Research progress on genetic map construction in fruit trees and QTL identification of major fruit quality traits

doi:10.3969/j.issn.1004-1524.20250224

Acta Agriculturae Zhejiangensis ›› 2026, Vol. 38 ›› Issue (3): 609-620.DOI: 10.3969/j.issn.1004-1524.20250224

Research progress on genetic map construction in fruit trees and QTL identification of major fruit quality traits

DU Chenfei¹^,²(), LI Wenjue¹^,², WEI Chunyan², CAI Danying², WANG Yuezhi², SHI Zebin², DAI Meisong²^,^*(), GAO Yongbin¹^,^*()

^1. College of Horticultural Science, Zhejiang A&F University, Hangzhou 311300, China
^2. Institute of Horticulture, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China

Received:2025-03-19 Online:2026-03-25 Published:2026-04-17
Contact: DAI Meisong,GAO Yongbin

Abstract

Abstract:

In breeding practice, traditional hybridization efficiency is constrained in major fruit crops such as apple, citrus, and pear due to their highly heterozygous genetic background, lengthy breeding cycles, and polygenic regulation of quantitative traits. In recent years, the construction of high-density genetic maps based on molecular markers and finemapping of quantitative trait loci (QTL) have become key approaches for unraveling the genetic mechanisms underlying key agronomic traits in fruit trees. Numerous genetic linkage maps have been developed worldwide, enabling the successfully mapping QTL associated with key quality traits including fruit size, shape, firmness, color, sugar-acid content, and aroma. Integrated multi-omics analyses have further revealed their molecular regulatory pathways. This review summarizes current progress in genetic mapping for fruit trees and QTL mapping achievements for major quality traits. Key findings indicate that optimized molecular marker techniques (SSR, SNP) have significantly enhanced genetic map resolution, while the “One-Ways-Pseudo-Testcross” and “Two-Ways-Pseudo-Testcross” theories have provided critical methodological frameworks for QTL analysis. Although QTLs related to major fruit quality traits have been identified in several fruit trees, their stability and cross-environment adaptability require further validation. Additionally, the synergistic genetic mechanisms of multiple traits and QTL-environment interactions remain poorly understood, limiting the precision of marker-assisted breeding. Future research should integrate high-throughput sequencing, multi-omics approaches, and machine learning to develop ultra-dense genetic maps, identify candidate genes, and establish theoretical foundations for efficient molecular design breeding in fruit trees.

Key words: fruit tree, linkage map, quality trait, QTL mapping

CLC Number:

DU Chenfei, LI Wenjue, WEI Chunyan, CAI Danying, WANG Yuezhi, SHI Zebin, DAI Meisong, GAO Yongbin. Research progress on genetic map construction in fruit trees and QTL identification of major fruit quality traits[J]. Acta Agriculturae Zhejiangensis, 2026, 38(3): 609-620.

