Zhang Peng, Liu Xiangdong, Tong Hanhua, Lu Yonggen, Li Jinquan
State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources, South China Agricultural University, Guangzhou, China; State Key Laboratory of Rice Biology, China National Rice Research Institute, Hangzhou, China.
State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources, South China Agricultural University, Guangzhou, China.
PLoS One. 2014 Oct 31;9(10):e111508. doi: 10.1371/journal.pone.0111508. eCollection 2014.
Mining elite genes within rice landraces is of importance for the improvement of cultivated rice. An association mapping for 12 agronomic traits was carried out using a core collection of rice consisting of 150 landraces (Panel 1) with 274 simple sequence repeat (SSR) markers, and the mapping results were further verified using a Chinese national rice micro-core collection (Panel 2) and a collection from a global molecular breeding program (Panel 3). Our results showed that (1) 76 significant (P<0.05) trait-marker associations were detected using mixed linear model (MLM) within Panel 1 in two years, among which 32% were identical with previously mapped QTLs, and 11 significant associations had >10% explained ratio of genetic variation; (2) A total of seven aforementioned trait-marker associations were verified within Panel 2 and 3 when using a general linear model (GLM) and 55 SSR markers of the 76 significant trait-marker associations. However, no significant trait-marker association was found to be identical within three panels when using the MLM model; (3) several desirable alleles of the loci which showed significant trait-marker associations were identified. The research provided important information for further mining these elite genes within rice landraces and using them for rice breeding.
挖掘水稻地方品种中的优良基因对于改良栽培稻具有重要意义。利用由150个地方品种组成的水稻核心种质（图1）和274个简单序列重复（SSR）标记，对12个农艺性状进行了关联分析，并利用中国国家水稻微核心种质（图2）和一个全球分子育种项目的种质（图3）对分析结果进行了进一步验证。我们 的结果表明：（1）在两年内，利用混合线性模型（MLM）在图1中检测到76个显著（P<0.05）的性状-标记关联，其中32%与先前定位的QTL相同，11个显著关联的遗传变异解释率>10%；（2）当使用一般线性模型（GLM）和76个显著性状-标记关联中的55个SSR标记时，在图2和图3中总共验证了上述7个性状-标记关联。然而，当使用MLM模型时，在三个图中未发现显著的性状-标记关联是相同的；（3）鉴定出了几个显示出显著性状-标记关联的位点的优良等位基因。该研究为进一步挖掘水稻地方品种中的这些优良基因并将其用于水稻育种提供了重要信息。
