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ARS Home » Southeast Area » Stuttgart, Arkansas » Dale Bumpers National Rice Research Center » Research » Publications at this Location » Publication #267741

Title: Mapping QTLs for improving grain yield using the USDA rice mini-core collection

Author
item Li, Xiaobai - Zhejiang University
item Yan, Wengui
item Agrama, Hesham - University Of Arkansas
item Jia, Limeng - Zhejiang University
item Shen, Xihong - China National Rice Research Institute
item Jackson, Aaron
item Moldenhauer, Karen - University Of Arkansas
item Yeater, Kathleen
item Mcclung, Anna
item Mcclung, Anna
item Wu, Dianxing - Zhejiang University

Submitted to: Planta
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/23/2011
Publication Date: 4/10/2011
Citation: Li, X., Yan, W., Agrama, H., Jia, L., Shen, X., Jackson, A., Moldenhauer, K., Yeater, K., McClung, A., Wu, D. 2011. Mapping QTLs for improving grain yield using the USDA rice mini-core collection. Planta. 234(2):347-361.

Interpretive Summary: Yield is the most important and complex trait for genetic improvement in crops, and marker-assisted selection enhances the improvement efficiency. The USDA rice mini-core collection derived from over 18,000 accessions of global origins is an ideal panel for association mapping of quantitative trait loci (QTL) responsib1e for valuable traits. We evaluated 203 O. sativa accessions for 14 agronomic traits and identified five highly and significantly correlated with grain yield per plant: plant height, plant weight, tillers, panicle length, and kernels/branch. Genotyping with 155 genome-wide molecular markers demonstrated five main cluster groups. Model comparisons revealed that different dimensions of principle components analysis affected yield and its correlated traits for mapping accuracy, and kinship did not improve the mapping in this collection. Thirty marker-trait associations were highly significant, four for yield, three for plant height, six for plant weight, nine for tillers, five for panicle length and three for kernels/branch. Twenty-one markers contributed to the 30 associations because seven were co-associated with two and one with three markers. Tagging the QTLs responsible for multiple yield traits may simultaneously help dissect the complex yield traits and elevate the efficiency to improve grain yield using marker-assisted selection in rice.

Technical Abstract: Yield is the most important and complex trait for genetic improvement in crops, and marker-assisted selection enhances the improvement efficiency. The USDA rice mini-core collection derived from over 18,000 accessions of global origins is an ideal panel for association mapping. We phenotyped 203 O. sativa accessions for 14 agronomic traits and identified 5 that were highly and significantly correlated with grain yield per plant: plant height, plant weight, tillers, panicle length, and kernels/branch. Genotyping with 155 genome-wide molecular markers demonstrated 5 main cluster groups. Linkage disequilibrium (LD) decayed at least 20 cM and marker pairs with significant LD ranged from 4.64 to 6.06% in four main groups. Model comparisons revealed that different dimensions of principal component analysis affected yield and its correlated traits for mapping accuracy, and kinship did not improve the mapping in this collection. Thirty marker–trait associations were highly significant, 4 for yield, 3 for plant height, 6 for plant weight, 9 for tillers, 5 for panicle length and 3 for kernels/branch. Twenty-one markers contributed to the 30 associations, because 8 markers were co-associated with 2 or more traits. Allelic analysis of OSR13, RM471 and RM7003 for their coassociations with yield traits demonstrated that allele 126 bp of RM471 and 108 bp of RM7003 should receive greater attention, because they had the greatest positive effect on yield traits. Tagging the QTLs responsible for multiple yield traits may simultaneously help dissect the complex yield traits and elevate the efficiency to improve grain yield using marker-assisted selection in rice.