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The Application of Multi-Locus GWAS for the Detection of Salt-Tolerance Loci in Rice

机译:多位点GWAS在水稻耐盐基因座检测中的应用

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摘要

Improving the salt-tolerance of direct-seeding rice at the seed germination stage is a major goal of breeders. Efficiently identifying salt tolerance loci will help researchers develop effective rice breeding strategies. In this study, six multi-locus genome-wide association studies (GWAS) methods (mrMLM, FASTmrMLM, FASTmrEMMA, pLARmEB, pKWmEB, and ISIS EM-BLASSO) were applied to identify quantitative trait nucleotides (QTNs) for the salt tolerance traits of 478 rice accessions with 162,529 SNPs at the seed germination stage. Among the 371 QTNs detected by the six methods, 56 were identified by at least three methods. Among these 56 QTNs, 12, 6, 7, 4, 13, 12, and 12 were found to be associated with SSI-GI, SSI-VI, SSI-MGT, SSI-IR-24h, SSI-IR-48h, SSI-GR-5d, and SSI-GR-10d, respectively. Additionally, 66 candidate genes were identified in the vicinity of the 56 QTNs, and two of these genes (LOC_Os01g45760 and LOC_Os10g04860) are involved in auxin biosynthesis according to the enriched GO terms and KEGG pathways. This information will be useful for identifying the genes responsible for rice salt tolerance. A comparison of the six methods revealed that ISIS EM-BLASSO identified the most co-detected QTNs and performed best, with the smallest residual errors and highest computing speed, followed by FASTmrMLM, pLARmEB, mrMLM, pKWmEB, and FASTmrEMMA. Although multi-locus GWAS methods are superior to single-locus GWAS methods, their utility for identifying QTNs may be enhanced by adding a bin analysis to the models or by developing a hybrid method that merges the results from different methods.
机译:提高种子播种期水稻的耐盐性是育种者的主要目标。有效鉴定耐盐基因座将有助于研究人员制定有效的水稻育种策略。在这项研究中,应用了六种多基因座全基因组关联研究(GWAS)方法(mrMLM,FASTmrMLM,FASTmrEMMA,pLARmEB,pKWmEB和ISIS EM-BLASSO)来鉴定定量盐核苷酸(QTNs),以鉴定其耐盐性状。在种子萌发阶段有478个水稻品种,带有162,529个SNP。通过六种方法检测到的371个QTN中,至少有三种方法鉴定了56个。在这56个QTN中,发现12、6、7、4、13、12和12与SSI-GI,SSI-VI,SSI-MGT,SSI-IR-24h,SSI-IR-48h,SSI相关-GR-5d和SSI-GR-10d。此外,在56个QTN附近确定了66个候选基因,其中两个基因(LOC_Os01g45760和LOC_Os10g04860)根据丰富的GO术语和KEGG途径参与了植物生长素的生物合成。该信息将有助于鉴定引起水稻盐耐性的基因。对这六种方法的比较表明,ISIS EM-BLASSO识别出最多的共检测到的QTN,并且表现最佳,具有最小的残留误差和最高的计算速度,其次是FASTmrMLM,pLARmEB,mrMLM,pKWmEB和FASTmrEMMA。尽管多位点GWAS方法优于单位点GWAS方法,但可以通过在模型中添加bin分析或开发可合并来自不同方法的结果的混合方法来增强其用于识别QTN的实用性。

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