Figures/Tables 3

References 45

[46]	CHO S H, OH S, HAN H, et al. QTL analysis and development of HRM markers associated with fruit shape in interspecific pears (Pyrus pyrifolia× P. bretschneideri)[J]. Euphytica, 2022, 218(9): 122.
[47]	WANG J, ZHANG K C, ZHANG X M, et al. Construction of commercial sweet cherry linkage maps and QTL analysis for trunk diameter[J]. PLoS One, 2015, 10(10): e0141261.
[48]	CARRASCO-VALENZUELA T, MUÑOZ-ESPINOZA C, RIVEROS A, et al. Expression QTL (eQTLs) analyses reveal candidate genes associated with fruit flesh softening rate in peach [Prunus persica(L.) Batsch[J]. Frontiers in Plant Science, 2019, 10: 1581.
[49]	SERRA O, GINÉ-BORDONABA J, EDUARDO I, et al. Genetic analysis of the slow-melting flesh character in peach[J]. Tree Genetics & Genomes, 2017, 13(4): 77.
[50]	RAWANDOOZI Z J, HARTMANN T P, CARPENEDO S, et al. Identification and characterization of QTLs for fruit quality traits in peach through a multi-family approach[J]. BMC Genomics, 2020, 21: 522.
[51]	SAEED M, BREWER L, JOHNSTON J, et al. Genetic, metabolite and developmental determinism of fruit friction discolouration in pear[J]. BMC Plant Biology, 2014, 14: 241.
[1]	王力荣, 吴金龙. 中国果树种质资源研究与新品种选育70年[J]. 园艺学报, 2021, 48(4): 749-758.
	WANG L R, WU J L. Review for the research of fruit tree germplasm and breeding of new varieties in the past seven decades in China[J]. Acta Horticulturae Sinica, 2021, 48(4): 749-758.
[52]	IWATA H, HAYASHI T, TERAKAMI S, et al. Potential assessment of genome-wide association study and genomic selection in Japanese pear Pyrus pyrifolia[J]. Breeding Science, 2013, 63(1): 125-140.
[53]	CAI L C, QUERO-GARCÍA J, BARRENECHE T, et al. A fruit firmness QTL identified on linkage group 4 in sweet cherry (Prunus avium L.) is associated with domesticated and bred germplasm[J]. Scientific Reports, 2019, 9: 5008.
[2]	VAN NOCKER S, GARDINER S E. Breeding better cultivars, faster: applications of new technologies for the rapid deployment of superior horticultural tree crops[J]. Horticulture Research, 2014, 1: 14022.
[3]	常源升, 程来亮, 王海波, 等. 苹果分子标记及辅助育种研究进展[J]. 园艺学报, 2017, 44(9): 1658-1680.
[54]	WU B, SHEN F, WANG X, et al. Role of MdERF3 and MdERF118 natural variations in apple flesh firmness/crispness retainability and development of QTL-based genomics-assisted prediction[J]. Plant Biotechnology Journal, 2021, 19(5): 1022-1037.
[55]	KHAN S A, CHIBON P Y, DE VOS R C H, et al. Genetic analysis of metabolites in apple fruits indicates an mQTL hotspot for phenolic compounds on linkage group 16[J]. Journal of Experimental Botany, 2012, 63(8): 2895-2908.
[56]	VERDU C F, GUYOT S, CHILDEBRAND N, et al. QTL analysis and candidate gene mapping for the polyphenol content in cider apple[J]. PLoS One, 2014, 9(10): e107103.
[57]	CHAGNÉ D, KUI L W, ESPLEY R V, et al. An ancient duplication of apple MYB transcription factors is responsible for novel red fruit-flesh phenotypes[J]. Plant Physiology, 2012, 161(1): 225-239.
[58]	SOORIYAPATHIRANA S S, KHAN A, SEBOLT A M, et al. QTL analysis and candidate gene mapping for skin and flesh color in sweet cherry fruit (Prunus avium L.)[J]. Tree Genetics & Genomes, 2010, 6(6): 821-832.
[59]	KUMAR S, KIRK C, DENG C H, et al. Fine-mapping and validation of the genomic region underpinning pear red skin colour[J]. Horticulture Research, 2019, 6: 29.
[60]	XUE H B, SHI T, WANG F F, et al. Interval mapping for red/green skin color in Asian pears using a modified QTL-seq method[J]. Horticulture Research, 2017, 4: 17053.
[61]	郑丽静, 聂继云, 闫震. 糖酸组分及其对水果风味的影响研究进展[J]. 果树学报, 2015, 32(2): 304-312.
	ZHENG L J, NIE J Y, YAN Z. Advances in research on sugars, organic acids and their effects on taste of fruits[J]. Journal of Fruit Science, 2015, 32(2): 304-312.
[62]	KUNIHISA M, MORIYA S, ABE K, et al. Identification of QTLs for fruit quality traits in Japanese apples: QTLs for early ripening are tightly related to preharvest fruit drop[J]. Breeding Science, 2014, 64(3): 240-251.
[63]	GUAN Y Z, PEACE C, RUDELL D, et al. QTLs detected for individual sugars and soluble solids content in apple[J]. Molecular Breeding, 2015, 35(6): 135.
[64]	SUN R, CHANG Y S, YANG F Q, et al. A dense SNP genetic map constructed using restriction site-associated DNA sequencing enables detection of QTLs controlling apple fruit quality[J]. BMC Genomics, 2015, 16: 747.
[65]	WANG Z Y, MA B Q, YANG N X, et al. Variation in the promoter of the sorbitol dehydrogenase gene MdSDH2 affects binding of the transcription factor MdABI3 and alters fructose content in apple fruit[J]. The Plant Journal, 2022, 109(5): 1183-1198.
[66]	SHI P, XU Z, ZHANG S Y, et al. Construction of a high-density SNP-based genetic map and identification of fruit-related QTLs and candidate genes in peach [Prunus persica(L.) Batsch[J]. BMC Plant Biology, 2020, 20: 438.
[67]	DESNOUES E, BALDAZZI V, GÉNARD M, et al. Dynamic QTLs for sugars and enzyme activities provide an overview of genetic control of sugar metabolism during peach fruit development[J]. Journal of Experimental Botany, 2016, 67(11): 3419-3431.
[68]	NISHIO S, SAITO T, TERAKAMI S, et al. Identification of QTLs associated with conversion of sucrose to hexose in mature fruit of Japanese pear[J]. Plant Molecular Biology Reporter, 2018, 36(4): 643-652.
[69]	BAN S, XU K N. Identification of two QTLs associated with high fruit acidity in apple using pooled genome sequencing analysis[J]. Horticulture Research, 2020, 7: 171.
[70]	ZHANG B, HAN Y P. Genomics of fruit acidity and sugar content in apple[M]//KORBAN S S. The apple genome. Cham: Springer International Publishing, 2021, 7: 297-309.
[71]	YAMAMOTO T, TERAKAMI S, TAKADA N, et al. Identification of QTLs controlling harvest time and fruit skin color in Japanese pear (Pyrus pyrifolia Nakai)[J]. Breeding Science, 2014, 64(4): 351-361.
[72]	彭麟. 梨高密度SNP图谱构建和果实有机酸的QTL定位[D]. 杭州: 浙江大学, 2022.
	PENG L. Construction of high-density SNP genetic linkage map and QTL mapping for fruit organic acids in pear[D]. Hangzhou: Zhejiang University, 2022.
[73]	DUNEMANN F, ULRICH D, BOUDICHEVSKAIA A, et al. QTL mapping of aroma compounds analysed by headspace solid-phase microextraction gas chromatography in the apple progeny ‘Discovery’×‘Prima’[J]. Molecular Breeding, 2009, 23(3): 501-521.
[74]	YANG S B, YU J, YANG H J, et al. Genetic analysis and QTL mapping of aroma volatile compounds in the apple progeny ‘Fuji’×‘Cripps Pink’[J]. Frontiers in Plant Science, 2023, 14: 1048846.
[75]	YU Y, BAI J H, CHEN C X, et al. Identification of QTLs controlling aroma volatiles using a ‘Fortune’×‘Murcott’ (Citrus reticulata) population[J]. BMC Genomics, 2017, 18: 646.
[76]	FERRER V, COSTANTINO G, PAYMAL N, et al. Inheritance and quantitative trait loci mapping of aromatic compounds from clementine (Citrus×clementina Hort. ex Tan.) and sweet orange [C.×sinensis(L.) Osb.] fruit essential oils[J]. Genes, 2023, 14(9): 1800.
[77]	ZORRILLA-FONTANESI Y, RAMBLA J L, CABEZA A, et al. Genetic analysis of strawberry fruit aroma and identification of O-methyltransferase FaOMT as the locus controlling natural variation in mesifurane content[J]. Plant Physiology, 2012, 159(2): 851-870.
[78]	BOHRA A, CHAND JHA U, GODWIN I D, et al. Genomic interventions for sustainable agriculture[J]. Plant Biotechnology Journal, 2020, 18(12): 2388-2405.
[79]	ZHENG W Y, SHEN F, WANG W Q, et al. Quantitative trait loci-based genomics-assisted prediction for the degree of apple fruit cover color[J]. The Plant Genome, 2020, 13(3): e20047.
[3]	CHANG Y S, CHENG L L, WANG H B, et al. Review of molecular marker and marker assisted breeding of apple[J]. Acta Horticulturae Sinica, 2017, 44(9): 1658-1680.
[4]	张校立, 艾沙江·买买提, 薛华柏, 等. 梨遗传连锁图谱研究进展[J]. 果树学报, 2018, 35(S1): 1-8.
	ZHANG X L, AISHAJIANG M, XUE H B, et al. Recent advances in research on the genetic linkage maps in pears[J]. Journal of Fruit Science, 2018, 35(S1): 1-8.
[5]	韩国辉, 罗友进, 向素琼, 等. 柑橘遗传连锁图谱构建及重要性状QTL的研究[J]. 园艺学报, 2019, 46(9): 1739-1751.
	HAN G H, LUO Y J, XIANG S Q, et al. Advances in genetic linkage map construction and QTL mapping associated main traits of citrus[J]. Acta Horticulturae Sinica, 2019, 46(9): 1739-1751.
[6]	GAO C X. Genome engineering for crop improvement and future agriculture[J]. Cell, 2021, 184(6): 1621-1635.
