果树遗传图谱构建与果实主要品质性状QTL定位研究进展

doi:10.3969/j.issn.1004-1524.20250224

浙江农业学报 ›› 2026, Vol. 38 ›› Issue (3): 609-620.DOI: 10.3969/j.issn.1004-1524.20250224

• 综述 • 上一篇

果树遗传图谱构建与果实主要品质性状QTL定位研究进展

杜辰飞¹^,²(), 李文珏¹^,², 魏春艳², 蔡丹英², 王月志², 施泽彬², 戴美松²^,^*(), 高永彬¹^,^*()

^1. 浙江农林大学园艺科学学院, 浙江杭州 311300
^2. 浙江省农业科学院园艺研究所, 浙江杭州 310021

收稿日期:2025-03-19 出版日期:2026-03-25 发布日期:2026-04-17
作者简介:戴美松,E-mail:daims@zaas.ac.cn
^*高永彬,E-mail:gaoybin@zafu.edu.cn;
杜辰飞,研究方向为梨育种和品质形成机理。E-mail:245642047@qq.com
通讯作者: 戴美松,高永彬
基金资助:
国家自然科学基金(32302492);浙江省果品新品种选育重点研发计划(2021C02066-5);国家现代农业(梨)产业技术体系(CARS-28-03)

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

摘要/Abstract

摘要：

在育种实践中,苹果、柑橘、梨等主要果树作物因其高度杂合的遗传背景、漫长的育种周期,以及数量性状的多基因调控特性,导致传统杂交育种效率受到限制。近年来,基于分子标记的高密度遗传图谱构建与数量性状基因座(QTL)精细定位技术已成为解析果树重要农艺性状遗传机制的关键手段。国内外已构建多张果树遗传连锁图谱,成功定位了与果实大小、形状、硬度、色泽、糖酸含量及香气等关键品质性状相关的QTL,并借助多组学整合分析揭示了其分子调控通路。本文系统综述了果树遗传图谱的发展现状和主要品质性状QTL定位的研究进展,重点指出:优化后的分子标记技术(SSR、SNP)显著提升了遗传图谱分辨率,“拟测交”与“双假测交”理论为QTL分析提供了关键方法框架;尽管目前已在多种果树中定位到主要品质性状相关QTL,但其稳定性与跨环境适应性仍需进一步验证;此外,多性状协同遗传机制,以及QTL与环境互作研究仍较薄弱,限制了标记辅助育种的精准性。未来应结合高通量测序、多组学与机器学习技术,构建超密遗传图谱并挖掘候选基因,为果树高效分子设计育种奠定理论基础。

关键词: 果树, 遗传图谱, 品质性状, QTL定位

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

中图分类号:

杜辰飞, 李文珏, 魏春艳, 蔡丹英, 王月志, 施泽彬, 戴美松, 高永彬. 果树遗传图谱构建与果实主要品质性状QTL定位研究进展[J]. 浙江农业学报, 2026, 38(3): 609-620.

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.

图/表 3

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	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]

果树遗传图谱构建与果实主要品质性状QTL定位研究进展

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

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