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Article(id=1321790850334577244, tenantId=1146029695717560320, journalId=1316781310003478668, issueId=1321790773536866550, articleNumber=null, orderNo=null, doi=10.11983/CBB24196, pmid=null, cstr=32102.14.CBB24196, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=research-article, receivedDate=1734624000000, receivedDateStr=2024-12-20, revisedDate=null, revisedDateStr=null, acceptedDate=1742227200000, acceptedDateStr=2025-03-18, onlineDate=1793069096406, onlineDateStr=2026-10-27, pubDate=1752076800000, pubDateStr=2025-07-10, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793069096406, onlineIssueDateStr=2026-10-27, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793069096406, creator=13701087609, updateTime=1793069096406, updator=13701087609, issue=Issue{id=1321790773536866550, tenantId=1146029695717560320, journalId=1316781310003478668, year='2025', volume='60', issue='4', pageStart='489', pageEnd='663', issueExtLink='null', onlineDate='null', pubDate='1752076800000', pubDateStr='2025-07-10', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793069078096, creator='13701087609', updateTime=1793069146158, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1321791059068310443, tenantId=1146029695717560320, journalId=1316781310003478668, issueId=1321790773536866550, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1321791059068310444, tenantId=1146029695717560320, journalId=1316781310003478668, issueId=1321790773536866550, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=533, endPage=550, ext={EN=ArticleExt(id=1321790850552681053, articleId=1321790850334577244, tenantId=1146029695717560320, journalId=1316781310003478668, language=EN, title=Analysis of the Texture Factors and Genetic Basis Influencing the Differences in Eating Quality between Northeast China and Japanese Japonica Rice, columnId=1317485691135706105, journalTitle=Chinese Bulletin of Botany, columnName=RESEARCH ARTICLES, runingTitle=null, highlight=null, articleAbstract=

INTRODUCTION Due to differences in breeding objectives, northeast japonica rice (Oryza sativa subsp. geng or japonica) is more advantageous than Japanese japonica rice in terms of yield level, whereas Japanese japonica rice is significantly better than Chinese japonica rice in terms of eating quality. Clarifying the genetic basis of the differences in eating quality between Chinese and Japanese japonica rice is highly valuable for the cultivation of high-yield and high-quality japonica rice.

RATIONALEA total of 274 Chinese and Japanese japonica rice varieties were used as research materials to quantify the eating quality of the rice and to analyze the genetic basis of the taste differences between Chinese and Japanese japonica rice by combining genome-wide association analysis with the downscaling of many parameters.

RESULTSThe results revealed that the significant differences in the taste values of Chinese and Japanese japonica rice were reflected in three textural parameters: the adhesion force (ADF), first recoverable deformation cycle (FRDC), and elasticity index (EI). Moreover, the correlation analysis between the taste values and 30 textural characters showed that 24 characters were significantly correlated with the taste value of rice. The 30 metrics of textural characterization were downscaled to four principal components that explained 80% of the phenotypic variation in the population, and the genome-wide associations of their eigenvalues were mined to two primary effector loci affecting the textural characterization of Chinese-Japanese japonica rice, qFPC4.3 and qFPC9.2.

CONCLUSION In this study, we quantified the parameters of eating quality from a qualitative perspective, and thus analyzed the genetic basis of the differences in eating quality between Chinese and Japanese rice, which provided valuable genetic information and a theoretical basis for the genetic improvement of the eating quality of japonica rice in China.

PCA analysis and genome-wide association studies based on principal component eigenvalues of texture characteristics indicators. PCA analysis was performed using 2021 data.

, authors=Juan Cui, Xiaoyu Yu, Yuejiao Yu, Chengwei Liang, Jian Sun, Wenfu Chen, authorsList=Juan Cui, Xiaoyu Yu, Yuejiao Yu, Chengwei Liang, Jian Sun, Wenfu Chen, authorCompany=null, correspAuthors=Jian Sun, Wenfu Chen, authorNote=

†These authors contributed equally to this paper

, correspAuthorsNote=
*E-mail: ;
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†共同第一作者

, correspAuthorsNote=
*孙健, 沈阳农业大学教授, 博士生导师。入选首批国家“神农青年英才”计划, 获“辽宁青年科技奖”等奖励或荣誉。主要研究方向为水稻种质资源创新。主持“国家自然科学基金面上项目”等多项国家与省部级项目。围绕杂草稻种质创新开展了系统性研究工作, 阐明了杂草稻的分类学地位, 提出起源演化新假说, 克隆了多个对栽培稻遗传改良具有重要价值的新基因。带领团队设计研发了粳稻种质资源芯片, 在粳稻资源评价、分子设计育种、全基因组选择等方面开展了广泛应用。研究成果在Nature Communications、Molecular Plant、New Phytologist等国际著名期刊上发表。E-mail: ;
陈温福, 辽宁法库人, 我国著名水稻专家和生物炭专家, 中国工程院院士, 沈阳农业大学教授。第十一届、第十二届、第十三届全国人大代表, 第十四届全国政协委员。曾任国家重点学科“作物栽培学与耕作学”学科带头人, 国务院学位委员会第五届、第六届学科评议组成员和作物学科组召集人, 国家农作物品种审定委员会委员, 农业农村部第九届科技委常委、水稻专家组成员, 辽宁省科协副主席。曾获全国劳动模范和五一劳动奖章、全国模范教师、全国教学名师、创先争优奖、全国农业科技先进工作者、中华农业英才奖、辽宁省特等劳动模范、辽宁省科技功勋奖等多项荣誉。E-mail:
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(in Chinese), articleTitle=Correlation between texture property of cooked rice and main quality properties of rice, refAbstract=null), Reference(id=1321790877828240122, tenantId=1146029695717560320, journalId=1316781310003478668, articleId=1321790850334577244, doi=null, pmid=null, pmcid=null, year=2012, volume=33, issue=(5), pageStart=21, pageEnd=24, url=null, language=null, rfNumber=[54], rfOrder=88, authorNames=周显青, 任洪玲, 张玉荣, 王军锋, journalName=河南工业大学学报(自然科学版), refType=null, unstructuredReference=周显青, 任洪玲, 张玉荣, 王军锋 (2012). 大米主要品质指标与米饭质构的相关性分析. 河南工业大学学报(自然科学版) 33(5), 21-24., articleTitle=大米主要品质指标与米饭质构的相关性分析, refAbstract=null), Reference(id=1321790877941486331, tenantId=1146029695717560320, journalId=1316781310003478668, articleId=1321790850334577244, doi=null, pmid=null, pmcid=null, year=2022, volume=55, issue=null, pageStart=1271, pageEnd=1283, url=null, language=null, rfNumber=[55], rfOrder=89, authorNames=Zhu DW, Zhang LP, Chen MX, Fang CY, Yu YH, Zheng XL, Shao YF, journalName=Sci Agric Sin, refType=null, unstructuredReference=Zhu DW, Zhang LP, Chen MX, Fang CY, Yu YH, Zheng XL, Shao YF (2022). Characteristics of high-quality rice varieties and taste sensory evaluation values in China. Sci Agric Sin 55, 1271-1283. (in Chinese), articleTitle=Characteristics of high-quality rice varieties and taste sensory evaluation values in China, refAbstract=null), Reference(id=1321790878193144572, tenantId=1146029695717560320, journalId=1316781310003478668, articleId=1321790850334577244, doi=null, pmid=null, pmcid=null, year=2022, volume=55, issue=null, pageStart=1271, pageEnd=1283, url=null, language=null, rfNumber=[55], rfOrder=90, authorNames=朱大伟, 章林平, 陈铭学, 方长云, 于永红, 郑小龙, 邵雅芳, journalName=中国农业科学, refType=null, unstructuredReference=朱大伟, 章林平, 陈铭学, 方长云, 于永红, 郑小龙, 邵雅芳 (2022). 中国优质稻品种品质及食味感官评分值的特征. 中国农业科学 55, 1271-1283., articleTitle=中国优质稻品种品质及食味感官评分值的特征, refAbstract=null)], funds=[Fund(id=1321790859268444832, tenantId=1146029695717560320, journalId=1316781310003478668, articleId=1321790850334577244, awardId=32372107, language=CN, fundingSource=国家自然科学基金(32372107), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1321790852440117867, tenantId=1146029695717560320, journalId=1316781310003478668, articleId=1321790850334577244, xref=null, ext=[AuthorCompanyExt(id=1321790852448506476, tenantId=1146029695717560320, journalId=1316781310003478668, articleId=1321790850334577244, companyId=1321790852440117867, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=Rice Research Institute, Shenyang Agricultural University, Shenyang 110866, China), AuthorCompanyExt(id=1321790852456895085, tenantId=1146029695717560320, journalId=1316781310003478668, articleId=1321790850334577244, companyId=1321790852440117867, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=沈阳农业大学水稻研究所, 沈阳 110866)])], figs=[ArticleFig(id=1321790856156271248, tenantId=1146029695717560320, journalId=1316781310003478668, articleId=1321790850334577244, language=EN, label=Figure 1, caption=Differences in taste values of japonica rice between China and Japan and genome-wide association studies based on taste value in two years

(A) Differences in taste values of japonica rice between China and Japan in 2021 and 2022; (B) Changes in taste values of Chinese and Japanese japonica rice in 2022 compared with 2021 (CN: Chinese japonica rice; JP: Japanese japonica rice; * P<0.05; ** P<0.01); (C) Genome wide association studies based on taste value in 2021; (D) Genome-wide association studies based on taste value in 2022. The blue line represents the threshold for the significance of marker-trait association (0.05 significance level), and the yellow line represents the threshold for the extreme significance of marker-trait association (0.01 significance level).

