi: 10.3864/j.issn.0578-1752.2017.23.014
ZHANG N N, LU Z H, CUI G C, PAN L, ZENG W F, NIU L, WANG Z Q. Gene mapping of aphid-resistant for peach using SNP markers. Scientia Agricultura Sinica, 2017, 50(23): 4613-4621. doi: 10.3864/j.issn.0578-1752.2017.23.014. (in Chinese)

doi: 10.3864/j.issn.0578-1752.2017.23.014
[26]
瞿贵军, 林毅. 桃蚜关键抗性基因挖掘及抗蚜Cry蛋白预测. 华侨大学学报(自然科学版), 2023, 44(1): 94-103.
QU G J, LIN Y. Discovery of key resistance genes of Myzus persicae and prediction of anti-aphid cry proteins. Journal of Huaqiao University (Natural Science), 2023, 44(1): 94-103. (in Chinese)
[27]
HASAN M M, RAFII M Y, ISMAIL M R, MAHMOOD M, RAHIM H A, ALAM M A, ASHKANI S, MALEK M A, LATIF M A. Marker-assisted backcrossing: A useful method for rice improvement. Biotechnology, Biotechnological Equipment, 2015, 29(2): 237-254.

doi: 10.1080/13102818.2014.995920
[28]
WING R A, PURUGGANAN M D, ZHANG Q F. The rice genome revolution: from an ancient grain to Green Super Rice. Nature Reviews Genetics, 2018, 19(8): 505-517.

doi: 10.1038/s41576-018-0024-z pmid: 29872215
[29]
FRISCH M. Breeding strategies: Optimum design of marker-assisted backcross programs//Biotechnology in Agriculture and Forestry. Berlin/Heidelberg: Springer-Verlag, 2005: 319-334.
[30]
KARUNARATHNA N L, PATIRANAGE D S R, HARLOFF H J, SASHIDHAR N, JUNG C. Genomic background selection to reduce the mutation load after random mutagenesis. Scientific Reports, 2021, 11: 19404.

doi: 10.1038/s41598-021-98934-5 pmid: 34593904
[31]
CHANDRAN S, PUKALENTHY B, ADHIMOOLAM K, MANICKAM D, SAMPATHRAJAN V, CHOCKLINGAM V, ESWARAN K, ARUNACHALAM K, JOIKUMAR MEETEI L, RAJASEKARAN R, MUTHUSAMY V, HOSSAIN F, NATESAN S. Marker-assisted selection to pyramid the opaque-2 (O2) and β-carotene (crtRB1) genes in maize. Frontiers in Genetics, 2019, 10: 859.

doi: 10.3389/fgene.2019.00859
[32]
YU H H, XIE W B, LI J, ZHOU F S, ZHANG Q F. A whole-genome SNP array (RICE 6K) for genomic breeding in rice. Plant Biotechnology Journal, 2014, 12(1): 28-37.

doi: 10.1111/pbi.2013.12.issue-1
[33]
YANG D B, TANG J H, YANG D, CHEN Y, ALI J, MOU T M. Improving rice blast resistance of Feng39S through molecular marker-assisted backcrossing. Rice, 2019, 12: 70.

