Although genome-wide association studies (GWAS) based on single-marker analysis have been widely applied in plant breeding programs, the effectivity of the methodology is still undermined by high false-positive rates and the limited power to detect associations. Bayesian methods, which estimate marker effects simultaneously, proved to be efficient, indicating genes with important effects. Regional heritability mapping (RHM), on the other hand, determines the genome region (group of markers) associated with the phenotype, considers population structure and familial relatedness, and is more powerful to detect quantitative trait loci (QTL) and reduced false-positive rates than single-marker methodologies. A single-marker mixed model (SM-MM) Bayesian approach and RHM were used for 11 traits in 413 rice (Oryza sativa L.) accessions genotyped for 44,100 single-nucleotide polymorphism (SNP) markers. Using RHM in regions of 0.21 and 0.69 Mb, respectively, detected five and seven associated regions with 163 and 569 SNPs. Bayesian method with regions of 0.21 and 0.69 Mb detected regions for all traits, whereas SM-MM detected four single SNP–trait associations. For the 11 traits, RHM explained approximately 25–40 and 25–76% using genome regions of 0.21 and 0.69 Mb, respectively, and SM-MM using single markers explained 1–7% of the genomic heritability. Regional heritability mapping was more effective than SM-MM in capturing major proportions of genomic heritability. The regions found in this study were within or close to the QTL noted in the Q-TARO and Gramene QTL databases.

中文翻译：

---

区域遗传力作图和全基因组关联确定水稻性状的基因座

尽管基于单标记分析的全基因组关联研究 (GWAS) 已广泛应用于植物育种计划，但该方法的有效性仍然受到高假阳性率和检测关联能力有限的影响。同时估计标记效应的贝叶斯方法被证明是有效的，表明基因具有重要影响。另一方面，区域遗传力图谱 (RHM) 确定与表型相关的基因组区域（标记组），考虑种群结构和家族相关性，并且更有效地检测数量性状基因座 (QTL) 和减少假阳性率高于单标记方法。单标记混合模型 (SM-MM) 贝叶斯方法和 RHM 用于 413 水稻 ( Oryza sativa ) 的 11 个性状L.) 对 44,100 个单核苷酸多态性 (SNP) 标记进行基因分型的种质。分别在 0.21 和 0.69 Mb 的区域中使用 RHM，检测到具有 163 和 569 个 SNP 的 5 个和 7 个相关区域。具有 0.21 和 0.69 Mb 区域的贝叶斯方法检测到所有性状的区域，而 SM-MM 检测到四个单一 SNP-性状关联。对于这 11 个性状，RHM 分别使用 0.21 和 0.69 Mb 的基因组区域解释了大约 25-40% 和 25-76%，而使用单个标记的 SM-MM 解释了 1-7% 的基因组遗传力。区域遗传力作图比 SM-MM 在捕获大部分基因组遗传力方面更有效。本研究中发现的区域位于或接近 Q-TARO 和 Gramene QTL 数据库中记录的 QTL。