[7]	ZHANG M Y, XUE C, HU H J, et al. Genome-wide association studies provide insights into the genetic determination of fruit traits of pear[J]. Nature Communications, 2021, 12: 1144.
[8]	GRATTAPAGLIA D, SEDEROFF R. Genetic linkage maps of Eucalyptus grandis and Eucalyptus urophylla using a pseudo-testcross: mapping strategy and RAPD markers[J]. Genetics, 1994, 137(4): 1121-1137.
[9]	WEEDEN N F, HEMMATT M, LAWSON D M, et al. Development and application of molecular marker linkage maps in woody fruit crops[J]. Euphytica, 1994, 77(1/2): 71-75.
[10]	张新忠, 邓明琴, 洪建源, 等. 正态检验法在果树遗传学研究中应用的可行性[J]. 落叶果树, 1994(1): 1-5.
	ZHANG X Z, DENG M Q, HONG J Y, et al. Feasability of normal distributive testing in genetic studies on fruit and berry crops[J]. Deciduous Fruits, 1994(1): 1-5.
[11]	唐志鹏, 甘霖, 熊兴耀. 果树基因工程研究的现状与展望[J]. 福建果树, 2005(3): 14-17.
	TANG Z P, GAN L, XIONG X Y. Advances in research of gene-engineering in fruit trees[J]. Fujian Fruits, 2005(3): 14-17.
[12]	OH S, AHN S, HAN H, et al. Genetic linkage maps and QTLs associated with fruit skin color and acidity in apple (Malus×domestica)[J]. Horticulture, Environment, and Biotechnology, 2023, 64(2): 299-310.
[13]	YANG C Q, SHA G Y, WEI T, et al. Linkage map and QTL mapping of red flesh locus in apple using a R1R1×R6R6 population[J]. Horticultural Plant Journal, 2021, 7(5): 393-400.
[14]	LIU Z C, BAO D E, LIU D L, et al. Construction of a genetic linkage map and QTL analysis of fruit-related traits in an F1 Red Fuji×Hongrou apple hybrid[J]. Open Life Sciences, 2016, 11(1): 487-497.
[15]	KUMAR K, YU Q B, BHATIA D, et al. Construction of a high density genetic linkage map to define the locus conferring seedlessness from Mukaku Kishu mandarin[J]. Frontiers in Plant Science, 2023, 14: 1087023.
[16]	王炯. 基于COS marker构建柑橘连锁图谱及作图群体的光合特性研究[D]. 重庆: 西南大学, 2017.
	WANG J. Citrus linkage map construction based on COS marker and photosynthetic characteristics research of the mapping population[D]. Chongqing: Southwest University, 2017.
[17]	DA SILVA LINGE C, ANTANAVICIUTE L, ABDELGHAFAR A, et al. High-density multi-population consensus genetic linkage map for peach[J]. PLoS One, 2018, 13(11): e0207724.
[18]	ZEBALLOS J L, ABIDI W, GIMÉNEZ R, et al. Mapping QTLs associated with fruit quality traits in peach [Prunus persica(L.) Batsch] using SNP maps[J]. Tree Genetics & Genomes, 2016, 12(3): 37.
[19]	RESHEF N, KARN A, MANNS D C, et al. Stable QTL for malate levels in ripe fruit and their transferability across Vitiss pecies[J]. Horticulture Research, 2022, 9: uhac009.
[20]	KARN A, DIAZ-GARCIA L, RESHEF N, et al. The genetic basis of anthocyanin acylation in North American grapes (Vitis spp.)[J]. Genes, 2021, 12(12): 1962.
[21]	QIN M F, LI L T, SINGH J, et al. Construction of a high-density bin-map and identification of fruit quality-related quantitative trait loci and functional genes in pear[J]. Horticulture Research, 2022, 9: uhac141.
[22]	WU J, LI L T, LI M, et al. High-density genetic linkage map construction and identification of fruit-related QTLs in pear using SNP and SSR markers[J]. Journal of Experimental Botany, 2014, 65(20): 5771-5781.
[23]	PENG Z, ZHAO C B, LI S Q, et al. Integration of genomics, transcriptomics and metabolomics identifies candidate loci underlying fruit weight in loquat[J]. Horticulture Research, 2022, 9: uhac037.
[24]	SAMANEH N K. 基于分子生物学方法揭示枇杷属植物遗传关系和构建遗传图谱[D]. 广州: 华南农业大学, 2017.
	SAMANEH N K. Genetic relationship by genome-wide rad sequence data and establishment of genetic maps based on molecular markers of Eriobotrya plants[D]. Guangzhou: South China Agricultural University, 2017.
[25]	CURTOLO M, CRISTOFANI-YALY M, GAZAFFI R, et al. QTL mapping for fruit quality in citrus using DArTseq markers[J]. BMC Genomics, 2017, 18: 289.
[26]	连晓东. 基于高密度遗传图谱的桃重要性状基因定位及其形成机制研究[D]. 郑州: 河南农业大学, 2019.
	LIAN X D. Based on high-density genetic linkage map, gene mapping and molecular mechanism analysis of important traits in peach (Prunus persica)[D]. Zhengzhou: Henan Agricultural University, 2019.
[27]	WANG L, LI X G, WANG L, et al. Construction of a high-density genetic linkage map in pear (Pyrus communis×Pyrus pyrifolia Nakai) using SSRs and SNPs developed by SLAF-seq[J]. Scientia Horticulturae, 2017, 218: 198-204.
[28]	LEWTER J, WORTHINGTON M L, CLARK J R, et al. High-density linkage maps and loci for berry color and flower sex in muscadine grape (Vitis rotundifolia)[J]. Theoretical and Applied Genetics, 2019, 132(5): 1571-1585.
[29]	FUKUDA S, NAGANO Y, MATSUGUMA K, et al. Construction of a high-density linkage map for bronze loquat using RAD-Seq[J]. Scientia Horticulturae, 2019, 251: 59-64.
[30]	单大鹏, 刘春燕, 蒋洪蔚, 等. 两种方法定位5个地点大豆蛋白质含量QTL[J]. 中国油料作物学报, 2011, 33(1): 9-14.
	SHAN D P, LIU C Y, JIANG H W, et al. QTL analysis of soybean protein content using two methods in 5 different environments[J]. Chinese Journal of Oil Crop Sciences, 2011, 33(1): 9-14.
[31]	仕相林, 孙亚男, 王家麟, 等. 大豆叶片性状QTL的定位及Meta分析[J]. 作物学报, 2012, 38(2): 256-263.
	SHI X L, SUN Y N, WANG J L, et al. Mapping and meta-analysis of QTLs for leaf traits in soybean[J]. Acta Agronomica Sinica, 2012, 38(2): 256-263.
[32]	方天, 刘继红. 主要果树重要性状QTL鉴定研究进展[J]. 园艺学报, 2022, 49(12): 2622-2640.
	FANG T, LIU J H. Advances in identification of QTLs associated with significant traits in major fruit trees[J]. Acta Horticulturae Sinica, 2022, 49(12): 2622-2640.
[33]	田雯, 李子琛, 王霖, 等. 苹果重要性状全基因组关联分析研究进展[J]. 园艺学报, 2024, 51(7): 1565-1579.
	TIAN W, LI Z C, WANG L, et al. Advances in genome-wide association study associated important traits in apple[J]. Acta Horticulturae Sinica, 2024, 51(7): 1565-1579.
[34]	SAHU T K, SINGH M, KALIA S, et al. Genome-wide association study (GWAS): concept and methodology for gene mapping in plants[M]//RAINA A, WANI M R, LASKAR R A, et al. Advanced crop improvement, volume 2: case studies of economically important crops. Cham: Springer International Publishing, 2023: 477-511.
[35]	TAM V, PATEL N, TURCOTTE M, et al. Benefits and limitations of genome-wide association studies[J]. Nature Reviews Genetics, 2019, 20(8): 467-484.
[36]	盛英华, 张延瑞, 戴亚楠, 等. 不同群体中大豆脂肪酸组分QTL定位研究[J]. 中国油料作物学报, 2020, 42(5): 796-806.
	SHENG Y H, ZHANG Y R, DAI Y N, et al. QTL mapping of fatty acids in different F₂ populations in soybean[J]. Chinese Journal of Oil Crop Sciences, 2020, 42(5): 796-806.
[37]	李华东, 杨会, 吕玉英, 等. 栽培花生花期相关QTL定位[J]. 山东农业科学, 2021, 53(2): 1-6.
	LI H D, YANG H, LYU Y Y, et al. QTL mapping for flowering time in cultivated peanut (Arachis hypogaea L.)[J]. Shandong Agricultural Sciences, 2021, 53(2): 1-6.
[38]	高静瑶, 刘春燕, 蒋洪蔚, 等. 多环境下大豆单株荚数性状的QTL分析[J]. 中国油料作物学报, 2012, 34(1): 1-7.
	GAO J Y, LIU C Y, JIANG H W, et al. QTL analysis of pod number per plant in soybean under multiple locations[J]. Chinese Journal of Oil Crop Sciences, 2012, 34(1): 1-7.
[39]	韩明丽, 刘永立, 郑小艳, 等. 梨遗传连锁图谱的构建及部分果实性状QTL的定位[J]. 果树学报, 2010, 27(4): 496-503.
	HAN M L, LIU Y L, ZHENG X Y, et al. Construction of a genetic linkage map and QTL analysis for some fruit traits in pear[J]. Journal of Fruit Science, 2010, 27(4): 496-503.
[40]	ZHANG J C, ZHANG D J, FAN Y W, et al. The identification of grain size genes by RapMap reveals directional selection during rice domestication[J]. Nature Communications, 2021, 12: 5673.
[41]	SHEN F, HUANG Z Y, ZHANG B G, et al. Mapping gene markers for apple fruit ring rot disease resistance using a multi-omics approach[J]. G3 Genes\|Genomes\|Genetics, 2019, 9(5): 1663-1678.
[42]	李嘉琦, 刘有春, 魏鑫, 等. 全基因组关联分析在果树品质及抗性性状中的研究进展[J]. 江苏农业科学, 2024, 52(11): 10-19.
	LI J Q, LIU Y C, WEI X, et al. Research progress on genome-wide association analysis (GWAS) for fruit quality and resistance traits[J]. Jiangsu Agricultural Sciences, 2024, 52(11): 10-19.
[43]	CIRILLI M, BACCICHET I, CHIOZZOTTO R, et al. Genetic and phenotypic analyses reveal major quantitative loci associated to fruit size and shape traits in a non-flat peach collection (P. persica L. Batsch)[J]. Horticulture Research, 2021, 8: 232.
[44]	FRESNEDO-RAMÍREZ J, FRETT T J, SANDEFUR P J, et al. QTL mapping and breeding value estimation through pedigree-based analysis of fruit size and weight in four diverse peach breeding programs[J]. Tree Genetics & Genomes, 2016, 12(2): 25.
[45]	KOSTICK S A, LUBY J J. Apple fruit size QTLs on chromosomes 8 and 16 characterized in ‘honeycrisp’-derived germplasm[J]. Agronomy, 2022, 12(6): 1279.