doi: 10.1186/s12284-019-0329-3 pmid: 31502096
[1] YE MeJin, WU Lei, MD NAHIBUZZAMAN Lohani, YIN Li, HU XinRong, LIU YaXi, JIANG YunFeng, CHEN GuoYue, PU ZhiEn, LI Yang, LI Ting, ZOU YaYa, WU JiaYi, MA Jian. Genome-Wide Association Study-Based Identification of Loci Controlling Mature Embryo Size in Chinese Wheat Landraces and Their Genetic Effects Analysis [J]. Scientia Agricultura Sinica, 2026, 59(6): 1157-1171.
[2] CAO HaiShun, ZHOU DongYuan, WANG Rui, SHI ZhaoWan, WU TingQuan, ZHANG ChangYuan. Identification of Short Hypocotyl Cucumber Germplasm Under Low Light Stress and QTL Mapping of the Trait [J]. Scientia Agricultura Sinica, 2026, 59(6): 1286-1301.
[3] WANG YongSheng, NIU Li, WANG ChangJie, MA LiHua, LIAN XiaoXiao, MENG YaXiong, MA XiaoLe, YAO LiRong, ZHANG Hong, YANG Ke, LI BaoChun, WANG HuaJun, SI ErJing, WANG JunCheng. Genome-Wide Association Study and Candidate Gene Identification for Thousand Grain Weight in Winter Wheat [J]. Scientia Agricultura Sinica, 2026, 59(3): 499-514.
[4] LI YunLi, DIAO DengChao, LIU YaRui, SUN YuChen, MENG XiangYu, WU ChenFang, WANG Yu, WU JianHui, LI ChunLian, ZENG QingDong, HAN DeJun, ZHENG WeiJun. Genome-Wide Association Study of Heat Tolerance at Seedling Stage in A Wheat Natural Population [J]. Scientia Agricultura Sinica, 2025, 58(9): 1663-1683.
[5] SUN Ping, ZHU WenCan, LIN XianRui, WU JiaQi, CAO YiWen, CHEN ChenFei, WANG Yi, ZHU JianXi, JIA HuiJuan, QIAN MinJie, SHEN JianSheng. Effects of Rainy and Low Light Conditions on Coloration and Flavonoid Accumulation in Peach Peel Based on Metabolomic and Transcriptomic Analyses [J]. Scientia Agricultura Sinica, 2025, 58(6): 1173-1194.
[6] GUO TianFa, WU JinLong, QIU QianQian, MA XinChao, WANG LiRong, WU CuiYun. Relationship Between the Formation of Non-Red Color in the Fruit Skin of Xinjiang Local Peach Varieties and the Variation of PpMYB10.1 Promoter [J]. Scientia Agricultura Sinica, 2025, 58(2): 326-338.
[7] LI Ming, CHENG YuKun, BAI Bin, LEI Bin, GENG HongWei. Genome-Wide Association Study on Spike Architecture Traits and Elite Haplotype Mining in Winter Wheat [J]. Scientia Agricultura Sinica, 2025, 58(18): 3583-3597.
[8] LIU HongXiang, ZHANG XuePing, WANG YiFei, WANG ZhiCheng, GU HaoTian, SONG WeiTao, TAO ZhiYun, XU WenJuan, ZHANG ShuangJie, LU LiZhi, LI HuiFang, ZHU ChunHong. Genome-Wide Association Study of Egg Production Traits in Jinding Duck [J]. Scientia Agricultura Sinica, 2025, 58(15): 3145-3158.
[9] GUO Lei, ZHANG BinBin, SHEN ZhiJun, YAN Juan, XU JianLan, CAI ZhiXiang, YU MingLiang, WANG FaLin, SONG HongFeng. The Release Characteristics of Medium and Trace Elements and Their Effects on Soil Available Nutrients after the Continuous Return of Green Manure in Peach Orchards [J]. Scientia Agricultura Sinica, 2025, 58(12): 2411-2426.
[10] ZHANG Ying, SHI TingRui, CAO Rui, PAN WenQiu, SONG WeiNing, WANG Li, NIE XiaoJun. Genome-Wide Association Study of Drought Tolerance at Seedling Stage in ICARDA-Introduced Wheat [J]. Scientia Agricultura Sinica, 2024, 57(9): 1658-1673.
[11] GUO Lei, HUANG ChenYan, SONG HongFeng, SHEN ZhiJun, ZHANG BinBin, MA RuiJuan, SUN Meng, HE Xin, YU MingLiang. Screening, Compounding and Safety Evaluation of Herbicides Suitable for Peach Nursery [J]. Scientia Agricultura Sinica, 2024, 57(9): 1734-1747.
[12] QI XiaoLei, WANG Jun, LÜ GuangDe, MU QiuHuan, MI Yong, SUN YingYing, YIN XunDong, QIAN ZhaoGuo, WANG RuiXia, WU Ke. Genetic Composition Analysis of a New High Quality and High Yield Wheat Cultivar Taikemai33 [J]. Scientia Agricultura Sinica, 2024, 57(22): 4391-4401.
[13] ZHANG MingQi, WANG Rui, ZHANG ChunXiao, SUN Bo, REN Jie, LI ShuFang, WANG Lu, ZHU ShaoXi, ZHANG JiangBin, SHI XinChen, WANG HaiJie, ZHANG YunLong, TIAN HongLi, ZHAO YiKun, KUANG Meng, WANG YuanDong, YI HongMei, LI XiaoHui, WANG FengGe. The Construction and Application of SSR and SNP Molecular ID for Maize Germplasm Resources of Jilin Province [J]. Scientia Agricultura Sinica, 2024, 57(2): 236-249.
[14] SHANG Hang, CHENG YuKun, REN Yi, GENG HongWei. Genome-Wide Association Analysis of Starch Gelatinization Traits in Winter Wheat [J]. Scientia Agricultura Sinica, 2024, 57(18): 3507-3521.
[15] LEI MengLin, LIU Xia, WANG YanZhen, CUI GuoQing, MU ZhiXin, LIU LongLong, LI Xin, LU LaHu, LI XiaoLi, ZHANG XiaoJun. Genetic Diversity Analysis of Winter Wheat Germplasm Resources in Shanxi Province Based on 55K SNP Array [J]. Scientia Agricultura Sinica, 2024, 57(10): 1845-1856.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!