, figureFileSmall=y7RGjq7wRH/fk/P73E0OGA==, figureFileBig=U2nF16FtftdjzqrQaphHoA==, tableContent=null), ArticleFig(id=1321790856227574417, tenantId=1146029695717560320, journalId=1316781310003478668, articleId=1321790850334577244, language=CN, label=图1, caption=两年间中日粳稻食味值差异以及基于食味值的全基因组关联分析

(A) 2021年和2022年中日粳米食味值差异; (B) 2022年中日粳米食味值相较于2021年变化幅度(CN: 中国粳稻; JP: 日本粳稻; * P<0.05; ** P<0.01); (C) 基于2021年食味值的全基因组关联分析; (D) 基于2022年食味值的全基因组关联分析。蓝线表示标记-性状关联的显著性阈值(0.05显著水平), 黄线表示标记-性状关联的极显著性阈值(0.01显著水平)。

, figureFileSmall=y7RGjq7wRH/fk/P73E0OGA==, figureFileBig=U2nF16FtftdjzqrQaphHoA==, tableContent=null), ArticleFig(id=1321790856424706706, tenantId=1146029695717560320, journalId=1316781310003478668, articleId=1321790850334577244, language=EN, label=Figure 2, caption=Correlation analysis of the taste and texture of japonica rice and differences in some textural characteristics of japonica rice between China and Japan in two years

(A) Correlation analysis of the taste and texture of japonica rice between China and Japan in two years (HN: Hardness; HND: Hardness deformation; HNDP: Hardness deformation percentage; FCWC: First compression work cycle; FRDC: First recoverable deformation cycle; FRWC: First recoverable work cycle; FTPC: First total power cycle; OL: Objective load; TDA: Target deformation amount; SDP: Sample deformation percentage; PS: Peak strain; ADF: Adhesion force; AND: Adhesion degree; EF: Elastic force; EL: Elastic length; EW: Elastic work; SCHN: Second cycle hardness; SCWC: Second compression work cycle; CON: Cohesiveness; SRDC: Second recoverable deformation cycle; SRWC: Second recoverable work cycle; STPC: Second total power cycle; SN: Springiness; EI: Elasticity index; GMN: Gumminess; CN: Chewiness; CI: Chewing index; CCON: Corrected cohesiveness; CGMN: Corrected gumminess; CCN: Corrected chewiness); (B) Differences in some textural characteristics of japonica rice between China and Japan in 2021; (C) Differences in some textural characteristics of japonica rice between China and Japan in 2022. CN and JP are the same as shown in Figure 1; * P<0.05; ** P<0.01

, figureFileSmall=oXqrJ2lmCusOgait3rfNbw==, figureFileBig=ZkQ57imoxrZMzzJ0vgwdCw==, tableContent=null), ArticleFig(id=1321790856558924435, tenantId=1146029695717560320, journalId=1316781310003478668, articleId=1321790850334577244, language=CN, label=图2, caption=两年间中日粳稻食味和质构相关性分析及其在质构特性上的差异

(A) 中日粳米食味值和质构特性指标的相关性分析(HN: 第一循环硬度; HND: 硬度形变量; HNDP: 硬度形变百分比; FCWC: 压缩功循环1; FRDC: 第一可恢复形变循环; FRWC: 可恢复功循环1; FTPC: 第一循环总功; OL: 目标负载; TDA: 目标变形量; SDP: 样品变形百分比; PS: 峰值应变; ADF: 粘力; AND: 粘性; EF: 弹力; EL: 弹性长度; EW: 弹性功; SCHN: 第二循环硬度; SCWC: 压缩功循环2; CON: 内聚性; SRDC: 可恢复形变循环2; SRWC: 可恢复功循环2; STPC: 总功循环2; SN: 弹性; EI: 弹性指数; GMN: 胶着性; CN: 咀嚼性; CI: 咀嚼指数; CCON: 校正内聚性; CGMN: 校正胶着性; CCN: 校正咀嚼性); (B) 2021年部分质构特性指标在中日粳米间的差异; (C) 2022年部分质构特性指标在中日粳米间的差异。CN和JP同图1; * P<0.05; ** P<0.01

, figureFileSmall=oXqrJ2lmCusOgait3rfNbw==, figureFileBig=ZkQ57imoxrZMzzJ0vgwdCw==, tableContent=null), ArticleFig(id=1321790856647004820, tenantId=1146029695717560320, journalId=1316781310003478668, articleId=1321790850334577244, language=EN, label=Figure 3, caption=Genome-wide association studies based on principal component eigenvalues of natural population texture characteristic indicators

(A) Genome-wide association studies based on texture characteristic feature values in 2021; (B) Genome-wide association studies based on texture characteristic feature values in 2022. The black lines represent the threshold for the significance of marker-trait association.

, figureFileSmall=cghUCyzYFkU6kPzgrBivUg==, figureFileBig=U4GAR2jvQm/Q3gX8BHfmtQ==, tableContent=null), ArticleFig(id=1321790856718307989, tenantId=1146029695717560320, journalId=1316781310003478668, articleId=1321790850334577244, language=CN, label=图3, caption=基于自然群体质构特性指标主成分特征值的全基因组关联分析

(A) 2021年基于质构特性特征值的全基因组关联分析; (B) 2022年基于质构特性特征值的全基因组关联分析。黑线表示标记-性状关联的显著性阈值。

, figureFileSmall=cghUCyzYFkU6kPzgrBivUg==, figureFileBig=U4GAR2jvQm/Q3gX8BHfmtQ==, tableContent=null), ArticleFig(id=1321790858370863766, tenantId=1146029695717560320, journalId=1316781310003478668, articleId=1321790850334577244, language=EN, label=Figure 4, caption=Haplotype analysis of candidate genes for qFPC4.3 and qFPC9.2

(A) Genome-wide association studies based on eigenvalues of texture properties in 2021 and 2022; (B) LD black near the qFPC4.3 locus; (C) Genes associated with phenotypic variation in the LD interval near the qFPC4.3 locus, whose vertical coordinates represent the principal component eigenvalue scores of PC1; (D) Genome-wide association studies based on eigenvalues of texture properties in 2021 and 2022; (E) LD black near the qFPC9.2 locus; (F) Genes associated with phenotypic variation in the LD interval near the qFPC9.2 locus, whose vertical coordinates represent the principal component eigenvalue scores of PC1. (C), (F) Different lowercase letters indicate significant differences.

, figureFileSmall=hkQQka8ov10mNscoVMzUWA==, figureFileBig=kqpXoDeJ/YLDHrwLJKJS9Q==, tableContent=null), ArticleFig(id=1321790858479915671, tenantId=1146029695717560320, journalId=1316781310003478668, articleId=1321790850334577244, language=CN, label=图4, caption=qFPC4.3和qFPC9.2位点的候选基因单倍型分析

(A) 2021年和2022年基于质构特性特征值的全基因组关联分析; (B) qFPC4.3位点附近的LD black图; (C) qFPC4.3位点附近LD区间内与表型变异相关的基因, 纵坐标代表PC1的主成分特征值得分; (D) 2021年和2022年基于质构特性特征值的全基因组关联分析; (E) qFPC9.2位点附近的LD black图; (F) qFPC9.2位点附近LD区间内与表型变异相关的基因, 纵坐标代表PC1的主成分特征值得分。(C), (F) 不同小写字母表示差异显著。

, figureFileSmall=hkQQka8ov10mNscoVMzUWA==, figureFileBig=kqpXoDeJ/YLDHrwLJKJS9Q==, tableContent=null), ArticleFig(id=1321790858563801752, tenantId=1146029695717560320, journalId=1316781310003478668, articleId=1321790850334577244, language=EN, label=Table 1, caption=

Factor load matrix, eigenvalues, contribution rates and cumulative contribution rates of the principal components of the qualitative and structural property indicators in 2021 and 2022