物种 Species	亲本 Parents	标记类型 Marker types	群体大小 (株) Population size (Number of plants)	连锁群数量 Number of linkage groups	标记数量 Marker number	平均标记间距/cM Average marker spacing/cM	构建年份 Construction year	参考文献 References
苹果 (Malus Mill.)	Gala×Jonathan	SNP/SSR	145	17	765	1.96	2023	[12]
	Fuji×Red3	SNP	168	17	630	0.30	2021	[13]
	Fuji×Hongrou	SSR/SRAP	110	17	280	4.60	2016	[14]
柑橘 (Citrus L.)	MK×SB, Daisy	SNP	165	9	2 588	0.54	2023	[15]
	梨橙2号×晚蜜2	SSR/COS	80	10	165	4.90	2017	[16]
	Licheng No. 2×Wanmi No. 2
	Murcott tangor×Pera	DArT	278	13	661	0.23	2017	[25]
桃 (Prunus L.)	中油桃14号×黄水蜜	In Del/SNP	86	8	948	1.43	2019	[26]
	Zhongyoutao No.14×Huangshuimi
	Zin Dai×Crimson Lady	SNP/SSR	90	8	1 476	1.38	2018	[17]
	Venus×Big Top	SNP/SSR	75	9	411	3.80	2016	[18]
梨 (Pyrus L.)	Niitaka×Hongxiangsu	SNP (Bin-Marker)	176	17	3 190	0.43	2022	[21]
	Red Clapp Favorite×Mansoo	SNP/SSR	161	17	4 865	0.56	2017	[27]
	八月红×砀山酥梨	SNP/SSR	102	17	3 241	0.70	2014	[22]
	Bayuehong×Dangshansu
葡萄 (Vitis L.)	B38×Horizon	rhAmpSeq	118	19	1 092	1.01	2021	[19-20]
	Horizon×Illinois 547-1	rhAmpSeq	142	19	1 171	1.16	2021	[20]
	Supreme×Nesbitt	GBS	172	20	2 069	1.01	2019	[28]
枇杷 (Eriobotrya L.)	宁海白×大丰	SNP (Bin-Marker)	130	17	3 859	0.52	2022	[23]
	Ninghaibai×Dafeng
	Japonica×Deflexa	SNP	96	17	960	1.78	2019	[29]
	栎叶枇杷×解放钟	RAD-seq/SRAP	207	17	500	14.53	2017	[24]
	Quercifolia Loquat×Jiefangzhong	SLAF-seq/SSR

