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ARS Home » Southeast Area » Stuttgart, Arkansas » Dale Bumpers National Rice Research Center » Research » Publications at this Location » Publication #346742

Research Project: Genomic Approaches and Genetic Resources for Improving Rice Yield and Grain Quality

Location: Dale Bumpers National Rice Research Center

Title: Validation of yield component traits identified by GWA mapping in a tropical japonica x tropical japonica rice mapping population

Author
item Eizenga, Georgia
item Jia, Melissa
item Jackson, Aaron
item Boykin, Deborah - Debbie
item LIAKAT, ALI - University Of Missouri
item SHAKIBA, EHSAN - University Of Arkansas
item TRAN, NHIEN - Cuu Long Delta Rice Research Institute
item MCCOUCH, SUSAN - Cornell University - New York
item Edwards, Jeremy

Submitted to: The Plant Genome
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 7/17/2018
Publication Date: 12/13/2018
Citation: Eizenga, G.C., Jia, M.H., Jackson, A.K., Boykin, D.L., Liakat, A.M., Shakiba, E., Tran, N.T., McCouch, S.R., Edwards, J. 2018. Validation of yield component traits identified by GWA mapping in a tropical japonica x tropical japonica rice mapping population. The Plant Genome. https://doi.org/10.3835/plantgenome2018.04.0021.
DOI: https://doi.org/10.3835/plantgenome2018.04.0021

Interpretive Summary: Rice is the stable food for over half of the world’s 7.6 billion people and population growth is predicted to reach 8.6 billion in 2030 and 9.8 billion in 2050. To meet this growing demand for food, it is essential to understand the plant processes that control grain yield, thus expediting breeding efforts to produce more pounds of rice per acre. Rice yield is determined by several factors including number of plants per acre, number of panicles per plant, number of seeds per panicle and seed size. To better understand the biological processes controlling the number of seeds per panicle and seed size, we evaluated a global collection of 400 diverse rice varieties for number of seeds per panicle and seed size, as well as, traits related to panicle architecture which affect the number of seeds per panicle. Subsequently, two diverse varieties were selected from this collection as parents and 256 progeny from a cross between these two parents were evaluated for the same panicle architecture and seed shape traits. Using DNA markers we identified at least 31 possible genes controlling the biological processes related to the panicle architecture and seed shape. These results will be used to develop DNA markers associated with the measured traits for panicle architecture and seed shape. Ultimately, the DNA markers developed will be used by rice breeders to accelerate selection for the desired panicle architecture, and required seed shape as mandated by the market class, i.e. short grain, medium grain or long grain.

Technical Abstract: The Rice Diversity Panel 1 (RDP1) was developed for genome-wide association mapping studies (GWAS) to explore the five diverse rice (Oryza sativa L.) subpopulations (indica, aus, aromatic, temperate japonica and tropical japonica). RDP1 was evaluated for over 30 agronomic and morphological traits, most of which were yield components, and with 700,000 SNPs from the high density rice array (HDRA). Most rice grown in the southern United States is classified as tropical japonica, thus the diversity in this subpopulation is of particular interest to U.S. breeders. Among the RDP1 tropical japonica accessions, ‘Estrela’ and NSFTV199, are both phenotypically and genotypically diverse, thus excellent parents for a bi-parental mapping population. The objectives of this study were to 1) develop the Estrela/NSFTV199 mapping population, evaluate the progeny for 15 traits related to agronomic traits (days to heading, plant height, culm habit and seed shattering), panicle architecture traits (panicle length; number primary branches, florets, seeds and sterile florets per panicle; and percent fertility) and seed traits (seed length, width and length to width ratio; and 100-seed weight), and identify associated QTL, 2) conduct a GWAS with the HDRA RDP1 genotypes for the same yield traits as the mapping population to validate QTL, and 3) identify possible candidate genes underlying these traits. The mapping population was genotyped with 132 SSR markers and a total of 70 QTL were found. Perl scripts were developed to automate the identification of underlying candidate genes in the GWAS-QTL regions. Fifty-five GWAS-QTL overlapped with 30 Estrela/NSFTV199 QTL regions and a total of 31 known genes were identified.