Analysis of the Texture Factors and Genetic Basis Influencing the Differences in Eating Quality between Northeast China and Japanese Japonica Rice

doi:10.11983/CBB24196

Abstract

Abstract: 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.

Key words: japonica rice, textural characteristics, eating quality, genome-wide association study, principal component analysis

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.

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References 55

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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

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

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

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

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