, figureFileSmall=null, figureFileBig=null, tableContent=
Year Variable V1 V2 V3 V4 Year Variable V1 V2 V3 V4
2021 HN -0.238 -0.077 -0.139 -0.153 2022 HN -0.250 0.110 -0.126 -0.050
HND -0.039 -0.343 -0.066 0.261 HND -0.076 0.285 0.210 0.002
HNDP 0.002 -0.139 0.028 0.459 HNDP -0.037 0.069 0.430 -0.448
FCWC -0.191 -0.235 -0.186 -0.089 FCWC -0.215 0.198 -0.071 -0.028
FRDC -0.176 0.186 -0.016 0.299 FRDC -0.144 -0.236 0.245 0.049
FRWC -0.249 0.038 -0.088 0.042 FRWC -0.264 -0.042 0.019 0.020
FTPC -0.214 -0.189 -0.177 -0.067 FTPC -0.231 0.169 -0.064 -0.022
OL -0.242 -0.060 -0.140 -0.117 OL -0.253 0.097 -0.116 -0.048
TDA -0.040 -0.344 -0.071 0.243 TDA -0.076 0.287 0.196 0.021
SDP -0.025 -0.186 -0.076 0.196 SDP -0.059 0.184 0.212 0.096
PS -0.001 -0.144 0.036 0.449 PS -0.033 0.068 0.443 -0.420
ADF -0.039 -0.267 0.296 -0.211 ADF 0.010 0.284 -0.125 0.078
AND 0.005 -0.286 0.299 -0.132 AND 0.061 0.186 0.034 0.311
EF -0.137 0.322 0.088 0.158 EF -0.031 -0.323 -0.012 -0.090
EL 0.045 -0.128 0.386 0.034 EL -0.012 -0.082 0.302 0.381
EW -0.036 -0.223 0.385 -0.124 EW 0.039 0.206 0.081 0.418
SCHN -0.244 -0.046 -0.127 -0.143 SCHN -0.252 0.100 -0.139 -0.051
SCWC -0.251 -0.110 -0.066 -0.031 SCWC -0.264 -0.081 -0.050 0.008
CON -0.153 0.256 0.236 0.089 CON -0.058 -0.315 -0.077 -0.052
SRDC -0.166 0.221 0.003 0.280 SRDC -0.128 -0.245 0.243 0.050
SRWC -0.240 0.090 -0.067 0.066 SRWC -0.263 -0.055 -0.004 -0.013
STPC -0.258 -0.050 -0.071 -0.010 STPC -0.269 0.043 -0.038 -0.003
SN -0.151 -0.166 0.248 0.206 SN -0.141 -0.060 0.378 0.329
EI -0.130 0.175 0.338 -0.019 EI -0.039 -0.296 0.096 0.212
GMN -0.256 0.047 -0.012 -0.080 GMN -0.265 -0.032 -0.100 -0.027
CN -0.259 -0.014 0.080 0.008 CN -0.264 -0.056 0.047 0.065
CI -0.249 0.093 0.086 -0.062 CI -0.241 -0.140 -0.025 0.054
CCON -0.152 0.129 0.321 0.029 CCON -0.073 -0.284 -0.104 0.004
CGMN -0.252 -0.013 0.018 -0.105 CGMN -0.264 -0.006 -0.109 -0.016
CCN -0.252 -0.069 0.102 -0.009 CCN -0.265 0.037 0.070 -0.092
Eigenvalue 14.250 5.510 3.588 2.393 Eigenvalue 13.354 8.352 2.513 1.728
Contribution
rate (%)		 53.28 14.80 8.69 6.32 Contribution rate (%) 44.49 27.81 8.35 5.80
Cumulative
contribution
rate (%)		 53.28 68.08 76.77 83.09 Cumulative contribution rate (%) 44.49 72.30 80.65 86.45
), ArticleFig(id=1321790858656076441, tenantId=1146029695717560320, journalId=1316781310003478668, articleId=1321790850334577244, language=CN, label=表1, caption=

2021年和2022年质构特性指标因子主成分的荷载矩阵、特征值、贡献率和累积贡献率

, figureFileSmall=null, figureFileBig=null, tableContent=
Year Variable V1 V2 V3 V4 Year Variable V1 V2 V3 V4
2021 HN -0.238 -0.077 -0.139 -0.153 2022 HN -0.250 0.110 -0.126 -0.050
HND -0.039 -0.343 -0.066 0.261 HND -0.076 0.285 0.210 0.002
HNDP 0.002 -0.139 0.028 0.459 HNDP -0.037 0.069 0.430 -0.448
FCWC -0.191 -0.235 -0.186 -0.089 FCWC -0.215 0.198 -0.071 -0.028
FRDC -0.176 0.186 -0.016 0.299 FRDC -0.144 -0.236 0.245 0.049
FRWC -0.249 0.038 -0.088 0.042 FRWC -0.264 -0.042 0.019 0.020
FTPC -0.214 -0.189 -0.177 -0.067 FTPC -0.231 0.169 -0.064 -0.022
OL -0.242 -0.060 -0.140 -0.117 OL -0.253 0.097 -0.116 -0.048
TDA -0.040 -0.344 -0.071 0.243 TDA -0.076 0.287 0.196 0.021
SDP -0.025 -0.186 -0.076 0.196 SDP -0.059 0.184 0.212 0.096
PS -0.001 -0.144 0.036 0.449 PS -0.033 0.068 0.443 -0.420
ADF -0.039 -0.267 0.296 -0.211 ADF 0.010 0.284 -0.125 0.078
AND 0.005 -0.286 0.299 -0.132 AND 0.061 0.186 0.034 0.311
EF -0.137 0.322 0.088 0.158 EF -0.031 -0.323 -0.012 -0.090
EL 0.045 -0.128 0.386 0.034 EL -0.012 -0.082 0.302 0.381
EW -0.036 -0.223 0.385 -0.124 EW 0.039 0.206 0.081 0.418
SCHN -0.244 -0.046 -0.127 -0.143 SCHN -0.252 0.100 -0.139 -0.051
SCWC -0.251 -0.110 -0.066 -0.031 SCWC -0.264 -0.081 -0.050 0.008
CON -0.153 0.256 0.236 0.089 CON -0.058 -0.315 -0.077 -0.052
SRDC -0.166 0.221 0.003 0.280 SRDC -0.128 -0.245 0.243 0.050
SRWC -0.240 0.090 -0.067 0.066 SRWC -0.263 -0.055 -0.004 -0.013
STPC -0.258 -0.050 -0.071 -0.010 STPC -0.269 0.043 -0.038 -0.003
SN -0.151 -0.166 0.248 0.206 SN -0.141 -0.060 0.378 0.329
EI -0.130 0.175 0.338 -0.019 EI -0.039 -0.296 0.096 0.212
GMN -0.256 0.047 -0.012 -0.080 GMN -0.265 -0.032 -0.100 -0.027
CN -0.259 -0.014 0.080 0.008 CN -0.264 -0.056 0.047 0.065
CI -0.249 0.093 0.086 -0.062 CI -0.241 -0.140 -0.025 0.054
CCON -0.152 0.129 0.321 0.029 CCON -0.073 -0.284 -0.104 0.004
CGMN -0.252 -0.013 0.018 -0.105 CGMN -0.264 -0.006 -0.109 -0.016
CCN -0.252 -0.069 0.102 -0.009 CCN -0.265 0.037 0.070 -0.092
Eigenvalue 14.250 5.510 3.588 2.393 Eigenvalue 13.354 8.352 2.513 1.728
Contribution
rate (%)		 53.28 14.80 8.69 6.32 Contribution rate (%) 44.49 27.81 8.35 5.80
Cumulative
contribution
rate (%)		 53.28 68.08 76.77 83.09 Cumulative contribution rate (%) 44.49 72.30 80.65 86.45
), ArticleFig(id=1321790858756739738, tenantId=1146029695717560320, journalId=1316781310003478668, articleId=1321790850334577244, language=EN, label=Table 2, caption=

Significant loci of important indicators of the texture characteristics of natural populations

, figureFileSmall=null, figureFileBig=null, tableContent=
Traits Chromosome Quanti-
tative
trait
nucleo-
tides		 Year Lead single
nucleotide
polymorphism		 P value Traits Chro-moso-me Quanti-
tative
trait
nucleo-
tides		 Year Lead single
nucleotide
polymorphism		 P value
PC1 1 qFPC1.1 2021 S01_4122448 1.77E-06 9 qFPC9.2 2021 S09_655033 2.84E-05
2 qFPC2.1 2021 S02_2111625 3.54E-05 2022 S09_707465 2.00E-05
2 qFPC2.2 2021 S02_18008119 1.89E-05 PC2 1 qSPC1.1 2021 S01_34361048 6.28E-06
2 qFPC2.3 2022 S02_24989754 9.26E-05 3 qSPC3.1 2021 S03_33811087 1.07E-06
3 qFPC3.1 2021 S03_14095190 8.10E-05 4 qSPC4.1 2021 S04_33424615 4.92E-05
4 qFPC4.1 2022 S04_1115264 5.54E-06 10 qSPC10.1 2022 S10_2285014 5.95E-05
4 qFPC4.2 2022 S04_4490974 2.53E-06 PC3 6 qTPC6.1 2021 S06_22391759 1.33E-05
4 qFPC4.3 2021 S04_13268045 7.14E-05 7 qTPC7.1 2021 S07_10551451 4.89E-05
2022 S04_13329698 2.47E-05 8 qTPC8.1 2021 S08_19868807 1.81E-05
6 qFPC6.1 2022 S06_23235180 5.02E-06 9 qTPC9.1 2021 S09_7944917 1.80E-05
7 qFPC7.1 2021 S07_9324722 7.40E-05 10 qTPC10.1 2022 S10_16314058 7.08E-05
8 qFPC8.1 2021 S08_21150875 6.47E-05 11 qTPC11.1 2021 S11_28894771 6.28E-07
9 qFPC9.1 2021 S09_38586 1.82E-05
), ArticleFig(id=1321790858836431515, tenantId=1146029695717560320, journalId=1316781310003478668, articleId=1321790850334577244, language=CN, label=表2, caption=

