Kumar Ilakiya Sharanee, Nadarajah Kalaivani
Department of Biological Sciences and Biotechnology, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, Bangi 43000, Malaysia.
Plants (Basel). 2020 Nov 5;9(11):1491. doi: 10.3390/plants9111491.
Rice blast, sheath blight and bacterial leaf blight are major rice diseases found worldwide. The development of resistant cultivars is generally perceived as the most effective way to combat these diseases. Plant disease resistance is a polygenic trait where a combinatorial effect of major and minor genes affects this trait. To locate the source of this trait, various quantitative trait loci (QTL) mapping studies have been performed in the past two decades. However, investigating the congruency between the reported QTL is a daunting task due to the heterogeneity amongst the QTLs studied. Hence, the aim of our study is to integrate the reported QTLs for resistance against rice blast, sheath blight and bacterial leaf blight and objectively analyze and consolidate the location of QTL clusters in the chromosomes, reducing the QTL intervals and thus identifying candidate genes within the selected meta-QTL. A total of twenty-seven studies for resistance QTLs to rice blast (8), sheath blight (15) and bacterial leaf blight (4) was compiled for QTL projection and analyses. Cumulatively, 333 QTLs associated with rice blast (114), sheath blight (151) and bacterial leaf blight (68) resistance were compiled, where 303 QTLs could be projected onto a consensus map saturated with 7633 loci. Meta-QTL analysis on 294 QTLs yielded 48 meta-QTLs, where QTLs with membership probability lower than 60% were excluded, reducing the number of QTLs within the meta-QTL to 274. Further, three meta-QTL regions (MQTL2.5, MQTL8.1 and MQTL9.1) were selected for functional analysis on the basis that MQTL2.5 harbors the highest number of QTLs; meanwhile, MQTL8.1 and MQTL9.1 have QTLs associated with all three diseases mentioned above. The functional analysis allows for determination of enriched gene ontology and resistance gene analogs (RGAs) and other defense-related genes. To summarize, MQTL2.5, MQTL8.1 and MQTL9.1 have a considerable number of R-genes that account for 10.21%, 4.08% and 6.42% of the total genes found in these meta-QTLs, respectively. Defense genes constitute around 3.70%, 8.16% and 6.42% of the total number of genes in MQTL2.5, MQTL8.1 and MQTL9.1, respectively. This frequency is higher than the total frequency of defense genes in the rice genome, which is 0.0096% (167 defense genes/17,272 total genes). The integration of the QTLs facilitates the identification of QTL hotspots for rice blast, sheath blight and bacterial blight resistance with reduced intervals, which helps to reduce linkage drag in breeding. The candidate genes within the promising regions could be utilized for improvement through genetical engineering.
稻瘟病、纹枯病和白叶枯病是全球范围内发现的主要水稻病害。培育抗病品种通常被认为是对抗这些病害最有效的方法。植物抗病性是一种多基因性状,其中主基因和微基因的组合效应影响这一性状。为了定位这一性状的来源,在过去二十年中进行了各种数量性状位点(QTL)定位研究。然而,由于所研究的QTL之间存在异质性,研究已报道的QTL之间的一致性是一项艰巨的任务。因此,我们研究的目的是整合已报道的抗稻瘟病、纹枯病和白叶枯病的QTL,并客观地分析和整合QTL簇在染色体上的位置,缩小QTL区间,从而在选定的元QTL中鉴定候选基因。总共收集了27项关于水稻抗稻瘟病(8项)、纹枯病(15项)和白叶枯病(4项)QTL的研究,用于QTL投影和分析。累计汇编了333个与水稻抗稻瘟病(114个)、纹枯病(151个)和白叶枯病(68个)相关的QTL,其中303个QTL可以投影到一个包含7633个位点的共识图谱上。对294个QTL进行元QTL分析产生了48个元QTL,其中成员概率低于60%的QTL被排除,将元QTL内的QTL数量减少到274个。此外,基于MQTL2.5包含的QTL数量最多,同时MQTL8.1和MQTL9.1具有与上述所有三种病害相关的QTL,选择了三个元QTL区域(MQTL2.5、MQTL8.1和MQTL9.1)进行功能分析。功能分析允许确定富集的基因本体和抗病基因类似物(RGA)以及其他防御相关基因。总之,MQTL2.5、MQTL8.1和MQTL9.1分别有相当数量的R基因,占这些元QTL中发现的总基因的10.21%、4.