Genome-wide Association Analysis and Candidate Gene Mining for Grain Type-Related Traits in Japonica Rice

doi:10.7668/hbnxb.20193821

Abstract

Abstract:

In order to promote the breeding of high quality grain shape Japonica rice varieties,a natural population consisting of 295 Japonica rice varieties collected from around the world was used as test material for phenotypic analysis of five grain shape-related traits,including grain length,grain width,grain thickness,aspect ratio and thousand grains weight,between 2018 and 2019,combined with 788,396 SNPs obtained by resequencing,and genome-wide association analysis using the MLM model of TASSEL 5.0.Haplotype analysis was performed on all the genes in the QTL intervals that were jointly detected during the two years and controlled multiple shape-related traits, and candidate genes for high quality rice grains were mined by combining the results of previous studies and functional annotation of the genes. The results showed that there was extensive phenotypic variation for the five grain shape-related traits,all of which were approximately normally distributed,and most of the grain shape-related traits were significantly or highly significantly correlated with each other. And a total of 221 QTLs significantly associated with rice grain shape-related traits were detected under the threshold condition of P≤5.46×10^-6,which were distributed on all 12 chromosomes of rice,and phenotypic contribution was 8.62%-20.73%, of which, seven QTLs were jointly detected in two years, and combined with the haplotype analysis and functional annotation of the genes, it was hypothesized that LOC_Os12g 44290 was a new candidate gene for rice grain shape. In summary,this study used genome-wide association analysis for QTL localization and candidate gene analysis for grain type-related traits in 295 Japonica rice varieties,providing a theoretical basis for breeding Japonica rice varieties with high quality grain types.

Key words: Japonica rice, Grain shape, Genome-wide association analysis, Candidate genes

DUAN Yuxuan, CUI Jingnan, XU Shanbin, WANG Jingguo, LIU Hualong, YANG Luomiao, JIA Yan, XIN Wei, WU Wenshen, ZHENG Hongliang, ZOU Detang. Genome-wide Association Analysis and Candidate Gene Mining for Grain Type-Related Traits in Japonica Rice[J]. Acta Agriculturae Boreali-Sinica, 2023, 38(5): 19-28. doi: 10.7668/hbnxb.20193821.

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References 40

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年份 Year	性状 Traits	平均值 Average	标准差 s	变异范围 Variation range	变异系数/% CV	峰度 Kurtosis	偏度 Skewness
2018	粒长	7.56	0.64	5.15~10.95	8.50	4.97	1.33
	粒宽	3.39	0.33	2.45~4.50	9.84	0.12	0.12
	粒厚	2.19	0.15	1.53~2.51	6.92	1.27	-0.73
	长宽比	2.25	0.33	1.56~3.77	14.70	3.32	1.45
	千粒质量	27.38	2.98	17.82~38.00	10.92	1.09	0.18
2019	粒长	7.54	0.64	5.63~10.92	8.60	3.88	0.94
	粒宽	3.20	0.31	2.04~4.64	9.93	0.51	0.15
	粒厚	2.16	0.16	1.55~2.59	7.54	0.92	-0.56
	长宽比	2.37	0.32	1.83~4.39	13.73	6.12	1.83
	千粒质量	24.46	3.42	13.56~44.50	14.01	3.60	0.51

年份 Year	性状 Traits	平均值 Average	标准差 s	变异范围 Variation range	变异系数/% CV	峰度 Kurtosis	偏度 Skewness
2018	粒长	7.56	0.64	5.15~10.95	8.50	4.97	1.33
	粒宽	3.39	0.33	2.45~4.50	9.84	0.12	0.12
	粒厚	2.19	0.15	1.53~2.51	6.92	1.27	-0.73
	长宽比	2.25	0.33	1.56~3.77	14.70	3.32	1.45
	千粒质量	27.38	2.98	17.82~38.00	10.92	1.09	0.18
2019	粒长	7.54	0.64	5.63~10.92	8.60	3.88	0.94
	粒宽	3.20	0.31	2.04~4.64	9.93	0.51	0.15
	粒厚	2.16	0.16	1.55~2.59	7.54	0.92	-0.56
	长宽比	2.37	0.32	1.83~4.39	13.73	6.12	1.83
	千粒质量	24.46	3.42	13.56~44.50	14.01	3.60	0.51

性状 Traits	粒长 GL	粒宽 GW	粒厚 GT	长宽比 AR	千粒质量 TGW
粒长 GL	1.000	-0.094	-0.132^*	0.471^**	0.173^**
粒宽 GW	-0.126	1.000	0.156^**	-0.585^**	0.322^**
粒厚 GT	-0.165^*	0.559^**	1.000	-0.306^**	0.287^**
长宽比 AR	0.577^**	-0.592^**	-0.438^**	1.000	-0.096
千粒质量 TGW	0.290^**	0.394^**	0.385^**	-0.064	1.000

性状 Traits	粒长 GL	粒宽 GW	粒厚 GT	长宽比 AR	千粒质量 TGW
粒长 GL	1.000	-0.094	-0.132^*	0.471^**	0.173^**
粒宽 GW	-0.126	1.000	0.156^**	-0.585^**	0.322^**
粒厚 GT	-0.165^*	0.559^**	1.000	-0.306^**	0.287^**
长宽比 AR	0.577^**	-0.592^**	-0.438^**	1.000	-0.096
千粒质量 TGW	0.290^**	0.394^**	0.385^**	-0.064	1.000

性状 Traits	年份 Year	QTL	染色体 Chromosome	位置 Position	峰值SNP Peak SNPs	贡献率/% R²	P值 P value	已知基因/QTL Known genes/QTLs
粒长 GL	2018	qGL8-1	8	2727555	Chr8__2727555	15.58	1.25E-09
	2019	qGL8-1	8	2727555	Chr8__2727555	14.55	2.68E-09
	2018	qGL11-1	11	4869155	Chr11__4869155	14.82	1.57E-09
	2019	qGL11-1	11	4869155	Chr11__4869155	14.22	3.61E-09
	2018	qGL12-4	12	27166122	Chr12__27166122	14.93	1.37E-09
	2019	qGL12-4	12	27166122	Chr12__27166122	14.18	3.80E-09
粒宽 GW	2018	qGW6-4	6	19873840	Chr6__19873840	9.87	1.05E-06	lwr6^[33]
	2019	qGW6-4	6	19873840	Chr6__19873840	9.93	9.72E-07	lwr6^[33]
粒厚 GT	2018	qGT6-2	6	13233278	Chr6__13233278	11.92	5.43E-08	lwr6^[33]
	2019	qGT6-2	6	13233278	Chr6__13233278	9.64	1.22E-06	lwr6^[33]
	2018	qGT12	12	27166122	Chr12__27166122	9.25	2.08E-06
	2019	qGT12	12	27166122	Chr12__27166122	9.25	2.08E-06
长宽比 AR	2018	qAR8-1	8	2727555	Chr8__2727555	20.58	1.17E-10
	2019	qAR8-1	8	2727555	Chr8__2727555	13.40	2.29E-08

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