自然群体质构特性重要指标显著位点

, figureFileSmall=null, figureFileBig=null, tableContent=
Traits Chromosome Quanti-
tative
trait
nucleo-
tides		 Year Lead single
nucleotide
polymorphism		 P value Traits Chro-moso-me Quanti-
tative
trait
nucleo-
tides		 Year Lead single
nucleotide
polymorphism		 P value
PC1 1 qFPC1.1 2021 S01_4122448 1.77E-06 9 qFPC9.2 2021 S09_655033 2.84E-05
2 qFPC2.1 2021 S02_2111625 3.54E-05 2022 S09_707465 2.00E-05
2 qFPC2.2 2021 S02_18008119 1.89E-05 PC2 1 qSPC1.1 2021 S01_34361048 6.28E-06
2 qFPC2.3 2022 S02_24989754 9.26E-05 3 qSPC3.1 2021 S03_33811087 1.07E-06
3 qFPC3.1 2021 S03_14095190 8.10E-05 4 qSPC4.1 2021 S04_33424615 4.92E-05
4 qFPC4.1 2022 S04_1115264 5.54E-06 10 qSPC10.1 2022 S10_2285014 5.95E-05
4 qFPC4.2 2022 S04_4490974 2.53E-06 PC3 6 qTPC6.1 2021 S06_22391759 1.33E-05
4 qFPC4.3 2021 S04_13268045 7.14E-05 7 qTPC7.1 2021 S07_10551451 4.89E-05
2022 S04_13329698 2.47E-05 8 qTPC8.1 2021 S08_19868807 1.81E-05
6 qFPC6.1 2022 S06_23235180 5.02E-06 9 qTPC9.1 2021 S09_7944917 1.80E-05
7 qFPC7.1 2021 S07_9324722 7.40E-05 10 qTPC10.1 2022 S10_16314058 7.08E-05
8 qFPC8.1 2021 S08_21150875 6.47E-05 11 qTPC11.1 2021 S11_28894771 6.28E-07
9 qFPC9.1 2021 S09_38586 1.82E-05
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Candidate gene annotation information of qFPC4.3

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Gene ID Annotation High expression site Gene ID Annotation High expression site
LOC_Os04g23070 Retrotransposon protein NA LOC_Os04g23330 Expressed
protein		 Young panicle and seed of filling stage
LOC_Os04g23250 Transposon protein NA LOC_Os04g23470 Transposon
protein		 Seed of filling stage
LOC_Os04g23300 Retrotransposon protein Young panicle and
seed of filling stage		 LOC_Os04g23710 Transposon
protein		 NA
), ArticleFig(id=1321790859008397981, tenantId=1146029695717560320, journalId=1316781310003478668, articleId=1321790850334577244, language=CN, label=表3, caption=

qFPC4.3的候选基因注释信息

, figureFileSmall=null, figureFileBig=null, tableContent=
Gene ID Annotation High expression site Gene ID Annotation High expression site
LOC_Os04g23070 Retrotransposon protein NA LOC_Os04g23330 Expressed
protein		 Young panicle and seed of filling stage
LOC_Os04g23250 Transposon protein NA LOC_Os04g23470 Transposon
protein		 Seed of filling stage
LOC_Os04g23300 Retrotransposon protein Young panicle and
seed of filling stage		 LOC_Os04g23710 Transposon
protein		 NA
), ArticleFig(id=1321790859088089758, tenantId=1146029695717560320, journalId=1316781310003478668, articleId=1321790850334577244, language=EN, label=Table 4, caption=

Candidate gene annotation information of qFPC9.2

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Gene ID Annotation High expression site
LOC_Os09g01600 Expressed protein Young panicle and seed of filling stage
LOC_Os09g01610 Clumping factor B Young panicle and seed of filling stage
LOC_Os09g01640 CAX-interacting protein 4 Young panicle and seed of filling stage
LOC_Os09g01660 Expressed protein Leaf
LOC_Os09g01680 DNA repair protein Young panicle and seed of filling stage
LOC_Os09g01690 Expressed protein Young panicle and seed of filling stage
LOC_Os09g01910 Transposon protein NA
LOC_Os09g01930 Expressed protein Young panicle and mature seed
LOC_Os09g01950 Expressed protein NA
LOC_Os09g02120 Expressed protein Young panicle and seed of filling stage
LOC_Os09g02160 DEFL47-defensin and Defensin-like DEFL family Pistils
LOC_Os09g02180 Oryza sativa drought and salt stress response-1 Mature seed
LOC_Os09g02214 Na+/H+ antiporter gene Mature leaf and SAM
LOC_Os09g02110 Retrotransposon protein Seed of filling stage
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qFPC9.2候选基因注释信息

, figureFileSmall=null, figureFileBig=null, tableContent=
Gene ID Annotation High expression site
LOC_Os09g01600 Expressed protein Young panicle and seed of filling stage
LOC_Os09g01610 Clumping factor B Young panicle and seed of filling stage
LOC_Os09g01640 CAX-interacting protein 4 Young panicle and seed of filling stage
LOC_Os09g01660 Expressed protein Leaf
LOC_Os09g01680 DNA repair protein Young panicle and seed of filling stage
LOC_Os09g01690 Expressed protein Young panicle and seed of filling stage
LOC_Os09g01910 Transposon protein NA
LOC_Os09g01930 Expressed protein Young panicle and mature seed
LOC_Os09g01950 Expressed protein NA
LOC_Os09g02120 Expressed protein Young panicle and seed of filling stage
LOC_Os09g02160 DEFL47-defensin and Defensin-like DEFL family Pistils
LOC_Os09g02180 Oryza sativa drought and salt stress response-1 Mature seed
LOC_Os09g02214 Na+/H+ antiporter gene Mature leaf and SAM
LOC_Os09g02110 Retrotransposon protein Seed of filling stage
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影响中国东北和日本粳稻食味品质差异的质构因素及其遗传基础解析
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崔娟 † , 于晓玉 † , 于跃娇 , 梁铖玮 , 孙健 * , 陈温福 *
植物学报 | 研究论文 2025,60(4): 533-550
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植物学报 |研究论文 2025 , 60 (4) : 533 -550
影响中国东北和日本粳稻食味品质差异的质构因素及其遗传基础解析
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崔娟†, 于晓玉†, 于跃娇, 梁铖玮, 孙健* , 陈温福*
作者信息
  • 沈阳农业大学水稻研究所, 沈阳 110866
通讯作者:
*孙健, 沈阳农业大学教授, 博士生导师。入选首批国家“神农青年英才”计划, 获“辽宁青年科技奖”等奖励或荣誉。主要研究方向为水稻种质资源创新。主持“国家自然科学基金面上项目”等多项国家与省部级项目。围绕杂草稻种质创新开展了系统性研究工作, 阐明了杂草稻的分类学地位, 提出起源演化新假说, 克隆了多个对栽培稻遗传改良具有重要价值的新基因。带领团队设计研发了粳稻种质资源芯片, 在粳稻资源评价、分子设计育种、全基因组选择等方面开展了广泛应用。研究成果在Nature Communications、Molecular Plant、New Phytologist等国际著名期刊上发表。E-mail: ;
陈温福, 辽宁法库人, 我国著名水稻专家和生物炭专家, 中国工程院院士, 沈阳农业大学教授。第十一届、第十二届、第十三届全国人大代表, 第十四届全国政协委员。曾任国家重点学科“作物栽培学与耕作学”学科带头人, 国务院学位委员会第五届、第六届学科评议组成员和作物学科组召集人, 国家农作物品种审定委员会委员, 农业农村部第九届科技委常委、水稻专家组成员, 辽宁省科协副主席。曾获全国劳动模范和五一劳动奖章、全国模范教师、全国教学名师、创先争优奖、全国农业科技先进工作者、中华农业英才奖、辽宁省特等劳动模范、辽宁省科技功勋奖等多项荣誉。E-mail:
作者简介:

