Association Analysis of the Amino Acid Contents in Rice

作     者：Weiguo Zhao Eun-Jin Park Jong-Wook Chung Yong-Jin Park III-Min Chung Joung-Kuk Ahn Gwang-Ho Kim 

作者机构：Department of Appfied Bioscience Konkuk University Seou1143-701 Korea Seficultural Research Institute Chinese Academy of Agricultural Sciences Jiangsu University of Science and Technology Zhenjiang 212018 China Department of Plant Resources Coflege of Industrial Science Kongju National University Yesan 340-802 Korea 

出 版 物：《Journal of Integrative Plant Biology》 (植物学报（英文版）)

年 卷 期：2009年第51卷第12期

页      面：1126-1137页

核心收录：

学科分类：0710[理学-生物学] 090502[农学-动物营养与饲料科学] 0905[农学-畜牧学] 09[农学] 0901[农学-作物学] 0703[理学-化学] 0902[农学-园艺学] 

基　　金：Supported by the Biogreen 21 project (#20080401034058) of the Rural Development Administration (RDA), Korea Dr Zhao Weiguo was supported by the 2008 KU Brain Pool grant from Konkuk University, Korea 

主　　题：Amino acid content association mapping linkage disequilibrium population structure simple sequence repeats. 

摘      要：The main objective of the present study was to identify simple sequence repeat （SSR） markers associated with the amino acid content of rice （Oryza sativa L.）. SSR markers were selected by prescreening for the relationship to amino acid content. Eighty-four rice landrace accessions from Korea were evaluated for 16 kinds of amino acids in brown rice and ganotyped with 25 SSR markers. Analysis of population structure revealed four subgroups in the population. Linkage disequilibrium （LD） patterns and distributions are of fundamental importance for genome-wide mapping associations. The mean r^2 value for all intrachromosomal loci pairs was 0.033. LD between linked markers decreased with distance. Marker-trait associations were investigated using the unified mixed-model approach, considering both population structure （Q） and kinship （K）. A total of 42 marker-trait associations with amino acids （P 〈 0.05） were identified using 15 different SSR markers covering three chromosomes and explaining more than 40% of the total variation. These results suggest that association analysis in rice is a viable alternative to quantitative trait loci mapping and should help rice breeders develop strategies for improving rice quality.