物种 Species	亲本 Parents	标记类型 Marker types	群体大小 (株) Population size (Number of plants)	连锁群数量 Number of linkage groups	标记数量 Marker number	平均标记间距/cM Average marker spacing/cM	构建年份 Construction year	参考文献 References
苹果 (Malus Mill.)	Gala×Jonathan	SNP/SSR	145	17	765	1.96	2023	[12]
	Fuji×Red3	SNP	168	17	630	0.30	2021	[13]
	Fuji×Hongrou	SSR/SRAP	110	17	280	4.60	2016	[14]
柑橘 (Citrus L.)	MK×SB, Daisy	SNP	165	9	2 588	0.54	2023	[15]
	梨橙2号×晚蜜2	SSR/COS	80	10	165	4.90	2017	[16]
	Licheng No. 2×Wanmi No. 2
	Murcott tangor×Pera	DArT	278	13	661	0.23	2017	[25]
桃 (Prunus L.)	中油桃14号×黄水蜜	In Del/SNP	86	8	948	1.43	2019	[26]
	Zhongyoutao No.14×Huangshuimi
	Zin Dai×Crimson Lady	SNP/SSR	90	8	1 476	1.38	2018	[17]
	Venus×Big Top	SNP/SSR	75	9	411	3.80	2016	[18]
梨 (Pyrus L.)	Niitaka×Hongxiangsu	SNP (Bin-Marker)	176	17	3 190	0.43	2022	[21]
	Red Clapp Favorite×Mansoo	SNP/SSR	161	17	4 865	0.56	2017	[27]
	八月红×砀山酥梨	SNP/SSR	102	17	3 241	0.70	2014	[22]
	Bayuehong×Dangshansu
葡萄 (Vitis L.)	B38×Horizon	rhAmpSeq	118	19	1 092	1.01	2021	[19-20]
	Horizon×Illinois 547-1	rhAmpSeq	142	19	1 171	1.16	2021	[20]
	Supreme×Nesbitt	GBS	172	20	2 069	1.01	2019	[28]
枇杷 (Eriobotrya L.)	宁海白×大丰	SNP (Bin-Marker)	130	17	3 859	0.52	2022	[23]
	Ninghaibai×Dafeng
	Japonica×Deflexa	SNP	96	17	960	1.78	2019	[29]
	栎叶枇杷×解放钟	RAD-seq/SRAP	207	17	500	14.53	2017	[24]
	Quercifolia Loquat×Jiefangzhong	SLAF-seq/SSR

