Weedy rice, a conspecific weedy counterpart of the cultivated rice (Oryza sativa L.), has been problematic in rice-production area worldwide. Although we started to know about the origin of some weedy traits for some rice-growing regions, an overall assessment of weedy trait-related loci was not yet available. On the other hand, the advances in sequencing technologies, together with community efforts, have made publicly available a large amount of genomic data. Given the availability of public data and the need of “weedy” allele mining for a better management of weedy rice, the objective of the present study was to explore the genetic architecture of weedy traits based on publicly available data, mainly from the 3000 Rice Genome Project (3K-RGP). Based on the results of population structure analysis, we have selected 1378 individuals from four sub-populations (aus, indica, temperate japonica, tropical japonica) without admixed genomic composition for genome-wide association analysis (GWAS). Five traits were investigated: awn color, seed shattering, seed threshability, seed coat color, and seedling height. GWAS was conducted for each sub-population × trait combination and we have identified 66 population-specific trait-associated SNPs. Eleven significant SNPs fell into an annotated gene and four other SNPs were close to a putative candidate gene (± 25 kb). SNPs located in or close to Rc were particularly predictive of the occurrence of seed coat color and our results showed that different sub-populations required different SNPs for a better seed coat color prediction. We compared the data of 3K-RGP to a publicly available weedy rice dataset. The profile of allele frequency, phenotype-genotype segregation of target SNP, as well as GWAS results for the presence and absence of awns diverged between the two sets of data. The genotype of trait-associated SNPs identified in this study, especially those located in or close to Rc, can be developed to diagnostic SNPs to trace the origin of weedy trait occurred in the field. The difference of results from the two publicly available datasets used in this study emphasized the importance of laboratory experiments to confirm the allele mining results based on publicly available data.

中文翻译：

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使用水稻3K数据库探索杂草性状的遗传

杂草稻是栽培稻（Oryza sativa L.）的同种杂草对应物，在世界范围的稻米生产地区一直存在问题。尽管我们开始了解某些水稻种植地区某些杂草性状的起源，但尚无法对杂草性状相关基因座进行全面评估。另一方面，测序技术的进步以及社区的努力使公众可以获取大量的基因组数据。考虑到公共数据的可用性以及需要对“杂草”等位基因进行挖掘以更好地管理杂草水稻，本研究的目的是基于公开数据（主要来自3000个水稻基因组）探索杂草性状的遗传结构。专案（3K-RGP）。根据人口结构分析的结果，我们从四个亚人群（澳大利亚，印度，温带粳稻，热带粳稻）中选择了1378个个体，没有混合的基因组组成进行全基因组关联分析（GWAS）。研究了五个特征：芒草颜色，种子破碎，种子脱粒性，种皮颜色和幼苗高度。对每个亚种群×性状组合进行了GWAS，我们已经鉴定出66个与人群特定性状相关的SNP。11个重要的SNP落入带注释的基因，另外四个SNP接近推定的候选基因（±25 kb）。位于Rc或附近的SNP特别能预测种皮颜色的发生，我们的结果表明，不同的亚群需要不同的SNP才能更好地预测种皮颜色。我们将3K-RGP的数据与可公开获取的杂草水稻数据集进行了比较。在两组数据之间存在和不存在芒的情况下，目标SNP的等位基因频率，表型-基因型分离以及GWAS的结果。这项研究中鉴定出的与性状相关的SNP的基因型，特别是位于Rc附近或附近的那些，可以发展为诊断性SNP，以追踪田间发生的杂草性状的起源。本研究中使用的两个公开可用数据集的结果差异强调了实验室实验对基于公开可用数据确认等位基因挖掘结果的重要性。尤其是位于Rc或附近的那些，可以用于诊断SNP，以追踪田间发生的杂草性状的起源。本研究中使用的两个公开可用数据集的结果差异强调了实验室实验对基于公开可用数据确认等位基因挖掘结果的重要性。尤其是位于Rc或附近的那些，可以用于诊断SNP，以追踪田间发生的杂草性状的起源。本研究中使用的两个公开可用数据集的结果差异强调了实验室实验对基于公开可用数据确认等位基因挖掘结果的重要性。