†共同第一作者

Analysis of the Texture Factors and Genetic Basis Influencing the Differences in Eating Quality between Northeast China and Japanese Japonica Rice
Juan Cui, Xiaoyu Yu, Yuejiao Yu, Chengwei Liang, Jian Sun , Wenfu Chen
Affiliations
  • Rice Research Institute, Shenyang Agricultural University, Shenyang 110866, China
    • Corresponding Author:
    *E-mail: ;
出版时间: 2025-07-10 doi: 10.11983/CBB24196
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摘要
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由于育种目标的差异, 使得东北粳稻(Oryza sativa subsp. geng or japonica)在单产水平上比日本粳稻更具优势, 而日本粳米食味品质则明显优于中国粳米。明确中日粳米间食味品质差异的遗传基础, 对于培育高产优质兼顾的粳稻具有重要意义。以274份中日粳稻为研究材料, 应用质构参数量化食味品质, 并将诸多参数降维后结合全基因组关联分析揭示影响中日粳米食味差异的遗传基础。结果表明, 中日粳稻食味值的显著差异体现在粘力(adhesion force, ADF)、第一可恢复形变循环(first recoverable deformation cycle, FRDC)和弹性指数(elasticity index, EI)三个质构特征参数上。同时, 食味值与30个质构特性指标相关性分析表明, 24个指标与米饭食味之间呈显著或极显著相关性。将30个质构特性指标降维为4个可解释群体80%表型变异的主成分, 通过对其特征值进行全基因组关联分析挖掘到2个影响中日粳米质构特性的主效位点qFPC4.3与qFPC9.2。该研究从质构角度量化了食味品质参数, 由此解析了中日稻米食味品质特性差异的遗传基础, 为我国粳稻食味品质遗传改良提供了有价值的遗传信息和理论依据。
粳米  /  质构特性  /  食味品质  /  全基因组关联分析  /  主成分分析

INTRODUCTION Due to differences in breeding objectives, northeast japonica rice (Oryza sativa subsp. geng or japonica) is more advantageous than Japanese japonica rice in terms of yield level, whereas Japanese japonica rice is significantly better than Chinese japonica rice in terms of eating quality. Clarifying the genetic basis of the differences in eating quality between Chinese and Japanese japonica rice is highly valuable for the cultivation of high-yield and high-quality japonica rice.

RATIONALEA total of 274 Chinese and Japanese japonica rice varieties were used as research materials to quantify the eating quality of the rice and to analyze the genetic basis of the taste differences between Chinese and Japanese japonica rice by combining genome-wide association analysis with the downscaling of many parameters.

RESULTSThe results revealed that the significant differences in the taste values of Chinese and Japanese japonica rice were reflected in three textural parameters: the adhesion force (ADF), first recoverable deformation cycle (FRDC), and elasticity index (EI). Moreover, the correlation analysis between the taste values and 30 textural characters showed that 24 characters were significantly correlated with the taste value of rice. The 30 metrics of textural characterization were downscaled to four principal components that explained 80% of the phenotypic variation in the population, and the genome-wide associations of their eigenvalues were mined to two primary effector loci affecting the textural characterization of Chinese-Japanese japonica rice, qFPC4.3 and qFPC9.2.

CONCLUSION In this study, we quantified the parameters of eating quality from a qualitative perspective, and thus analyzed the genetic basis of the differences in eating quality between Chinese and Japanese rice, which provided valuable genetic information and a theoretical basis for the genetic improvement of the eating quality of japonica rice in China.

PCA analysis and genome-wide association studies based on principal component eigenvalues of texture characteristics indicators. PCA analysis was performed using 2021 data.