维度 Dimension	果树 Fruit trees	大田作物 Field crops
群体类型 Group types	多采用杂交F₁群体或自然群体 Generally employ hybrid F₁ populations or natural populations	主要使用F₂群体、RILs(重组自交系)或双单倍体群体 Mainly using F₂ populations, RILs (recombinant inbred lines), or dihaploid populations
群体规模 Group size	规模较小(50~200株) Small scale (50-200 plants)	规模较大(200~1 000株) Large scale (200-1 000 plants)
标记类型 Marker type	偏向SNP等高通量标记或SSR标记 Towards SNP and other high-throughput markers or SSR markers	SSR、AFLP等传统标记较为常用^[36] SSR, AFLP, and other traditional markers are more commonly used^[36]
表型鉴定 Phenotypic identification	需多年数据(3~5年) Requires multi-year data (3-5 years)	单季/两年多点鉴定为主 Single season/over two years identification as main
统计方法 Statistical methods	需考虑多年数据混合模型,LOD阈值更高 Need to consider a multi-year data hybrid model with a higher LOD threshold	复合区间作图法(CIM)为主,LOD值2.5~3.0^[37] Composite interval mapping (CIM) as the primary method, with LOD values ranging from 2.5 to 3.0^[37]
环境影响 Environmental impact	受环境影响大,环境互作效应显著 Highly influenced by the environment, with significant environmental interaction effects	可通过多点重复降低误差^[38] Error can be reduced by multi-point repetition^[38]
应用方向 Application direction	侧重性状早期预测(成熟期、缩短童期)^[39] Focus on early prediction of traits (maturity period, shortened juvenile period)^[39]	直接指导分子标记辅助选择^[36] Direct guidance in molecular marker-assisted selection^[36]

