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United States Department of Agriculture

Agricultural Research Service

Research Project: DEVELOPMENT AND CHARACTERIZATION OF GENETIC RESOURCES FOR AGRONOMIC AND QUALITY TRAITS USING GENOMIC TOOLS Title: Unraveling the complex trait of harvest index with association mapping in rice (Oryza sativa L.)

Authors
item Li, Xiaobai -
item Yan, Wengui
item Agrama, Hesham -
item Jia, Limeng -
item Jackson, Aaron
item Moldenhauer, Karen -
item Yeater, Kathleen
item McClung, Anna
item McClung, Anna
item Wu, Dianxing -

Submitted to: PLoS One
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: November 27, 2011
Publication Date: January 23, 2012
Citation: Li, X., Yan, W., Agrama, H., Jia, L., Jackson, A.K., Moldenhauer, K., Yeater, K.M., McClung, A.M., Wu, D. 2012. Unraveling the complex trait of harvest index with association mapping in rice (Oryza sativa L.). PLoS One. 7(1):Article e29350.

Interpretive Summary: In food production, optimizing grain yield, reducing production costs, and minimizing risks to the environment have been the primary objectives. Food crops grow by developing a green canopy that transpires water and carries out photosynthesis, and a root system that takes up water and nutrition, which leads to the production of biomass. Harvest index is the ratio of grain yield to total biomass and is considered as a measure of biological success in partitioning assimilated photosynthate to the harvestable product. We phenotyped 203 O. sativa accessions in the USDA rice mini-core collection in both temperate (Arkansas) and subtropical (Texas) climates and genotyped them using 154 SSRs and an indel marker. Correlation analysis identified five agronomic traits significantly associated with harvest index. Model comparisons revealed that principle components analysis (PCA) was the best for mapping accuracy. In total, 36 markers in Arkansas and 28 markers in Texas were identified to be significantly associated with harvest index and its correlated traits. Seven and two markers were consistently associated with two or more harvest index correlated traits in Arkansas and Texas, respectively. Additionally, four markers were identified at both locations constitutively, while 32 and 24 markers were identified specifically adaptive to Arkansas and Texas, respectively.

Technical Abstract: Harvest index is a measure of success in partitioning assimilated photosynthate. An improvement of harvest index means an increase of economic portion of the plants. Our objective was to identify the markers associated with harvest index and its correlated traits using 203 O. sativa accessions. The phenotyping for 14 traits was conducted in both temperate (Arkansas) and subtropical (Texas) climates and the genotyping used 154 SSRs and an indel marker. Heading, plant height and weight, and panicle length had negative correlations, while seed set and grain weight/panicle had positive correlations with harvest index across both locations. Subsequent genetic diversity and population structure analysis identified five groups in this collection, which corresponded to their geographic originations. Model comparisons revealed that different dimensions of principle components analysis (PCA) affected harvest index and its correlated traits for mapping accuracy, and kinship did not help. In total, 36 markers in Arkansas and 28 markers in Texas were identified to be significantly associated with harvest index and its correlated traits. Seven and two markers were consistently associated with two or more harvest index correlated traits in Arkansas and Texas, respectively. Additionally, four markers were identified at both locations constitutively, while 32 and 24 markers were identified specifically adaptive to Arkansas and Texas, respectively. Allelic analysis of four constitutive markers demonstrated that allele 253bp of RM431 had significantly greater effect on decreasing plant height, and 390bp of RM24011 had the greatest effect on decreasing panicle length across both locations. Many of these identified markers are located either nearing or flanking the regions where the QTLs for harvest index have been reported. Thus, the results from our association mapping could complement and enrich the information from linkage-based QTL studies, and help improve harvest index directly and indirectly in rice.

Last Modified: 11/25/2014