Descalsota Gwen Iris L, Swamy B P Mallikarjuna, Zaw Hein, Inabangan-Asilo Mary Ann, Amparado Amery, Mauleon Ramil, Chadha-Mohanty Prabhjit, Arocena Emily C, Raghavan Chitra, Leung Hei, Hernandez Jose E, Lalusin Antonio B, Mendioro Merlyn S, Diaz Ma Genaleen Q, Reinke Russell
Strategic Innovation Platform, International Rice Research Institute, Manila, Philippines.
University of Southern Mindanao, Kabacan, Philippines.
Front Plant Sci. 2018 Sep 20;9:1347. doi: 10.3389/fpls.2018.01347. eCollection 2018.
The development of rice genotypes with micronutrient-dense grains and disease resistance is one of the major priorities in rice improvement programs. We conducted Genome-wide association studies (GWAS) using a Multi-parent Advanced Generation Inter-Cross (MAGIC) Plus population to identify QTLs and SNP markers that could potentially be integrated in biofortification and disease resistance breeding. We evaluated 144 MAGIC Plus lines for agronomic and biofortification traits over two locations for two seasons, while disease resistance was screened for one season in the screen house. X-ray fluorescence technology was used to measure grain Fe and Zn concentrations. Genotyping was carried out by genotype by sequencing and a total of 14,242 SNP markers were used in the association analysis. We used Mixed linear model (MLM) with kinship and detected 57 significant genomic regions with a -log10 (-value) ≥ 3.0. The and were consistently identified in all the four environments, ten QTLs , , , , and were detected in two environments, while two major loci and were identified for Bacterial Leaf Blight (BLB) resistance. The associated SNP markers were found to co-locate with known major genes and QTLs such as for days to flowering, for plant height, and for grain length. Similarly, and genes were identified for BLB resistance and , and genes were identified for Blast resistance. A number of metal homeostasis genes , , , , and were co-located with QTLs for Fe and Zn. The marker-trait relationships from Bayesian network analysis showed consistency with the results of GWAS. A number of promising candidate genes reported in our study can be further validated. We identified several QTLs/genes pyramided lines with high grain Zn and acceptable yield potential, which are a good resource for further evaluation to release as varieties as well as for use in breeding programs.
培育富含微量营养素且抗病的水稻基因型是水稻改良计划的主要优先事项之一。我们利用多亲本高世代杂交(MAGIC)Plus群体进行全基因组关联研究(GWAS),以鉴定可能整合到生物强化和抗病育种中的数量性状基因座(QTL)和单核苷酸多态性(SNP)标记。我们在两个地点对144个MAGIC Plus株系进行了两个季节的农艺和生物强化性状评估,同时在温室中对其抗病性进行了一个季节的筛选。使用X射线荧光技术测量籽粒铁和锌浓度。通过简化基因组测序进行基因分型,共14242个SNP标记用于关联分析。我们使用包含亲缘关系的混合线性模型(MLM),检测到57个显著的基因组区域,其-log10(p值)≥3.0。在所有四个环境中均一致鉴定出了 和 ,在两个环境中检测到10个QTL,即 、 、 、 、 、 、 、 、 和 ,同时鉴定出两个抗白叶枯病(BLB)的主要基因座 和 。发现相关的SNP标记与已知的主要基因和QTL共定位,如控制开花天数的 、控制株高的 和控制粒长的 。同样,鉴定出了抗BLB的 和 基因,以及抗稻瘟病的 和 基因。一些金属稳态基因 、 、 、 、 和 与铁和锌的QTL共定位。贝叶斯网络分析的标记-性状关系与GWAS结果一致。我们研究中报道的许多有前景的候选基因可以进一步验证。我们鉴定出了几个具有高籽粒锌含量和可接受产量潜力的QTL/基因聚合系,这是进一步评估以释放为品种以及用于育种计划的良好资源。