Genetic Dissection of Grain Size Traits Through Genome-Wide Association Study Based on Genic Markers in Rice

doi:10.1016/j.rsci.2022.07.006

Abstract

Abstract:

Grain size plays a significant role in rice, starting from affecting yield to consumer preference, which is the driving force for deep investigation and improvement of grain size characters. Quantitative inheritance makes these traits complex to breed on account of several alleles contributing to the complete trait expression. We employed genome-wide association study in an association panel of 88 rice genotypes using 142 new candidate gene based SSR (cgSSR) markers, derived from yield-related candidate genes, with the efficient mixed-model association coupled mixed linear model for dissecting complete genetic control of grain size traits. A total of 10 significant associations were identified for four grain size-related characters (grain weight, grain length, grain width, and length-width ratio). Among the identified associations, seven marker trait associations explain more than 10% of the phenotypic variation, indicating major putative QTLs for respective traits. The allelic variations at genes OsBC1L4, SHO1 and OsD2 showed association between 1000-grain weight and grain width, 1000-grain weight and grain length, and grain width and length-width ratio, respectively. The cgSSR markers, associated with corresponding traits, can be utilized for direct allelic selection, while other significantly associated cgSSRs may be utilized for allelic accumulation in the breeding programs or grain size improvement. The new cgSSR markers associated with grain size related characters have a significant impact on practical plant breeding to increase the number of causative alleles for these traits through marker aided rice breeding programs.

Key words: best linear unbiased predictor estimate, candidate gene based SSR, efficient mixed-model association approach, genome-wide association study, VanRaden kinship

Amrit Kumar Nayak, Anilkumar C, Sasmita Behera, Rameswar Prasad Sah, Gera Roopa Lavanya, Awadhesh Kumar, Lambodar Behera, Muhammed Azharudheen Tp. Genetic Dissection of Grain Size Traits Through Genome-Wide Association Study Based on Genic Markers in Rice[J]. Rice Science, 2022, 29(5): 462-472.

Figures/Tables 6

Table 1. Phenotype variation and distribution pattern of four grain size-related traits.

Trait	Phenotype				Skewness	Kurtosis	Shapiro-Wilks ‘p’
Trait	Min	Max	Mean ± SE	PV	Skewness	Kurtosis	Shapiro-Wilks ‘p’
TGW	10.6	31.9	21.50 ± 0.04	0.18	0.07	0.41	0.197
GL	5.2	10.6	8.39 ± 0.14	1.82	-0.09	-0.51	0.754
GW	1.7	3.3	2.62 ± 0.04	0.14	-0.22	-0.74	0.094
LWR	2.01	5.59	3.31 ± 2.01	0.52	0.67	0.24	0.016

Fig. 1. Variation and distribution pattern of grain size and related traits in association panel.

Fig. 2. Correlation coefficients and trend of distribution among grain size characters estimated based on across season best linear unbiased predictor values of phenotypes. TGW, 1000-grain weight; GL, Grain length; GW, Grain width; LWR, Length-width ratio. ***, P < 0.001 by Pearson’s correlation approach.

Fig. 3. Population structure analysis. A, Magnitude of ∆K values with k ranging from 2 to 8 (x-axis) in association mapping panel. B, Population structure of association panel based on 142 new candidate gene based SSR markers at K = 3. Different color columns represent different sub-populations. C, 3D representation of principle component (PC) analysis showing three sub-populations. D, Heat map of kinship matrix. The heat map shows the level of relatedness among the population. The darker areas show the level of relatedness between varieties and the dendrogram depicts clustering of sub-populations.

Table 2. Significant marker-trait associations identified for four grain size-related traits based on mixed line model.

Trait	Marker	Chr	Position (bp)	P-value	PVE (%)	Known gene
TGW	M69	5	18 724 905	0.01	11.01	OSBC1L4
	Sd14	6	5 315 178	0.02	10.23	OsC1
	M55	4	25 489 003	0.02	10.00	SHO1
	Sdi21	5	1 160 267	0.04	9.54	RSR1
GL	M55	4	25 489 003	0.04	6.34	SHO1
GW	M35	8	26 439 584	0.01	13.25	NPP1
	Sdi1	1	5 236 623	0.01	13.07	OsD2
	M99	1	25 382 698	0.02	11.00	Rd
	M69	5	18 724 905	0.02	10.56	OSBC1L4
LWR	Sdi1	1	5 236 623	0.02	8.00	OsD2

Fig. 4. Manhattan plots and Quantile-quantile plots for markers associated with grain traits across the genome. A, 1000-grain weight; B, Grain length; C, Grain width; D, Length-width ratio. In Manhattan plots, x-axis represents chromosomes and explains chromosome-wise marker distribution, and -log10P values on y-axis indicates significant associations. Quantile-quantile plots show deviation of observed -log10P values and expected -log10P values indicating the significant marker trait associations.

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