维度 Dimension	果树 Fruit trees	大田作物 Field crops
群体类型 Group types	多采用杂交F₁群体或自然群体 Generally employ hybrid F₁ populations or natural populations	主要使用F₂群体、RILs(重组自交系)或双单倍体群体 Mainly using F₂ populations, RILs (recombinant inbred lines), or dihaploid populations
群体规模 Group size	规模较小(50~200株) Small scale (50-200 plants)	规模较大(200~1 000株) Large scale (200-1 000 plants)
标记类型 Marker type	偏向SNP等高通量标记或SSR标记 Towards SNP and other high-throughput markers or SSR markers	SSR、AFLP等传统标记较为常用^[36] SSR, AFLP, and other traditional markers are more commonly used^[36]
表型鉴定 Phenotypic identification	需多年数据(3~5年) Requires multi-year data (3-5 years)	单季/两年多点鉴定为主 Single season/over two years identification as main
统计方法 Statistical methods	需考虑多年数据混合模型,LOD阈值更高 Need to consider a multi-year data hybrid model with a higher LOD threshold	复合区间作图法(CIM)为主,LOD值2.5~3.0^[37] Composite interval mapping (CIM) as the primary method, with LOD values ranging from 2.5 to 3.0^[37]
环境影响 Environmental impact	受环境影响大,环境互作效应显著 Highly influenced by the environment, with significant environmental interaction effects	可通过多点重复降低误差^[38] Error can be reduced by multi-point repetition^[38]
应用方向 Application direction	侧重性状早期预测(成熟期、缩短童期)^[39] Focus on early prediction of traits (maturity period, shortened juvenile period)^[39]	直接指导分子标记辅助选择^[36] Direct guidance in molecular marker-assisted selection^[36]

QTL鉴定技术 QTL identification techniques	优势 Advantages	局限 Limitations	参考文献 References
遗传图谱QTL定位 Genetic map QTL mapping	适用于主效QTL定位且结果稳定,辅助比较基因组分析 Applicable for major QTL localization with stable results, assisting in comparative genome analysis	依赖高通量测序,成本较高;对群体类型要求严格,仅能解析亲本间有限的等位变异 Relies on high-throughput sequencing, which is costly; requires strict population types and can only resolve a limited number of allelic variations between parents	[32]
RapMap高效克隆技术 RapMap high-efficiency cloning technology	高效克隆主效QTL,适用于多亲本遗传多样性分析 Efficient cloning of major QTL for genetic diversity analysis in multi-parental populations	依赖杂交群体构建,不适合自然群体,对微效QTL检测能力有限 Relies on hybrid population construction, not suitable for natural populations, and has limited capability in detecting minor QTL	[40]
BSA(混池分离分析) BSA(bulked segregant analysis)	成本低,无需完整遗传图谱,适合单一主效QTL分析 Low cost, without the need for a complete genetic map, suitable for the analysis of single major QTL	分辨率较低,依赖极端表型材料;对候选区域后续验证工作量大 Low-resolution, relying on extreme phenotypic materials; substantial workload required for subsequent validation of candidate regions	[41]
多组学整合分析 Multi-omics integrated analysis	适用于复杂性状(如风味、抗逆性)的多层次解析 Multilayered analysis for complex traits (such as flavor and stress resistance)	数据量大、分析复杂、成本高,对样本处理和实验设计要求严格 Large data volume, complex analysis, high cost, and strict requirements for sample processing and experimental design	[41]
GWAS(全基因组关联分析) GWAS(genome-wide association study)	可同时检测多个等位变异,适用于复杂性状 Can detect multiple allelic variants simultaneously, applicable to complex traits	需大量样本控制假阳性;对微效QTL检测能力弱,且难以区分连锁位点 Requires a large number of samples to control false positives; weak detection capability for minor QTL and difficulty in distinguishing linked loci	[42]