japonica rice  /  textural characteristics  /  eating quality  /  genome-wide association study  /  principal component analysis
崔娟, 于晓玉, 于跃娇, 梁铖玮, 孙健, 陈温福. 影响中国东北和日本粳稻食味品质差异的质构因素及其遗传基础解析. 植物学报, 2025 , 60 (4) : 533 -550 . DOI: 10.11983/CBB24196
Juan Cui, Xiaoyu Yu, Yuejiao Yu, Chengwei Liang, Jian Sun, Wenfu Chen. Analysis of the Texture Factors and Genetic Basis Influencing the Differences in Eating Quality between Northeast China and Japanese Japonica Rice[J]. Chinese Bulletin of Botany, 2025 , 60 (4) : 533 -550 . DOI: 10.11983/CBB24196
正文
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水稻(Oryza sativa)是我国重要的粮食作物, 更是四大主食之一, 可满足全球一半以上人口的日常能量需求(Tu et al., 2025)。近年来, 消费者对稻米品质的要求不断提高(徐铨等, 2017)。稻米品质主要包括外观品质、食味品质、碾磨品质、营养品质和储存品质(Gong et al., 2017; Sultana et al., 2022), 其中食味品质是决定稻米在消费者中受欢迎程度的重要因素(常俊楠等, 2021), 是稻米经济价值的最终体现和关键决定因素(Shi et al., 2022), 也是最受育种家和消费者关注的品质指标。20世纪60年代以来, 我国东北粳稻在籼粳亚种有利性状利用和理想株型相结合的育种理论指导下, 育成大量高产优质粳型超级稻品种。高虹等(2018)发现东北稻区主栽粳稻品种产量比日本典型粳稻高15.79%。徐海等(2023)研究也表明, 一些粳型超级稻品种产量潜力显著高于传统日本粳稻品种。近年来, 随着品质育种愈加受到重视, 我国东北粳稻的外观品质和加工品质虽然也有明显改善, 但食味品质与日本优质米品种仍有较大差异(徐正进等, 2005, 2010)。迄今为止, 我国引进了大量日本优质种质资源并用于育种实践, 在一定程度上使我国东北粳稻品质有了进一步提高, 但中日粳稻品质差异依然存在(徐铨等, 2017)。因此, 解析中日粳稻食味品质差异的遗传基础尤为重要。
目前, 评价大米食味品质的方法主要有感官试验评价法、理化指标评价法和仪器评价法3种(卞金龙等, 2023)。但感官评价法主观性较强, 受评价人员的自身喜好影响较大, 且稻米的理化指标与食味品质并非简单的线性相关, 在一些具有相似理化指标的品种之间, 食味依然存在着很大差异(Li et al., 2016; 卢慧等, 2021)。因此, 品质评价的客观性、可靠性以及高通量检测方法对于其遗传研究与育种改良至关重要。
质构特性是一个可以区别并描述米饭感官特征的物理性状(李霞辉等, 2007), 能将感官评价的指标客观量化, 在一定程度上可反映米饭的适口性和食味品质的优劣, 是一种可用于获取高通量品质指标的重要策略, 在遗传研究方面优势明显(官斌等, 2011; 汪长钢等, 2015; 周小理等, 2017)。米饭蒸煮过程实质上是在水、热作用下, 其中包含的淀粉、蛋白质和脂类等成分发生质变的过程(金丽晨等, 2011)。因此, 米饭的质构特性很大程度上取决于稻米组成成分(如淀粉、蛋白质和脂肪)的含量。同时, 环境、样品制备方式和测试条件等也会对质构特性产生一定程度的影响(常俊楠等, 2021)。但对米饭质构特性起决定性作用的还是遗传因素。目前, 对于质构特性的遗传机理研究主要集中在果蔬类(卜远鹏, 2019; 姜建福, 2020; 孙蕾, 2021), 而对于米饭质构特性主要侧重于研究其与稻米外观和理化性质等的相关性, 从遗传角度的研究报道相对较少(朱大伟等, 2022; 杨陶陶等, 2023)。吴长明等(2003)以Asominori/IR24的71份重组自交系群体为实验材料, 对控制稻米食味品质性状的数量性状位点(quantitative trait loci, QTL)进行分析, 采用2种分析方法重复检测到3个控制米饭硬度的QTLs, 以及3个控制粘弹性的QTLs。余峥嵘(2007)利用重组自交系群体发现Waxy基因区段以及第10号染色体的RM388A-RM591区段是控制米饭质构特性的主要区段。王翠丽等(2022)利用“Asominori × IR24”衍生的重组自交系群体, 在第7号染色体的RM505-RM118区段重复定位到与米饭硬度和胶着性相关的QTL位点, 在第2号染色体RM5390- RM1313区段重复定位到与米饭内聚性相关的QTL位点。
米饭质构特性指标众多, 且米饭食味品质受多个质构特性指标综合影响, 很难通过单一指标直接反映米饭食味。因此, 将多个变量转化为少数几个相互独立的主成分来进行综合评价是一种行之有效的方法。米饭质构特性与食味品质同样是复杂的数量性状, 受基因和环境双重影响, 遗传基础复杂。但由于QTL传统作图群体间遗传背景差异较大, 群体构建年限较长, 且QTL对遗传背景和环境较为敏感, 目前只定位了极少数的质构特性指标QTLs (Liao et al., 2001; Monna et al., 2002)。全基因组关联分析因其无须专门构建作图群体, 且具有检测效率快、解析精度高等优点, 被认为是可用于寻找目标性状相关候选基因, 从而揭示基因型与表型性状之间联系的有效手段(Cantor et al., 2010; Huang et al., 2010; Zhao et al., 2011)。近年来, 随着二代测序技术的不断发展, 很多动植物的基因组完成了完整测序, 这使得基因组信息愈加丰富, 全基因组关联分析得以彰显出巨大优势, 从而广泛应用于各种植物多种性状相关基因的鉴定(Lin et al., 2014; 杨行海等, 2016; 武棒棒等, 2023; 李晓明等, 2023)。日本名古屋大学松冈信教授课题组通过对8个与水稻株型有关的典型性状进行主成分分析, 结果显示第一主成分反映了水稻株型性状的大部分信息, 利用PC1作为因变量进行全基因组关联分析, 分离出1个编码水稻细长秆的基因OsSPY, 该基因可激活赤霉素信号抑制蛋白SLR1 (Yano et al., 2019)。由此可见, 将主成分分析与全基因组关联分析相结合, 是鉴定涉及复杂特征性状基因的有效手段。
稻米的质构特性被认为是米饭食味品质评价中最关键的因素。前人在探究稻米的质构特性时多注重研究影响米饭质构特性的因素以及稻米质构特性与其它理化指标之间的相关性, 对米饭质构特性的遗传基础研究相对较少。而由于育种目标和育种方法的差异, 中国粳稻和日本粳稻之间的遗传组成存在很大差异, 这也造成了粳米质构的变化, 进而引起食味差异。因此, 本研究基于中日粳米食味品质的多样性变化, 从质构角度量化食味品质的参数, 同时应用基于质构特性指标主成分特征值的全基因组关联分析, 鉴定中日粳米质构特性变异相关位点, 旨在解析中日稻米食味品质差异的遗传基础, 为我国粳稻食味品质遗传改良提供有价值的遗传信息和理论依据。
1 材料与方法
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1.1 实验材料
本研究材料为来自中国和日本不同地区的274份种质资源组成的自然群体。其中包括来自中国北方粳稻(Oryza sativa subsp. geng or japonica) 189份(农家品种75份, 现代栽培稻114份)、日本粳稻(O. sativa subsp. geng or japonica) 85份(农家品种40份, 现代栽培稻45份)。
1.2 材料种植
供试材料于2021年和2022年种植于沈阳农业大学科学实验基地(123.25°E, 41.48°N)。种植采用单株插秧, 每个品种种植3行, 每行种植10株, 株行间距12 cm × 30 cm, 所有田间管理遵循当地常规大田生产管理。肥料施用: 尿素150 kg·hm-2, 硫酸钾75 kg·hm-2, 磷酸二铵150 kg·hm-2。
1.3 食味值测定
米饭的蒸煮制备主要参照GB/T 15682-2008《粮油检验稻谷、大米蒸煮食用品质感官评价方法》中的小量样品制备方法并结合实际情况进行适当修改。称取30 g精米于蒸饭皿中, 快速加入100 mL水, 顺时针搅拌10圈, 逆时针搅拌10圈, 快速换水并重复上述操作1次。再用100 mL蒸馏水冲洗1次, 沥尽余水, 整个洗米时间控制在3-5分钟。然后按照米:水=1:1.4 (m/m)加入蒸馏水, 蒸饭皿加盖纸盖, 浸泡30分钟。当蒸锅沸腾时, 将蒸饭皿放入蒸锅蒸煮30分钟, 再焖10分钟。取下纸盖沿蒸饭皿壁轻轻上下搅动米饭, 注意不要破坏米粒结构。盖上纸盖, 在通风橱内冷却20分钟, 再换成铁盖, 室温下冷却1.5小时。称取8 g冷却后的米饭样品压成米饼, 用日本株式会社生产的STA-1A米饭食味计测定其外观、硬度、黏度、平衡度和食味值, 每个样品重复测定3次。
1.4 质构特性测定
质构特性的测定方法参考孟庆虹等(2017)提出的三粒法, 结合实际情况进行适当修改。在中间部位米饭层每次随机挑选3粒饱满完整的米饭粒, 3个饭粒对称放置在质构仪的载物平板上, 使用配备TA43球形探头的CT34500型质构仪(BROOKFIELD ENGINEERING LABORTORIES)进行TPA质构测定, 每个样品重复测试10次。测试参数设置: 测前速度为1.00 mm·s-1, 测后速度为2.00 mm·s-1, 触发力设置为5 g, 循环次数为2, 测试速度为0.5 mm·s-1。对获得的10次测定结果, 每个品种分别去掉2个极大值和极小值, 取剩余6次测定结果的平均值作为最终测定结果。共测定30个质构性状: 第一循环硬度(hardness, HN)、粘力(adhesion force, ADF)、粘性(adhesion degree, AND)、内聚性(cohesiveness, CON)、弹性(springiness, SN)、胶着性(gumminess, GMN)、咀嚼性(chewiness, CN)、弹力(elastic force, EF)、硬度形变量(hardness deformation, HND)、硬度形变百分比(hardness deformation percentage, HNDP)、压缩功循环1 (first compression work cycle, FCWC)、第一可恢复形变循环(first recoverable deformation cycle, FRDC)、可恢复功循环1 (first recoverable work cycle, FRWC)、第一循环总功(first total power cycle, FTPC)、目标负载(objective load, OL)、目标变形量(target deformation amount, TDA)、样品变形百分比(sample deformation percentage, SDP)、峰值应变(peak strain, PS)、弹性长度(elastic length, EL)、弹性功(elastic work, EW)、第二循环硬度(second cycle hardness, SCHN)、压缩功循环2 (second compression work cycle, SCWC)、可恢复形变循环2 (second recoverable deformation cycle, SRDC)、可恢复功循环2 (second recoverable work cycle, SRWC)、总功循环2 (second total power cycle, STPC)、弹性指数(elasticity index, EI)、咀嚼指数(chewing index, CI)、校正内聚性(corrected cohesiveness, CCON)、校正胶着性(corrected gumminess, CGMN)和校正咀嚼性(corrected chewiness, CCN)。
1.5 全基因组关联分析
使用llumina HiSeqTM 2500测序仪获得原始序列数据(raw data或raw reads), 通过fastq格式进行保存, 使用BWA (Burrows-Wheeler Alignment)序列比对软件中的BWA-MEM算法, 将全基因组重测序原始数据与水稻参考基因组IRGSP v1.0进行比对, 将比对结果保存于sam格式文件中, 根据水稻参考基因组使用Samtools软件将经过比对的sam文件转化为二进制bam格式文件, 使用GATK4生成群体vcf文件, 通过MAF小于0.05过滤得到1.3 Mb的SNP和indel。利用emmax软件的混合线性模型, 结合274份水稻样品质构特性表型值与基因型值, 进行粳稻质构特性的全基因组关联分析(genome-wide association study, GWAS)。
1.6 数据分析与绘图
使用GraphPad Prism 9.0.0.121进行方差分析并绘制箱型图, 采用IBM SPSS Statistics 22进行相关性分析, 使用R × 64 4.1.0进行主成分分析并绘制相关性热图、碎石图和Manhattan图(Friendly, 2002)。
2 结果与分析
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2.1 中日粳米在食味上的差异以及基于食味值的全基因组关联分析
单产的提升是长期以来符合我国国情的水稻育种目标, 而优质尤其是高食味是长期以来商品稻米的必要条件。因此, 本研究分析了中日两国粳稻食味品质及其构成因素的差异。结果表明, 在2021年和2022年沈阳的同一种植环境条件下, 日本粳米的食味值均显著高于中国粳米, 且中国粳米和日本粳米在2022年的食味值均显著高于2021年。表明食味值受遗传因素影响的同时, 受到不同年份间环境影响也较大(图1A)。进一步分析表明, 环境对食味的影响趋势一致, 即相对于2021年, 2022年所有材料的食味值均有所提高, 多数品种食味值对于环境影响的响应程度相近, 少数环境敏感品种食味值变异幅度较大(图1B)。
本研究首先对自然群体开展基于食味值的全基因组关联分析(GWAS), 结果表明2021年和2022年在6号染色体存在1个与稻米食味显著关联的位点(图1C, D)。进一步的单体型分析和基因注释显示, 该位点位于调控稻米食味品质的主效基因Wx所在区域。对该群体材料的Wx分型发现, 群体中只包含Wxb和Wxin两种等位基因, 中日粳稻材料中均包含这两种等位基因, 且以Wxb为主(附图1A)。我们比较了具有相同Wx等位基因的中日粳稻的食味值, 发现即使在Wx基因型相同的背景下, 日本粳稻的食味值依然显著高于中国粳稻(附图1B)。这表明, 还存在独立于Wx基因之外的影响中日粳米食味品质的位点。且由于中日现代栽培粳稻中大部分食味品质相关主效基因均已被固定, 许多品种的直链淀粉和蛋白质含量差异不大, 但食味品质仍然体现出明显差异。因此我们引入在一定程度上可将米饭食味品质的描述性判别转化为可用于遗传研究的质构特性指标, 用于揭示中日粳米之间产生细微食味差异的遗传机制。
2.2 中日粳米食味值与质构特性指标相关性分析及其在质构特性指标上的差异
为明确各项质构特性指标与稻米食味间的关系, 我们将两年间的食味值和质构特性指标分别取平均值, 然后对两者进行相关性分析。结果表明, 食味值与米饭硬度、硬度形变量、第一可恢复形变循环、可恢复功循环1、第一循环总功、目标负载、目标变形量、粘力、粘性、弹力、弹性长度、弹性功、第二循环硬度、可恢复形变循环2、可恢复功循环2、总功循环2、胶着性、咀嚼指数以及校正胶着性这些质构特性指标均具有极显著相关性; 与压缩功循环1、峰值应变、压缩功循环2、弹性和咀嚼性呈显著相关性(图2A), 这表明大部分米饭质构特性指标与稻米食味之间具有密切联系。
2021年中国粳稻和日本粳稻群体在所有质构指标上均无统计学上的显著差异(图2B, 附图2); 2022年中国粳稻和日本粳稻在粘力、弹性指数两个指标上呈现极显著性差异, 第一可恢复形变循环指数呈现显著性差异, 在硬度、粘性、内聚性、弹性、弹力、胶着性和咀嚼性等质构重要指标及其它质构特性指标上均无显著性差异(图2C, 附图3)。综合两年间的质构特性结果, 发现中国粳稻和日本粳稻在质构特性指标上的差异并不明显, 其差异更多体现在品种个体之间。随后对硬度、弹力和粘力指标遗传力进行分析(附表1), 结果表明中日粳稻食味差异不是由单一的质构特性指标决定, 而是整体质构特性共同作用的结果。
2.3 粳稻自然群体质构特性指标的主成分分析
稻米的质构指标繁多, 而且我们的研究发现, 稻米食味变化是由多个质构指标综合得出的结果, 再加上稻米的质构特性具有复杂的遗传基础, 因此采用主成分分析法对复杂的稻米质构特性降维, 提取主成分, 综合两年主成分分析结果, 发现前4个主成分的累积贡献率均达到80%以上, 可以解释群体表型遗传80%以上的变异(表1; 附图4)。因此, 通过主成分分析将水稻质构特性的30个指标降维成4个主成分。
由表1可知, 两年间第一主成分贡献率达44.49%以上, 该主成分中总功循环2 (STPC)、胶着性(GMN)、校正咀嚼性(CCN)、可恢复功循环1 (FRWC)、压缩功循环2 (SCWC)、咀嚼性(CN)、校正胶着性(CGMN)、可恢复功循环2 (SRWC)、目标负载(OL)、第二循环硬度(SCHN)和第一循环硬度(HN)的荷载均较大, 因此第一主成分主要反映上述11个质构特性指标。两年间第二主成分的贡献率达14.80%以上, 2021年第二主成分中载荷较大的指标是目标负载(TDA)、硬度形变量(HND)、弹力(EF)、粘性(AND)、粘力(ADF)、内聚性(CON)、压缩功循环1 (FCWC)、弹性功(EW)和可恢复形变循环2 (SRDC), 而2022年该主成分中载荷较大的是弹力(EF)、内聚性(CON)、弹性指数(EI)、目标变形量(TDA)、硬度形变量(HND)、粘力(ADF)、校正内聚性(CCON)、可恢复形变循环2 (SRDC)和第一可恢复形变循环(FRDC), 其中目标变形量(TDA)、硬度形变量(HND)、弹力(EF)、粘力(ADF)、内聚性(CON)和可恢复形变循环2 (SRDC)指标在两年间的载荷均较大, 表明这6个指标受不同年际间的环境影响较小。两年间第3主成分和第4主成分的贡献率分别达8.35%和5.8%以上, 第3主成分和第4主成分在两年间的载荷均较大的分别为弹性长度(EL)和弹性(SN)以及硬度形变百分比(HNDP)和峰值应变(PS)。综上所述, 第1主成分主要表示胶着性和咀嚼性相关性状, 第2主成分主要表示硬度、粘性和形变相关性状, 第3和第4主成分分别表示弹性相关性状和硬度形变相关性状。
2.4 基于质构特性指标主成分特征值的全基因组关联分析
本研究以中日粳稻组成的自然群体的主成分特征值为表型数据, 结合基因型数据进行全基因组关联分析。结果显示, 共检测到23个对水稻质构特性主成分产生影响的位点(图3; 表2)。其中2021年共检测到17个显著性位点, 分布在除第5、10和12以外的9条染色体上; 2022年共检测到8个显著性位点, 分布在除第6、9和10以外的染色体上。位于第4号染色体的qFPC4.3和位于第9号染色体的qFPC9.2两个位点在两年间被共定位。
2.5 qFPC4.3和qFPC9.2区域的候选基因分析
我们绘制了目标峰值附近的LD图, qFPC4.3位点候选基因的范围被缩小至486.3 Kb的基因组区域(图4A, B), 在qFPC4.3和区域内注释到6个与表型变异相关的基因(图4C)。基因注释表明, 这6个基因编码的蛋白多为表达蛋白, 随后我们根据国家水稻数据库中心(https://ricedata.cn/gene/)和The Bio-Analytic Resource for Plant Biology网站(https://bar.utoronto.ca/eplant_rice/)基因表达和蛋白工具模块获取各基因的表达量数据, 结果表明, LOC_Os04g23300、LOC_Os04g23330和LOC_Os04g23470在灌浆期的种子中高表达(表3)。
同样, 我们绘制了目标峰值附近的LD图, 将qFPC9.2候选基因的范围缩小至500 Kb的基因组区域(图4D, E), 结合单体型分析, 在qFPC9.2区域内注释到15个与表型变异相关的基因(图4F)。基因注释信息显示, 这15个基因功能各异, 基因表达分析显示, 有7个基因在灌浆期籽粒中高表达, 2个在成熟种子中高表达(表4)。
值得关注的是, 仅在2022年被定位到的qFPC2.3 位点附近存在1个已报道来源于突变体的品质相关基因Du-3 (附图5A-C; 附表2)。du3突变体中, Wxb mRNA前体的剪接效率降低, 从而导致直链淀粉含量降低(Isshiki et al., 2008)。根据群体中存在的变异, 将Du-3基因分为4种单体型(附表3)。对Du-3基因4种单体型材料间的直链淀粉含量、胶稠度和蛋白质含量进行分析, 发现携带Du-3Hap1的材料直链淀粉含量和胶稠度分别显著低于和高于携带其它3种单体型材料, 而不同单体型材料间的蛋白质含量则无显著差异(附图5D-F)。本研究应用GWAS方法检测到该基因可能存在的自然变异, 同时该基因仅在2022年被定位到, 说明该基因的表达可能易受温度和光照等环境因素影响。
3 讨论
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稻米食味品质受到包括遗传背景和环境条件在内多种因素的影响。环境因素中对稻米食味品质产生影响的主要是温度、光照和水分, 灌浆前期和中期温度过高或过低均不利于食味品质形成(习敏等, 2020); 同时, 灌浆期和结实期的平均积温和日照时数均严重影响稻米的食味品质(王乐惠等, 2023)。灌浆期尤其是抽穗后15天开始缺乏足够的光照会严重降低稻米的食味品质(单智超和陈富忠, 2023); 雨量充沛、灌溉水水质优良以及间歇灌溉方式有利于食味品质的提升(陈宏法等, 2023)。但遗传基因是决定食味品质的基石, 影响食味品质的潜力。我们通过对两年间不同环境条件下的食味品质和质构特性进行研究, 以排除部分环境因素的影响, 从而在相对排除环境影响的前提下, 挖掘影响中国东北和日本粳稻食味品质的遗传基础。研究表明, 淀粉和蛋白质含量对稻米食味品质起决定性作用(姚晓云等, 2016; 唐子雯和张冬平, 2023; 许锐等, 2024)。因此, 中日粳稻品质育种过程中, 淀粉和蛋白质含量相关基因优势单体型逐渐被固定下来, 如携带Wxb型基因的水稻, 其直链淀粉含量低, 食味品质佳(Sreenivasulu et al., 2022)。我们以食味值为表型进行关联分析, 在两年间重复定位到Wx基因, 这是由于本研究材料中包含早期水稻品种, 这些品种中淀粉和蛋白质相关基因未被固定, 所以Wx基因效应得以凸显出来。而中日粳稻现代栽培品种中, 调控淀粉和蛋白质含量的主效基因已被固定, 因此各品种间直链淀粉和蛋白质含量基本趋于一致, 但其食味值仍存在一定差异。我们对该群体材料间的Wx分型后发现, 携带相同Wx等位基因的中日粳稻材料食味值仍存在显著差异, 这表明除已被固定的主效基因外, 还存在一些微效基因对中日粳稻食味品质差异产生不可忽视的影响。基于此, 我们从质构的角度探索影响中日粳米食味品质的遗传机制, 揭示独立于Wx之外的基因组变异位点。
质构特性是由微效多基因控制的综合性复杂性状, 由环境条件和遗传因素共同决定。且质构指标繁多, 质构特性对稻米蒸煮食味品质的影响是由多个指标共同作用的结果。当前, 国内外对稻米质构特性的研究主要通过对稻米质构特性与其理化指标的关系进行相关分析, 以探索影响稻米质构特性的主要因素。研究发现, 水稻的直链淀粉含量、蛋白质含量、糊化温度、碱消度和胶稠度等是影响稻米质构特性的主要因素(周显青等, 2012; 王鹏跃等, 2016), 但从遗传角度对质构特性进行研究的较少。本研究以中日粳稻质构特性指标主成分特征值为表型数据进行全基因组关联分析, 在两年间共定位到23个相关QTN位点。余峥嵘(2007)利用珍汕97和德陇208的重组自交系群体为材料对稻米质构特性进行QTL定位分析, 结果表明Waxy基因区段是控制稻米质构特性的主要区段。王翠丽等(2022)利用“Asominori × IR24”衍生的重组自交系群体在4号染色体上定位到1个调控粘附力的QTL: F-Qadf-4-1, 在9号染色体上分别定位到调控硬度、粘附力、弹性和咀嚼性的QTLs, 但上述位点均与本研究定位到的2个位点不重合, 这表明调控中日粳米质构特性的变异位点独立于上述位点之外, 这可能是由于日本从育种之初便开始以优质作为育种目标, 在一代代的人工选择中, 保留了某些调控质构特性的独特的等位变异, 但与中国现代栽培粳稻中的单体型不一致。
在对某一问题进行研究时, 为了获取更加全面和详细的信息, 研究者通常选取多个变量对相关性状进行说明(许淑娜和李长坡, 2011)。但如果选取的变量过多且变量之间反映的信息具有重叠性, 反而会增加研究工作的复杂度和工作量。因此, 基于原始变量之间的相关关系, 利用少数几个包涵原始变量大部分变异信息的线性组合来代替原始变量进行综合评价, PCA便是一种从高度相关的复杂、多个特征中收集信息的有效方法。在GWAS分析中, PCA结果能作为协变量, 用于校正群体分层给关联分析带来的假阳性。因此, 在前人研究中提出了一种使用PC分数作为因变量执行高效GWAS的策略。首先, 这种策略可以通过避免多次测试降低I型错误率的可能性(Holberg et al., 2001; He et al., 2008)。其次, PCA生成的PC分数可以将倾斜的原始变量转化为近似的正态分布, 从而得到稳健可靠的GWAS结果(Boomsma and Dolan, 1998; Goh and Yap, 2009)。第三, 由于PC分数代表集成变量的特性, 使用PC分数的GWAS可以检测到使用个体性状可能被忽略的基因组区域(Yano et al., 2019)。
此外, 将复杂性状表型参数应用主成分分析降维后的全基因组关联分析也有报道。日本名古屋大学松冈信教授课题组对8个与水稻株型有关的典型性状进行了主成分分析, 结果显示第一主成分反映了水稻株型性状的大部分信息, 利用PC1作为因变量进行全基因组关联分析, 分离到1个编码水稻细长秆的基因 OsSPY (Yano et al., 2019)。张旭等(2021)在解析甘蓝型油菜(Brassica napus)株型相关性状的遗传调控机制时, 组合利用全基因组关联分析和主成分分析方法, 充分挖掘了复杂性状的调控机制, 而且协调了株型相关性状之间的相互影响。本研究将PCA与GWAS相结合, 成功地将30个质构特性指标降维成4个可解释群体80%以上表型变异的主成分, 并定位到2个稳定遗传的调控中日粳米质构特性的变异位点以及1个已克隆影响直链淀粉含量的基因Du-3。由此可见, 在GWAS中使用主成分分析是挖掘涉及复杂特征性状基因的有效手段。
本研究发现中国粳米食味值显著低于日本粳米, 质构特性指标与食味值相关性分析表明, 30个米饭质构特性指标中24个指标与稻米食味之间显著或极显著相关。进一步的质构特性指标分析显示, 中日粳米食味差异是由多个质构特性指标综合作用的结果。同时, 本研究成功地将稻米食味品质的描述性判别, 转化为可用于遗传研究的30个可量化的质构指标, 并降维成4个能解释80%以上表型变异的主成分。利用基于质构特性指标主成分特征值的全基因组关联分析, 揭示了中日粳米之间产生细微食味差异的遗传机制, 为东北稻米品质遗传改良提供了新的参考。
4 作者贡献声明
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崔娟: 完成实验并撰写论文; 于晓玉: 部分数据分析和部分制图; 于跃娇: 协助完成实验; 梁铖玮: 部分制图; 孙健: 设计实验和指导论文修改; 陈温福: 资金支持及实验设计指导。
附图1 自然群体Wx基因分型及相同Wx基因型间食味值比较
(A) 自然群体中的Wx基因分型; (B) 两年间相同Wx基因型的中日粳稻食味值比较。** P<0.01
Appendix figure 1 Wx genotyping and comparison of taste values among identical Wx genotypes in natural populations
(A) Typing of Wx in natural populations; (B) Comparison of taste values between Chinese and Japanese japonica rice with the same Wx genotype in two years. ** P<0.01
附图2 2021年自然群体中日粳稻质构特性指标差异对比
Appendix figure 2 Comparison of the differences in texture characteristics of japonica rice in Chinese and Japanese in 2021
附图3 2022年自然群体中日粳稻质构特性指标差异对比
Appendix figure 3 Comparison of the differences in texture characteristics of japonica rice in Chinese and Japanese in 2022
附图4 质构特性PCA碎石图
Appendix figure 4 Texture characteristics PCA lithotripsy diagram
附图5 qFPC2.3的候选基因单倍型分析及Du-3基因不同单体型材料间直链淀粉、蛋白质含量和胶稠度
(A) 2022年基于质构特性特征值的全基因组关联分析; (B) qFPC2.3位点附近的LD black图; (C) qFPC2.3位点附近LD区间内与表型变异相关的基因, 纵坐标代表PC1的主成分特征值得分; (D) Du-3基因不同单体型材料间的直链淀粉含量; (E) Du-3基因不同单体型材料间的胶稠度; (F) Du-3基因不同单体型材料间的蛋白质含量。** P<0.01
Appendix figure 5 Haplotype analysis of candidate genes for qFPC2.3 and amylose content, protein content, and gel consistency among different haplotypes of the Du-3 gene
(A) Genome wide association studies based on texture characteristic feature values in 2022; (B) LD black near the qFPC2.3 locus; (C) Genes associated with phenotypic variation in the LD interval near the qFPC2.3 locus, whose vertical coordinates represent the principal component eigenvalue scores of PC1; (D) Amylose content among different haplotypes of Du-3; (E) Gel consistency among different haplotypes of Du-3; (F) Protein content different haplotypes of Du-3. ** P<0.01
附表1 自然群体硬度(HN)、弹力(EF)和粘力(ADF)指标广义遗传力分析
Appendix table 1 Genetic power analysis of the natural population hardness (HN), elastic force (EF), and adhesion force (ADF) indicators
附表2 qFPC2.3候选基因注释信息
Appendix table 2 Candidate gene annotation information of qFPC2.3
附表3 Du-3基因变异信息
Appendix table 3 Variant information of the Du-3 gene
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  • 国家自然科学基金(32372107)
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2025年第60卷第4期
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doi: 10.11983/CBB24196
  • 接收时间：2024-12-20
  • 首发时间：2026-10-27
  • 出版时间：2025-07-10
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  • 收稿日期：2024-12-20
  • 录用日期：2025-03-18
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国家自然科学基金(32372107)
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    沈阳农业大学水稻研究所, 沈阳 110866