Research progress on genetic map construction in fruit trees and QTL identification of major fruit quality traits

RichHTML

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 3

References 45

Related Articles 15

Recommended Articles

Metrics

Comments

[1]	LEI Zhiwei, LI Xinxin, XU Heng, ZHANG Heng, ZHU Ying, ZHANG Hua. Identification of QTLs for stem borer resistance using chromosome segment substitution lines in rice [J]. Acta Agriculturae Zhejiangensis, 2025, 37(3): 530-537.
[2]	CUI Junjie, LYU Zhen, YANG Tianwen, WANG Jing, HONG Yu, CAO Yi. Construction of high-density bin marker genetic map and QTL mapping for fruit length in Luffa spp. [J]. Acta Agriculturae Zhejiangensis, 2023, 35(3): 590-597.
[3]	MO Yanling, ZHANG Wenjing, LUO Yalan, ZENG Jing, CHEN Jingjing, LIU Yihua. Identification and evaluation of agronomic traits and nutritional quality traits in wide handle mustard germplam resources [J]. Acta Agriculturae Zhejiangensis, 2022, 34(2): 317-328.
[4]	ZHOU Xinxing, ZHAO Lin, ZHANG Wenjie, TAN Changwei, LI Gangbo, SHI Mengyun, ZHANG Ting, YANG Feng. Remote sensing extraction of fruit tree planting area based on Sentinel-2 multi-temporal images [J]. Acta Agriculturae Zhejiangensis, 2022, 34(12): 2767-2777.
[5]	YANG Youbing, BIAN Junping, WU Yan, LOU Ran, LI Shihao. Association analysis between polymorphism in FTO gene and meat quality traits in pigs [J]. , 2019, 31(12): 1971-1978.
[6]	ZHANG Kexin, DAI Dongyang, WANG Haonan, YU Mingyue, SHENG Yunyan. Genetic and QTL analysis of seed traits in melon (Cucumis melon L.) [J]. , 2018, 30(9): 1496-1503.
[7]	HUANG Shengjia, YE Shuang, LIU Xinya, XI Lijuan, WANG Zhihui, FU Jialing. Effects of different fertilizes on phenolics of Huangguogan fruit [J]. , 2018, 30(1): 91-98.
[8]	CHEN Jie1， TIAN Zhili1， HU Jiang1，*， LUO Yuzhu1， LIU Xiu1， WANG Jiqin1， SHI Hongmei2， LEI Junjie2. Polymorphism in the intron 1 of CAPN1 and its association with carcass and meat quality traits in yak [J]. , 2016, 28(8): 1315-.
[9]	WANG Hua， WANG Jianfu， WANG Xinrong， CHENG Shuru*， LI Xiaomei， WANG Zhiming， FAN Qingshan， FU Lixia， LI Shuang， ZHOU Xiaoxia. Association analysis of CAST gene polymorphism with meat quality in five sheep breeds [J]. , 2016, 28(8): 1309-.
[10]	LI Xiao-peng;XIE Zhen-qiang;ZHANG Yan-yi;MU Qian;WANG Chen;FANG Jing-gui;*. Application of 2-in researches of fruit trees [J]. , 2013, 25(5): 0-1166.
[11]	YANG Yong;CHEN Li-na;YAN Cheng-qi;CHENG Ye;CHENG Xiao-yue;CHEN Jian-ping;*. Construction of a genetic linkage map of a bacterial blight resistance rice line derived from Oryza meyeriana L. [J]. , 2012, 24(5): 0-852.
[12]	WANG Zhen;SHAO Gao-neng;WEI Xiang-jin;HU Pei-song;TANG Shao-qing;*. Mapping QTLs for starch traits and its association analysis with starch synthesis-related genes in rice (Oryza sativa L.) [J]. , 2011, 23(3): 0-445.
[13]	TIAN He-bin;WANG Jun-mei;HUA Wei;YANG Jian-ming;*. Correlation and grey relational analysis of main agronomic traits and malting quality traits of barley [J]. , 2011, 23(3): 0-438.
[14]	TANG Zhong-hou;LI Hong-min;ZHANG Ai-jun;SHI Xin-min;XU Fei;SUN Jian. Effect of potassium fertilizer application on main quality traits and starch RVA characters of sweetpotato [J]. , 2011, 23(1): 46-51.
[15]	BAO Qin—xing;CHEN Chu—wen;*. Application of the ornamental fruit trees to lanndscaping of city in Zhejiang [J]. , 2009, 21(4): 0-389.