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*孙健, 沈阳农业大学教授, 博士生导师。入选首批国家“神农青年英才”计划, 获“辽宁青年科技奖”等奖励或荣誉。主要研究方向为水稻种质资源创新。主持“国家自然科学基金面上项目”等多项国家与省部级项目。围绕杂草稻种质创新开展了系统性研究工作, 阐明了杂草稻的分类学地位, 提出起源演化新假说, 克隆了多个对栽培稻遗传改良具有重要价值的新基因。带领团队设计研发了粳稻种质资源芯片, 在粳稻资源评价、分子设计育种、全基因组选择等方面开展了广泛应用。研究成果在Nature Communications、Molecular Plant、New Phytologist等国际著名期刊上发表。E-mail: ;
陈温福, 辽宁法库人, 我国著名水稻专家和生物炭专家, 中国工程院院士, 沈阳农业大学教授。第十一届、第十二届、第十三届全国人大代表, 第十四届全国政协委员。曾任国家重点学科“作物栽培学与耕作学”学科带头人, 国务院学位委员会第五届、第六届学科评议组成员和作物学科组召集人, 国家农作物品种审定委员会委员, 农业农村部第九届科技委常委、水稻专家组成员, 辽宁省科协副主席。曾获全国劳动模范和五一劳动奖章、全国模范教师、全国教学名师、创先争优奖、全国农业科技先进工作者、中华农业英才奖、辽宁省特等劳动模范、辽宁省科技功勋奖等多项荣誉。E-mail:
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2种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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